diff --git a/crypto/example_model.ipynb b/crypto/example_model.ipynb
new file mode 100644
index 0000000..97c3b13
--- /dev/null
+++ b/crypto/example_model.ipynb
@@ -0,0 +1,3791 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ekw8Z93ljC3v",
+        "outputId": "06b318dc-3d3f-47a6-a782-3f5980a5bd49"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Python 3.12.11\n"
+          ]
+        }
+      ],
+      "source": [
+        "!python --version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yoy_wT1rhMqF",
+        "outputId": "f61b4fd7-5ef5-4550-b114-5cb6f300ae52"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install dependencies\n",
+        "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
+        "!pip install -q --no-deps numerai-tools"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "crypto/v2.0_beta/train.parquet: 3.58MB [00:00, 14.5MB/s]                            \n"
+          ]
+        }
+      ],
+      "source": [
+        "from numerapi import NumerAPI\n",
+        "import pandas as pd\n",
+        "import lightgbm as lgb\n",
+        "\n",
+        "napi = NumerAPI()\n",
+        "\n",
+        "# use one of the latest data versions\n",
+        "DATA_VERSION = \"crypto/v2.0\"\n",
+        "\n",
+        "# Download and read training data\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
+        "data = pd.read_parquet(f\"{DATA_VERSION}/train.parquet\")\n",
+        "\n",
+        "# split data into train / val at a ~70/30 ratio\n",
+        "split = int(len(data) * 0.7)\n",
+        "train = data[:split].copy()\n",
+        "val = data[split:].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "train"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b6ad5a88-7777-4921-b9d2-1caf1e7868d4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>feature_bollinger_20d</th>\n",
+              "      <th>feature_bollinger_60d</th>\n",
+              "      <th>feature_close_avg_20d</th>\n",
+              "      <th>feature_close_avg_60d</th>\n",
+              "      <th>feature_close_ewa_20d</th>\n",
+              "      <th>feature_close_ewa_60d</th>\n",
+              "      <th>feature_market_cap_avg_20d</th>\n",
+              "      <th>feature_market_cap_avg_60d</th>\n",
+              "      <th>feature_market_cap_ewa_20d</th>\n",
+              "      <th>...</th>\n",
+              "      <th>feature_sharpe_ratio_20d</th>\n",
+              "      <th>feature_sharpe_ratio_60d</th>\n",
+              "      <th>feature_volatility_20d</th>\n",
+              "      <th>feature_volatility_60d</th>\n",
+              "      <th>feature_volume_avg_20d</th>\n",
+              "      <th>feature_volume_avg_60d</th>\n",
+              "      <th>feature_volume_ewa_20d</th>\n",
+              "      <th>feature_volume_ewa_60d</th>\n",
+              "      <th>target_binned_return_20</th>\n",
+              "      <th>target_binned_return_60</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>symbol</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>BTC</th>\n",
+              "      <td>2020-01-01</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LTC</th>\n",
+              "      <td>2020-01-01</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>XRP</th>\n",
+              "      <td>2020-01-01</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DOGE</th>\n",
+              "      <td>2020-01-01</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>VTC</th>\n",
+              "      <td>2020-01-01</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>FIL</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ELF</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>WAXP</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>MED</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IOST</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>307157 rows × 25 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6ad5a88-7777-4921-b9d2-1caf1e7868d4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b6ad5a88-7777-4921-b9d2-1caf1e7868d4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b6ad5a88-7777-4921-b9d2-1caf1e7868d4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3680e98f-32c7-4383-8ff6-f273c8a7342c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3680e98f-32c7-4383-8ff6-f273c8a7342c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3680e98f-32c7-4383-8ff6-f273c8a7342c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_594609ae-eda3-4149-a9c2-71545d487251\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_594609ae-eda3-4149-a9c2-71545d487251 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('train');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "             date  feature_bollinger_20d  feature_bollinger_60d  \\\n",
+              "symbol                                                            \n",
+              "BTC    2020-01-01                   0.50                   0.50   \n",
+              "LTC    2020-01-01                   0.75                   0.50   \n",
+              "XRP    2020-01-01                   0.50                   0.50   \n",
+              "DOGE   2020-01-01                   0.25                   0.25   \n",
+              "VTC    2020-01-01                   0.25                   0.25   \n",
+              "...           ...                    ...                    ...   \n",
+              "FIL    2024-01-02                   0.75                   0.75   \n",
+              "ELF    2024-01-02                   0.25                   0.25   \n",
+              "WAXP   2024-01-02                   0.50                   0.50   \n",
+              "MED    2024-01-02                   0.50                   0.50   \n",
+              "IOST   2024-01-02                   0.50                   0.50   \n",
+              "\n",
+              "        feature_close_avg_20d  feature_close_avg_60d  feature_close_ewa_20d  \\\n",
+              "symbol                                                                        \n",
+              "BTC                      1.00                   1.00                   1.00   \n",
+              "LTC                      1.00                   1.00                   1.00   \n",
+              "XRP                      0.50                   0.50                   0.50   \n",
+              "DOGE                     0.25                   0.25                   0.25   \n",
+              "VTC                      0.50                   0.50                   0.50   \n",
+              "...                       ...                    ...                    ...   \n",
+              "FIL                      0.75                   0.75                   0.75   \n",
+              "ELF                      0.50                   0.50                   0.50   \n",
+              "WAXP                     0.25                   0.25                   0.25   \n",
+              "MED                      0.25                   0.25                   0.25   \n",
+              "IOST                     0.25                   0.25                   0.25   \n",
+              "\n",
+              "        feature_close_ewa_60d  feature_market_cap_avg_20d  \\\n",
+              "symbol                                                      \n",
+              "BTC                      1.00                        1.00   \n",
+              "LTC                      1.00                        1.00   \n",
+              "XRP                      0.50                        1.00   \n",
+              "DOGE                     0.25                        0.75   \n",
+              "VTC                      0.50                        0.50   \n",
+              "...                       ...                         ...   \n",
+              "FIL                      0.75                        0.75   \n",
+              "ELF                      0.50                        0.50   \n",
+              "WAXP                     0.25                        0.50   \n",
+              "MED                      0.25                        0.25   \n",
+              "IOST                     0.25                        0.50   \n",
+              "\n",
+              "        feature_market_cap_avg_60d  feature_market_cap_ewa_20d  ...  \\\n",
+              "symbol                                                          ...   \n",
+              "BTC                           1.00                        1.00  ...   \n",
+              "LTC                           1.00                        1.00  ...   \n",
+              "XRP                           1.00                        1.00  ...   \n",
+              "DOGE                          0.75                        0.75  ...   \n",
+              "VTC                           0.50                        0.50  ...   \n",
+              "...                            ...                         ...  ...   \n",
+              "FIL                           0.75                        0.75  ...   \n",
+              "ELF                           0.50                        0.50  ...   \n",
+              "WAXP                          0.50                        0.50  ...   \n",
+              "MED                           0.25                        0.00  ...   \n",
+              "IOST                          0.50                        0.50  ...   \n",
+              "\n",
+              "        feature_sharpe_ratio_20d  feature_sharpe_ratio_60d  \\\n",
+              "symbol                                                       \n",
+              "BTC                         0.50                      0.50   \n",
+              "LTC                         0.50                      0.25   \n",
+              "XRP                         0.50                      0.25   \n",
+              "DOGE                        0.50                      0.50   \n",
+              "VTC                         0.25                      0.50   \n",
+              "...                          ...                       ...   \n",
+              "FIL                         0.75                      0.50   \n",
+              "ELF                         0.25                      0.50   \n",
+              "WAXP                        0.50                      0.25   \n",
+              "MED                         0.50                      0.50   \n",
+              "IOST                        0.50                      0.50   \n",
+              "\n",
+              "        feature_volatility_20d  feature_volatility_60d  \\\n",
+              "symbol                                                   \n",
+              "BTC                       0.00                    0.00   \n",
+              "LTC                       0.25                    0.25   \n",
+              "XRP                       0.25                    0.00   \n",
+              "DOGE                      0.00                    0.00   \n",
+              "VTC                       0.50                    0.75   \n",
+              "...                        ...                     ...   \n",
+              "FIL                       0.75                    0.50   \n",
+              "ELF                       0.50                    0.50   \n",
+              "WAXP                      0.25                    0.50   \n",
+              "MED                       0.25                    0.25   \n",
+              "IOST                      0.50                    0.25   \n",
+              "\n",
+              "        feature_volume_avg_20d  feature_volume_avg_60d  \\\n",
+              "symbol                                                   \n",
+              "BTC                       1.00                    1.00   \n",
+              "LTC                       1.00                    1.00   \n",
+              "XRP                       1.00                    1.00   \n",
+              "DOGE                      0.75                    0.75   \n",
+              "VTC                       0.25                    0.25   \n",
+              "...                        ...                     ...   \n",
+              "FIL                       0.75                    0.75   \n",
+              "ELF                       0.50                    0.50   \n",
+              "WAXP                      0.50                    0.50   \n",
+              "MED                       0.25                    0.25   \n",
+              "IOST                      0.50                    0.50   \n",
+              "\n",
+              "        feature_volume_ewa_20d  feature_volume_ewa_60d  \\\n",
+              "symbol                                                   \n",
+              "BTC                       1.00                    1.00   \n",
+              "LTC                       1.00                    1.00   \n",
+              "XRP                       1.00                    1.00   \n",
+              "DOGE                      0.75                    0.75   \n",
+              "VTC                       0.25                    0.25   \n",
+              "...                        ...                     ...   \n",
+              "FIL                       0.75                    0.75   \n",
+              "ELF                       0.50                    0.50   \n",
+              "WAXP                      0.50                    0.50   \n",
+              "MED                       0.25                    0.25   \n",
+              "IOST                      0.50                    0.50   \n",
+              "\n",
+              "        target_binned_return_20  target_binned_return_60  \n",
+              "symbol                                                    \n",
+              "BTC                        0.50                     0.50  \n",
+              "LTC                        0.75                     0.50  \n",
+              "XRP                        0.50                     0.50  \n",
+              "DOGE                       0.50                     0.50  \n",
+              "VTC                        1.00                     0.75  \n",
+              "...                         ...                      ...  \n",
+              "FIL                        0.25                     0.50  \n",
+              "ELF                        0.75                     0.50  \n",
+              "WAXP                       0.50                     0.50  \n",
+              "MED                        0.50                     0.50  \n",
+              "IOST                       0.50                     0.50  \n",
+              "\n",
+              "[307157 rows x 25 columns]"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "\n",
+        "train"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160
+        },
+        "id": "13hdRk9ghMqI",
+        "outputId": "d2274374-fd85-4189-f27b-d9d466cc63ca"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.357879 seconds.\n",
+            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Total Bins 112\n",
+            "[LightGBM] [Info] Number of data points in the train set: 307157, number of used features: 22\n",
+            "[LightGBM] [Info] Start training from score 0.499976\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('boosting_type',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">boosting_type&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('num_leaves',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">num_leaves&nbsp;</td>\n",
+              "            <td class=\"value\">31</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_depth',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_depth&nbsp;</td>\n",
+              "            <td class=\"value\">5</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('learning_rate',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+              "            <td class=\"value\">0.01</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_estimators',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+              "            <td class=\"value\">2000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
+              "            <td class=\"value\">200000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('objective',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">objective&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('class_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">class_weight&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_split_gain',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_child_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
+              "            <td class=\"value\">0.001</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_child_samples',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
+              "            <td class=\"value\">20</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample&nbsp;</td>\n",
+              "            <td class=\"value\">1.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample_freq',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
+              "            <td class=\"value\">0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
+              "            <td class=\"value\">0.1</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_alpha',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_lambda',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">random_state&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('importance_type',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">importance_type&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;split&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000)"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train model\n",
+        "model = lgb.LGBMRegressor(\n",
+        "    n_estimators=2000,\n",
+        "    learning_rate=0.01,\n",
+        "    max_depth=5,\n",
+        "    num_leaves=2**5-1,\n",
+        "    colsample_bytree=0.1\n",
+        ")\n",
+        "# We've found the following \"deep\" parameters perform much better,\n",
+        "#   but they require much more CPU and RAM\n",
+        "# model = lgb.LGBMRegressor(\n",
+        "#     n_estimators=30_000,\n",
+        "#     learning_rate=0.001,\n",
+        "#     max_depth=10,\n",
+        "#     num_leaves=2**10,\n",
+        "#     colsample_bytree=0.1,\n",
+        "#     min_data_in_leaf=10000,\n",
+        "# )\n",
+        "model.fit(\n",
+        "    train.filter(like=\"feature_\"),\n",
+        "    train[\"target_binned_return_20\"]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/var/folders/vm/jlf_h6td3b5dh_kg5p3xr45c0000gq/T/ipykernel_91204/2873597676.py:6: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  ].groupby(\"date\").apply(\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative 20D Numerai Corr over Validation'}, xlabel='date'>"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAHXCAYAAAAcDjiVAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAZWpJREFUeJzt3Xd8E/X/B/BXOpLOpHvRQZltKRQoq2wQKIgoUoYIUgTEUVBAVHABCoIiCv4UFUEQFAVkyFAZZSmWbdmUVWihdNCR7qRNPr8/sPkSWqAzadrX8/HIQ/u5y937jvTy6ufuPicRQggQERERGYiZsQsgIiKi+oXhg4iIiAyK4YOIiIgMiuGDiIiIDIrhg4iIiAyK4YOIiIgMiuGDiIiIDIrhg4iIiAyK4YOIiIgMiuGDqmzs2LFo2LBhtS5z1apVkEgkuH79erUul+qGmvjMkWH17NkTPXv21P18/fp1SCQSrFq16pHv5THH9DF81BJXr17Fiy++iEaNGsHKygpyuRxdunTBkiVLUFBQYOzyasxHH32ELVu2GLsMnWPHjmHSpElo0aIFbG1t4evri+HDh+PSpUtlzn/hwgX0798fdnZ2cHJywnPPPYe0tDS9efbv3w+JRKJ7yWQyuLu7o2fPnvjoo49Kzf8gJQdniUSCjRs3lpo+e/ZsSCQS3Llzp+IbXsdt3rwZAwYMgIuLC6RSKby8vDB8+HDs3bvX2KXVeps2bYJEIsHy5csfOM/u3bshkUjwxRdfGLCyyqltx5x6S5DRbd++XVhbWwsHBwfx6quvimXLlokvv/xSPPPMM8LS0lK88MILxi7xoSIjI4Wfn1+l3mtraysiIyNLtRcXF4uCggKh1WqrVlwFRURECA8PDzF58mTx3XffiQ8//FC4u7sLW1tbcebMGb15ExMThYuLi2jcuLFYsmSJmDdvnnB0dBQhISFCpVLp5tu3b58AIF599VWxZs0asWrVKrFw4ULx9NNPCwsLC+Hs7Cyio6MfWVt8fLwAIACIVq1aldo3s2bNEgBEWlpa9eyMWkytVovCwsJHzqfVasXYsWMFANGmTRsxb948sWLFCjF37lwRGhoqAIhDhw4ZoGLTVVhYKBQKhejVq9cD5xk7dqwwNzcXKSkp5V5ujx49RI8ePXQ/a7VaUVBQIIqLix/53rp0zKmvGD6M7Nq1a8LOzk4EBASIpKSkUtMvX74sFi9ebITKyq8mDgTGcujQIb3gIIQQly5dEjKZTIwaNUqv/eWXXxbW1tbixo0burbdu3cLAOLbb7/VtZWEjw0bNpRaX2xsrHBzcxMODg5l/vvfqyR8tG7dWgAQGzdu1JtuyuEjLy+vRpa7cOFCAUBMmTKlzC+V1atXiyNHjlR5PQUFBUKj0ZQ5LTc3t8rLN4SHbcP48eOFmZmZuHXrVpnvUygUon///hVa3/3hoyLq0jGnvmL4MLKXXnqp3H99lXz5rFy5stQ0AGLWrFm6n0u+iOLi4sSoUaOEXC4XLi4u4t133xVarVYkJCSIJ598Utjb2wt3d3fx6aef6i1v5cqVAoCIj4/Xay/5It23b5+urawDwcKFC0VYWJhwcnISVlZWom3btqW+fEv+ir/3VXJQuH/9AwcOFP7+/mXul06dOonQ0FC9tjVr1oi2bdsKKysr4ejoKEaMGCESEhLKfH95tG3bVrRt21avzc3NTQwbNqzUvM2aNROPPfaY7ueHhQ8hhFi7dq0AIN5+++2H1lDy779gwQLRrFkzERISoveFWlb48PPzK/NAe/+Bv6TGdevWidmzZwsvLy9hZ2cnIiIiRFZWligsLBSvvfaacHV1Fba2tmLs2LFl9jyUZ7/36NFDtGjRQhw/flx069ZNWFtbi9dee00IIcSWLVvE448/Ljw9PYVUKhWNGjUSH3zwQam/hsvz5ZOfny+cnJxEQEBAuf6aFkKIq1eviqFDhwpHR0dhbW0tOnbsKLZv3643T8m++vnnn8U777wjvLy8hEQiEZmZmSIyMlLY2tqKK1euiAEDBgg7Ozvx1FNPPXSdJ0+eFP379xf29vbC1tZW9O7dW8TExOimHzt2TAAQq1atKvXeP//8UwAQ27Zt07XdvHlTPP/888LNzU1IpVIRFBQkVqxYUe5tKEvJ/IsWLSo17ddffxUAxJo1a4QQQnz//feiV69ewtXVVUilUhEYGCiWLl1a6n33fwYfdHzbvHmzaNGihZDJZKJFixZi06ZNBjnmlPjqq69EUFCQkEqlwtPTU7zyyiul9lPJZ/rcuXOiZ8+ewtraWnh5eYmPP/64zP1JQvCaDyPbtm0bGjVqhM6dO9fI8keMGAGtVosFCxagY8eOmDt3LhYvXoy+ffuiQYMG+Pjjj9GkSRNMnz4dBw8erLb1LlmyBG3atMEHH3yAjz76CBYWFhg2bBh27Nihm2fNmjWQyWTo1q0b1qxZgzVr1uDFF1984HbEx8fj2LFjeu03btzA4cOH8cwzz+ja5s2bhzFjxqBp06b47LPPMGXKFERHR6N79+7Iysqq8LYIIZCSkgIXFxdd261bt5Camop27dqVmr9Dhw74999/y738oUOHwtraGrt27SrX/Obm5nj33Xdx6tQpbN68udzrKY/58+dj586dmDFjBsaNG4dNmzbhpZdewrhx43Dp0iXMnj0bQ4YMwapVq/Dxxx/rvbci+z09PR0DBgxA69atsXjxYvTq1QvA3Yv+7OzsMG3aNCxZsgShoaF4//33MWPGjApvy99//42MjAw8++yzMDc3f+T8KSkp6Ny5M3bu3IlXXnkF8+bNQ2FhIZ588sky9/OHH36IHTt2YPr06fjoo48glUoBAMXFxQgPD4ebmxs+/fRTREREPHCd586dQ7du3XDq1Cm8+eabeO+99xAfH4+ePXviyJEjAIB27dqhUaNGWL9+fan3r1u3Do6OjggPD9dtQ6dOnbBnzx5MmjQJS5YsQZMmTTB+/HgsXry43Ntwv+7du8Pb2xtr164tNW3t2rWwsbHB4MGDAQBff/01/Pz88Pbbb2PRokXw8fHBK6+8gq+++uqB++FBdu3ahYiICEgkEsyfPx+DBw/G888/j+PHj5eat7qPOcDd66iioqLg5eWFRYsWISIiAt9++y369euHoqIivXkzMzPRv39/hISEYNGiRQgICMBbb72FP/74o8LbXS8YO/3UZ0qlUgB45F9GJSrT8zFx4kRdW3FxsfD29hYSiUQsWLBA156ZmSmsra31/kKuas9Hfn6+3s9qtVoEBweL3r1767U/qAv0/vUrlUohk8nE66+/rjffJ598IiQSie7Ux/Xr14W5ubmYN2+e3nxnzpwRFhYWpdrLY82aNQKA3l+PJX+Nrl69utT8b7zxhgCg6xl4VM+HEEKEhIQIR0fHh9ZR8u+/cOFCUVxcLJo2barX+1EdPR/BwcFCrVbr2keOHCkkEokYMGCA3vvDwsL0/s0rst979OghAIhvvvmmVF33f26EEOLFF18UNjY2ej0t5en5WLJkiQAgNm/e/ND5SkyZMkUAEH/99ZeuLScnR/j7+4uGDRvqTkmU7KtGjRqVqjcyMlIAEDNmzCjXOgcPHiykUqm4evWqri0pKUnY29uL7t2769pmzpwpLC0tRUZGhq5NpVIJBwcHMW7cOF3b+PHjhaenp7hz547eep555hmhUCh09T5sGx6k5HMdFxena1MqlcLKykqMHDlS11bW8sLDw0WjRo302srT89G6dWvh6ekpsrKydG27du0SAGr8mJOamiqkUqno16+f3umoL7/8UgAQ33//vd623H88UKlUwsPDQ0RERJRaF7Hnw6iys7MBAPb29jW2jgkTJuj+39zcHO3atYMQAuPHj9e1Ozg4oHnz5rh27Vq1rdfa2lr3/5mZmVAqlejWrRtOnjxZqeXJ5XIMGDAA69evhxBC175u3Tp06tQJvr6+AO5ema/VajF8+HDcuXNH9/Lw8EDTpk2xb9++Cq334sWLiIqKQlhYGCIjI3XtJXcgyWSyUu+xsrLSm6c87OzskJOTU+757+39qM4r98eMGQNLS0vdzx07doQQAuPGjdObr2PHjkhMTERxcTGAiu93mUyG559/vtT67/3c5OTk4M6dO+jWrRvy8/Nx8eLFCm1LRX+/fv/9d3To0AFdu3bVtdnZ2WHixIm4fv06zp8/rzd/ZGSkXr33evnllx+5Po1Gg127dmHw4MFo1KiRrt3T0xPPPvss/v77b902jBgxAkVFRdi0aZNuvl27diErKwsjRowAcLeHbuPGjRg0aBCEEHr/DuHh4VAqlaV+/x62DfcbPXo0AOj1fmzcuBGFhYUYNWqUru3e5SmVSty5cwc9evTAtWvXoFQqy7UuALh9+zZiY2MRGRkJhUKha+/bty+CgoJKzV/dx5w9e/ZArVZjypQpMDP731flCy+8ALlcrtejAtz9rJTsIwCQSqXo0KFDtR5X6xKGDyOSy+UAUKEvnYoq+VIuoVAoYGVlpXcKoaQ9MzOz2ta7fft2dOrUCVZWVnBycoKrqyu+/vrrCh187jdixAgkJiYiJiYGwN3bk0+cOKE7+ALA5cuXIYRA06ZN4erqqve6cOECUlNTy72+5ORkDBw4EAqFAr/++qte133JgU6lUpV6X2Fhod485ZGbm1vhEDpq1Cg0adIEH3zwgV4gq4qyPi8A4OPjU6pdq9Xq/j0rut8bNGhQZhf/uXPn8PTTT0OhUEAul8PV1VV3QK/oZ6eiv183btxA8+bNS7UHBgbqpt/L39+/zOVYWFjA29v7ketLS0tDfn7+A9ep1WqRmJgIAAgJCUFAQADWrVunm2fdunVwcXFB7969dcvLysrCsmXLSv0blAS9+/8dHrQNZWnVqhWCg4Px888/69rWrl0LFxcX3WkfADh06BD69OkDW1tbODg4wNXVFW+//TaAiv0bluzvpk2blppW1j6r7mNOyfrvX5dUKkWjRo1KfR68vb0hkUj02hwdHav1uFqXWBi7gPpMLpfDy8sLZ8+eLdf893+wS2g0mge+p6xz3Q86/33vF1hl1lXir7/+wpNPPonu3btj6dKl8PT0hKWlJVauXFnmOePyGjRoEGxsbLB+/Xp07twZ69evh5mZGYYNG6abR6vVQiKR4I8//ihzO+3s7Mq1LqVSiQEDBiArKwt//fUXvLy89KZ7enoCuPvX2f1u374NJyenMntFylJUVIRLly4hODi4XPOXKOn9GDt2LH777bcy53nYv2NFPhuP+sxUdL+XFcyysrLQo0cPyOVyfPDBB2jcuDGsrKxw8uRJvPXWW9BqtWXW8CABAQEAgDNnzuiuR6hODwqXMplM7y/l6jJixAjMmzcPd+7cgb29PbZu3YqRI0fCwuLuYbxk/4wePVqvl+5erVq10vu5IgG5ZNkzZszA8ePH4e3tjX379uHFF1/U1XD16lU89thjCAgIwGeffQYfHx9IpVL8/vvv+Pzzzyv8b1heNXXMqYjyHFfpfxg+jOyJJ57AsmXLEBMTg7CwsIfO6+joCAClLt67P4FXh6qsa+PGjbCyssLOnTv1voBXrlxZat4HfTmWxdbWFk888QQ2bNiAzz77DOvWrUO3bt30gkHjxo0hhIC/vz+aNWtW7mXfq7CwEIMGDcKlS5ewZ8+eMrt4GzRoAFdX1zIvfDt69Chat25d7vX9+uuvKCgo0PvrsbxGjx6NuXPnYs6cOXjyySdLTXd0dCzzItsbN27odfVXVXXs9/379yM9PR2bNm1C9+7dde3x8fGVWl7Xrl3h6OiIn3/+GW+//fYjLzr18/NDXFxcqfaS0z1+fn6VquNBXF1dYWNj88B1mpmZ6fU4jRgxAnPmzMHGjRvh7u6O7OxsvQutXV1dYW9vD41Ggz59+lRrrSVGjhyJmTNnYu3atfDz84NGo9E75bJt2zaoVCps3bpVrxetoqc7gf/t78uXL5eadv8+q4ljTsn64+Li9H5X1Go14uPja2wf1xc87WJkb775JmxtbTFhwgSkpKSUmn716lUsWbIEwN2eEhcXl1J3pSxdurTa62rcuDEA6K1Lo9Fg2bJlj3yvubk5JBKJXi/J9evXy7w2wdbWtkJ3oIwYMQJJSUlYvnw5Tp06pXfKBQCGDBkCc3NzzJkzp9RfHEIIpKenP3T5Go0GI0aMQExMDDZs2PDQQBgREYHt27frusYBIDo6GpcuXdLrjXmYU6dOYcqUKXB0dERUVFS53nOvkt6P2NhYbN26tdT0xo0b4/Dhw1Cr1bq2+2uuDlXd78D//nK89/1qtbrSn28bGxu89dZbuHDhAt56660y/wL98ccfcfToUQDA448/jqNHj+pO6wFAXl4eli1bhoYNG5YZQqvC3Nwc/fr1w2+//aY3pHdKSgrWrl2Lrl276k4dAXdPxbRs2RLr1q3DunXr4OnpqRfSzM3NERERgY0bN5bZm1rekXQfxtfXF926dcO6devw448/wt/fX+9OvbL+DZVKZZkh4FE8PT3RunVr/PDDD3qnTnbv3l3q+puaOOb06dMHUqkUX3zxhd72rFixAkqlEgMHDqzwNtH/sOfDyBo3boy1a9dixIgRCAwMxJgxYxAcHAy1Wo1//vkHGzZswNixY3XzT5gwAQsWLMCECRPQrl07HDx48IFDf1dFixYt0KlTJ8ycORMZGRlwcnLCL7/8orvA8GEGDhyIzz77DP3798ezzz6L1NRUfPXVV2jSpAlOnz6tN29oaCj27NmDzz77DF5eXvD390fHjh0fuOzHH38c9vb2mD59uu5ge6/GjRtj7ty5mDlzJq5fv47BgwfD3t4e8fHx2Lx5MyZOnIjp06c/cPmvv/46tm7dikGDBiEjIwM//vij3vR7Lyh7++23sWHDBvTq1QuvvfYacnNzsXDhQrRs2bLMiyn/+usvFBYWQqPRID09HYcOHcLWrVuhUCiwefNmeHh4PHS/PsioUaPw4YcfIjY2ttS0CRMm4Ndff0X//v0xfPhwXL16FT/++KMuXFaXqu53AOjcuTMcHR0RGRmJV199FRKJBGvWrKlSt/Ubb7yBc+fOYdGiRdi3bx+GDh0KDw8PJCcnY8uWLTh69Cj++ecfAMCMGTPw888/Y8CAAXj11Vfh5OSEH374AfHx8di4cWONnEqZO3cudu/eja5du+KVV16BhYUFvv32W6hUKnzyySel5h8xYgTef/99WFlZYfz48aVqWrBgAfbt24eOHTvihRdeQFBQEDIyMnDy5Ens2bMHGRkZVa559OjRmDhxIpKSkvDOO+/oTevXrx+kUikGDRqEF198Ebm5ufjuu+/g5uZW5inKR5k/fz4GDhyIrl27Yty4ccjIyMD//d//oUWLFsjNzdXNVxPHHFdXV8ycORNz5sxB//798eSTTyIuLg5Lly5F+/bt9Y4FVAmGvLWGHuzSpUvihRdeEA0bNhRSqVTY29uLLl26iP/7v//Tu8UwPz9fjB8/XigUCmFvby+GDx8uUlNTH3ir7f2jXZYMgnS/kkFy7nX16lXRp08fIZPJhLu7u3j77bd1I3g+6lbbFStWiKZNmwqZTCYCAgLEypUrdTXd6+LFi6J79+7C2tq6XAP+CCHEqFGjBADRp0+fB+7PjRs3iq5duwpbW1tha2srAgICRFRUlN5tgmUpuWXuQa/7nT17VvTr10/Y2NgIBwcHMWrUKJGcnKw3T8ltjSUvS0tL4erqKrp37y7mzZsnUlNTH1pTiXtvtb1fyf4q69980aJFokGDBkImk4kuXbqI48ePP/BW2/tvBy5Z7rFjx/TaH/T5Ks9+L+uzVuLQoUOiU6dOukGa3nzzTbFz585yfeYe5tdffxX9+vUTTk5OwsLCQnh6eooRI0aI/fv3681XMsiYg4ODsLKyEh06dHjgIGNl3Tr9oN+vhzl58qQIDw8XdnZ2wsbGRvTq1Uv8888/Zc57+fJl3b/z33//XeY8KSkpIioqSvj4+AhLS0vh4eEhHnvsMbFs2bJybcOjZGRkCJlMJgCI8+fPl5q+detW0apVK2FlZSUaNmwoPv74Y/H999+X+n0u7yBjGzduFIGBgUImk4mgoKAHDjJWU8ecL7/8UgQEBAhLS0vh7u4uXn755QcOMna/qozEWtdJhODVMERERGQ4vOaDiIiIDIrhg4iIiAyK4YOIiIgMiuGDiIiIDIrhg4iIiAyqQuGjYcOGkEgkpV4lgyMVFhYiKioKzs7OsLOzQ0RERJkDZxEREVH9VaFbbdPS0vRGkDt79iz69u2Lffv2oWfPnnj55ZexY8cOrFq1CgqFApMmTYKZmRkOHTpU7oK0Wi2SkpJgb29foaG3iYiIyHiEEMjJyYGXl9ejB+WryiAhr732mmjcuLHQarUiKytLWFpa6g1ac+HCBQFAxMTElHuZiYmJDx3kiS+++OKLL774qr2vxMTER37XV3p4dbVajR9//BHTpk2DRCLBiRMnUFRUpPewnYCAAPj6+iImJgadOnUqczkqlUrvseTiv46YxMREvecaEBERUe2VnZ0NHx8f2NvbP3LeSoePLVu2ICsrS/fckeTkZEilUjg4OOjN5+7ujuTk5AcuZ/78+ZgzZ06pdrlczvBBRERkYspzyUSl73ZZsWIFBgwYoPc488qYOXMmlEql7lXdT9skIiKi2qVSPR83btzAnj17sGnTJl2bh4cH1Go1srKy9Ho/UlJSHvq0TplMBplMVpkyiIiIyARVqudj5cqVcHNzw8CBA3VtoaGhsLS0RHR0tK4tLi4OCQkJCAsLq3qlREREVCdUuOdDq9Vi5cqViIyMhIXF/96uUCgwfvx4TJs2DU5OTpDL5Zg8eTLCwsIeeLFpVWg0GhQVFVX7cusaS0tLmJubG7sMIiIinQqHjz179iAhIQHjxo0rNe3zzz+HmZkZIiIioFKpEB4ejqVLl1ZLoSWEEEhOTkZWVla1Lrcuc3BwgIeHB8dNISKiWqFCg4wZQnZ2NhQKBZRKZZl3u9y+fRtZWVlwc3ODjY0Nv1AfQgiB/Px8pKamwsHBAZ6ensYuiYiI6qhHfX/fq9K32hqDRqPRBQ9nZ2djl2MSrK2tAQCpqalwc3PjKRgiIjI6k3qwXMk1HjY2NkauxLSU7C9eI0NERLWBSYWPEjzVUjHcX0REVJuYZPggIiIi08XwQURERAbF8EFEREQGxfBhgtRqdZntvKCUiIiMJTOv7O+msjB8GIhWq8Unn3yCJk2aQCaTwdfXF/PmzQMAnDlzBr1794a1tTWcnZ0xceJE5Obm6t47duxYDB48GPPmzYOXlxeaN2+O69evQyKRYN26dejRowesrKzw008/GWvziIioHios0uDApTTM2XYOA5YcLPf7TGqcj7IIIVBQpDHKuq0tzct9J8nMmTPx3Xff4fPPP0fXrl1x+/ZtXLx4EXl5eQgPD0dYWBiOHTuG1NRUTJgwAZMmTcKqVat074+OjoZcLsfu3bv1ljtjxgwsWrQIbdq0gZWVVXVuHhERUZky89TY9O8tfLn3MjLz7/a6a1Xl/y42+fBRUKRB0Ps7jbLu8x+Ew0b66F2Yk5ODJUuW4Msvv0RkZCQAoHHjxujatSu+++47FBYWYvXq1bC1tQUAfPnllxg0aBA+/vhjuLu7AwBsbW2xfPlySKVSAMD169cBAFOmTMGQIUNqYOuIiIj0HbuegW/2X8WBS2ko1t4dIN1DboWezV3R0dsaQxaXbzkmHz5MwYULF6BSqfDYY4+VOS0kJEQXPACgS5cu0Gq1iIuL04WPli1b6oLHvdq1a1dzhRMREQG4lpaLj36/gD0XUnVtgZ5yjO7kixHtfGBhbobs7OxyL8/kw4e1pTnOfxButHWXa77/hjivinvDSXnaiYiIqsOO07fxxq+nkK/WwNxMguHtfDC+a0M0cbOv9DJNPnxIJJJynfowpqZNm8La2hrR0dGYMGGC3rTAwECsWrUKeXl5uiBx6NAhmJmZoXnz5sYol4iICADw59lkTP75JLQCCGvkjLlPB6Oxq12Vl1u7v7XrCCsrK7z11lt48803IZVK0aVLF6SlpeHcuXMYNWoUZs2ahcjISMyePRtpaWmYPHkynnvuOd0pFyIiIkOLuZqOV3/+F1oBDAv1xoKIVjA3q57HdTB8GMh7770HCwsLvP/++0hKSoKnpydeeukl2NjYYOfOnXjttdfQvn172NjYICIiAp999pmxSyYionrqfFI2Jq4+DrVGi35B7pg/pGW1BQ8AkAghRLUtrRpkZ2dDoVBAqVRCLpfrTSssLER8fDz8/f15W2kFcL8REVF5XUrJwejlR5Cao0IHfyesHtcBVuW4xvFh39/3Y88HERERQQiBraeS8O7ms8hRFSPAwx7fjWlXruBRUQwfRERE9VxajgrvbD6DXedTAAAd/J3w7ehQKKwta2R9DB9ERET12LW0XIz5/ihuZhbA0lyCyb2b4uWejWFpXnNPYGH4ICIiqoeEEPi/vVfw5d4rUGu08HO2wTejQxHo+fDrNaqDSYaPWnaNbK3H/UVERPdbdvAaPtt9CQDQubEzFj/TGm72hrkpwaTCh6Xl3XNP+fn51TJqaH2Rn58P4H/7j4iI6rffYm9h/h8XAQDvPB6ICd38y/2g1OpgUuHD3NwcDg4OSE29O7a8jY2NQXeWqRFCID8/H6mpqXBwcIC5efVfsUxERKbln6t3MH3DKQDA+K7+eKF7I4PXYFLhAwA8PDwAQBdA6NEcHBx0+42IiOqvi8nZeHH1CRRpBAa29MQ7jwcapQ6TCx8SiQSenp5wc3NDUVGRscup9SwtLdnjQURESFYW4vmVx5CjKkb7ho5YNDwEZtU4amlFmFz4KGFubs4vVSIionLQaAVe/eVf3FYWorGrbY0NHlZeNXcTLxEREdUK3xy4iqPxGbCRmmN5ZHs42EiNWg/DBxERUR32b0Km7pbaD54Khr+LrZErYvggIiKqs67fycMLq09AoxUYFOKFiLYNjF0SAIYPIiKiOikpqwCjlh/BnVwVAjzsMe/p4FozPAXDBxERUR2TpyrGmO+P4lZWARq52GLN+I6QW9WegSYZPoiIiOqYWVvP4UpqLjzkVvhxQke42suMXZIehg8iIqI6ZPlf1/DriZuQSIDFz7SGl0PtexwJwwcREVEdse1UEubuuAAAeKt/ADo1cjZyRWVj+CAiIqoDTiZk4vX1d5/ZMrZzQ7xohGe2lBfDBxERkYnLUxVj6rpYqDVahLdwx3tPBNWaO1vKwvBBRERkwoQQeGfzGdxIz4eXwgoLh4XA3EjPbCkvhg8iIiIT9u3Ba9gSmwRzMwk+G9G6Vt1S+yAMH0RERCYq+kIKPv7zIgBg9qCgWnuB6f0qHD5u3bqF0aNHw9nZGdbW1mjZsiWOHz+umy6EwPvvvw9PT09YW1ujT58+uHz5crUWTUREVN/tOH0bL/94EkIAIzv4YnQnP2OXVG4VCh+ZmZno0qULLC0t8ccff+D8+fNYtGgRHB0ddfN88skn+OKLL/DNN9/gyJEjsLW1RXh4OAoLC6u9eCIiovpodcx1TPr5JNQaLfq38MCcJ1vU6gtM7ycRQojyzjxjxgwcOnQIf/31V5nThRDw8vLC66+/junTpwMAlEol3N3dsWrVKjzzzDOPXEd2djYUCgWUSiXkcnl5SyMiIqrzhBD4bPcl/N/eKwCA0Z18MefJ4FpxgWlFvr8r1POxdetWtGvXDsOGDYObmxvatGmD7777Tjc9Pj4eycnJ6NOnj65NoVCgY8eOiImJKXOZKpUK2dnZei8iIiLSl68uxsxNZ3TBY1rfZvjwqdoRPCqqQuHj2rVr+Prrr9G0aVPs3LkTL7/8Ml599VX88MMPAIDk5GQAgLu7u9773N3dddPuN3/+fCgUCt3Lx8enMttBRERUZ209lYSuH+/DL8cSYSYBPnq6JV59rKlJnWq5l0VFZtZqtWjXrh0++ugjAECbNm1w9uxZfPPNN4iMjKxUATNnzsS0adN0P2dnZzOAEBER4W5vx+yt57D++E0AgJ+zDWYNCkLvAPdHvLN2q1D48PT0RFBQkF5bYGAgNm7cCADw8PAAAKSkpMDT01M3T0pKClq3bl3mMmUyGWSy2vW0PSIiImOLTczC6+tjcTUtDxIJMLl3U7zauwkszE1/lIwKhY8uXbogLi5Or+3SpUvw87t7e4+/vz88PDwQHR2tCxvZ2dk4cuQIXn755eqpmIiIqA7bfT4F3x64iuM3MgEA7nIZFo9og7DGpjGGR3lUKHxMnToVnTt3xkcffYThw4fj6NGjWLZsGZYtWwYAkEgkmDJlCubOnYumTZvC398f7733Hry8vDB48OCaqJ+IiKhOKCzSYMEfF7Hqn+sAAEtzCQaFeOHdgUFwspUat7hqVqHw0b59e2zevBkzZ87EBx98AH9/fyxevBijRo3SzfPmm28iLy8PEydORFZWFrp27Yo///wTVlZW1V48ERGRqRNC4M+zyZj3+wXczCwAAEzs3ggTuvrDTV43vzsrNM6HIXCcDyIiqi9OJmTikz8v4vC1DACAp8IKHzwVjL5BpndBaUW+vyvU80FERERVV6DWYPqGU9hx5jYAQGZhhhd7NMZLPRrBRlr3v5rr/hYSERHVEkIIxFxLx+yt53ApJRcWZhI83aYBXuvTFN6ONsYuz2AYPoiIiGpYsUaLP88l47uD13DqphIA4GwrxTfPhaJ9QycjV2d4DB9EREQ1JFdVjHXHEvH93/G4lXX3YlKphRlGtPPBtL7N4FjH7mIpL4YPIiKiapasLMTKf+Kx9kgCcgqLAQBOtlI818kPz4X5wcWufg+uyfBBRERUTVJzCrFo5yVsPHkTxdq7N5M2crHF+G7+iGjrDStLcyNXWDswfBAREVWDfxMyMXblMSgLigAAHfydMLFbI/QOcIOZCT55tiYxfBAREVXRX5fT8NKaE8hTaxDkKceHg1sg1K/+XUhaXgwfREREVbD1VBJeXx+LIo1A1yYuWDYmtF6M1VEV3DtERESVUKTR4ovoy/i/vVcAAE+08sSi4SGQWfC6jkdh+CAiIqogrVbgtV/+xe9nkgEAYzs3xPtPBPHajnJi+CAiIqqgxXsu4fczybA0l+Cz4a0xKMTL2CWZFDNjF0BERGRKfou9hS/+O9Xy0dMtGTwqgeGDiIionGITs/DGr6cB3H3s/bB2PkauyDQxfBAREZXDbWUBXlh9HOpiLR4LcMNb/QOMXZLJYvggIiJ6BFWxBi+sPo60HBWau9tjycg2MOfFpZXG8EFERPQI3x28hrO3suFkK8XyyHawk/F+japg+CAiInqIxIx8fLnv7gWmswYFwcfJxsgVmT6GDyIioof4YPt5FBZp0amRE57knS3VguGDiIjoAbaeSsLu8ymwMJPgg6eCIZHwOo/qwPBBRERUhkNX7mD6+lMA7t5W28zd3sgV1R0MH0RERPc5c1OJiauPQ63R4vGWHni9X3Njl1SnMHwQERHdIyNPjRfXHEeeWoPOjZ3x+YjWvK22mjF8EBER/UerFZiyLhZJykI0crHFt8+F8im1NYDhg4iI6D9f7ruCg5fSYGVphqWj28LeytLYJdVJDB9ERES4+8C4z3ZfAgB8+FQwAjzkRq6o7uIQbUREVK9ptQKf7b6EpfvvDiQ2tnNDPjCuhjF8EBFRvaXVCry9+Qx+OZYIABjZwRfvPxFk5KrqPoYPIiKql1TFGrz562n8FpsEMwmwcGgIIkK9jV1WvcDwQURE9U52YRFeWnMC/1xNh4WZBJ8OC8HgNg2MXVa9wfBBRET1SrKyEGNXHsXF5BzYSs3x9ehQdG/mauyy6hWGDyIiqjcupeRg7PdHkaQshKu9DCvHtkdwA4Wxy6p3GD6IiKheuJSSgxHfxiAzvwiNXG3xw/Md4ONkY+yy6iWGDyIiqvMOXErDtHWxyMwvQitvBX54vgMcbaXGLqveYvggIqI6q1ijxaLdl/D1/qsAgOAGcqwe1wEONgwexsTwQUREdc5tZQF+P5OM7aeT8G9CFgBgdCdfvDswCFaWfFaLsTF8EBGRyVMWFOFWZgEOXEpD9IUUxCZmoVgrAAD2MgssiGiFga08jVwllWD4ICIikySEwN9X7uCXo4nYdT4ZRRqhN71DQyeENXbG0FBvXlhayzB8EBGRybmalovZW8/hr8t3dG3OtlL4OdtgaKgPOjd2RkMXWyNWSA9Toafazp49GxKJRO8VEBCgm15YWIioqCg4OzvDzs4OERERSElJqfaiiYiofipQa7Bw50X0X3wQf12+A6mFGZ7r5Icdr3bFiff6YtMrXfBsR18Gj1quwj0fLVq0wJ49e/63AIv/LWLq1KnYsWMHNmzYAIVCgUmTJmHIkCE4dOhQ9VRLRET11tlbSkxdF4vLqbkAgJ7NXTF7UAsGDRNU4fBhYWEBDw+PUu1KpRIrVqzA2rVr0bt3bwDAypUrERgYiMOHD6NTp05Vr5aIiOqV28oC/BabhC3/3sLF5BwAgJu9DB88FYzwFu6QSCRGrpAqo8Lh4/Lly/Dy8oKVlRXCwsIwf/58+Pr64sSJEygqKkKfPn108wYEBMDX1xcxMTEPDB8qlQoqlUr3c3Z2diU2g4iI6pI7uSos2XMZa48mQPPfXSuW5hL0C/LA7CdbwNVeZuQKqSoqFD46duyIVatWoXnz5rh9+zbmzJmDbt264ezZs0hOToZUKoWDg4Pee9zd3ZGcnPzAZc6fPx9z5sypVPFERFS3FBZp8P2heCzddxW5qmIAQPuGjhjcpgEGtvTk4GB1RIXCx4ABA3T/36pVK3Ts2BF+fn5Yv349rK2tK1XAzJkzMW3aNN3P2dnZ8PHxqdSyiIjIdKVkF2LCD8dx5pYSwN3RSN9+PBCdG7sYuTKqblW61dbBwQHNmjXDlStX0LdvX6jVamRlZen1fqSkpJR5jUgJmUwGmYzdZ0RE9ZUQAttP38asreeQkaeGo40l3h8UhKdCGsDMjNd01EUVutX2frm5ubh69So8PT0RGhoKS0tLREdH66bHxcUhISEBYWFhVS6UiIjqnn+u3MHgrw5h8s//IiNPjUBPOX6L6oqn23gzeNRhFer5mD59OgYNGgQ/Pz8kJSVh1qxZMDc3x8iRI6FQKDB+/HhMmzYNTk5OkMvlmDx5MsLCwninCxER6WTkqbHp5E3svZiKf66mAwBspOaY2L0RXu7ZGDILPnulrqtQ+Lh58yZGjhyJ9PR0uLq6omvXrjh8+DBcXV0BAJ9//jnMzMwQEREBlUqF8PBwLF26tEYKJyIi05Keq8Kyv65hTcwN5Ks1AO7ewfJsB19M6t2Ud7DUIxIhhHj0bIaTnZ0NhUIBpVIJuVxu7HKIiAiAMr8INzLycCM9HzfS7/43MTMfDtZShPg4oLWPA1p5K2ArK/03bVxyDn46cgMbjt9EQdHd0NHCS44nWnnhiVaefO5KHVGR728+24WIiPQkZuTjenoekpWF+ONsMk4mZCIrv+iB8/957u5wClILMwwL9UbHRs5o6GyD+Dt5+PvyHWw8eRP/DdWBVt4KvPZYU/QOcOMAYfUYez6IiAgAoC7W4qPfL2DVP9fLnO5qL4Ofkw18nW3Q0NkWPk7WSMtRITYxC7EJWUhSFj5w2f2C3PFcmB+6NnFh6Kij2PNBREQVkpRVgKi1J/FvQhYAoJm7HVztZQhuoMCgVl7wd7Et85RKCSEEYq6mY+upJFxOzcWN9Dw0cLRBGx8HhLfwQFhjZwNtCZkChg8ionpMCIFd51MwY+NpZOYXQW5lgc+Gt0afIPcKLUcikaBzExd0bsIBwejRGD6IiOqZkl6KbaeTcORaBq7dyQNwd0TRpc+GwteZF4BSzWL4ICKqR07cyMDHf8Th6PUMXZvUwgzju/rjtceawsqSY2xQzWP4ICKqJ1YeisecbecBADILMwxp640+gW5o6+sIR1s+sI0Mh+GDiKiOE0JgdcwNfLD9bvCIaOuN6eHN4Kmo3ANBiaqK4YOIqA4r0mgxfcMp/BabBAAYE+aHOU+24O2uZFQMH0REddRtZQHe2HAaf1+5A0tzCWYMCMTznRsyeJDRMXwQEdVBF25n47kVR3AnVw2ZhRm+GR2KXgFuxi6LCADDBxFRnRObmIXI749CWVCEAA97fPlsGzRxszd2WUQ6DB9ERHVEsUaLFX/HY0n0ZeSrNWjr64CVz3eAwtrS2KUR6WH4ICKqA7Ly1Zi09l/8feUOAKBrExd8+1zoQ4dEJzIWfiqJiEzc5ZQcTFh9HDfS82EjNcfsQS0wNNQbZma8sJRqJ4YPIiITFn0hBa/9EotcVTEaOFhjeWQ7BHryieBUuzF8EBGZoCKNFkv3XcXi6EsQAujo74Slo9rC2U5m7NKIHonhg4jIxCjzizDm+yM4dVMJABjV0Rezn2wBS3MzI1dGVD4MH0REJiRPVYyxq47i1E0lFNaWmP1kEJ5u423ssogqhOGDiMhEFBZp8OKaE/g3IQsKa0use7ETAjx4fQeZHoYPIiITkJmnxoTVx3HiRiZspOZY9Xx7Bg8yWQwfRES13K2sAjy34giupeVBbmWB5ZHt0cbX0dhlEVUawwcRUS2Wml2IZ787jBvp+fBSWGHVuA5o5s6h0sm0MXwQEdVSmXlqjF5xBDfS8+HjZI31L4bBU2Ft7LKIqoz3ZRER1UJFGi1eWH0cl1Jy4S6X4afxnRg8qM5g+CAiqoUW7ozD8RuZsLeywI/jO8LX2cbYJRFVG4YPIqJaZs/5FCw7eA0AsHBoCJryGg+qYxg+iIhqkZuZ+Xh9wykAwPNdGqJ/sIeRKyKqfgwfRES1hLpYi0lr/4WyoAgh3grMHBBo7JKIagTDBxFRLfHJnxcRm5gFuZUFvny2LaQWPERT3cRPNhFRLbA65jqW/x0PAPh0WAh8nHiBKdVdDB9EREa2JuY63v/tHADgpR6N0a8Fr/Oguo2DjBERGVFsYhY+2H4eADC5dxNM69vMyBUR1Tz2fBARGcmtrAK8sPo4ijQCA4I9MK1vM0gkEmOXRVTjGD6IiIwgp7AI41cdQ1qOCgEe9vhkaCsGD6o3GD6IiAxMCIGp62JxMTkHrvYyrBjbHvZWlsYui8hgGD6IiAxsw/Gb2HMhFVILMywf0w4NHPjMFqpfGD6IiAzoVlYBPvzvAtPp/ZohxMfBuAURGQHDBxGRgQghMGPjaeSoitHW1wHjuzYydklERlGl8LFgwQJIJBJMmTJF11ZYWIioqCg4OzvDzs4OERERSElJqWqdREQm74+zyfjr8h3ILMzw6bAQmJvxAlOqnyodPo4dO4Zvv/0WrVq10mufOnUqtm3bhg0bNuDAgQNISkrCkCFDqlwoEZEpUxdr8fGfFwHcHUiskaudkSsiMp5KhY/c3FyMGjUK3333HRwdHXXtSqUSK1aswGeffYbevXsjNDQUK1euxD///IPDhw9XW9FERKbm6/1XcSM9H672MkzsztMtVL9VKnxERUVh4MCB6NOnj177iRMnUFRUpNceEBAAX19fxMTElLkslUqF7OxsvRcRUV1yMTkbX+67DAB4d2AgbGUcXJrqtwr/Bvzyyy84efIkjh07VmpacnIypFIpHBwc9Nrd3d2RnJxc5vLmz5+POXPmVLQMIiKTUKzR4o0Np1GkEegT6I4nQ7yMXRKR0VWo5yMxMRGvvfYafvrpJ1hZWVVLATNnzoRSqdS9EhMTq2W5RES1wbcHr+HMLSXkVhb46OlgjmJKhAqGjxMnTiA1NRVt27aFhYUFLCwscODAAXzxxRewsLCAu7s71Go1srKy9N6XkpICD4+yn9Iok8kgl8v1XkREdUFccg6W7Ll7uuX9QS3gJq+eP9qITF2FTrs89thjOHPmjF7b888/j4CAALz11lvw8fGBpaUloqOjERERAQCIi4tDQkICwsLCqq9qIqJarlijxRu/noJao0XvADdEtG1g7JKIao0KhQ97e3sEBwfrtdna2sLZ2VnXPn78eEybNg1OTk6Qy+WYPHkywsLC0KlTp+qrmoiolvv24DWcvnn3dMv8IS15uoXoHtV+yfXnn38OMzMzREREQKVSITw8HEuXLq3u1RAR1Vo3M/PxRfTd0y2zBrWAO0+3EOmRCCGEsYu4V3Z2NhQKBZRKJa//ICKTFPXTSew4cxsd/Z3wy8RO7PWgeqEi3998tgsRUTU6fC0dO87chpnkbq8HgwdRaQwfRETVpEijxZxtd59YO7KDL4K82HtLVBaGDyKiavJF9GVcuJ0NhbUlXu/X3NjlENVaDB9ERNXg2PUMfLXvCgBg3tPBcLKVGrkiotqL4YOIqIpyCoswdV0stAIY0rYBnmjFIdSJHobhg4ioimZtPYebmQXwdrTGnCdbGLscolqP4YOIqAo2/3sTm07egpkEWDyiNeytLI1dElGtx/BBRFRJJxMy8dbGu4+cmNSrCdo1dDJyRUSmgeGDiKgSsguLMHntv1AXa9EvyB1T+jQzdklEJoPhg4iogtTFWkxbF4tbWQXwcbLGouEhMDPjYGJE5VXtz3YhIqrLCos0eOWnk9h7MRVSCzMsHtGG13kQVRB7PoiIykkIgaj/gofMwgzLx7RDqJ+jscsiMjns+SAiKqfN/95C9MVUWFma4fux7dG5sYuxSyIySez5ICIqh+zCInz0+0UAwKuPNWXwIKoChg8ionL4fPcl3MlVoZGLLSZ0bWTscohMGsMHEdEjnL2lxOqYGwCA2U+2gNSCh06iquBvEBHRQxQWaTBlXSw0WoGBLT3RvZmrsUsiMnkMH0RED6DVCkxbH4srqblwtZfhw8HBxi6JqE5g+CAiKoMQAh9sP4/fzyTD0lyCL55pAydbqbHLIqoTGD6IiMrw3V/XsOqf6wCARcNbI6yxs3ELIqpDGD6IiO7zW+wt3W217w4MxJMhXkauiKhuYfggIrrHP1fuYPqGUwCAcV38MaEbb6slqm4MH0RE/zmflI0X15xAkUZgYCtPvDsw0NglEdVJDB9ERAAuJmcjcuVR5KiK0dHfCYuG8Um1RDWFz3YhonrvaHwGxv9wDDmFxQjwsMeyMe1gZWlu7LKI6iyGDyKqlwqLNNh7MRXbTiUh+kIq1Bot2vk5YkVkeyisLY1dHlGdxvBBRPVKZp4a/7f3CtYdS0CeWqNr7xfkjiXPtIG1lD0eRDWN4YOI6oXCIg2+PxSPr/dfRU5hMQCggYM1BoV4YVCIJ4I85ZBIeI0HkSEwfBBRnXf8egZe+yUWt7IKAACBnnK81b85ejRzZeAgMgKGDyIyaTFX0/HDP9dhZ2WBx1t6wM/ZFvZWFkjPVQMAdp1LwRd7L0OjFfBSWGF6eHMMbt2Ad7IQGRHDBxGZJCEElkRfxuI9l3Vtv564+cD5n2rthbmDg2FvxYtJiYyN4YOITIIQAsVaAY1WYOe5ZHy9/youJucAAEZ28IFGK3D6phKJGfnIL9LAyUYKrRDwc7bFc538MKRtA55iIaolGD6IqFa7k6vC6n+u45djiUjLVcFMIoFGKwAAtlJzvD0wEKM6+unmF+JuQLEw5xiKRLUVwwcR1UrKgiJ8tisOvxxLhKpYq2vXCAF3uQyjO/phTFhDKGz0T6NIJBJYmLOHg6g2Y/ggolrnSmoOnl91DIkZd+9OCfFW4IXujdDB3wlaLeAul/EUCpEJY/ggololKasAz604itvKQng7WmPBkFbo0sSZYYOoDmH4IKJaQ5lfhLEr7waPxq622PBSZzjZSo1dFhFVM16RRUS1QmGRBi+sOY5LKblwl8vww7gODB5EdRTDBxEZnUYrMG19LI7GZ8BeZoFVz3eAt6ONscsiohpSofDx9ddfo1WrVpDL5ZDL5QgLC8Mff/yhm15YWIioqCg4OzvDzs4OERERSElJqfaiiajuEELgw+3n8fuZZEjNzfDtmFAEesqNXRYR1aAKhQ9vb28sWLAAJ06cwPHjx9G7d2889dRTOHfuHABg6tSp2LZtGzZs2IADBw4gKSkJQ4YMqZHCiahu+PbgNaz65zoAYNHwEHRu7GLcgoioxkmEEKIqC3BycsLChQsxdOhQuLq6Yu3atRg6dCgA4OLFiwgMDERMTAw6depUruVlZ2dDoVBAqVRCLudfP0R12Q//XMesrXf/eHl3YCAmdGtk5IqIqLIq8v1d6Ws+NBoNfvnlF+Tl5SEsLAwnTpxAUVER+vTpo5snICAAvr6+iImJqexqiKiOir6QogseL/dszOBBVI9U+FbbM2fOICwsDIWFhbCzs8PmzZsRFBSE2NhYSKVSODg46M3v7u6O5OTkBy5PpVJBpVLpfs7Ozq5oSURkYrILi/DO5rMAgDFhfngzvLmRKyIiQ6pwz0fz5s0RGxuLI0eO4OWXX0ZkZCTOnz9f6QLmz58PhUKhe/n4+FR6WURkGhb8cRHJ2YVo6GyDtx8P5ABiRPVMhcOHVCpFkyZNEBoaivnz5yMkJARLliyBh4cH1Go1srKy9OZPSUmBh4fHA5c3c+ZMKJVK3SsxMbHCG0FEpiPmajrWHkkAACyIaAUrS3MjV0REhlblcT60Wi1UKhVCQ0NhaWmJ6Oho3bS4uDgkJCQgLCzsge+XyWS6W3dLXkRUNxWoNZix6TQA4NmOvujUyNnIFRGRMVTomo+ZM2diwIAB8PX1RU5ODtauXYv9+/dj586dUCgUGD9+PKZNmwYnJyfI5XJMnjwZYWFh5b7ThYjqtk93xeFGej485FaYMSDA2OUQkZFUKHykpqZizJgxuH37NhQKBVq1aoWdO3eib9++AIDPP/8cZmZmiIiIgEqlQnh4OJYuXVojhRORadkXl4oVf8cDAOY9HQy5laWRKyIiY6nyOB/VjeN8ENU9N9Lz8NRXh5CVX4TIMD/MeSrY2CURUTUzyDgfRETloSrWYOLqE8jKL0KItwIzHw80dklEZGQMH0RUo76Ivoy4lBy42EmxbEw73t1CRAwfRFRzYhOz8M2BawCAuYOD4S63MnJFRFQbMHwQUY3IVxdj6rpYaLQCg0K80D/Y09glEVEtwfBBRDVi3o4LiL+TBw+5FebyAlMiugfDBxFVu70XU/DTf6OYLhoeAoUNb6slov9h+CCiapWeq8Kbv54BAIzr4o8uTVyMXBER1TYMH0RUbYQQeGvjadzJVaGZux3e7M+n1RJRaQwfRFRt1hy+gT0XUiE1N8PiEW14Wy0RlYnhg4iqRVxyDubuuAAAmPl4AIK8OEIxEZWN4YOIqiwzT42XfjwBdbEWPZu7YmznhsYuiYhqMYYPIqqSwiINJq45jvg7eWjgYI1Ph4VAIpEYuywiqsUYPoio0rRagTd/PY1j1zNhb2WBlc+3h4udzNhlEVEtx/BBRJX22e5L2HoqCRZmEnwzOhTN3O2NXRIRmQCGDyKqlPXHEvHlvisAgPlDWnI8DyIqNwtjF0BEpkVVrMFX+67iq/+Cx6u9m2BYOx8jV0VEpoThg4jKRZlfhD/P3cayg9dwNS0PADC8nTem9m1m5MqIyNQwfBBRKVqtgEQCSCQS5KuL8dHvF7DuWCKKNAIA4GInw5wnW+Dxlh68s4WIKozhg6iWKFBrkFNYhIvJOTCTSBDkJYeTrdSgNZScUvnu4DU42ljC19kGF27nQFlQBAAI8LDHoBAvjO7ox4fFEVGlMXwQGZG6WIt1xxKw/O943EjP15smszDDsHbe6N/CE50bO8PMrPp6GBLS87H11C1k5RdBYW0Jd7kVZJZm+GrfFVxKyQUAFCg1SFIWAgA8FVZYODQEXZvyolIiqjqJEEIYu4h7ZWdnQ6FQQKlUQi7n8MxUd9zKKsCRa+m4kZ6P28oCnL6pxKWUHGjv+w1s6GwDALh+TxjxdbJBCy85RnX0K3cAUBVroNUCVpZmUBVrkZajQsy1dPx64iaOxmc88H0udlK8P6gFFNaWUBYUoYGDFUK8HWBhzpvjiOjBKvL9zZ4PohqWkafGx39cxLrjiWVOV1hbYkqfpniqdQM4WFvCzEwCIQQOXUnHtlNJ+P3sbSRk5CMhIx9/nE3G4y09MKVPMzR1s4NWAPF3cnHhdg5yCouh0WpxITkHp29m4eLtHBRrBcwkKBVwJBKgaxMXBHrKocwvQkJGPjLz1egT6I7xXf3haODTPURUv7Dng6gG7TqXjDc3nkZW/t1rJtr4OiDAQw5PhRUaudqinZ8T3OWyh160masqxpFr6Th4KQ1rDt/QBQmZhRk0WoHi+5PFA1iYSRDgaY8BwZ4Y0rYBPBXWVd4+IqISFfn+ZvggqiGb/72J6RtOQ6MVCPCwx4eDg9G+oVOVlnnhdjYW7YrDwct3oC7WArh7WqWFlwKONlIAAo1d7dDK2wEhPgrIrS1RoNbARmoOG6kFzKvxuhEionsxfBAZ2S9HEzBz8xkIAQwN9caCIS2r9ZqJwiIN0nJUMDeTwM1exusxiMjoeM0HkREdvJSGt/8LHpFhfpg1qEW13qkCAFaW5vBxsqnWZRIRGQrDB1E1ir+Th0lrT0IrgGGh3pj9ZAsOwkVEdB/21RJVk4T0fIz/4RiyC4vR1tcBc58OZvAgIioDez6IqsH5pGyMWBaDnMJieCqs8M1zoZBZmBu7LCKiWok9H0RVlJmnRtTak8gpLEaIjwN+fbkz3OytjF0WEVGtxZ4Poio4dOUOJv/8LzLy1PBUWGHV2PYcoIuI6BEYPogq6VySEhNXH0eeWoOmbnZY/ExrBg8ionJg+CCqhKSsAoxbdQx5ag3CGjlj1bj2vMaDiKiceM0HUQVlFxbh+ZXHkJKtQlM3O15cSkRUQQwfRBWgLtbi5R9PIC4lB272Mqwa1wEKa0tjl0VEZFIYPojKSQiBGZtO49CVdNhIzfH92PZo4MCHsxERVRTDB1E5fb7nMjadvAVzMwm+GtUWwQ0Uxi6JiMgkMXwQlUP0hRR8EX0ZADB3cDB6NXczckVERKaL4YPoETLz1Jix6QwAYFwXf4zs4GvkioiITBvDB9EjzNp6Dmk5KjRxs8Ob/ZsbuxwiIpNXofAxf/58tG/fHvb29nBzc8PgwYMRFxenN09hYSGioqLg7OwMOzs7REREICUlpVqLJjKUP88mY+upJJibSbBoWAisLHlLLRFRVVUofBw4cABRUVE4fPgwdu/ejaKiIvTr1w95eXm6eaZOnYpt27Zhw4YNOHDgAJKSkjBkyJBqL5yopuWri/HBtnMAgBe7N0KIj4NxCyIiqiMkQghR2TenpaXBzc0NBw4cQPfu3aFUKuHq6oq1a9di6NChAICLFy8iMDAQMTEx6NSp0yOXmZ2dDYVCAaVSCblcXtnSiKps4c6L+GrfVXg7WmPPtB7s9SAieoiKfH9X6ZoPpVIJAHBycgIAnDhxAkVFRejTp49unoCAAPj6+iImJqbMZahUKmRnZ+u9iIwt/k4evjsYDwB474kgBg8iompU6fCh1WoxZcoUdOnSBcHBwQCA5ORkSKVSODg46M3r7u6O5OTkMpczf/58KBQK3cvHx6eyJRFVCyEEZm89B7VGix7NXNEvyN3YJRER1SmVDh9RUVE4e/YsfvnllyoVMHPmTCiVSt0rMTGxSssjqqpd51Nw4FIapOZmmP1kC0gkEmOXRERUp1TqqbaTJk3C9u3bcfDgQXh7e+vaPTw8oFarkZWVpdf7kZKSAg8PjzKXJZPJIJPJKlMGUbUrLNLgg23nAQATuvnD38XWyBUREdU9Fer5EEJg0qRJ2Lx5M/bu3Qt/f3+96aGhobC0tER0dLSuLS4uDgkJCQgLC6ueiolq0NL9V3ErqwBeCitM6t3E2OUQEdVJFer5iIqKwtq1a/Hbb7/B3t5edx2HQqGAtbU1FAoFxo8fj2nTpsHJyQlyuRyTJ09GWFhYue50ITKmI9fS8fX+KwCAd58Igo20Uh2DRET0CBU6un799dcAgJ49e+q1r1y5EmPHjgUAfP755zAzM0NERARUKhXCw8OxdOnSaimWqKbE38nDiz+eQJFGYGBLTwwILvs0IRERVV2VxvmoCRzngwwtM0+NIV//g/g7eWjt44BfJnbirbVERBVksHE+iExdYZEGL6w+jvg7eWjgYI3vxrRj8CAiqmEMH1RvabUC0zecwvEbmbC3ssDK59vD1Z53XhER1TReUUf1zm1lAX4+mojtp5Jw7U4eLM0l+HZ0KJq52xu7NCKieoHhg+qVw9fS8dKPJ5CVXwQAsLI0w8cRrdC5iYuRKyMiqj8YPqheKFBr8OmuOHx/KB5CAC285JjYvREeC3SHnYy/BkREhsSjLtUZGq3ArcwCHLp6B9tPJyE2IQvuCit4O9rg4u1spOaoAAARbb0xd3AwrKW8sJSIyBgYPsgkCSGQlqtCarYKNzMLsP10Eg5eSkN2YbHefNfS8nAtLQ8A4KmwwkdDWqJXczdjlExERP9h+CCTkpiRjy/3XsHvZ28j576gAQBSCzMEeNijf7AHejZzQ2a+GgkZ+WjgYI32DZ3Y20FEVAswfJDJ2HTyJt7bchZ5ag0AwEwCONvJ4GInQ0d/JzzV2gstGyhgYa5/B3kXYxRLREQPxPBBtV5OYRHe23IWW2KTAADtGzpier/maOPrCKkFh6ohIjI1DB9Uq93MzMeo5UdwIz0f5mYSvPZYU0T1agJzM4mxSyMiokpi+KBaKz1XhdH/BY8GDtZY8kxrtGvoZOyyiIioihg+qFbSagWmrT+F6+n58Ha0xoaXwuCpsDZ2WUREVA14wpxqpU93xeHApTTILMywIrI9gwcRUR3C8EG1ztL9V7B0/1UAwIeDg9Hcg89cISKqSxg+qFb56cgNfPJnHABg5oAADG/nY+SKiIioujF8UK2x92IK3ttyFgAwqVcTvNijsZErIiKimsDwQbXCuSQlJq39F1oBjGjng9f7NTN2SUREVEMYPsjojl3PQOT3R5Gv1qBLE2fMfToYEgnH8SAiqqt4qy0ZjUYr8NORG/hg23kUawVaeMmxdFQoLM2ZiYmI6jKGDzKYkifR3slRY/vpJGz59xaSlIUAgIGtPLFwaCvYSPmRJCKq63ikJ4M4fC0ds347h7iUHL12RxtLRPVqgvFd/XmqhYionmD4oBqlLtbi3S1nsP74TV2b1NwMPZu74qnWDfBYoBusLPmYeyKi+oThg2pMWo4K0zecwoFLaZBIgGc7+OKN8OZQWFuyl4OIqB5j+KAasfNcMqZvOIWcwmJYWZrh2+faoUczV2OXRUREtQDDB1WrrHw1voi+gu8PxQMAWjZQ4KOnW6Klt8LIlRERUW3B8EFVpswvwp/nbmPHmWT8c+UOirUCADC2c0O8MzCQt84SEZEehg+qtGKNFmsO38Bnuy8hp7BY1x7kKccb/ZujV3M3I1ZHRES1FcMHVcqRa+mYtfUcLibfvXW2iZsdnm7TAAOCPdDI1c7I1RERUW3G8FFP3czMR0JGPs7cVCIxMx92MksMbOmJ4AZySCQSFGu0+OvyHVxIzkZhkRa2UnNohMC1tDycuanUjdfhYGOJN8Kb45n2vjA34x0sRET0aAwf9czha+mY/8dFnErMKjXtmwNX0djVFl4O1riYnIO0HNUDl2NpLsGwdj54o19zONpKa7BiIiKqaxg+6oE8VTEOXbmDNYdv4K/LdwAA5mYS+DnZoJGrLYI85bh2Jw+7zqfgaloerqblAQCcbKXo0cwVtjJz5Ks0EAD8XWzRxM0OXRq7QGFjacStIiIiU8XwUYedTMjEvB0XcOJGpq5NIgGeae+L1/s1g4udTG9+ZUERDl9LR3ZBEXycbNDax4GjjxIRUbVj+KiDijVaLP87Hp/8eRH/3fUKFzspIkK9MbqjH3ycbMp8n8LaEuEtPAxYKRER1UcMH3VIYZEGq/65jl+OJuB6ej4A4MkQL8x8PACeCmsjV0dERHQXw0cdUaDWYNLak4i+mArg7vUad+9C8eFzVIiIqFZh+DBhQgicTMjC94fisftcCtQaLWQWZnh/UBCeDPGCvRUvCCUiotqH4cMECSGw4u94/Hripm6QLwBo4GCNeU8HoydHFiUiolqM4cPECCEwe+s5/BBzAwAgNTfDU629ENm5IVp4yXmKhYiIar0KP/Hr4MGDGDRoELy8vCCRSLBlyxa96UIIvP/++/D09IS1tTX69OmDy5cvV1e99ZpWK/Dh9gv4IeYGJBLg7ccDcOTtx7BwWAiCGygYPIiIyCRUOHzk5eUhJCQEX331VZnTP/nkE3zxxRf45ptvcOTIEdja2iI8PByFhYVVLra+un4nDx//eREDlvyle1T9giEtMbF7Y44uSkREJqfCp10GDBiAAQMGlDlNCIHFixfj3XffxVNPPQUAWL16Ndzd3bFlyxY888wzVau2ntFoBb7efwVfRF+BWqMFANjLLPD+oCAMa+dj5OqIiIgqp1qv+YiPj0dycjL69Omja1MoFOjYsSNiYmLKDB8qlQoq1f+eIZKdnV2dJZkkIQS2n76Nr/dfxfnbd/dHt6YueLylJwYEe8DBhr0dRERkuqo1fCQnJwMA3N3d9drd3d110+43f/58zJkzpzrLMGnX7+ThnS1ncOhKOgDARmqOD54KRkTbBrymg4iI6gSj3+0yc+ZMTJs2TfdzdnY2fHzq5ymFfXGpePnHEygsujtex8s9G2NMWEM48boOIiKqQ6o1fHh43H0uSEpKCjw9PXXtKSkpaN26dZnvkclkkMlkZU6rT07cyMCLq09ArdEirJEzFkS0hJ+zrbHLIiIiqnYVvtvlYfz9/eHh4YHo6GhdW3Z2No4cOYKwsLDqXFWdUlikwfQNp6HWaNE3yB2rx3dg8CAiojqrwj0fubm5uHLliu7n+Ph4xMbGwsnJCb6+vpgyZQrmzp2Lpk2bwt/fH++99x68vLwwePDg6qy7zigs0mDKL7GIv5MHd7kMnw4LgaV5tWZCIiKiWqXC4eP48ePo1auX7ueS6zUiIyOxatUqvPnmm8jLy8PEiRORlZWFrl274s8//4SVlVX1VV1HKPOL8MLq4zh6PQNSczN8MjQECms+j4WIiOo2iRBCGLuIe2VnZ0OhUECpVEIulxu7nBpzK6sAY78/isupubCXWeDbMaHo3NjF2GURERFVSkW+v41+t0t9dFtZgOHfxOBWVgHc5TL8MK4DAjzqbtAiIiK6F8OHASkLirD+WCJW/XMdt7IK4O9iix8ndEQDB2tjl0ZERGQwDB8GkF1YhCV7LuOXownIU2sAAA0crBk8iIioXmL4qGG3sgrwwg/HdcOkN3e3x9guDTEoxAt2Mu5+IiKqf/jtV0PyVMVYuDMOPx6+gWKtgIudFAuHhaBnM1cOk05ERPUaw0c10moF/jibjG2nknDwchry/zvF0qmREz56uiUaudoZuUIiIiLjY/ioJicTMjFn6zmcuqnUtfk52+DDp4LRvZmrESsjIiKqXRg+qkgIgSXRl7F4z2UAgJ3MApGd/TAg2BMtvOQ8xUJERHQfho8qKCzS4PUNp7Dj9G0AQERbb7w1oDnc7DmaKxER0YMwfFRSSnYhXlh9HKdvKmFpLsHcwcEY0d7X2GURERHVegwflZCSXYjh38bgRno+HG0s8c3oUHRs5GzssoiIiEwCw0cFZeap8dyKI7iRng8fJ2v8NL4TfJ1tjF0WERGRyWD4qIDUnEKMW3UMl1Jy4S6XYe2ETvBxYvAgIiKqCIaPcjqflI0JPxxDkrIQTrZS/Di+I4MHERFRJTB8lMOuc8mYsi4W+WoNGrnaYkVke/i72Bq7LCIiIpPE8PEIPx9NwNubz0AIoGsTF3z1bFsobCyNXRYREZHJYvh4iI0nbuqCx6iOvpj9ZAtYmpsZuywiIiKTxvDxAH+cuY03fj0FIYDIMD/MfrIFRyslIiKqBvwzvgwXk7Mxbf0paAUwsoMPZg1i8CAiIqouDB/3ua0swIQfjqOgSINuTV0wd3BLmJkxeBAREVUXho973MlVYdTyI7iZWYCGzjb44pk2MGfwICIiqlYMH//JyFPjuRVHcS0tDw0crPHTC53gaCs1dllERER1Di84BZCWo8Lo5UcQl5IDFzsZfpzQEQ0crI1dFhERUZ1U78PHbWUBRi0/gmtpeXCzl2HtC504gBgREVENqtfh41paLp5bcRS3sgrgpbDC2hc6oSGDBxERUY2qt+Hj7C0lIr8/ivQ8NRq52GL1+A7wduSzWoiIiGpavQwfMVfT8cLq48hVFSO4gRyrnu8AFzuZscsiIiKqF+pd+PjjzG28ti4W6mItOvo7YXlkO9hb8VktREREhlJvwoeqWIOZm85g08lbAIC+Qe74v5FtYGVpbuTKiIiI6pd6ET6UBUWYtPYk/rp8B2YSYEK3RngzvDks+JA4IiIig6vz4ePMTSVeWXsCiRkFsLY0x3dj2qFrUxdjl0VERFRv1dnwIYTAmsM3MHf7Bag1Wng7WmPpqLZo5e1g7NKIiIjqtToVPtTFWhyJT8ee8ynYcyEVt7IKANy9vuPToSFQ2PDCUiIiImMz+fCRmlOIDcdv4t+ELBy5lo4cVbFumrWlOV7v1wzju/pDIuED4oiIiGoDkw0f2YVF+GrfFaw8dB3qYq2u3cVOhj6BbugT6I4uTVxgLeXdLERERLWJyYWPXFUxfjp8A98evIaMPDUAoK2vA55o5YU2vg4I8XaAmRl7OYiIiGorkwoff1++gzd+PYXbykIAQGNXW7w7MAg9m7vytAoREZGJMJnwse1UEqaui0WxVsDXyQaTejfB020awJJjdRAREZmUWh8+MvLUmLv9PDb9e3dk0qdae2HBkFa8loOIiMhE1Vi3wVdffYWGDRvCysoKHTt2xNGjRyu8jAOX0tDnswPY9O8tSCTAC9388dnw1gweREREJqxGwse6deswbdo0zJo1CydPnkRISAjCw8ORmppa7mUs3XcFY1ceRUaeGs3d7bHp5c54Z2AQzHkxKRERkUmTCCFEdS+0Y8eOaN++Pb788ksAgFarhY+PDyZPnowZM2Y89L3Z2dlQKBTwmbIeZjIbPNvRF7MGBUFmwd4OIiKi2qrk+1upVEIulz903mq/5kOtVuPEiROYOXOmrs3MzAx9+vRBTExMqflVKhVUKpXu5+zsbACA1MIMC4aFYGiod3WXSEREREZU7add7ty5A41GA3d3d712d3d3JCcnl5p//vz5UCgUupePjw8AYO2EjgweREREdZDR71OdOXMmlEql7pWYmAgACPB8eJcNERERmaZqP+3i4uICc3NzpKSk6LWnpKTAw8Oj1PwymQwymay6yyAiIqJaqtp7PqRSKUJDQxEdHa1r02q1iI6ORlhYWHWvjoiIiExMjQwyNm3aNERGRqJdu3bo0KEDFi9ejLy8PDz//PM1sToiIiIyITUSPkaMGIG0tDS8//77SE5ORuvWrfHnn3+WugiViIiI6p8aGeejKipynzARERHVDhX5/jb63S5ERERUvzB8EBERkUExfBAREZFBMXwQERGRQTF8EBERkUExfBAREZFBMXwQERGRQTF8EBERkUExfBAREZFB1cjw6lVRMuBqdna2kSshIiKi8ir53i7PwOm1Lnykp6cDAHx8fIxcCREREVVUeno6FArFQ+epdeHDyckJAJCQkPDI4quqffv2OHbsmEkuPzs7Gz4+PkhMTKzRZ+DU9D6q6fUYaj8BhtlX3E/GX35d2Vemfnwy5d83oO58ju5dvlKphK+vr+57/GFqXfgwM7t7GYpCoajxfxBzc/MaXUdNLx8A5HK5yW+DIdZT0/sJMMy+4n4y/vJLmPq+MvXjU134fQNM/3NU1vJLvscfpl5fcBoVFWXSyzcEQ20D91XtWUdN4+9d+dXktpj6fuLvW/nVxt85iSjPlSEGVJFH8tZn3E/lw/1UPtxP5cd99WjcR49WF/dRRbap1vV8yGQyzJo1CzKZzNil1GrcT+XD/VQ+3E/lx331aNxHj1YX91FFtqnW9XwQERFR3Vbrej6IiIiobmP4ICIiIoNi+CAiIiKDYvigekkikWDLli3GLoOISE99OTYZPHyMHTsWgwcPNvRqTU5MTAzMzc0xcOBAY5diEvi5erjExESMGzcOXl5ekEql8PPzw2uvvaZ7nMGj7N+/HxKJBFlZWTVbaC3Az9Kj8fhUfvw8lY09H7XUihUrMHnyZBw8eBBJSUlVWpZGo4FWq62mysjUXLt2De3atcPly5fx888/48qVK/jmm28QHR2NsLAwZGRkGLtEMjE8PlFVGTV8/Pnnn+jatSscHBzg7OyMJ554AlevXtVNv379OiQSCTZt2oRevXrBxsYGISEhiImJMWLVNS83Nxfr1q3Dyy+/jIEDB2LVqlW6aSV/ge7YsQOtWrWClZUVOnXqhLNnz+rmWbVqFRwcHLB161YEBQVBJpMhISHBCFtiHA0bNsTixYv12lq3bo3Zs2cbpR5ji4qKglQqxa5du9CjRw/4+vpiwIAB2LNnD27duoV33nkHAKBSqfDWW2/Bx8cHMpkMTZo0wYoVK3D9+nX06tULAODo6AiJRIKxY8cacYsMh8eo0nh8qjwem/7HqOEjLy8P06ZNw/HjxxEdHQ0zMzM8/fTTpVLwO++8g+nTpyM2NhbNmjXDyJEjUVxcbKSqa9769esREBCA5s2bY/To0fj+++9LPaL4jTfewKJFi3Ds2DG4urpi0KBBKCoq0k3Pz8/Hxx9/jOXLl+PcuXNwc3Mz9GZQLZCRkYGdO3filVdegbW1td40Dw8PjBo1CuvWrYMQAmPGjMHPP/+ML774AhcuXMC3334LOzs7+Pj4YOPGjQCAuLg43L59G0uWLDHG5hgcj1Gl8fhE1cGoD5aLiIjQ+/n777+Hq6srzp8/j+DgYF379OnTdecW58yZgxYtWuDKlSsICAgwaL2GsmLFCowePRoA0L9/fyiVShw4cAA9e/bUzTNr1iz07dsXAPDDDz/A29sbmzdvxvDhwwEARUVFWLp0KUJCQgxeP9Uely9fhhACgYGBZU4PDAxEZmYmjh07hvXr12P37t3o06cPAKBRo0a6+UqeUunm5gYHB4car7u24DGqNB6fqDoYtefj8uXLGDlyJBo1agS5XI6GDRsCQKkuuFatWun+39PTEwCQmppqsDoNKS4uDkePHsXIkSMBABYWFhgxYgRWrFihN19YWJju/52cnNC8eXNcuHBB1yaVSvX2G9VvjxrI+Pr16zA3N0ePHj0MVJFp4DFKH49PVF2M2vMxaNAg+Pn54bvvvoOXlxe0Wi2Cg4OhVqv15rO0tNT9v0QiAYA6e4HSihUrUFxcDC8vL12bEAIymQxffvlluZdjbW2t21f1jZmZWakv23u7fOuTJk2aQCKR4MKFC3j66adLTb9w4QIcHR1LnZKhu3iM0sfjU9Xw2PQ/Ruv5SE9PR1xcHN5991089thjuu7f+qy4uBirV6/GokWLEBsbq3udOnUKXl5e+Pnnn3XzHj58WPf/mZmZuHTp0gO71usbV1dX3L59W/dzdnY24uPjjViR8Tg7O6Nv375YunQpCgoK9KYlJyfjp59+wogRI9CyZUtotVocOHCgzOVIpVIAd+9MqC94jNLH41PV8dj0P0YLH46OjnB2dsayZctw5coV7N27F9OmTTNWObXC9u3bkZmZifHjxyM4OFjvFRERode1+cEHHyA6Ohpnz57F2LFj4eLiwnvJ/9O7d2+sWbMGf/31F86cOYPIyEiYm5sbuyyj+fLLL6FSqRAeHo6DBw8iMTERf/75J/r27YsGDRpg3rx5aNiwISIjIzFu3Dhs2bIF8fHx2L9/P9avXw8A8PPzg0Qiwfbt25GWlobc3Fwjb1XN4zFKH49PVcdj0/8YPHxotVpYWFjAzMwMv/zyC06cOIHg4GBMnToVCxcuNHQ5tcqKFSvQp08fKBSKUtMiIiJw/PhxnD59GgCwYMECvPbaawgNDUVycjK2bdum++u0Pir5XAHAzJkz0aNHDzzxxBMYOHAgBg8ejMaNGxu5QuNp2rQpjh8/jkaNGmH48OFo3LgxJk6ciF69eiEmJkZ3MenXX3+NoUOH4pVXXkFAQABeeOEF5OXlAQAaNGiAOXPmYMaMGXB3d8ekSZOMuUk1iseosvH4VDk8NpVNIh51JVo169+/P5o0aVKh84P0P/v370evXr2QmZlZr+46eBR+rqi68LNUeTw+lcbPU9kM1vORmZmJ7du3Y//+/bpb+Yiqip8rqi78LFF14ufp4Qx2t8u4ceNw7NgxvP7663jqqacMtVqq4/i5ourCzxJVJ36eHs7gp12IiIiofuOD5YiIiMigGD6IiIjIoGokfMyfPx/t27eHvb093NzcMHjwYMTFxenNU1hYiKioKDg7O8POzg4RERFISUnRTT916hRGjhwJHx8fWFtbIzAw8KEPszp06BAsLCzQunXrmtgkIqpDDHWMKnnK6/2v5ORkg2wnUW1VI+HjwIEDiIqKwuHDh7F7924UFRWhX79+ujEDAGDq1KnYtm0bNmzYgAMHDiApKQlDhgzRTT9x4gTc3Nzw448/4ty5c3jnnXcwc+bMMm9XysrKwpgxY/DYY4/VxOYQUR1j6GNUydOAS158iivVdwa54DQtLQ1ubm44cOAAunfvDqVSCVdXV6xduxZDhw4FAFy8eBGBgYGIiYlBp06dylxOVFQULly4gL179+q1P/PMM2jatCnMzc2xZcsWxMbG1vQmEVEdUlPHKI57QVQ2g1zzoVQqAfzvsdwnTpxAUVGR3r3PAQEB8PX1RUxMzEOXU7KMEitXrsS1a9cwa9asGqiciOqDmjxGAUDr1q3h6emJvn374tChQ9VcPZHpqfFxPrRaLaZMmYIuXbogODgYwN0HWkml0lJ/Cbi7uz/wXOg///yDdevWYceOHbq2y5cvY8aMGfjrr790w9cSEVVETR6jPD098c0336Bdu3ZQqVRYvnw5evbsiSNHjqBt27Y1tk1EtV2Nf2NHRUXh7Nmz+Pvvvyu9jLNnz+Kpp57CrFmz0K9fPwB3n6757LPPYs6cOWjWrFl1lUtE9UxNHaMAoHnz5mjevLnu586dO+Pq1av4/PPPsWbNmirVTWTKajR8TJo0Cdu3b8fBgwfh7e2ta/fw8IBarUZWVpbeXxYpKSnw8PDQW8b58+fx2GOPYeLEiXj33Xd17Tk5OTh+/Dj+/fdf3UOutFothBCwsLDArl270Lt375rcPCIycTV5jHqQDh06VCnoENUFNXLNhxACkyZNwubNm7F37174+/vrTQ8NDYWlpSWio6N1bXFxcUhISEBYWJiu7dy5c+jVqxciIyMxb948vWXI5XKcOXMGsbGxutdLL72E5s2bIzY2Fh07dqyJTSOiOsAQx6gHiY2NhaenZ/VsCJGJqpGej6ioKKxduxa//fYb7O3tdedIFQoFrK2toVAoMH78eEybNg1OTk6Qy+WYPHkywsLCdFeRnz17Fr1790Z4eDimTZumW4a5uTlcXV1hZmamOz9bws3NDVZWVqXaiYjuZYhjFAAsXrwY/v7+aNGiBQoLC7F8+XLs3bsXu3btMs6GE9UWogYAKPO1cuVK3TwFBQXilVdeEY6OjsLGxkY8/fTT4vbt27rps2bNKnMZfn5+D1zvrFmzREhISE1sEhHVIYY6Rn388ceicePGwsrKSjg5OYmePXuKvXv3GnBLiWonPliOiIiIDIrPdiEiIiKDYvggIiIig2L4ICIiIoNi+CAiIiKDYvggIiIig2L4ICIiIoNi+CAiIiKDYvggomrTs2dPTJkyxdhlEFEtx/BBREaxf/9+SCQSZGVlGbsUIjIwhg8iIiIyKIYPIqqUvLw8jBkzBnZ2dvD09MSiRYv0pq9Zswbt2rWDvb09PDw88OyzzyI1NRUAcP36dfTq1QsA4OjoCIlEgrFjxwIAtFot5s+fD39/f1hbWyMkJAS//vqrQbeNiGoWwwcRVcobb7yBAwcO4LfffsOuXbuwf/9+nDx5Uje9qKgIH374IU6dOoUtW7bg+vXruoDh4+ODjRs3Arj7qPrbt29jyZIlAID58+dj9erV+Oabb3Du3DlMnToVo0ePxoEDBwy+jURUM/hgOSKqsNzcXDg7O+PHH3/EsGHDAAAZGRnw9vbGxIkTsXjx4lLvOX78ONq3b4+cnBzY2dlh//796NWrFzIzM+Hg4AAAUKlUcHJywp49exAWFqZ774QJE5Cfn4+1a9caYvOIqIZZGLsAIjI9V69ehVqtRseOHXVtTk5OaN68ue7nEydOYPbs2Th16hQyMzOh1WoBAAkJCQgKCipzuVeuXEF+fj769u2r165Wq9GmTZsa2BIiMgaGDyKqdnl5eQgPD0d4eDh++uknuLq6IiEhAeHh4VCr1Q98X25uLgBgx44daNCggd40mUxWozUTkeEwfBBRhTVu3BiWlpY4cuQIfH19AQCZmZm4dOkSevTogYsXLyI9PR0LFiyAj48PgLunXe4llUoBABqNRtcWFBQEmUyGhIQE9OjRw0BbQ0SGxvBBRBVmZ2eH8ePH44033oCzszPc3NzwzjvvwMzs7jXsvr6+kEql+L//+z+89NJLOHv2LD788EO9Zfj5+UEikWD79u14/PHHYW1tDXt7e0yfPh1Tp06FVqtF165doVQqcejQIcjlckRGRhpjc4momvFuFyKqlIULF6Jbt24YNGgQ+vTpg65duyI0NBQA4OrqilWrVmHDhg0ICgrCggUL8Omnn+q9v0GDBpgzZw5mzJgBd3d3TJo0CQDw4Ycf4r333sP8+fMRGBiI/v37Y8eOHfD39zf4NhJRzeDdLkRERGRQ7PkgIiIig2L4ICIiIoNi+CAiIiKDYvggIiIig2L4ICIiIoNi+CAiIiKDYvggIiIig2L4ICIiIoNi+CAiIiKDYvggIiIig2L4ICIiIoNi+CAiIiKD+n+8AQukyH73AAAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from numerai_tools.scoring import numerai_corr\n",
+        "\n",
+        "val[\"prediction\"] = model.predict(val.filter(like=\"feature_\"))\n",
+        "validation_corr = val[\n",
+        "    [\"date\", \"prediction\", \"target_binned_return_20\"]\n",
+        "].groupby(\"date\").apply(\n",
+        "    lambda df: numerai_corr(df[[\"prediction\"]], df[\"target_binned_return_20\"])\n",
+        ").rename(columns={\"prediction\": \"corr\"})\n",
+        "validation_corr.cumsum().plot(\n",
+        "    title=\"Cumulative 20D Numerai Corr over Validation\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "crypto/v2.0_beta/live.parquet: 21.5kB [00:00, 11.0MB/s]                   \n"
+          ]
+        }
+      ],
+      "source": [
+        "# download and read live data\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
+        "live_data = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\")\n",
+        "\n",
+        "# generate live predictions\n",
+        "live_data[\"prediction\"] = model.predict(live_data.filter(like=\"feature_\"))\n",
+        "live_data.to_parquet(\"predictions.parquet\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "EX-tGFkGY_mI"
+      },
+      "outputs": [],
+      "source": [
+        "# Define your prediction pipeline as a function\n",
+        "def predict(live_features: pd.DataFrame) -> pd.DataFrame:\n",
+        "    live_predictions = model.predict(live_data.filter(like=\"feature_\"))\n",
+        "    submission = pd.Series(live_predictions, index=live_features.index)\n",
+        "    return submission.to_frame(\"prediction\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "lXl3qyWKZBsP"
+      },
+      "outputs": [],
+      "source": [
+        "# Use the cloudpickle library to serialize your function\n",
+        "import cloudpickle\n",
+        "p = cloudpickle.dumps(predict)\n",
+        "with open(\"crypto_example_model.pkl\", \"wb\") as f:\n",
+        "    f.write(p)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "USljDjorZCqj",
+        "outputId": "94809fd6-89ab-4637-b435-957ebe6c07a1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "download(\"download_8b3fb0bb-d59e-4532-9af9-f9a24c435c82\", \"crypto_example_model.pkl\", 4115725)",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Download file if running in Google Colab\n",
+        "try:\n",
+        "    from google.colab import files\n",
+        "    files.download('crypto_example_model.pkl')\n",
+        "except:\n",
+        "    pass"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.11"
+    },
+    "orig_nbformat": 4
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/numerai/example_model.ipynb b/numerai/example_model.ipynb
new file mode 100644
index 0000000..f3f0a15
--- /dev/null
+++ b/numerai/example_model.ipynb
@@ -0,0 +1,275 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZqK_u9k-hMqE"
+      },
+      "source": [
+        "# Model Upload"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ekw8Z93ljC3v",
+        "outputId": "bdd16698-2ad0-4423-b090-c5ce55fe3053"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Python 3.11.13\n"
+          ]
+        }
+      ],
+      "source": [
+        "!python --version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yoy_wT1rhMqF",
+        "outputId": "e038b50f-1b61-4334-be62-28f4dc40a0a0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.9/61.9 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m49.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.3/42.3 MB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m118.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m75.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.9/12.9 MB\u001b[0m \u001b[31m99.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.3/35.3 MB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.1 which is incompatible.\n",
+            "plotnine 0.14.6 requires scipy<1.16.0,>=1.8.0, but you have scipy 1.16.0 which is incompatible.\n",
+            "pylibcudf-cu12 25.2.1 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 20.0.0 which is incompatible.\n",
+            "sklearn-compat 0.1.3 requires scikit-learn<1.7,>=1.2, but you have scikit-learn 1.7.0 which is incompatible.\n",
+            "dask-cudf-cu12 25.2.2 requires pandas<2.2.4dev0,>=2.0, but you have pandas 2.3.1 which is incompatible.\n",
+            "cudf-cu12 25.2.1 requires pandas<2.2.4dev0,>=2.0, but you have pandas 2.3.1 which is incompatible.\n",
+            "cudf-cu12 25.2.1 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 20.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "# Install dependencies\n",
+        "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160
+        },
+        "id": "13hdRk9ghMqI",
+        "outputId": "d2274374-fd85-4189-f27b-d9d466cc63ca"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/train.parquet: 2.37GB [01:28, 26.7MB/s]                            \n",
+            "v5.0/features.json: 291kB [00:00, 1.45MB/s]                           \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011829 seconds.\n",
+            "You can set `force_row_wise=true` to remove the overhead.\n",
+            "And if memory is not enough, you can set `force_col_wise=true`.\n",
+            "[LightGBM] [Info] Total Bins 210\n",
+            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
+            "[LightGBM] [Info] Start training from score 0.500008\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_a959b0e2-3a84-4ffd-9205-035cdf0c5587\", \"example_model.pkl\", 6513463)"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from numerapi import NumerAPI\n",
+        "import pandas as pd\n",
+        "import json\n",
+        "napi = NumerAPI()\n",
+        "\n",
+        "# use one of the latest data versions\n",
+        "DATA_VERSION = \"v5.0\"\n",
+        "\n",
+        "# Download data\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n",
+        "\n",
+        "# Load data\n",
+        "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
+        "features = feature_metadata[\"feature_sets\"][\"small\"]\n",
+        "# use \"medium\" or \"all\" for better performance. Requires more RAM.\n",
+        "# features = feature_metadata[\"feature_sets\"][\"medium\"]\n",
+        "# features = feature_metadata[\"feature_sets\"][\"all\"]\n",
+        "train = pd.read_parquet(f\"{DATA_VERSION}/train.parquet\", columns=[\"era\"]+features+[\"target\"])\n",
+        "\n",
+        "# For better models, join train and validation data and train on all of it.\n",
+        "# This would cause diagnostics to be misleading though.\n",
+        "# napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
+        "# validation = pd.read_parquet(f\"{DATA_VERSION}/validation.parquet\", columns=[\"era\"]+features+[\"target\"])\n",
+        "# validation = validation[validation[\"data_type\"] == \"validation\"] # drop rows which don't have targets yet\n",
+        "# train = pd.concat([train, validation])\n",
+        "\n",
+        "# Downsample for speed\n",
+        "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]  # skip this step for better performance\n",
+        "\n",
+        "# Train model\n",
+        "import lightgbm as lgb\n",
+        "model = lgb.LGBMRegressor(\n",
+        "    n_estimators=2000,\n",
+        "    learning_rate=0.01,\n",
+        "    max_depth=5,\n",
+        "    num_leaves=2**5-1,\n",
+        "    colsample_bytree=0.1\n",
+        ")\n",
+        "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
+        "# model = lgb.LGBMRegressor(\n",
+        "#     n_estimators=30_000,\n",
+        "#     learning_rate=0.001,\n",
+        "#     max_depth=10,\n",
+        "#     num_leaves=2**10,\n",
+        "#     colsample_bytree=0.1,\n",
+        "#     min_data_in_leaf=10000,\n",
+        "# )\n",
+        "model.fit(\n",
+        "    train[features],\n",
+        "    train[\"target\"]\n",
+        ")\n",
+        "\n",
+        "# Define predict function\n",
+        "def predict(\n",
+        "    live_features: pd.DataFrame,\n",
+        "    live_benchmark_models: pd.DataFrame\n",
+        " ) -> pd.DataFrame:\n",
+        "    live_predictions = model.predict(live_features[features])\n",
+        "    submission = pd.Series(live_predictions, index=live_features.index)\n",
+        "    return submission.to_frame(\"prediction\")\n",
+        "\n",
+        "# Pickle predict function\n",
+        "import cloudpickle\n",
+        "p = cloudpickle.dumps(predict)\n",
+        "with open(\"example_model.pkl\", \"wb\") as f:\n",
+        "    f.write(p)\n",
+        "\n",
+        "# Download file if running in Google Colab\n",
+        "try:\n",
+        "    from google.colab import files\n",
+        "    files.download('example_model.pkl')\n",
+        "except:\n",
+        "    pass"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    },
+    "orig_nbformat": 4
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/numerai/feature_neutralization.ipynb b/numerai/feature_neutralization.ipynb
new file mode 100644
index 0000000..ad303eb
--- /dev/null
+++ b/numerai/feature_neutralization.ipynb
@@ -0,0 +1,4401 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6jGRS-9syu-L"
+      },
+      "source": [
+        "# Feature Neutralization\n",
+        "\n",
+        "One thing that makes predicting the stock market so hard is the \"non-stationary\" relationship between features and returns. Features can have strong predictive power some eras but not others - or may completely reverse over time.\n",
+        "\n",
+        "This uncertainty is what we call \"feature risk\". In order to create models that have consistent performance, it is helpful to reduce this feature risk via \"feature neutralization\". In this notebook, we will:\n",
+        "\n",
+        "1. Learn how to quantify feature risk\n",
+        "2. Measure our model's feature exposure\n",
+        "3. Apply feature neutralization to our predictions\n",
+        "4. Measure the performance of our neutralized predictions\n",
+        "5. Pickle and upload our feature-neutral model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python --version"
+      ],
+      "metadata": {
+        "id": "ws4qrSssFC9T",
+        "outputId": "3860d6e5-38ec-4638-82b2-bce4c7365966",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Python 3.11.13\n",
+            "Python 3.11.13\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iHzZde7Tyu-N",
+        "outputId": "f9cb52f5-88f3-4776-a1be-cef458e718f5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.9/61.9 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m119.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m115.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m91.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.9/12.9 MB\u001b[0m \u001b[31m86.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.3/35.3 MB\u001b[0m \u001b[31m43.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.1 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "# Install dependencies\n",
+        "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
+        "\n",
+        "# Inline plots\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZGyNf56dyu-O"
+      },
+      "source": [
+        "## 1. Feature Risk\n",
+        "\n",
+        "In order to quantify feature risk, we evaluate the performance of each feature on their own."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K7uvuNlAyu-P"
+      },
+      "source": [
+        "### Feature Groups\n",
+        "In the last notebook, you learned about the basic feature sets that Numerai offers. There are also 8 feature groups: `intelligence`, `wisdom`, `charisma`, `dexterity`, `strength`, `constitution`, `agility`, `serenity`. Each group contains a different type of feature. For example all technical signals would be in one group, while all analyst predictions and ratings would be in another group.\n",
+        "\n",
+        "Let us take a look at feature groups in the small, medium, and all feature sets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 385
+        },
+        "id": "JTN8-MUmyu-P",
+        "outputId": "b8d0557f-ae8f-48e8-e707-806ac4683ad4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/features.json: 291kB [00:00, 3.52MB/s]                   \n",
+            "/tmp/ipython-input-4-462694130.py:39: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
+            "  pd.DataFrame(subgroups).applymap(len).sort_values(by=\"all\", ascending=False)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              small  medium   all\n",
+              "all              42     705  2376\n",
+              "constitution      2     134   335\n",
+              "charisma          3     116   290\n",
+              "agility           2      58   145\n",
+              "wisdom            3      56   140\n",
+              "strength          1      54   135\n",
+              "serenity          3      34    95\n",
+              "dexterity         4      21    51\n",
+              "intelligence      2      14    35"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8ab76cd7-c3fb-457b-b86e-e876e2376aad\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>small</th>\n",
+              "      <th>medium</th>\n",
+              "      <th>all</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>all</th>\n",
+              "      <td>42</td>\n",
+              "      <td>705</td>\n",
+              "      <td>2376</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>constitution</th>\n",
+              "      <td>2</td>\n",
+              "      <td>134</td>\n",
+              "      <td>335</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charisma</th>\n",
+              "      <td>3</td>\n",
+              "      <td>116</td>\n",
+              "      <td>290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>agility</th>\n",
+              "      <td>2</td>\n",
+              "      <td>58</td>\n",
+              "      <td>145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>wisdom</th>\n",
+              "      <td>3</td>\n",
+              "      <td>56</td>\n",
+              "      <td>140</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>strength</th>\n",
+              "      <td>1</td>\n",
+              "      <td>54</td>\n",
+              "      <td>135</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>serenity</th>\n",
+              "      <td>3</td>\n",
+              "      <td>34</td>\n",
+              "      <td>95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dexterity</th>\n",
+              "      <td>4</td>\n",
+              "      <td>21</td>\n",
+              "      <td>51</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>intelligence</th>\n",
+              "      <td>2</td>\n",
+              "      <td>14</td>\n",
+              "      <td>35</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ab76cd7-c3fb-457b-b86e-e876e2376aad')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8ab76cd7-c3fb-457b-b86e-e876e2376aad button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8ab76cd7-c3fb-457b-b86e-e876e2376aad');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5edaa251-0e04-4b89-ab30-25d3077e2db1\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5edaa251-0e04-4b89-ab30-25d3077e2db1')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5edaa251-0e04-4b89-ab30-25d3077e2db1 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"small\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1,\n        \"max\": 42,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"medium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 218,\n        \"min\": 14,\n        \"max\": 705,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          21,\n          134,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"all\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 747,\n        \"min\": 35,\n        \"max\": 2376,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          51,\n          335,\n          135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "import json\n",
+        "import pandas as pd\n",
+        "from numerapi import NumerAPI\n",
+        "\n",
+        "# initialize our API client\n",
+        "napi = NumerAPI()\n",
+        "\n",
+        "# Set data version to one of the latest datasets\n",
+        "DATA_VERSION = \"v5.0\"\n",
+        "\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n",
+        "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
+        "feature_sets = feature_metadata[\"feature_sets\"]\n",
+        "\n",
+        "sizes = [\"small\", \"medium\", \"all\"]\n",
+        "groups = [\n",
+        "  \"intelligence\",\n",
+        "  \"wisdom\",\n",
+        "  \"charisma\",\n",
+        "  \"dexterity\",\n",
+        "  \"strength\",\n",
+        "  \"constitution\",\n",
+        "  \"agility\",\n",
+        "  \"serenity\",\n",
+        "  \"all\"\n",
+        "]\n",
+        "\n",
+        "# compile the intersections of feature sets and feature groups\n",
+        "subgroups = {}\n",
+        "for size in sizes:\n",
+        "    subgroups[size] = {}\n",
+        "    for group in groups:\n",
+        "        subgroups[size][group] = (\n",
+        "            set(feature_sets[size])\n",
+        "            .intersection(set(feature_sets[group]))\n",
+        "        )\n",
+        "\n",
+        "# convert to data frame and display the feature count of each intersection\n",
+        "pd.DataFrame(subgroups).applymap(len).sort_values(by=\"all\", ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HMy4I8cBO3aG"
+      },
+      "source": [
+        "For this tutorial we are going to analyze the `serenity` features from the `small` feature sets.\n",
+        "\n",
+        "We read the entire `small` training feature set to train on it later:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "meowEBs-PwtB",
+        "outputId": "b82484be-38ce-4524-fdf9-60bbd038f09f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/train.parquet: 2.37GB [00:51, 45.9MB/s]                            \n"
+          ]
+        }
+      ],
+      "source": [
+        "# define the small features and small serenity features\n",
+        "# use \"all\" for better performance. Requires more RAM.\n",
+        "feature_size = \"small\"\n",
+        "# feature_size = \"all\"\n",
+        "small_features = feature_sets[feature_size]\n",
+        "med_serenity_feats = list(subgroups[feature_size][\"serenity\"])\n",
+        "\n",
+        "# Download the training data and feature metadata\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
+        "\n",
+        "# Load the just the small feature set,\n",
+        "# this is a great feature of the parquet file format\n",
+        "train = pd.read_parquet(\n",
+        "    f\"{DATA_VERSION}/train.parquet\",\n",
+        "    columns=[\"era\", \"target\"] + small_features\n",
+        ")\n",
+        "\n",
+        "# Downsample to every 4th era to reduce memory usage and\n",
+        "# speedup model training (suggested for Colab free tier).\n",
+        "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qUdZvVSPyu-Q"
+      },
+      "source": [
+        "### Evaluating feature performance\n",
+        "\n",
+        "When thinking about feature risk, the first thing to check might be the correlation of each feature with the target over the training dataset. This will tell us what kind of relationship each feature has with the target:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 561
+        },
+        "id": "SE9QmW6ryu-Q",
+        "outputId": "53d5a554-bfbe-4719-c359-8f0ffa1fd970"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-6-648902385.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  per_era_corr = train.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative Absolute Value CORR of Features and the Target'}, xlabel='Era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABNsAAAHWCAYAAABOs4+oAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdYFFcXwOHf7tKrIk0FQUDFgg0Ve2+xG3uJJTFRY0w0Gj9NM6bZYhK7xhiNxsQaS+y9994LChaUqhTpy873B7IRKSIsgua8z8MTmblz587u7MQ9nnuPSlEUBSGEEEIIIYQQQgghRJ6pC3oAQgghhBBCCCGEEEK8LiTYJoQQQgghhBBCCCGEgUiwTQghhBBCCCGEEEIIA5FgmxBCCCGEEEIIIYQQBiLBNiGEEEIIIYQQQgghDESCbUIIIYQQQgghhBBCGIgE24QQQgghhBBCCCGEMBAJtgkhhBBCCCGEEEIIYSASbBNCCCGEEEIIIYQQwkAk2CaEEEIAAwYMwN3d3aB9Ll68GJVKRWBgoEH7NaTAwEBUKhU//PDDSz1vfrzehc3evXtRqVTs3bu3oIfyn7N161aqVq2KmZkZKpWKyMjIgh6SeEFpz6bFixfnug93d3fatWtnuEEJIYQQOSTBNiGEEAZz8+ZNBg8ejIeHB2ZmZtjY2FCvXj2mT59OfHx8QQ8v33z//fesW7euoIeRqTlz5qBSqfDz8yvooRjM5s2b+eqrrwzaZ+XKlSlVqhSKomTZpl69ejg5OaHVag167vy0d+9e3nzzTZydnTExMcHR0ZH27dvz999/Z2gbERHBJ598Qrly5TAzM8POzo5WrVqxcePGDG3TAiFpP2q1Gjs7O9544w2OHDmSof1XX32Vrr2xsTHu7u58+OGHBg+ERURE0L17d8zNzZk9ezZLly7F0tIy07ZpAfHMfsaOHWvQcaU5fPgwX331lQQADeTy5ct89dVXBfKPGtndP0//FMZ/WJgzZ06eAplCCCGyZ1TQAxBCCPF62LRpE926dcPU1JR+/fpRqVIlkpKSOHjwIJ988gmXLl3il19+Kehh5ovvv/+erl270qlTp3Tb33rrLXr27ImpqWnBDAxYtmwZ7u7uHD9+HH9/f7y8vApsLIayefNmZs+ebdCAW58+fRg7diwHDhygYcOGGfYHBgZy5MgRPvjgA4yMXo2/Po0fP56vv/6aMmXKMHjwYNzc3IiIiGDz5s106dKFZcuW0bt3bwCuXbtGs2bNCAsLY+DAgdSoUYPIyEiWLVtG+/btGT16NFOnTs1wjl69etGmTRtSUlK4fv06c+bMoUmTJpw4cQIfH58M7efOnYuVlRWxsbHs2rWLmTNncvr0aQ4ePGiw6z5x4gQxMTF88803NG/ePEfHfP3115QuXTrdtkqVKhlsTE87fPgwEyZMYMCAARQpUiRfzvFfcvnyZSZMmEDjxo1felCrYcOGLF26NN22QYMGUatWLd577z39Nisrq5c6rpyYM2cO9vb2DBgwoKCHIoQQr6VX42+LQgghCrWAgAB69uyJm5sbu3fvpnjx4vp9w4YNw9/fn02bNhXgCAuGRqNBo9EU2PkDAgI4fPgwf//9N4MHD2bZsmWMHz++wMZTmPXu3Ztx48bx559/Zhps++uvv1AUhT59+hTA6F7c6tWr+frrr+natSt//vknxsbG+n2ffPIJ27ZtIzk5GYDk5GS6du3Ko0eP2L9/f7osyJEjR9KnTx9++OEHatSoQY8ePdKdp3r16vTt21f/e4MGDXjjjTeYO3cuc+bMyTCurl27Ym9vD8DgwYPp2bMnK1as4Pjx49SqVcsg1x4aGgrwQoGsN954gxo1ahjk/AUlNjY2yww+kT88PDzw8PBIt23IkCF4eHik+1zkVkJCAiYmJqjVMhlJCCFeNfLkFkIIkWdTpkzh8ePHLFy4MF2gLY2XlxcfffQRkP06PCqVKl22UtrUs+vXr9O3b19sbW1xcHDgiy++QFEU7t69S8eOHbGxscHZ2Zlp06al6y+rNdNyupbWDz/8QN26dSlWrBjm5ub4+vqyevXqDGOOjY3l999/108ZSssUePb87dq1y/DFLE2dOnUyfNn/448/8PX1xdzcHDs7O3r27Mndu3ezHfPTli1bRtGiRWnbti1du3Zl2bJl2bb/6aefcHNzw9zcnEaNGnHx4sV0+4ODgxk4cCAuLi6YmppSvHhxOnbsmOH1nTNnDhUrVsTU1JQSJUowbNiw506Zy+o9efZ+GTBgALNnzwZIN00rjU6n4+eff6ZixYqYmZnh5OTE4MGDefToUbbnd3V1pWHDhqxevVofhHran3/+iaenJ35+fty+fZv333+fcuXKYW5uTrFixejWrVuOprG5u7tnmknSuHFjGjdunG5bYmIi48ePx8vLC1NTU1xdXRkzZgyJiYnPPc8XX3yBnZ0dv/32W7pAW5pWrVrp17Jas2YNFy9eZOzYsRmmG2s0GubPn0+RIkVylEnYoEEDIHVKeU68aPtVq1bpPxP29vb07duXoKAg/f7GjRvTv39/AGrWrJnu85gXW7ZsoUGDBlhaWmJtbU3btm25dOlSujbnz59nwIAB+mn0zs7OvP3220REROjbfPXVV3zyyScAlC5dWn//BgYG5urZePnyZXr37k3RokWpX7++fn9Onh03btygS5cuODs7Y2ZmhouLCz179iQqKirb1+LAgQN069aNUqVK6e/LkSNHZlgqYMCAAVhZWREUFESnTp2wsrLCwcGB0aNHk5KSkq5tZGQkAwYMwNbWliJFitC/f/8cTbNdvHgx3bp1A6BJkyb61/PZ58jBgwepVasWZmZmeHh4sGTJkgx9RUZGMmLECFxdXTE1NcXLy4vJkyej0+meO47sPHz4kNGjR+Pj44OVlRU2Nja88cYbnDt3Ll27tGfg8uXL+fzzzylZsiQWFhZER0cDqfd+hQoVMDMzo1KlSqxduzbTtS9z8gx0d3fn0qVL7Nu3T/+aPfv8EUIIkTeS2SaEECLP/vnnHzw8PKhbt26+9N+jRw/Kly/PpEmT2LRpE99++y12dnbMnz+fpk2bMnnyZJYtW8bo0aOpWbNmpplJuTF9+nQ6dOhAnz59SEpKYvny5XTr1o2NGzfStm1bAJYuXZph2pCnp2eW19GvXz9OnDhBzZo19dtv377N0aNH003T++677/jiiy/o3r07gwYNIiwsjJkzZ9KwYUPOnDmTo6ydZcuW8eabb2JiYkKvXr2YO3duhnOnWbJkCTExMQwbNoyEhASmT59O06ZNuXDhAk5OTgB06dKFS5cuMXz4cNzd3QkNDWXHjh3cuXNH/4Xvq6++YsKECTRv3pyhQ4dy7do1/XkPHTqUaeDnRQwePJj79++zY8eODNO30vYvXryYgQMH8uGHHxIQEMCsWbM4c+bMc8/fp08f3nvvPbZt25ZuUfULFy5w8eJFvvzySyB1muLhw4fp2bMnLi4uBAYGMnfuXBo3bszly5exsLDI0zVC6hfmDh06cPDgQd577z3Kly/PhQsX+Omnn7h+/Xq2awTeuHGDq1ev8vbbb2Ntbf3cc/3zzz8A9OvXL9P9tra2dOzYkd9///25U5HTAo5FixZ97nlftH3a+1qzZk0mTpxISEgI06dP59ChQ/rPxGeffUa5cuX45Zdf9FNDs/o8Pi0qKorw8PB029Iy8JYuXUr//v1p1aoVkydPJi4ujrlz51K/fn3OnDmjv/d37NjBrVu3GDhwIM7Ozvqp85cuXeLo0aOoVCrefPNNrl+/zl9//cVPP/2kP4eDgwNhYWE5es2e1q1bN8qUKcP333+vX28wJ8+OpKQkWrVqRWJiIsOHD8fZ2ZmgoCA2btxIZGQktra2WZ5z1apVxMXFMXToUIoVK8bx48eZOXMm9+7dY9WqVenapqSk0KpVK/z8/Pjhhx/YuXMn06ZNw9PTk6FDhwKgKAodO3bk4MGDDBkyhPLly7N27Vp90DQ7DRs25MMPP2TGjBl8+umnlC9fHkD/XwB/f3+6du3KO++8Q//+/fntt98YMGAAvr6+VKxYEYC4uDgaNWpEUFAQgwcPplSpUhw+fJhx48bx4MEDfv755xd6X55269Yt1q1bR7du3ShdujQhISHMnz+fRo0acfnyZUqUKJGu/TfffIOJiQmjR48mMTERExMTNm3aRI8ePfDx8WHixIk8evSId955h5IlS2Y4X06egT///DPDhw/HysqKzz77DED/nBdCCGEgihBCCJEHUVFRCqB07NgxR+0DAgIUQFm0aFGGfYAyfvx4/e/jx49XAOW9997Tb9NqtYqLi4uiUqmUSZMm6bc/evRIMTc3V/r376/ftmjRIgVQAgIC0p1nz549CqDs2bNHv61///6Km5tbunZxcXHpfk9KSlIqVaqkNG3aNN12S0vLdOfN6vxRUVGKqampMmrUqHTtpkyZoqhUKuX27duKoihKYGCgotFolO+++y5duwsXLihGRkYZtmfm5MmTCqDs2LFDURRF0el0iouLi/LRRx+la5f2fpibmyv37t3Tbz927JgCKCNHjlQUJfX1BZSpU6dmec7Q0FDFxMREadmypZKSkqLfPmvWLAVQfvvtN/22Z1/vzN6Tp8f39P0ybNgwJbO/whw4cEABlGXLlqXbvnXr1ky3P+vhw4eKqamp0qtXr3Tbx44dqwDKtWvXFEXJeF8oiqIcOXJEAZQlS5Zke01ubm6Z3iuNGjVSGjVqpP996dKlilqtVg4cOJCu3bx58xRAOXToUJbXsX79egVQfvrpp2yu9l9Vq1ZVbG1ts23z448/KoCyYcMGRVH+fV8mTJighIWFKcHBwcqBAweUmjVrKoCyatWqdMenfZavXbumhIWFKYGBgcpvv/2mmJubKw4ODkpsbGy2509KSlIcHR2VSpUqKfHx8frtGzduVADlyy+/1G9L+9ydOHHiudee1jazH0VRlJiYGKVIkSLKu+++m+644OBgxdbWNt32zO6Lv/76SwGU/fv367dNnTo10+dSbp6Nz96rOX12nDlzJtP3KScyu86JEyeme4YpSupnHFC+/vrrdG2rVaum+Pr66n9ft26dAihTpkzRb9NqtUqDBg2yfD2etmrVqkyfHYqS+nl79vUPDQ3N8Bz+5ptvFEtLS+X69evpjh87dqyi0WiUO3fuZDuGpz37/4OEhIR0z0NFSX2vTU1N0702ac8LDw+PDK+xj4+P4uLiosTExOi37d27VwHSPUdf5BlYsWLFdM8cIYQQhiXTSIUQQuRJ2hSXnGTQ5NagQYP0f9ZoNNSoUQNFUXjnnXf024sUKUK5cuW4deuWwc5rbm6u//OjR4+IioqiQYMGnD59Olf9pU0fWrlyZbqqlytWrKB27dqUKlUKgL///hudTkf37t0JDw/X/zg7O1OmTBn27Nnz3HMtW7YMJycnmjRpAqROQ+vRowfLly/PMIULoFOnTumyJGrVqoWfnx+bN2/WvxYmJibs3bs3yymZO3fuJCkpiREjRqRbY+jdd9/FxsYm39ftW7VqFba2trRo0SLd6+br64uVldVzX7eiRYvSpk0bNmzYQGxsLJCadbN8+XJq1KhB2bJlgfT3RXJyMhEREXh5eVGkSJFc3xuZXUv58uXx9vZOdy1NmzYFyPZaXvQzGRMT89y2afvT+k4zfvx4HBwccHZ2pkGDBly5coVp06bRtWvXTPspV64cDg4OuLu78/bbb+Pl5cWWLVuemw148uRJQkNDef/99zEzM9Nvb9u2Ld7e3nm+t2bPns2OHTvS/UBqtlpkZCS9evVK9z5oNBr8/PzSvQ9P3xcJCQmEh4dTu3ZtAIPdF88aMmRIut9z+uxIy1zbtm0bcXFxL3TOp68zNjaW8PBw6tati6IonDlz5rljbNCgQbrn9ObNmzEyMtJnukHqc3748OEvNK6sVKhQQT9dGVKzCJ/9f8WqVato0KABRYsWTfe6NW/enJSUFPbv35/r85uamuqfhykpKURERGBlZUW5cuUyvS/69++f7jW+f/8+Fy5coF+/fukKLTRq1ChDEZK8PgOFEEIYjkwjFUIIkSc2NjZA6hf2/JIWhEpja2uLmZmZfgrW09ufXh8przZu3Mi3337L2bNn062T9fQaYS+qR48erFu3jiNHjlC3bl1u3rzJqVOn0k1TunHjBoqiUKZMmUz7eN5UzJSUFJYvX06TJk0ICAjQb/fz82PatGns2rWLli1bpjsms3OVLVuWlStXAqlfGCdPnsyoUaNwcnKidu3atGvXjn79+uHs7AykToeF1IDK00xMTPDw8NDvzy83btwgKioKR0fHTPenLZyfnT59+rB27VrWr19P7969OXz4MIGBgfo1BwHi4+OZOHEiixYtIigoKF3g9HnrXeXUjRs3uHLlCg4ODpnuz+5aXvQzaW1tnWEK5bPS+no2KPfee+/RrVs3EhIS2L17NzNmzMg0mJtmzZo12NjYEBYWxowZMwgICEgXWMhKVvcWgLe3d56rmdaqVSvTAgk3btwA0Ac5n5X2WkPq2lwTJkxg+fLlGd4fQ90Xz3q2gmpOnx2lS5fm448/5scff2TZsmU0aNCADh066NfGzM6dO3f48ssv2bBhQ4bA+7PXaWZmluEeLlq0aLrjbt++TfHixTNU7Mzsvc6NZ///kdkYbty4wfnz53P1eXsenU7H9OnTmTNnDgEBAek+H8WKFcvQ/tn3NO3ez2z6tpeXV7qAnSGegUIIIQxDgm1CCCHyxMbGhhIlSmRYTD8rWQWqsvuCnllFz6yqfD4d+MjNudIcOHCADh060LBhQ+bMmUPx4sUxNjZm0aJF/Pnnn889Pivt27fHwsKClStXUrduXVauXIlardYv8g2pX85UKhVbtmzJ9Dqf/VL6rN27d/PgwQOWL1/O8uXLM+xftmxZhmBbTowYMYL27duzbt06tm3bxhdffMHEiRPZvXs31apVe+H+npaX9yqNTqfD0dExy0IQWX2Rflq7du2wtbXlzz//pHfv3vz5559oNBp69uypbzN8+HAWLVrEiBEjqFOnDra2tqhUKnr27PncxdSzu86n32udToePjw8//vhjpu1dXV2zPIe3tzeQutZcTpQvX56zZ89y586dTAMTkLr4P6RmCT2tTJkyNG/eHEh97TQaDWPHjqVJkyaZBq8aNmyoD5K3b98eHx8f+vTpw6lTpwplxcW093Pp0qX6oPLTjIz+/at09+7dOXz4MJ988glVq1bFysoKnU5H69atc7TIfm4+A88GKl/k2TFt2jQGDBjA+vXr2b59Ox9++CETJ07k6NGjuLi4ZDmWFi1a8PDhQ/73v//h7e2NpaUlQUFBDBgwIMN1FmQ15ueN4en/V+h0Olq0aMGYMWMybZuW1Zob33//PV988QVvv/0233zzDXZ2dqjVakaMGJHpfZGT4HNWDPEMFEIIYRgSbBNCCJFn7dq145dffuHIkSPUqVMn27ZpC6E/W2kuP7Ke8nKuNWvWYGZmxrZt2zA1NdVvX7RoUYa2L5LpZmlpSbt27Vi1ahU//vgjK1asoEGDBukWyfb09ERRFEqXLp2rL3nLli3D0dFRX7XzaX///Tdr165l3rx56b7UpWXwPO369esZKt15enoyatQoRo0axY0bN6hatSrTpk3jjz/+wM3NDYBr166lq7qalJREQECAPiiTmRd5r7J6vT09Pdm5cyf16tXL9RdWU1NTunbtypIlSwgJCWHVqlU0bdo0XaBl9erV9O/fP13124SEhBxVTyxatGim7W7fvp3uNfP09OTcuXM0a9bshTMpy5YtS7ly5Vi/fj3Tp09/bnC2Xbt2/PXXXyxZsoTPP/88w/7o6GjWr1+Pt7d3tsURAD777DMWLFjA559/ztatW7Nta2Vlxfjx4xk4cCArV65MF9B81tP31rNZZteuXdPvN7S04gqOjo7Z3r+PHj1i165dTJgwQV9IAzL/XGX1fhri2fiizw4fHx98fHz4/PPPOXz4MPXq1WPevHl8++23mba/cOEC169f5/fff09XUCNt2m1uuLm5sWvXLh4/fpzuXr127VqOjs9LpnEaT09PHj9+nO17nFurV6+mSZMmLFy4MN32yMjIDNnZmUm7t/39/TPse3bbizwDDfG6CSGEyFrh+ydEIYQQr5wxY8ZgaWnJoEGDCAkJybD/5s2bTJ8+HUjNhLO3t8+wBs6cOXMMPq60L8pPnyslJYVffvnlucdqNBpUKlW6rJLAwMBMq0BaWlrmKNCSpkePHty/f59ff/2Vc+fO0aNHj3T733zzTTQaDRMmTEiXfQGp2RjZTZWNj4/n77//pl27dnTt2jXDzwcffEBMTAwbNmxId9y6desICgrS/378+HGOHTvGG2+8AaRW60tISEh3jKenJ9bW1vopts2bN8fExIQZM2akG/fChQuJiorSV3DNjJubGxqNJkf3haWlJZAxKNG9e3dSUlL45ptvMhyj1Wpz/B716dOH5ORkBg8eTFhYGH369Em3X6PRZHhfZs6cmaMsPE9PT44ePUpSUpJ+28aNG7l7926GawkKCmLBggUZ+oiPj9evKZeVCRMmEBERwaBBg9BqtRn2b9++nY0bNwLQtWtXKlSowKRJkzh58mS6djqdjqFDh/Lo0SPGjx//3OsrUqQIgwcPZtu2bZw9e/a57fv06YOLiwuTJ0/Otl2NGjVwdHRk3rx56aZ0b9myhStXrmR7b+VFq1atsLGx4fvvvyc5OTnD/rQKomnZU8/eF5lVsczq/jXEszGnz47o6OgM94WPjw9qtTrd6/uszK5TURT98z032rRpg1arZe7cufptKSkpzJw5M0fHZ/V6voju3btz5MgRtm3blmFfZGRkpp+hnMrsebFq1ap0z9vslChRgkqVKrFkyRIeP36s375v374M2asv8gx80f9vCSGEeDGS2SaEECLPPD09+fPPP+nRowfly5enX79+VKpUiaSkJA4fPsyqVasYMGCAvv2gQYOYNGkSgwYNokaNGuzfv5/r168bfFwVK1akdu3ajBs3jocPH2JnZ8fy5ctz9MWpbdu2/Pjjj7Ru3ZrevXsTGhrK7Nmz8fLy0k+pS+Pr68vOnTv58ccfKVGiBKVLl8bPzy/Lvtu0aYO1tTWjR49Go9HQpUuXdPs9PT359ttvGTduHIGBgXTq1Alra2sCAgJYu3Yt7733HqNHj8607w0bNhATE0OHDh0y3V+7dm0cHBxYtmxZuiCfl5cX9evXZ+jQoSQmJvLzzz9TrFgx/bSq69ev06xZM7p3706FChUwMjJi7dq1hISE6DOSHBwcGDduHBMmTKB169Z06NCBa9euMWfOHGrWrEnfvn2zfE1sbW3p1q0bM2fORKVS4enpycaNGzNdY8jX1xeADz/8kFatWumneTZq1IjBgwczceJEzp49S8uWLTE2NubGjRusWrWK6dOnZ7lw/9MaNWqEi4sL69evx9zcnDfffDPd/nbt2rF06VJsbW2pUKECR44cYefOnZmuv/SsQYMGsXr1alq3bk337t25efMmf/zxhz4wnOatt95i5cqVDBkyhD179lCvXj1SUlK4evUqK1euZNu2bZlO00zTo0cPLly4wHfffceZM2fo1asXbm5uREREsHXrVnbt2qWfDm1iYsLq1atp1qwZ9evXZ+DAgdSoUYPIyEj+/PNPTp8+zahRo7LNPHvaRx99xM8//8ykSZMyncb8NGNjYz766CM++eQTtm7dSuvWrbNsN3nyZAYOHEijRo3o1asXISEhTJ8+HXd3d0aOHJmjsb0oGxsb5s6dy1tvvUX16tXp2bMnDg4O3Llzh02bNlGvXj1mzZqFjY0NDRs2ZMqUKSQnJ1OyZEm2b9+ebs3ENGn372effUbPnj0xNjamffv2+n+wyMuzMafPjt27d/PBBx/QrVs3ypYti1arZenSpZk+j57m7e2Np6cno0ePJigoCBsbG9asWZNl0ZScaN++PfXq1WPs2LEEBgZSoUIF/v777xyvc1e1alU0Gg2TJ08mKioKU1NTmjZtmuW6ZZn55JNP2LBhA+3atWPAgAH4+voSGxvLhQsXWL16NYGBgTnKQstMu3bt+Prrrxk4cCB169blwoULLFu2LF0m6/N8//33dOzYkXr16jFw4EAePXrErFmzqFSpUroA3Is8A319fZk7dy7ffvstXl5eODo6Zrk2oRBCiFx4qbVPhRBCvNauX7+uvPvuu4q7u7tiYmKiWFtbK/Xq1VNmzpypJCQk6NvFxcUp77zzjmJra6tYW1sr3bt3V0JDQxVAGT9+vL7d+PHjFUAJCwtLd57+/fsrlpaWGc7fqFEjpWLFium23bx5U2nevLliamqqODk5KZ9++qmyY8cOBVD27NmTrk83N7d0xy5cuFApU6aMYmpqqnh7eyuLFi3Sj+lpV69eVRo2bKiYm5srgNK/f39FURRl0aJFCqAEBARkGGufPn0UQGnevHmWr+eaNWuU+vXrK5aWloqlpaXi7e2tDBs2TLl27VqWx7Rv314xMzNTYmNjs2wzYMAAxdjYWAkPD1cCAgIUQJk6daoybdo0xdXVVTE1NVUaNGignDt3Tn9MeHi4MmzYMMXb21uxtLRUbG1tFT8/P2XlypUZ+p81a5bi7e2tGBsbK05OTsrQoUOVR48epWuT2esdFhamdOnSRbGwsFCKFi2qDB48WLl48aICKIsWLdK302q1yvDhwxUHBwdFpVJleD9++eUXxdfXVzE3N1esra0VHx8fZcyYMcr9+/ezfE2e9cknnyiA0r179wz7Hj16pAwcOFCxt7dXrKyslFatWilXr15V3Nzc9O+9oijKnj17MtxniqIo06ZNU0qWLKmYmpoq9erVU06ePKk0atRIadSoUbp2SUlJyuTJk5WKFSsqpqamStGiRRVfX19lwoQJSlRUVI6uY9euXUrHjh0VR0dHxcjISHFwcFDat2+vrF+/PkPb0NBQ5eOPP1a8vLwUU1NTpUiRIkrz5s2VDRs2ZGj79H2TmQEDBigajUbx9/dXFCXrz7KiKEpUVJRia2ub4fozs2LFCqVatWqKqampYmdnp/Tp00e5d+9eujZpn7sTJ048t7+ctt2zZ4/SqlUrxdbWVjEzM1M8PT2VAQMGKCdPntS3uXfvntK5c2elSJEiiq2trdKtWzfl/v37GZ5riqIo33zzjVKyZElFrVane0bk9dmY5nnPjlu3bilvv/224unpqZiZmSl2dnZKkyZNlJ07dz73Nbt8+bLSvHlzxcrKSrG3t1feffdd5dy5cxk+p1k9pzN7hkZERChvvfWWYmNjo9ja2ipvvfWWcubMmQx9ZmXBggWKh4eHotFo0n3m3NzclLZt22Zon9nnLSYmRhk3bpzi5eWlmJiYKPb29krdunWVH374QUlKSnruGNJYWlqmew4kJCQoo0aNUooXL66Ym5sr9erVU44cOZJhDGnPi1WrVmXa7/LlyxVvb2/F1NRUqVSpkrJhwwalS5cuire3d4a2OXkGBgcHK23btlWsra0VIEefPyGEEDmnUpRn8pqFEEIIIYQQQhRqVatWxcHBIU9r5gkhhMgfsmabEEIIIYQQQhRSycnJGZY/2Lt3L+fOnaNx48YFMyghhBDZksw2IYQQQgghhCikAgMDad68OX379qVEiRJcvXqVefPmYWtry8WLF3O0XqQQQoiXSwokCCGEEEIIIUQhVbRoUXx9ffn1118JCwvD0tKStm3bMmnSJAm0CSFEISWZbUIIIYQQQgghhBBCGIis2SaEEEIIIYQQQgghhIFIsE0IIYQQQgghhBBCCAORNduyoNPpuH//PtbW1qhUqoIejhBCCCGEEEIIIYQoIIqiEBMTQ4kSJVCrs89dk2BbFu7fv4+rq2tBD0MIIYQQQgghhBBCFBJ3797FxcUl2zYSbMuCtbU1kPoi2tjYFPBohBBCCCGEEEIIIURBiY6OxtXVVR8vyo4E27KQNnXUxsZGgm1CCCGEEEIIIYQQIkdLjUmBBCGEEEIIIYQQQgghDESCbUIIIYQQQgghhBBCGIgE24QQQgghhBBCCCGEMBAJtgkhhBBCCCGEEEIIYSASbBNCCCGEEEIIIYQQwkAk2CaEEEIIIYQQQgghhIFIsE0IIYQQQgghhBBCCAORYJsQQgghhBBCCCGEEAYiwTYhhBBCCCGEEEIIIQxEgm1CCCGEEEIIIYQQQhiIBNuEEEIIIYQQQgghhDAQCbYJIYQQQgghhBBCCGEgEmwTQgghhBBCCCGEEMJAjAp6AEIIIYQQQgghhBDi1aHVafnh5A+oUDGqxiiM1BJeepq8GkIIIYQQQgghhBAixxZeWMiyK8sA0Kg0jK45uoBHVLjINFIhhBBCCCGEEEIIkSPnw84z99xc/e+/X/6djbc2FuCICh8JtgkhhBBCCCGEEEKI54pNjmXsgbGkKCm84f4G7/q8C8BXh7/iUsSlAh5d4SHBNiGEEEIIIYQQQgjxXJOPT+ZuzF2KWxbn8zqfM6zqMBq6NCQxJZERe0YQER9R0EMsFCTYJoQQQgghhBBCCCGytT1wO2v916JCxXf1v8PGxAaNWsOkBpNwt3EnODaYUftGkaxLLuihFjgJtgkhhBBCCCGEEEKILAXHBjPhyAQA3vF5h5rONfX7rE2smd50OpbGlpwKOcWU41MKapiFhgTbhBBCCCGEEEIIIUSmdIqOzw9+TnRSNBWLVeT9Ku9naONh68GkBpMAWH5tOX/f+PtlD7NQkWCbEEIIIYQQQgghhMjUkktLOBZ8DHMjcyY1mISxxjjTdo1dGzOs6jAAvj36LefCzr3MYRYqEmwTQgghhBBCCCGEEBlcfXiV6WemAzCm5hjcbd2zbf9e5fdoVqoZybpkRu4ZSWhc6EsYZeEjwTYhhBBCCCGEEEIIkU68Np7/7f8fWp2WJq5N6FKmy3OPUavUfFf/OzxtPQmLD2PO2TkvYaSFjwTbhBBCCCGEEEIIIV5Rybpkbjy6gU7RGbTfeefmcSvqFvbm9kyoOwGVSpWj4yyNLZnRdAbdy3ZnbK2xBh3Tq0KCbUIIIYQQQgghhBCvoGsPr9FzY0/e3PAmI/aM4HHSY4P0GxAVwJLLSwD4svaXFDUr+kLHl7IpxRd1vsDMyMwg43nVSLBNCCGEEEIIIYQQ4hWi1Wn55fwv9NzUk+uPrgOw5+4eem/uTUBUQJ76VhSFyccno9VpaVCyAY1dGxtgxP8tEmwTQgghhBBCCCGEeEXcirrFW5vfYuaZmWh1Wpq6NmVOszk4WjgSEBVA70292Xd3X67733N3D4fuH8JYbcz/av0vx9NHxb8k2CaEEEIIIYQQQghRyOkUHUsvL6X7P925GHERa2Nrvq//PT83+ZkGLg1Y0W4F1R2r8zj5McN3D2f+ufkvvI5bgjaBKSemANC/Yn/cbNzy41Jee/kebJs9ezbu7u6YmZnh5+fH8ePHs2x76dIlunTpgru7OyqVip9//jlDm4kTJ1KzZk2sra1xdHSkU6dOXLt2LV2bxo0bo1Kp0v0MGTLE0JcmhBBCCCGEEEK89nSKjrU31rLh5ga0Om1BD+c/KehxEO9se4cpJ6aQmJJI3RJ1+bvj37T3bK/PPLM3t+fXlr/So1wPFBRmnZ3Fx3s/JjY5NsfnWXRpEUGPg3CycOJdn3fz63Jee/kabFuxYgUff/wx48eP5/Tp01SpUoVWrVoRGhqaafu4uDg8PDyYNGkSzs7OmbbZt28fw4YN4+jRo+zYsYPk5GRatmxJbGz6m+fdd9/lwYMH+p8pU6YY/PqEEEIIIYQQQojX2cOEh7y/632+PPwlnx38jC4burD37l4URSnoof1nxGvj6be5HydDTmJuZM4Xtb9gXvN5OFtmjJsYa4z5vPbnTKg7AWO1Mbvu7KLPpj4ERgU+9zxBj4NYeGEhAKNrjMbC2MLQl/KfoVLy8RPi5+dHzZo1mTVrFgA6nQ5XV1eGDx/O2LHZl391d3dnxIgRjBgxItt2YWFhODo6sm/fPho2bAikZrZVrVo108y4nIqOjsbW1paoqChsbGxy3Y8QQgghhBBCCPEqOhVyijH7xhAaH4qpxhRzI3MiEyMBqOFUg9E1RlPRvmLBDvI/YOW1lXxz9BuKWxZnYcuFuNq45ui482HnGblnJKHxoVgYWfBFnS9o59Euy/Yj9oxg151d1HSuycKWC2Wttme8SJwo3zLbkpKSOHXqFM2bN//3ZGo1zZs358iRIwY7T1RUFAB2dnbpti9btgx7e3sqVarEuHHjiIuLy7afxMREoqOj0/0IIYQQQgghhBD/NTpFxy/nf+HtbW8TGh+Ku407y9osY/Obm3m70tuYqE04GXKSnpt6Mmb/GIIeBxX0kF9baeu0QeoaajkNtAFUdqjMivYr8HXyJU4bx7gD4/js4GfEJWeMjxwOOsyuO7vQqDSMqzVOAm15lG/BtvDwcFJSUnByckq33cnJieDgYIOcQ6fTMWLECOrVq0elSpX023v37s0ff/zBnj17GDduHEuXLqVv377Z9jVx4kRsbW31P66uOb+BhRBCCCGEEEKI10FEfARDdw5l5pmZ6BQd7T3as6LdCsrZlcPaxJqRviPZ2Hkj7T3aA7AlYAvt17Zn2slpJGgTCnj0r5/99/YTGB2ItbE1nbw6vfDx9ub2LGy5kPervI9apWbDzQ302NiDqw+v6tskpyQz8fhEAHp596JM0TKGGv5/1itdjXTYsGFcvHiR5cuXp9v+3nvv0apVK3x8fOjTpw9Llixh7dq13Lx5M8u+xo0bR1RUlP7n7t27+T18IYQQQgghhBCi0DgRfIJu/3Tj8P3DmGnM+Lru13xX/7sMa3cVtyrO9w2+Z0W7Ffg5+5GsS2bxpcUM3z1cAm4G9vul3wHoWq4rlsaWuepDo9YwtOpQFrZciKOFI4HRgfTe1JtlV5ahKAp/XPmDwOhA7MzseL/q+4Yc/n9WvgXb7O3t0Wg0hISEpNseEhKSZfGDF/HBBx+wceNG9uzZg4uLS7Zt/fz8APD398+yjampKTY2Nul+hBBCCCGEEEKI111iSiI/n/qZQdsHERYfhoetB3+1/YvOZTpnO52wQrEKLGi5gBlNZmBuZM7RB0cZvns48dr4lzj619eliEucDDmJkcqI3t6989xfDecarGm/hsaujUnWJTPp+CTe3/U+887NA2Ck70isTazzfB6Rj8E2ExMTfH192bVrl36bTqdj165d1KlTJ9f9KorCBx98wNq1a9m9ezelS5d+7jFnz54FoHjx4rk+rxBCCCGEEEII8bo5G3qWbv90Y+HFhegUHR09O/JX27/wKuqVo+NVKhVNSjVhbvO5EnAzsCWXlgDQqnSrTCuP5kYRsyLMaDKDcbXGYaw25mDQQeK0cVR2qEwHzw4GOYfI52mkH3/8MQsWLOD333/nypUrDB06lNjYWAYOHAhAv379GDdunL59UlISZ8+e5ezZsyQlJREUFMTZs2fTZaQNGzaMP/74gz///BNra2uCg4MJDg4mPj71g3zz5k2++eYbTp06RWBgIBs2bKBfv340bNiQypUr5+flCiGEEEIIIYQQAJy9G8nCgwFoU3QZ9l0Kv0SjFY0YuWckDx4/KIDRQbw2nqknptJvSz8CogKwN7dnepPpfFv/2wzTRnPC18mXec3nYWFkwbEHxxi+SwJueREcG8z2wO0A9KvQz6B9q1QqepfvzV9t/6K0bWksjS35zO8z1KpXeqWxQkWlKIqSnyeYNWsWU6dOJTg4mKpVqzJjxgz9tM7GjRvj7u7O4sWLAQgMDMw0U61Ro0bs3bs3dcBZpLAuWrSIAQMGcPfuXfr27cvFixeJjY3F1dWVzp078/nnn7/Q1NAXKekqhBBCCCGEEEKkURSFJj/sJTAijq/aV2BAvfTfc8ceGMumW5sAMDcyZ3DlwfSr0A9jjTHaFB3D/zoDwOze1VGrDV8V8lTIKb489CV3Yu4A0MGzA2NqjsHW1DbPfZ8JPcOQHUOI08bh5+zHzGYzMTcyz3O//zU/nvyRRZcWUdO5Jr+1+i3fzqMoCnHauFyvB/df8iJxonwPtr2qJNgmhBBCCCGEECI3AsJjafLDXgAcrE3Z/0kTzE00QOr6aI1WNCI2ORavIl74R6bO5PKw9eDz2p8TcNeZMWvOA7DlowaUL26476NxyXFMPz2dv67+hYKCo4Uj4+uMp6FLQ4OdA9IH3Go512Jm05m5ypb7r4pNjqXFqhbEJMcws+lMGrs2LughCV4sTiQ5gkIIIYQQQgghhAHtuxaq/3NYTCJLjwbqfz8UdIjY5FicLJxY3X4139X/DjszO25F3eLtbW8z8eQXqDQxABy7FWGwMSmKwrBdw/jz6p8oKLxZ5k3WdVxn8EAbQDXHasxvMR9LY0uOBx9n+O7hxCXHGfw8r6u1N9YSkxyDu417vrw/Iv9JsE0IIYQQQgghhDCgfdfDAKhYIjX7Zd6+WzxO1AKw/XbqOlwt3VuiUWvo4NmBDZ020KNcD0CF1uIUlp4/YFzkKEcNGGw7fP8wJ0NOYqYxY37z+UyoOyFfK09WdazKvObz9AG3t7e9zd2Yu/l2vtdFii6FP678AcBbFd6SddReUfKuCSGEEEIIIYQQBpKQnMKRJ0GyKV0r417MgoexSSw+FEBiSiJ77+4FoKVbS/0xtqa2DK/8Pwj6kJR4F1SaRMyKr+NIzHQeJz3O85gURWHuubkAdC/Xnbol6+a5z5xIC7jZmNhwKeIS3f7pxuZbm1/KuV9Vu+7sIuhxEEVMi9Des31BD0fkkgTbhBBCCCGEEEK80tb5r2PknpHcf3y/oIfC8YCHJCTrcLYxo0JxG0Y0LwvAL/tvsSNgP7HJsThbOlPZoXK64+buu0lMdHFc4v/HyOqjURQ1OsuzdNvQi1uRt/I0pqMPjnIu7BymGlMGVhqYp75eVFXHqqxuv5rqjtWJTY7lfwf+xxeHvpBppVn4/fLvQGpQVApLvLok2CaEEEIIIYQQ4pW18/ZOvjj0BTvv7GTYrmEGyQTLi7QppI3KOqBSqWhfpQRlHK2ITtCy4PRaAFq4tUg3PfBBVDyLDgUAMPaN8rzt0x9P7Rh0yTbciw2k16ZebA/cnqvxKIrCvHPzAOhWthv25vZ5ubxcKW5VnIWtFjKkyhDUKjXr/NfRY2MPrkRceeljKSg6Rcc3R76h0YpGjNk/hq0BWzPcq2dDz3I+7DzGamN6efcqoJEKQ5BgmxBCCCGEEEKIV9KliEuMOzAOAI1Kg3+kP2P2jyFFl1JgY9IH28o5pI5LrWJki7KgSuZm7HEAWrm3SnfMzztukKjVUcvdjiblHAFo6l6LuIDh2Kq8idPGMWrfKKaemEqyLvmFxnMi+ASnQ09jojZ56VltTzNSGzGs6jB+bfkrjhaOBEYH0mdzH/64/AeKohTYuF4GRVGYdnIaK6+v5GHCQ7YEbOGT/Z/QYEUDBu8YzIqrKwiJDWHJ5SUAtPVoWyBBUWE4EmwTQgghhBBCCPHKCYkN4cNdH5KQkkC9EvVY3HoxphpTDgQd4IeTP+S4n0sRlzgcdBidosvzmO49isM/9DEatYp6Xv8GS1pXdMbd9S4qdSIW6mJUtv93Cql/aAyrTqUWDvjfG96oVCoA/DzsUFKsSbg7iIEVU4NkSy4vYdC2QYTHh+d4TPPOp2a1dSnbBUcLxzxfY17VdK7JmvZraOLahGRdMpNPTObD3R+SoE0o6KHlm0WXFukDaR9V/4iBFQfibuOOVqfl8P3DfHvsW5qvbs6O2zuA1MII4tUmwTYhhBBCCCGEEK+UuOQ4hu8eTmh8KJ62nkxtNJWqjlX5rv53APxx5Q9WXluZbR/JumRmnJ5Br429GLxzMB3XdWTltZXEa+NzPa7911ODYNVci2BrbqzfrlarcHP1B+BxREXCHyfp903Zeg2dAi0rOOHrVlS/vaprEUyM1ITHaOnk9h4/Nf4JS2NLToeepts/3XI0BfNk8ElOBJ/AWG3M25XezvV1GVoRsyJMbzKdT/0+xURtwt57e/ni0BcGCXhCaibZjNMz6LCuA6dCThmkz9xa57+On079BEBX96GE3q3Hez4f8k/nf1jfaT0jqo+gikMVVKQGWeuXrE/ZomULcsjCACTYJoQQQgghhBDilaFTdHx28DOuPLxCUdOizGo2C2sTayB1eubwasMB+P7Y9xy+fzjTPu7G3GXAlgEsuLAABQVzI3MCowP55ug3tFzdkllnZr1Q9liafddDgdT12p6WoE3gavRRAOIjKzF3700ATt1+yPbLIahVMKZ1uXTHmBlrqOpaBICjtx7S3K05f7X9C68iXoTHh/Pejve4/uh6tuNJy2rr7NUZZ0vnF76e/KRSqejl3Ys5zedgpDJia+BW5pydY5C+556by4ILCwiICmDYrmFcCLtgkH5f1N67e/nq8FcADKw4kIOnKvLL/lt8tvYCiqLgYevBOz7v8EebP9jdfTc/N/mZSQ0mFchYhWFJsE0IIYQQQgghxCtj5pmZ7LyzE2O1MdObTsfF2iXd/nd93qW9R3tSlBRG7x2doZLnplub6PZPN86Hn8faxJppjaaxt/textYaS0mrkkQmRjL//HxarW7FV4e/4mbkzRyNKzlFxyH/CODf9drSHLp/iDhtHEVNHNEluPLHsds8iIpn8pZrAHSv4YqXo3WGPmuXtgPgWEBqv6VtS7P0jaX42PsQmRjJu9vf5VZU5pVKz4Se4diDYxipjRjkMyhH11AQ/Ir78WWdLwGYf34+G25uyFN/y64sY+65uQCUsi5FbHIsQ3YO4drDa3ke64s4E3qG0ftGk6Kk0MGzA+9U/AD/sNSCCOvP3ufv00Hp2tub29OsVDNsTW1f6jhF/pBgmxBCCCGEEEKIV8KGmxv49cKvAEyoO4FqjtUytFGpVHxV9yuqOVYjJjmGYbuG8SjhEbHJsXx28DPGHhhLbHIs1R2rs6b9Glq6t8TC2II+5fuwqfMmpjWaRmX7yiTpklhzYw2d1ndi/OHxRCZEZju207cf8ThRSzFLEyqVSB8w2Ra4DYD2Xq2p6W5HklbHoN9PcjzwIaZGakY0z3zaoJ9HMQCO3XqoLyJgZWLF3OZzKW9XnocJDxm0bRB3ou9kODatAmlHz44Utyqe7dgLWucynXmn0jsAjD88npPBJ3PVz6Zbm5h0PDUz7P2q77Oy/UoqO1QmOima93a8l2Vg8lk6RUdscmyup7XeeHSDYbuGkZiSSEOXhnxV9ysuBUXzdB2IL9dfJCA8Nlf9i8JPpbzuZT9yKTo6GltbW6KiorCxsSno4QghhBBCCCHEf5aiKJwOPc2g7YPQ6rS86/MuH1b/MNtjHiY8pPem3gQ9DsLH3oeoxCjuxNxBrVIzpPIQ3q38LkZqoyzPdzbsLL9f+p1dd3YBUNS0KKNrjqa9R3t9EYOnTd56lbl7b9K5Wkl+6lFVvz1Bm0DDFQ2J18azrM0y4mJK0vOXo/r9Qxp5MvYN70zHEZ+UQuUJ20hOUdj3SWPcilnq9z1KeMQ729/hxqMbOFs6s7j1YkpalQTgXNg5+m7ui5HKiH86/5Mh+68w0ik6Ru8bzY7bO7A1teXPNn9SyqZUjo8/cO8AH+7+EK2ipbd3b8bWGotKpSI6KZpB2wZx5eEVHM0dWfzGYlytXTPtQ6vTsuHmBuafm8/92PsAWBpb6n+sjK2wNLbE2sQaB3MHnCydcLJwwtnSGScLJxwtHAmPD+etzW8RGh9KFYcqLGi5AHMjc+bs9WfK1mu8UcmZh7FJHAt4iE9JW9YMrYuJkeRBvQpeJE6U+ZNFCCGEEEIIIYR4iS6EXeCX87/wMOEh8SnxxCfHk5CSQLw2ngRtAilKCgAt3FrwQbUPntufnZkds5vNpu/mvlwIT12zq7hlcSY1mER1p+rZHqtSqajmWI1qjtU4E3qGr498jX+kP58d/Iz1/uv5vPbnlLYtne6YfdfCgIzrtR0KOkS8Np7ilsXxsfdB5aCinlcxDvlHYGtuzNBGnlmOw9xEQ2WXIpy6/Yhjtx6mC7YVNSvKLy1+4e1tbxMQFcA7295hcevFOFs667Pa2nu2fyUCbQBqlZrv639PcGwwF8IvMGzXMP5o80eOplWeDT3Lx3s/RqtoaVO6Df+r9T99QNTGxIb5LeYzcOtAbkbd5N3t7+pfpzQ6RcfWgK3MOTeH29G30/UdmxxLbHLOM9BM1CYk6ZLwtPVkdrPZmBuZA3DubiQA1UsVpV2V4rwx/QAXgqKYtv0a49qUz3H/4tUgmW1ZkMw2IYQQQgghhHg5zoaeZcjOIc8NatQuXpsZTWfoAxg5cfj+YT498Cm1itfiM7/PcrUmVrIumSWXljDv3DwSUhIwVhvzjs87DPIZhKnGlNDoBGp9vwuVCk5+1pxiVqb6Y8fsG8OWwC30r9Cf0TVHA3AtOIZRq87ybgMPOlYtme25p2y9ypy9N3mzekl+7F41w/7QuFAGbh3InZg7uNm4Mcp3FB/u+RCNSsM/nf7B1SbzLK7CKjw+nN6bevMg9gE1nWsyv/l8jDXGWba//ug6A7YOICYphvol6zOjyYxM24fFhTFg6wDuxNzB3cadRa0XUcysGLvv7mbWmVn4R6ZWiy1qWpR3fN6hc5nOJKckE5scy+Pkx/qg2+Pkx0QlRhEWF0ZIXAghcSEExwYTEhtCki61ymxxy+IseWNJuoBe7e93ERydwMrBdahV2o5tl4IZvDS1UuqSt2vR8JkgrSh8XiROJMG2LEiwTQghhBBCCCHy39OBtprONelbvi/mRub6HzMjM/2frYytMp3C+TyKouTquGfdi7nH98e+50DQAQDcbNz4zO8zgh64MnrVOSq72LLhg/r69s9OIa3sUPmFz7nvehj9fztOySLmHBrbNNM2wbHBDNg6gKDH/y6638GzA9/V/+6Fz1cY3Hh0g7e2vEVsciwdPTvyee3P0ag1GKmM0r2P92Lu0W9LP8Liw6jiUIVfWvyChbFFlv0+ePyA/lv78yD2AV5FvDDTmHEx4iIA1sbWDKg0gD7l+2BpbJllH1lRFIXIxEhC40IpblUcG5N/4wgh0Qn4fb8LtQouTmiFhUnqJMPP113gj6N3sLcyZeuIBtg/FaQVhY8E2wxAgm1CCCGEEEIIkb+eDrTVcq7FzKYzsw2WFAaKorDj9g4mH59MaHwoAI7quty62oQPGlVjVMty+rY7b+9k5N6RFLcszrYu23IV8HucqKXKhO2k6BQO/q8JLkUzf33uxdxjwNYBhMSFoFapWd9xPe627rm6xsLgUNAhhu0app8+nEatUqNRaTBSG5GsS0ar0+JVxIvFrRfnKGvxbvRd+m/tT1h86rRfcyNz+pbvS/+K/fOtEuj2S8G8t/QU3s7WbB3RUL89ITmFDrMOcj3kMY3LOfBb/5qo1XkPCov88SJxIlmFTwghhBBCCCHES/dsRturEGiD1PXcWrq3ZH2n9fT27o0KFaG6w1h6TkOxOpKuguX2wO0AtHRrmevMOitTIyqVTA0CHbv1MMt2LtYuLGy1kMr2lXmv8nuvdKANoF7JenxZ58sMU4Z1io5kXTLx2ni0Oi3uNu7MbzE/x4EyVxtXfm35K37F/ehXoR9b3tzCh9U/zLdAG8C5e5EAVHEpkm67mbGGmb2qY2qkZu+1MBYdDsy3MYiXSzLbsiCZbUIIIYQQQgiRP54NtM1qOuuVCLRlZs3FI3xxcAIa89QpnFUdqvJFnS8oZV1KP4X0zzZ/4uPgk+tzTNx8hfn7b9G9hgtTulYx1NALnKIoTN56Df/Qx/zcsypWphlrOCbrkklOSUaraEnRpZCipKDVaUlRUkjRpVDCqkSWVWULi76/HuOgfzjfd/aht1/GCqtLjwTyxfpLGGtUfNqmPKXsLHC0NsPJxpRiVqZoJNutUJBqpEIIIYQQQgghCqVzYedem0AbwN3gYsQFvk/Vipd5oF7L2bCz9PinB7VL1CZeG08JyxJUsq+Up3P4edgxf/8tjgVkndn2KlpzOoh5+24C8MO2a3zVoWKGNsZqY4zVWRdIKOx0OuXfzDbXzLPn+tZ2Y/+NcHZcDmHCP5fT7VOrwMHaFCcbMxqXdWBE87Iy1fQVINNIhRBCCCGEEEK8FOfCzjF4x+DXJtAGqQUMQENv7z6s77Se5qWao1W0HAw6CEBL99xPIU1Tw90OtQpuR8QRHJVggFEXvMDwWMavv6j//fcjgZy586gAR5Q/AiJiiUnQYmaspqyTdaZtVCoV07pXYWhjT5qXd8SnpC2O1qaoVaBTICQ6kfP3opix25+lR2+/5CsQuSGZbUIIIYQQQggh8l1YXBgf7PqA2ORYajjVeC0CbY9ikzh7NxKAhmUdcLY046cmP7Hv7j6+O/YdkYmRdPTsmOfz2JgZU6GEDReDojkWEEHHqiXz3GdBSk7R8dGKs8QmpVCrtB0lbM1Yd/Y+4/6+wD/D62OseX3ygs4/yWqrWMI22+uyMTPmf629023Tpuh4GJtESHQi2y8HM3O3P5O2XKVxOQfcir14xVTx8rw+d7AQQgghhBBCiEJJURS+PPwlkYmReNt5M7vZ7Fc+0AZwwD8cRQFvZ2ucbc302xu5NmLzm5vZ2W0nXkW9DHIuv9LFADiaTZGEV8X0nTc4dzcSGzMjfupRlS/bV6SohTFXg2P4Zf+tgh6eQZ27GwVkLI6QE0YaNY42Zvi42DKyeVlqe9gRn5zCJ6vPo9PJ8vuFmQTbhBBCCCGEEELkq1XXV3Ew6CAmahMm1p/4WgTaAPZdCwOgUVmHDPuM1EbYmBiu2J5faTsAjgVEGKzPgnDsVgSz9/oD8P2bPpQsYo6dpQlftKsAwPRdNwgIjy3IIRpUWuZjVuu15ZRarWJKlypYmGg4HvCQ348E5n1wIt9IsE0IIYQQQgghRL4JjArkh5M/ADDCd4TBMr0Kmk6nPFmvLfNgm6HVKm2HSgW3wmIJjXk1122Lik/m45XnUBTo6utCu8ol9Ps6VytJgzL2JGl1fLb2Aory6mduJWl1XL4fDUBV1yJ57q9UMQvGvZE61XTy1qsEvkZBydeNBNuEEEIIIYQQQuQLrU7Lpwc/JV4bj5+zH33K9ynoIRnMH8duE/44EQsTDTXc7fL9fEUsTCj3ZIH9469gVVJFUfhs7QWCIuNxK2aRofKoSqXiu04+mBmrOXwzgtWn7hXQSA3nanA0SSk6ilgYU8rOMNmcffzcqOtZjIRkHZ+sPifTSQspCbYJIYQQQgghRAEJi0mk5y9HGPT7idfyS/OCCwu4EH4Ba2Nrvq3/LWpV4fsKeiMk5oWrYK47E8T4DZcAeL+xJyZGL+e6anukrtt27CWv25aiUxj+1xk+Xnk21/fpmtNBbDz/ACO1iuk9q2FlmrFeY6liFoxsXhaA7zZfIfxxYp7GXdDOpU0hdSmS54q0adRqFZO7VMbSRMOJwEcsOhxokH6FYRW+J50QQgghhBBC5EGiNoUkra6gh/Fc9x7F0W3eYY7eesjOK6HsvxFW0EMyqIvhF5l/bj4An9X+DGdL5wIeUUYJySl0m3+EznMOM2XrVVJyEEjaeTmEUatSp0IOqOvOsCYvb1psQa3bdu5eJP+cu8/fp4P468SdFz4+MDyW8esvAjCyRdlsp1S+U780FYrbEBmXzDcbL+d2yIXC2bTiCAaYQvo0VzsLPm1bHoCp265yK+yxQfsXeZcxlCyEEEIIIYQQr6izdyPpMf8IiVodpkZqrM2MsTEzwtrMCGszY6zNjPB2tmFAXXdsLYwLbJz+oTH0/fU4wdEJqFSgKPDH0ds0LudYYGMypHhtPOMOjCNFSaG1e2valG5T0EPK1JFbEUTGJQMwZ+9NLj+IZnrPatiaZ35vHLkZwft/niZFp/BmtZJ82a6CwTKWcqLWk2Db9ZDHPIxNws7S5KWcN60QBMDkLVdpWcEZB2vTHB2bnKLjoxVniU1Kwa+0HUMaeWbb3kijZnKXynScfZD1Z+/TqVpJmryin4vz9yIBqOKSt+IImeldqxRbLgRz0D+cT1afZ+XgOmjUL+9eFNmTzDYhhBBCCCHEa2PT+fskPslqS9TqCH+cyK3wWM7di+KgfzhbLgbz087r1J+ym9l7/IlL0r70MV64F0X3+UcJjk7Ay9GKZYP8ANh1NZS7D+Ne+njyw48nfyQwOhBHc0c+r/35Sw1IvYhdV0IA8Clpi5mxmr3Xwug0+xA3QmIytD1/L5JBv58gSaujeXknJnetjPolBzeKWZlSxtEKgM0XHry086YVgjA1UhOdoOX7zVdyfOyUrVc5dzcSGzMjfupRNUcBIR8XW96uVxqAz9deLJDPaV7FJCTj/yTjrLJLEYP3r1KpmNTFBytTI07dfsSiQwEGP4fIPQm2CSGEEEIIIV4bp26nrr31TadKHBjThM0fNmDFe7X5tV8NfupRhS/bVaCckzUxCVqmbrtGwyl7+f1w4EubdnrsVgS9FhzlYWwSlV1sWTm4DnU97anvZY+iwF/HX3yKXmFzMOggy68tB+Cb+t9ga2r4rB5DUBSF3VdCARjZogyrh9SlZBFzAsJj6TT7EFsvBuvb3giJof9vx4lNSqGORzFm9a6GsaZgvk63rpQ6HffL9RdZeiQw38/3KDaJc08ytGb0qoZKBWvPBHH4Zvhzj9168QELDqQGgaZ0rUKJIuY5Pu/IFmUpWcScoMh4vv7n1ZtOeiEoCkWBkkXMc5wF+KJcilrwmX466TVuynTSQkOCbUIIIYQQQojXQkJyCheDogFoWMYeVzsLKpSwwc+jGM0rONG5mgtv1y/N5o8a8FOPKrjamRP+OJHxGy7RdNpe1py6l6M1u3Jr99UQ+v12nMeJWvxK27FskJ9+GmDf2m4ArDhxl0RtSr6NIb9FJUbx5aEvAejt3Zu6JeoW8IiydvlBNPejEjAzVlPX055KJW3Z8EE96ngUIzYphSF/nOLHHde5ExFH34XHeBSXTBXXIizoXwMzY02BjfvDZmXoUcMVnQJfrL/ExC1X8rW4xgH/cBQFvJ2taVXRmb5+qffq5+suZnuvBoTH8smq8wC819BDHyTMKUtTIyZ3qYxKBctP3GXZsdu5v4gCcO7Jem3ZrU9nCD1rutKgjD2JWh3j119CUV6/QiuvIgm2CSGEEEIIIV4Ll+5HkZSiw97KhFJ2Flm206hVdK7mwq6PG/NNx4o4WJty71E8o1ad443p+znk//yMnRe1/mwQ7y05RaJWRzNvR35/uxbWZv+uC9a8vCPFbc2IiE1iy4XgbHoq3H6/9Dth8WG427gzwndEQQ8nW7ueZLXV93LQB8+KWZmy5J1aDKznDsCMXTdo/tM+QqITKetkxeIBNTOtovkyGWvUTOriw6gWqVU75++7xUcrzuZbkDZtvbZGZR0AGN2qHPZWptwKi+WXfbcyPSY+KYWhf5wiJlFLLXc7xrQql6tz1y9jz5hW3gB8teESJwJfbhXWvNBXInXN38xOlUrFt50qYWKk5qB/eLqMTFFwJNgmhBBCCCGEeC2kTSGtXqpojtYIMzFS81Ydd/Z90pj/tfbGxsyI6yGP6fPrMT5ecZaIx4kGGdeOyyGMWHEWrU6hY9USzHvLN0NmlJFGTe9apQBYevTVyuBJE5MUw/KrqdNHP6r+EeZGOZ8yWBDS1mtrXj794vvGGjXj21dkWrcqmBipSdLqcLUzZ+k7fhR9SQUJnkelUjG8WRmmdauCkVrFP+fu89bC40Q9KfZgKDqdol+vLS3YZmtuzBftUqcuztrjz+2I2HTHKIrC5+sucjU4BnsrU2b1roZRHqbcDmnkQdvKxUlOURj6x2keRMXnuq+X6Zy+OEKRfD+XWzFLhjT0AOCbjZeJT3p1s2NfFxJsE0IIIYQQQrwW0oJtvm5FX+g4CxMjhjb25MCYpvSv44ZKBX+fCaLZj/tYeeJunqZlRSck89naCyhK6nSvn7pXzXKtrx61XDFSqzh1+xGX7kfl+pwFZcW1FcQkx1DatjRNSzUt6OFkKzQ6gXP3Ul/jpt6ZV7rs4uvC30PrMrihB38Oqo2TjdnLHGKOdPF1Sc2SNDXieMBDusw7bNAiG5cfRBP+OBELEw2+7v9+rjpUKUF9r9Spi18+M3VxxYm7rDl9D7UKZvaqhmMeXzeVSsXUrpXxdrYm/HEiQ5aeIiG5cAeTQqMTeBCVgFoFlUq+nDULhzb2omQRc+5HJTBnr/9LOafImgTbhBBCCCGEEK88RVE4dTsSePFgWxpbC2MmdKzE2vfrUb64DZFxyYxZc54evxzFPzRjdcqcmLL1KqExiZS2t+SrDhWzrV7paG2mX9fqj1csuy1Bm8DSy0sBGOQzCLWqcH/V3H01dQppFRfbbINBlUraMq5NeVyzmZZc0Op52bNqaB2cbczwD33Mm3MPczHIMMHatKy2up7FMDX6NxtTpVLxdceKmGjU7LsexuYnU58vBkXx5YZLQOp00zqexQwyDgsTIxb0q0ERC2PO3Yvis7UXC/XaZGmB3DKO1li+pGnH5iYafcbh/H23MmQcipercD8BhRBCCCGEECIH7jyMI/xxIiYadZ4zSaq6FuGfD+rxWZvymBtrOB7wkDemH+DH7ddeKKPm1O2H/HE0tbrod50r5WhR/X513AFYd+Y+UfGGnRKYn9b6r+VhwkNKWJbgjdJvFPRwnmvXk2Bbs/JOBTwSw/B2tmHtsLp4O1sTFpPIYANlfz07hfRpHg5WDG3sCcCEfy4RFBnP0GWnSNLqaF7ekSENPfN8/qe52lkwu3d11CpYc/oevx8ONGj/hvSy1mt7VquKzjQoY09Sio5vNr56FVxfJxJsE0IIIYQQQrzy0qaQVippY5BKkUYaNe829GDHxw1p6u1IcorCjN3+dJ9/hOiE5wfBkrQ6xv19AYBuvi7U9bTP0XlruhelnJM18ckp/H36Xp6u4WVJ1iWz6OIiAAZWGoix2vg5RxSshOQUDt5ILYLRrHzmU0hfRcVtzVk5pA7Fbc0IiozPc3ZkdEIyp598rhqVzfx1GtrYE/diFoTGJPLGz/u5+zAeVztzpnWrmm0WZ27V87Ln0zap2VvfbLrCkZsRBj+HIejXa8vnSqTPUqlUjG9fESO1ip1XQtl9NeSlnl/8S4JtQgghhBBCiFdebtdrex6XohYs7F+DuX2qY2dpwvl7Ubyz+ARxSdpsj/tl/02uhzymmKWJPjiQEyqVir513IDUQgmFeapcms23NvMg9gHFzIrRyatTgYxBm6IjPIcFLY7cjCA+OYUStmZUKG6TzyN7uWzMjBnZPLVK6aw9/jkKDGflsH8EWp1CaXtLShXLfBqtmbGGbzpVAiA6QYuJkZq5fXyxtci/gOs79UvTuVpJUnQKw/48zb1HhlujzhB0OuXfzLaXUBzhWV6OVrxTvzQAE/65XOjXt3tdSbBNCCGEEEII8crLr2AbpAbA3vApztJ3amFjZsSJwEfZTtO7FfaYGbtTFyj/sn2FF65g2blaSSxNNNwKi+VwIc3cSZOiS+HXC78C0K9iP8yMXn4RgSsPomn1837qTNyln/aYnZ1PqpA2Le+Yo6q1r5o3q5fEy9GKyLhkftl3K9f9ZDeF9GkNyjjQpboLAN92rJTvBQFUKhUT3/ShUkkbHsYmMeSPUyRqC09AKTAilugELaZGaso5WxfIGIY3K4OjtSm3I+JYeDCgQMbwXyfBNiGEEEIIIcQrLSYhmWshqQUMqpcyfLAtTcUStix+uxYWJhoO3Ahn+F9nSE7RpWujKAqfrb1IklZHgzL2dKhS4oXPY2VqxJtPghdLjxTuQgm77+4mMDoQaxNrupft/lLPrSgKS44E0nH2IW6GxZKcovDl+ovZZvIoiqIvjvC6rNf2LCONmk9alQPg14O3CI1OeOE+FEVhf1qwrVz2wTaAKV0rc+zTZnSv6frC58oNM2MN89+qQVELYy4GRTN167WXct6cSJtCWrGETZaVh/OblakRn7VNzaidufsGQZHxBTKO/zIJtgkhhBBCCCFeaWfvRqIo4Gpnnm1lSUOoXqoov/argYmRmh2XQxi96hwpun+neq4+dY8jtyIwM1bzXSefXGdOvfVkKumOKyE8iCqcX5QVRWHB+QUA9PbujZWJVZ76OnX7Ib8euPXk/cx++mxkXBKDl57iy/WXSNLqaOrtqM/kWbA/62yuS/ejeRCVgIWJhjoehqmUWRi1rOBE9VJFSEjWMX3XjRc+/mbYY4Ii4zExUlO79PNfJ41ahVM+f/aeVbKIOVO6VgHg14MB+uBgQTt3N7US6cter+1ZHaqUoJa7HQnJOr7fdCVXfSSn6DjkH058UuHJHHxVSLBNCCGEEEII8UrTTyHNx6y2p9X1smde3+oYqVWsP3ufz9ddQFEUwh8n8t3m1C+1I5uXzXKdq5wo62SNX2k7UnQKfx27Y6ihG0SiNoW7D+M4cv8IVx5ewdzInD7l+7xwP4qicPl+NJO2XKX+5D10mXuEbzddodPsQzT7cR+z9/hnmpFzPOAhbaYfYPvlEEw0ar5sV4GF/WvoM3lm7/XPch2vXVdSs9rqe9kbpJBGYaVSqfhfa28Alp+4S0B47Asdv/daauDKr7Qd5iaF93VqUcGJt2qnBqZHrTpHRA7X7ctPaZltVQs42KZSqZjQsSJqFWy68IBD/uEvdPyj2CTeWniMPr8eo+/CYyRpdc8/SOjle7Bt9uzZuLu7Y2Zmhp+fH8ePH8+y7aVLl+jSpQvu7u6oVCp+/vnnXPWZkJDAsGHDKFasGFZWVnTp0oWQEKnCIYQQQgghxOsoP9dry0pTbyem96yGWgV/Hb/Lt5uu8M3Gy0TGJVOhuI1+gfK8SMtu++vE3UL1RfeztRdpMGUP3x6aCUDXsl0papbz1/52RCwzd92g5U/7aTPjAPP23SQoMh5LE82TIJiaW2GxTN12jfqTd9Prl6OsOnmXqPhkpu+8Qc9fjnA/KoHS9pb8/X5d3q5fGpVKRYcqJfArnZrJ8+3GzDN5dj2pztj8NZ1C+jQ/j2I0KedAik7hh+0vNs0yp+u1FQaftS1PGUcrwmISGbP6fIEWFUnS6rh0PxoomOIIzypf3IZ+ddwBGP7XGbZeDM7Rcf6hj+k85xBHbz0EUp+xX2+8lF/DfC3la7BtxYoVfPzxx4wfP57Tp09TpUoVWrVqRWhoaKbt4+Li8PDwYNKkSTg7O+e6z5EjR/LPP/+watUq9u3bx/3793nzzTfz5RqFEEIIIYQQBSdFp3DmTiQAvm52L/XcbSsXZ3KXygAsPBjA+rP3Uatg4ps+GBlgraaWFZxxsDYlLCaRbzddzrA+XEF4GJvE+rNBaMwDuRt/EY3KiP4V+ufo2CStjqF/nKLR1L1M23GdG6GPMdGoaVXRidm9q3Py8xb8MciPE581Z2rXytT2sENR4MitCD5ZfZ5qX2/np53X0SmpRQD+GV4/3WL8KpWKrztWQqNWsfVScIZphSHRCZy/F4VKBU28HQ36uhRWY1p7o1LBpvMPOP8k4+p54pNSOBaQGmRpnIP12gqambGGGb2qYWKkZtfVUJYeLZh1DhOSUxix4gxJWh3FLE1wy0NmqyGNbFGW8sX/LSbx8cqz2VapPXAjjM5zDhEYEUfJIuZ83rY8KhX8cfQOK0/efYkjf7Xla7Dtxx9/5N1332XgwIFUqFCBefPmYWFhwW+//ZZp+5o1azJ16lR69uyJqalprvqMiopi4cKF/PjjjzRt2hRfX18WLVrE4cOHOXr0aL5dqxBCCCGEEOLlux4Sw+NELZYmmgKp/NethisTOlTU/96/rrvB1moyMVIzonkZAJYcuU2P+Ue4X8ALna87E0RyioJJsb0AqB/XwJjnZ7XpdAqfrD7HlovBqFXQoIw9U7tW5uQXzZn/Vg3aVi6un65obWZMtxquLH+vDgf/14TRLcviYW+JTgFLEw0/9ajCj92rYmVqlOE85Zyt6f8kk+erDZfSZQSmFUao4lIEB+vMv2++bsoXt6Fz1ZIATN56NUfHHA2IIEmro2QRczwdcr8O38tUvrgN495InTb77aYrXAuOyba9NkXH4ZvhXA+JMUgmXFR8Mv1+O87mC8GYaNR81zn36zUamq25MeuG1WVII09UKvj7dBCtf9qf6bTSpUcCGbDoBDEJWnzdirL+g3oMauDBiGZlAfh83UXO3Y18yVfwasq3YFtSUhKnTp2iefPm/55MraZ58+YcOXIk3/o8deoUycnJ6dp4e3tTqlSpbM+bmJhIdHR0uh8hhBBCCCFE4ZY2hbRaqaJo1AXz5bZ/XXemdavCgLrujG5ZzqB99/FzY/5bvlibGXH6TiRtZxzQT/F72RRFYeXJu6hN72NkfRUUFY8e1Oej5WfSFYnIzMQtV1h/9j5GahW/DajJ0nf86FbDFRsz42yPcylqwQdNy7BrVCM2fVifXaMa07maS7bHjGhRBnsrU26Fx7LwYIB++64raVNI/xtZbWlGtiiLiUbNIf8IDtx4/r2z78l6bQ3LOhSagFFODKjrTuNyDiRpdXz415lMq9ImaXUsP36HJtP20nvBMVr+tJ/aE3fx8cqzrD1zj9CYF6/cGhyVQPd5Rzge8BBrUyMWv12T1pUyn6lXUEyNNIx9w5tVg+vgVsyC+1EJ9Pn1GF9tuER8UgraFB3j11/ki/WXSNEpvFmtJH++64e9VWpQenhTL5qXdyJJq2PIH6cILwRr4xV2+RZsCw8PJyUlBSen9HPhnZycCA7O2Tzh3PQZHByMiYkJRYoUeaHzTpw4EVtbW/2Pq+vLKVkshBBCCCGEyL3TT4Jt1V/iem2Z6eLrwlcdKmKZSbZVXrWq6Mym4Q2oWMKGR3HJDFh0nB93XH9ugMvQLgZFczU4EnPnzQDUK94MUxw5cCOcn3dez/K4BftvseBAatBrStfKNC734sEulUpFxRK2ONs+v+KljZmxPstp5u4bPIiKJyE5hYNPMnma/QfWa3uaq50FfWqXAlKz23TPuW9epfXanqZSqZjatQr2ViZcC4lh0pZ/M/kSklNYeiSQJj/sZezfF7j7MB5bc2NMjdSERCfy9+kgRq44R63vdtH65/18t+kyB2+EP3fq9o2QGN6cc4hrITE4Wpuyckgd6nra5/el5loNdzs2f9iAPn6p98Piw4G0nXGAfr8d5/cjqdNvx7Qux7TuVTA1+rcwhlqt4sceVfBwsORBVALDlp0uFNPaCzOpRvrEuHHjiIqK0v/cvStzkYUQQgghhCjsTt15+cURCkKpYhasGVqX3n6lUBSYsesG/X87/lIzTFaevIOp8wbUFv6YG5kzxm84k95MXbNu5m5/febY09afDdJXaB37hjdvVs8+K81Q3qxekhpuRYlLSuG7TVc45B9OQnLq1EjvAphuXNA+aOKFlakRF4Oi2XThQZbtbkfEEhAei5FaRV2vYi9xhIbhYG3KD92qAKmBpM0XHvDbwQAaTd3DF+svERQZj4O1KZ+3Lc+RcU05N74lywb5MaSRJxVL2ABwNTiGBQcC6LvwGDW/28mY1efYey00Q5GSk4EP6TovtViHh0NqsY7yxW1e+jW/KEtTI77r7MPigTVxsknNAD18MwJzYw3z+vryfmOvTDMabcyM+eUtXyxNNBwLeMjEzTmblvxfZfh/dnnC3t4ejUaToQpoSEhIlsUPDNGns7MzSUlJREZGpstue955TU1Ns1wnTgghhBBCCFH4hMUkcjsiDpUKqhponbTCzMxYw/edfajlbse4vy9w0D+ctjMOMKePb74HGxOSU1h3609Mih1DhYrJDSbjUcQDj2pw+s4jlhy5zcgVZ9k4vAGlniwMf+BGGKNXnQNgYD13Bjf0yNcxPk2lUjGhY0XazzzIxvMPCAiPBaBZecdXamqkoRSzMuXdBh78tPM6U7ddw9PBigolMgaG0opKVHcr+twpvoVV43KODKznzqJDgby/7LR+e3FbM4Y08qRHTVfMjP/N2qrnZU89L3vGvuFNxONEDt2M4MD1MHZfDSUiNomVJ++x8uQ9bMyMaFHBmTY+ziQk6/h45VkStTqqlyrCwv41KWppUhCXm2uNyzmybURDvt10heshMXzf2SddwZHMeDlaM617VYb8cYrfDgVQxdWWjk/WBBTp5Vtmm4mJCb6+vuzatUu/TafTsWvXLurUqZNvffr6+mJsbJyuzbVr17hz506uzyuEEEIIIYQofE4/yWor62iNrfmrGRjIjU7VSrLhg3p4OVoREp3IgEXH9cGk/PLT4b9R7DYBMKrGaJqUaqLf93nbClQrVYToBC1D/jhFQnIKF4OiGLL0FMkpCm0rF+eLthVeepCrYglb+tZ2A+DS/dQ1uf9rU0ifNqhBaeytTLnzMI42Mw7Qec4hVp28S3zSv2ubvapTSJ/1v9be+iwzl6LmfN/Zh72fNKZ/Xfd0gbZnFbMypUOVEkztVoVjnzbjz3f9eKu2G/ZWpkQnaFlz+h7v/H6SYX+eJlGro3l5R5YNqv3KBdrSFLEw4YduVdjwQf3nBtrStK7kzAdNvAD435rzXLoflZ9DfGWpFEOU3sjCihUr6N+/P/Pnz6dWrVr8/PPPrFy5kqtXr+Lk5ES/fv0oWbIkEydOBFILIFy+fBmANm3a0KdPH/r06YOVlRVeXl456hNg6NChbN68mcWLF2NjY8Pw4cMBOHz4cI7HHh0dja2tLVFRUdjYFP5UUCGEEEIIIf5rJm6+wvz9t+jtV4rvO/sU9HBeuthELf1+O86p24/wcrRi7ft1sc6HbKSL4RfpvbE/iioJb4vWrOw6JUPg7EFUPO1mHCQiNonWFZ05efsh4Y+TqONRjMVv10y3/tPLFBWXTNNpe4mITcLSRMPpL1sU2FgKg+shMUzfeYNtl4LRPlm7zcbMiDeru9Cthgvd5h0hLimFjcNzHnwprKLikzl/L5LaHsUw1uQtzyhFp3Dq9iM2X3jAlosPCIlOpFetUnzTsSJGeez7VZSiU3jn9xPsvRZGKTsLdo1qlOfX+FXwInGifA22AcyaNYupU6cSHBxM1apVmTFjBn5+fgA0btwYd3d3Fi9eDEBgYCClS5fO0EejRo3Yu3dvjvoESEhIYNSoUfz1118kJibSqlUr5syZ80LTVyXYJoQQQgghxMv3KDaJU7cfcfZuJJVdbGlZMeu/w3eZe5hTtx8xrVsVuvi+nLXACpvQ6ATazzpISHQiLSo4Mb+vL2oDVmW9//g+PTf24lHiQ7SPy7Glx2+422f+/eiwfzh9Fx4jbf398sVtWDG4doFPR1xz6h6jVp2jq6+Lfj2v/7rQmARWnbzHX8fvcO9RfLp99lamHP+0mUHvo9eJTqcQGpOYo2Idr7OouGSa/biP8MeJzO5dnbaVixf0kPJdoQq2vaok2CaEEEIIIUT+UhSFOw/jOBn4iJO3H3Ii8BH+oY/1+1UqmNUr8y9xidoUfMZvJylFx97RjXG3t3yZQy9Uzt6NpPv8IyRpdXzUrAwjW5Q1SL8xSTH029IP/0h/UhKcqaT6jJXvNc72mDl7/Zmy9Roli5iz9v26ONoUjoCEf2gMLkUtsp1C+F+k0ynsvxHGn8fusOtqKCk6hV61XJn4pPCFENn5ccd1Zuy6QW0PO5a/9/ov2/UicaJ8K5AghBBCCCFEmseJWo7cjKBxOYf/xFQTkTltio7LD6LTBdfCYjJW0/R0sMTO0oQTgY8YseIMtubG1C9jn67NxaBoklJ0FLM0we3Jgvz/VVVdi/B9Zx9GrzrH9F03KF/chtaVcleULk2yLpnR+0bjH+mPKsWG+LsD6NXF67nHDW3kSS13O8o4WmNrUXjW0fNy/O9VIM0JtVpF43KONC7nSHBUAscDH9K43Ku9Xpt4eXrVcmX2Hn+O3nrI9ZAYyjrJ5yyNBNuEEEIIIUS+m7b9GosOBdKlugvTuss0rv+KhOQUTgQ+1AfXztyJJO6pxdgBjDUqfEraUtPdjhrudvi6FcXO0oQUncLwv06z+UIwg5ee5K/3alPZpYj+uNO3U4sjVHcrWuirS0YlRjH//Hysja0pXaQ0nraeuNm4YaIx3KLqXX1duHQ/ikWHAhm18iweDvVy/cU3Ij6CH0/9yOH7hzFRm/EooB9WGntaV3z+NDGVSkUNd7tcnVcULGdbMzpUKVHQwxCvkOK25rQo78TWS8H8cfQ2X3esVNBDKjQk2CaEEEIIIfKVoihsvxQCwJrT92hczoH28oXutRcak0CP+UczVMm0NjOihltRarjbUdPdjsoutplO7dOoVfzUoypR8Sc45B/BgEUnWDm4Dl6OVgCcehJs83Urmv8Xk0cLzi9g6eWl6bZpVBpcrF3wsPXAw9aDZqWa4eOQtyIPn7Ypz9UHMRy5FcF7S06yflj9HGeX6RQdx4OPs+raKnbf3Y1Wp0WFCm/NEA4lONPerwTmJjIFUwiR3lt13Nh6KZi/TwcxprU3VqYSZgIJtgkhhBBCiHx2KzyWoMh/F+D+bO0FqrsVpWQR8wIc1X/L5fvRfL/5CiOal3kpWUcxCckMXHSCgPBY7CxNaFjGXh9cK+NoleOF102NNMx/qwa9Fxzl/L0o+v92nNVD6+BsY8apO69GsE2r07Lx1kYAGpRsQFRSFLcib/E4+TG3o29zO/o2e+7uYfGlxSx5YwmVHXK/VpaxRs3sPtXpMOsggRFxfPDXaRYPrIUmm9c7Ij6C9TfXs+b6Gu7E3NFvr2xfmd7eA/l4UTKgo3sN11yPSwjx+qrrWQwPB0tuhcWy7kwQfWu7FfSQCgVZMEMIIYQQQuSrfdfCAKjtYUdV1yJEJ2gZueIsKTqp0/WyzNpzg4P+4Xy0/Cyxidp8PVeSVseQP05x6X409lYm/D20Lj/3rEbf2m6Uc7Z+4QqHVqZGLBpQEw97S4Ii4+m38DgXgqIIi0nUT0EtzA7fP0xEQgRFTYsyvcl0lrVZxuFeh9nVbRcLWi5gXK1x1HCqQYqSwpj9Y4hJisnT+ewsTfjlrRqYG2s4cCOcKduuZtruUcIjxuwfQ/PVzfnp1E/cibmDpbElPcr1YHX71Sxru4yo8LIkanWUcbSiikvhfp2FEAVDpVLR1y81wPbH0dtIDc5UEmwTQgghhBD5av+N1GBbM28npvesiqWJhuMBD5m372YBj+y/IT4phT1XU9+DoMh4ftpxPd/OpdMpjF51jkP+EViYaFg0oJZBqoQWszJlyTu1cLYx40boY/r+egyASiUzn4JamGy4uQGANh5tMNakTulUqVQ4WjhSu3htepfvzfSm0ylhWYKgx0F8e/TbPH9ZrVDChqndUjPk5u+7xWH/8Axtvj36LVsCtqDVaalUrBIT6k5gd7fdfF77c8rZlQNg5cm7AHSv4Vro18UTQhScLr4umBmruRocw8knU/z/6yTYJoQQQggh8k1CcgpHb0UA0LCsA27FLPmqQ0UAftpxnXN3IwtwdP8N+66HEZ+cguWT9bZ+OxTAxaAog59HURS+3XSFDefuY6RWMa+vLz4GzIZyKWrBkndqYWtuTHRCanaeb6nCPYU0KjGKPXf2ANDBs0OW7WxMbJjccDIalYbNAZv559Y/eT53u8oleOvJdK6v/rmENkWn33ch7ALbb29HhYrfWv3GX+3+4s0yb2Jh/G9V1+shMZy9G4mRWkWnaiXzPB4hxOvL1tyYTlVTnxNLj9wu4NEUDhJsE0IIIYQQ+eZE4EMSknU425hR1il1Yfuuvi609SmOVqcwYkX+T2v8r9ty8QEAvWqVol3l4ugU+HTtBYNP411w4Ba/HQoAYGq3yjQs62DQ/gHKOlnz24CamD/JZqtZunBXvdwWuI0kXRJeRbwob1c+27ZVHasytMpQAL47+h13ou9k2z4nRrcsR1ELY66HPGbp0dQvwIqi8OOpH4HUAGBN55qZHrvqSVZbU29HHKxN8zwWIcTrLW2tti0XHxAWk1jAoyl4EmwTQgghhBD5Zv/11OmLDcva66ehqVQqvu/sQ3FbMwLCY/n6n8v5dv7QmARCoxPyrf/CLlGbwu4roQC84ePMl+0rYG1mxPl7USw5Emiw86w9c4/vN6euDfZpG286V3MxWN/P8nUryorBtfm8bXmal3fKt/MYQtoU0o6eHXM0DXOQzyBqONUgThvHmP1jSE5JztP5bS2M+aSVNwA/7rhOxONEDgQd4GTISUzUJgyrOizT4x4nall7JghACiMIIXKkUklbqpUqQnKKop+C/l8mwTYhhBBCCJFv9umDbemznGwtjPmxe1VUKlhx8i5bLjww+LnjkrS0mX6QFj/tJzTmvxlwO+QfTkyiFicbU6q5FsXR2oyxb6QGX37Ydo0HUfHP6eH59l0P45NV5wF4p35p3m3gkaPjtDot58LOkZSS9MLnrOxShEENPLKtslnQAqMCORd2DrVKTVuPtjk6RqPWMLHBRGxMbLgUcYmZZ2fmeRw9arpSqaQNMQlapmy9wk+nfgKgT/k+FLcqnqG9TqcwauVZwh8nUcLWjEblDJ+hKIR4PaVNXV929Ha6qev/RRJsE0IIIYQQ+eJBVDzXQx6jVkF9L/sM++t4FmNII08Axv59wSCBn6ftvRZG+ONEouKTmbXb36B9vyo2XwgGoHVFZ30V0F41S+HrVpTYpBTGr7+U676vPIjm242XGbL0FFqdQocqJfisTfkcZXAlpiTywa4P6Lu5L63XtOa3i7/luQpnTiWlJOUqwPei0rLa6paoi4NFzgNWzpbOfF33awAWXVzE4fuH8zQOjVrFV+1T10n8+8YG/CP9sTax5h2fdzJtP3O3P9suhWCiUTOrT3WMNfKVUQiRM218ilPUwpj7UQnsvhpa0MMpUPLkFEIIIYQQ+SJtCmkV1yIUsTDJtM3I5mWp7GJLVHwyn6w6n+cqjE/b/FS23J/H7nA7ItZgfb8KklN07LgcAkDrSv9mMKnVqdN4jdQqtl8OYdul4Bz3GfE4kd8OBtBm+gHemH6AXw8GEJ+cQoMy9vzQrYo+oJedxJREPtrzEYfuHwIgLD6Mn079RIvVLZh2chrBsTkfz4u48egG3xz5hvrL69NhXQfikuPy5TwAOkWnL3LQ0bPjCx/fzK0Z3ct2B+Czg5/xMOFhnsZTw92ODlUdMHHYDsCgSoOwNc1YvGLbpWB+2plarfbbzpWoXsgLUAghChczYw3da6ZOPU9bJ/K/yqigByCEEEIIIV5P+6+HA9CwTNZZPSZGan7uUZU3ph/goH84my8E07ZyxqltLyohOYU9T/5V3b2YBYERcUzbfp0Zvarlue9XxZGbEUTFJ1PM0oRazxQSKOdszXsNPZiz9yZfbbhEPS97rEwz/2qQkJzC3muhrD4VxN5roWifFFYw1qho5u1EF18XmpRzwCgHGVBJKUmM3DOSQ0GHMDcy5+cmPxMWF8biS4vxj/Rn8aXF/HH5D9p4tGFAxQF42HoQFh/GvZh7BD0O0v/ci7lHUkoSlR0q4+vkS3Wn6tibZ8yeTNYls+vOLpZfXc6pkFP67UGPg9gWuI3OZTq/yEuaY8eDjxMcG4y1sTVNSjXJVR+ja47mVMgpbkbd5ItDXzCr6awcZQ1mxcvrHHsuRaFLtsUioXGG/ddDYvh4xVkABtR1l7XahBC50qeWG7/sv8WBG+EEhMdS2t6yoIdUICTYJoQQQgghDE6bouPAjdTMtuet+eThYMXQxp78vPMG3266TBNvByxM8vbX1P3Xw4hNSqGErRmzelen3cyDbDh3n8GNPKhYImNGz+toy8XUDLGWFZ0zXdvsw2Zl2Hj+AXcexjFt+zXGP5lqCHA/Mp4910LZczWUQ/4RxCen6PdVcbGli68L7SuXoKhl5hmLmUlKSWLk3pEcCDqAmcaMWU1nUat4LSC1KuaBoAMsuriIkyEn2XBzAxtubsBIbYRWl3W12osRF/nz6p8AuNu44+vki6+TL2WLlmXXnV2svr6asPjU+1Cj0tC0VFMsjCxYf3M96/zX5VuwbYN/6hTSVqVbYarJXSVPcyNzpjSaQq+Nvdh/bz/TT0/no+of5SrgFpUYxfIbiwBIDGvJD9tu0dbHFWszYwAi45J4d8lJYpNSqOtZjM/aZl85VQghslKqmAWNyzqw51oYy47e5vN2FQp6SAVCgm1CCCGEEMLgzt2LIjpBi625MVVcijy3/ZBGnqw+dY97j+KZvcdfX0Ext9ICTW/4FKdSSVvaVynBP+fuM2XrNX5/u1ae+n4VpOgUtj+ZHvpGJWciEyJZdnUZjV0aU9E+NahmZqzh206V6PfbcX4/HEjFErb4hz5m77VQrganXz/N2caMjtVK0LW6C2WcrF94PMkpyYzaO4r99/ZjqjFlVrN/A22QWqG2oUtDGro05ELYBRZdWsSuO7vQ6rQYqYwoblWcElYlcLFyoaRVSUpalUStUnM69DSnQk5x49ENAqMDCYwOZM2NNenOXcysGF3LdqVr2a44WzoTFhfGxlsbOR16moCoAErbln7h68lObHIsO+/sBHI3hfRpZYuW5VO/T/nqyFcsvLgQtUrN8GrDXzjgtvDCQmKSYvAqUobIsPoERMUzc7c/n7YpjzZFx/C/znA7Ig5XO3Nm95Z12oQQedOvjjt7roWx6tQ9RrUsh7mJpqCH9NJJsE0IIYQQQhhc2npt9cvY56hipJmxhi/aVWDw0lMs2B9AN19X3HM59SRRm8LOJ2uVtfFxBmBUi7JsufCAfdfDOHIzgjqexXLV96vieMBDImKTsDU3po5nMb4+Op51/uuYf24+3ct158PqH2JjYkPDsg50rFqC9WfvM3rVOf3xahVUK1WUpt6ONC7nQIXiNrmewpicksyofaPYe28vphpTZjadiV9xvyzb+zj48GPjHwmPDyc5JRlHC0c06sy/qLUu3RpIzdw6E3qGUyGnOBVyiqsPr+Jj70NP7540L9UcY42x/hgHCwfql6zPvnv7WOe/jpG+I3N1XVnZcXsH8dp43GzcqOJQJc/9dSnbhThtHFNOTGHBhQVo1BqGVR2W4+MfPH7AsivLABjpO4IUb28GLjrBbwcD6F7DlRUn7nDgRjjmxhp+eavGC2UrCiFEZhqWdcDVzpy7D+P55/z9/+S0dAm2CSGEEEIIg9v3JNjWKJv12p7VsoITDcs6sP96GF9vvMxvA2rm6tyH/MOJSdTiZGNKNdfUBd7d7S3pVasUS4/eZtLWq6x7v26e1r8q7LZeTC0O0aKCE8m6BLYFbgNAQWHFtRXsuL2D0TVG086jHZ+3rcDJwEfEJWlpVNaBJt6ONCzjYJCgS7IumdH7RrPn7h5M1CbMaDqDOiXq5OjYzNZgy4qtqS2NXRvT2LUxAIqiZPv+dvbqzL57+9hwcwPDqw3HSG24r0VpVUjbe7Q32D32VoW3UBSFqSenMu/cPNSoGVp1aI6OnX12Nkm6JGo616RByQaoVCqaeTuy62ooby8+wZ2HqYUifuxehfLFbQwyXiHEf5tGraKPnxtLj9zGKAf/4PY6kvxgIYQQQghhUI9ikzh/LxKABmVzHjBRqVSMb18BY42K3VdD2XUlJFfn33whbfpk8XTVMYc388LcWMO5u5Fsu5S7vl8FOp3C1idTSNv4OOszrUpZl+LXlr9S2rY0DxMe8unBT3l729tEp9zj4P+acPqLFvzcsxodq5Y0SKBNp+gYu38su+/u1gfa6paom+d+c+J5Qa6Grg2xM7MjPD6cg0EHDXbeoMdBnAg+AUB7z/YG6xegX8V+jPIdBcCcc3OYd27ec4+5/ui6Pvg3svpI/evyRbsKmGjU+kDbh029eMMn74VJhBAizYC67uwf04Q3q7sU9FAKhATbhBBCCCGEQR30D0enQDkna4rbmr/QsZ4OVrxT3wOACf9cJuGphflzIjlFx44nU0hbV3JOt8/R2ox36qeuzzV121W0KboX6vtVcebuI0KiE7E2NaKelz3rb64HoKNXR/yK+7Gm/Ro+qv4RZhozToacpOuGrkw/PZ14bbxBxzH77Gy2396OsdqY6U2nU69kPYP2nxfGamPae6QGw9beWGuwfv+5+Q8AtZxrUcKqhMH6TTOg0gD9tNfZZ2ez4PyCDG10io6L4ReZfXY2I/aMQEGhpVtLfBx89G3c7S0Z3Cj1c9a8vBMjmpc1+FiFEP9tZsaaHC0j8bqSYJsQQgghhDCotPXaGr5AVtvThjf1wsnGlDsP41iw/9YLHXvkZgRR8cnYW5lQ090uw/73GnlQ1MKYm2GxrDl9L9M+ouKTmbX7Bo2n7uGHbddydQ0FacuTzL6m5R0Jjb/PieATqFDRwbMDAMYaYwb5DGJdp3U0dm2MVtGy8OJC+m/tT3JKskHGsC1wG7+c/wWACXUnUL9kfYP0a0hplUj339tPeHx4nvtTFEUfbEt7rfPD25Xe5qPqHwEw48wMfr3wK7HJsey6vYsvD31J05VN6bWpF/POzeNuzF1sTW317Z82snlZVg2pw9y+1dNlgAohhMg7CbYJIYQQQgiDURSF/TfSgm05X6/taZamRnzapjwAs/f6c+9RXI6P3fJkrbJWFZ0z/Rd1GzNjhjXxAuCnHTfSZc5FPE5kytar1J+0mx+2XycwIo7fDgWQ/AplwCmK8m8l1krF9cGf2sVr42yZPtOvpFVJZjadyYwmMyhiWoSrD6/y++Xf8zyGqw+v8sWhLwDoX6G/wadTGopnEU8qO1RGq2jZeHNjnvs7G3aWOzF3MDcyp4VbCwOMMGuDfAYxvNpwAKafnk795fUZsXcEa/3XEpEQgYWRBS3cWvBNvW/4p9M/lLIplaEPtVpFTXc7qTwqhBD5QJ6sQgghhBDCYK6FxBASnYiZsTrTzLKc6lClBLVK25GQrOO7TVdydIw2Radfi61NNutP9a3tRglbM4KjE1hyJJAHUfFM+OcS9SbvZs7em8QkainrZIW1qRFxSSlcDIrK9XUYkk6ncCLwIRGPE7NscyEoiqDIeMyNNTQoU0y/XldHr45ZHtOkVBPG1BwDwPxz8wl6HJTrMUbER/Dh7g+J18ZTr0Q9g1f6NLTOXqnZbX/7/42iKLnqIzw+nO2B25l+ejoALdxaYGFsYbAxZuW9yu/pq5JqdVpcrV3pW74vC1ou4GDPg/zY+Ec6eXWiqFnRfB+LwehS4M6x1P8KIcQrTKqRCiGEEEIIg0mbQlrboxhmxppc96NSqZjQoSLtZh5ky8VgDt4Ip36Z7KelHg94yMPYJIpaGONXOutAn5mxhpEtyvLJ6vP8tOMGU7ddIzklNdBS2cWWYU28aFHeiSF/nGL75RCOBTykWqmCC1jodKnZajN33+BqcAyWJhreb+LFO/VLZ3iN07Lamng7cOnhGYIeB2FlbEWzUs2yPUc7j3as9V/LieATTDo+iZlNZ77wOJNTkvl478c8iH2Am40bkxtORqPO/T3wMrR2b82UE1MIiArgXNg5qjpWfe4xDx4/4GTISU6FnOJUyCkCowPT7U8L4L0MQ6oMoU6JOlibWFPapvSrX2F351dweAZU6Q2d5sCrfj1CiP8syWwTQgghhBAGs+9JsK1RLqeQPq18cRvequ0GwPgNF0nUZp/tsvmpKaRGz5ka92Z1F8o6WRGfnEJyioJfaTuWvlOL9cPq0aqiM2q1Cj+PYgAcuxWR52vJjRSdwvqzQbT6eT/D/jzN1eAYNGoVsUkpTN12jWbT9vHPufv6jCxFUdhyIfU1eKNScX1hhNalW2NmZJbtuVQqFZ/7fY6Ryoi9d/ey586eFxqroih8f/x7ToeexsrYihlNZ2BravviF/2SWZlY6ad8rvNfl23bW1G36Ly+My3XtOTTg5+y5sYaAqMDUaGibNGy9PLuxexms6nhXOMljPxfVRyq4GHr8eoH2iLvwrEnFVbP/QmnlxTseIQQIg8ks00IIYQQQhhEXJKWEwGPgNyv1/askS3K8s+5+9wMi+XjFeeY0atapmuxpegUtl5MnUL6RjZTSNNo1Cpm9a7OX8fv0NanODUymfKalh13MvARKTrlpVVV06boWH/2PrP3+HMrPBYAGzMj3q5fmgF13dl7LYzJW68SFBnP8L/O8PvhQL5oVwETIzWBEXGYGKmp5WnJN+t3ANDRM+sppE/zKOJB/4r9WXhxIZOOT8KvuF+Op0OuuLaC1ddXo0LF5IaT8bD1yN3FF4DOXp3ZcHMDWwK2MKbmmEyv+erDqwzeMZiHCQ/RqDRUKFYBXydffJ18qeZY7ZUILBZ6eydBShKYFYGESNj8CZSoBsUrF/TIhBDihUmwTQghhBBCGMTRWxEkpegoWcQcD3tLg/Rpa27Mzz2r8vbiE2y68ABLUw2T3qycoXriycCHhD9OxMbMiDpPMtKep6yTNePbV8xyf/niNlibGRGToOXy/Wh8XPI3oKIoCpsuPGDqtmvcjkgtClHEwphB9UvTr647NmbGAHSqVpJWFZ35Zf8t5u27ycnbj+g4+xCln7zmjco6cPjBbuK18bjbuFPFoUqOxzC4ymC2BGzhfux9FlxYkGkVy2edCD7B5OOTAfio+kc0dGn4opdeoHydfCllXYo7MXfYfns7nbw6pdt/NvQs7+96n5ikGMrblWdu87kUM8/ZPVboxUeCiRVoCvhrYejV1Gw2gD6rYf8UuLEdVvaDwfvATIKZQohXi0wjFUIIIYQQBrH/ejgAjco5GHRKW4MyDszoWQ21ClaevMe3m65kWMw+ba2yFhWcMTEyzF9xNU+qNQIcC8jfqaQB4bH0++04H/x5htsRcdhZmvC/1t4c/F9TPmhaRh9oS2NuouGj5mXYM7oxXaq76PsAeKOSs35KZEevji/0XpgbmTO21lgAFl9azK3IW9m2Px1ymo/3foxW0dKmdBvervR2js9VWKhUKjqXSV1nbe2Nten2HXtwjPd2vEdMUgzVHKuxsNXClx9o0+kgNh/uvzPLYLIbfOcMs2rCX71g++dwajEEHoKYEMhN0QidDkIuQ3J8zo/Z/Q0oOvBuB641ofN8sC0FjwJg3fu5G4cQQhQgyWwTQgghhBA5kqJTWHsmiPuR8UTFJxMdn0zUk5/oBC0B4Y8BaFjGMFNIn/aGT3GmdK3C6FXn+O1QANZmRoxsURZILSCw9UmwrY2Ps0HP61fajt1XQzl66yGDGhh+amRCcgpz995k7r6bJGl1mBipeb+xJ+819MDC5Pl/VXe2NWNa9yr0r+vG1G3XSEzWUb5UEuMvnEatUtPeo/0Lj6lJqSY0dmnM3nt7+fbYtyxsuTBDwC42OZafT/3M8mvLAahQrAIT6k54ZdcNa+/RnplnZnI69DSBUYG427qz7+4+Pt77MUm6JGoXr830JtNfSpXRDHZNgEPTodNcqNrLMH3GPYTtn6X+WZcM4ddTf55lUxK826b+uNUDjXHGNpAaDHtwFi6ugYtrIfoelKgOAzaByXNes7sn4OpGUKmh2Zep2yzsoNti+K1V6r6jc6DOsNxerRBCvHQSbBNCCCGEEDkyb99Npm67lm0bO0sT6nnlT+ZPV18XHick89U/l5m+6wbWZkYMauDBmbuRBEcnYGVq9NyKpS8qrUjCicCH6HRKhumrebH/ehhfrr9I4JMpow3K2PNNx0q452IKbmWXIix9xw+AmWdSK4nWKVEHJ0unXI1trN9Yjj44yongE2wK2EQ7j3b6fQeDDvL1ka95EJtajKGzV2dG1xz93CIMhZmTpRP1S9Zn/739rPVfS/li5Rm3fxxaRUsT1yZMbTQVU43pyx9YcgKcXAQosHk0lKoNdqXz3u+e7yH+EThWgJ5/pmaQRdyECP/Un/AbEHkHooPg+C+pP2a2ULZ1auDNsxmYWkHolScBtjXw8JksyPunYd1Q6LoI1FlkmypKagVSgKq9waHcv/tcfKH1xNTr3vEllKwBpfzyfu1CCPESSLBNCCGEEEI8V0h0ArP3+AOp0xRL2VlgY26Mrbmx/r+25saUtrfE2iyL7BcDGFCvNDEJWqbtuM63m65gZWqEf2hqRl3z8o6YGmkMer5KJWywNNEQFZ/MtZAYyhe3yXOfIdEJfL3xMpvOpwarHK1N+bJ9Bdr6FM9zZphO0bHh5gYAOnl2ynU/Ja1KMrjKYKafns7UE1Np6NIQnU7H1JNT9f2XtCrJ+DrjqVOiTp7GXFh09urM/nv7WX51OQkpCegUHW1Kt+Hb+t9irM6/ezpb17dAYlTqn5MepwavBmwCdR7u8+CLcHJh6p/fmJwavLMrDZ5N07dLjoeA/amZZVc3Q1w4nF+R+qMxBduS6QNsRuZQrjVU6gLGFvBnD7i8DvaVgyafZj6Wm7vg9sHU/hqNzbi/5iC4cyQ1mLdqAAw5AJaGDagDEHwBEh+D2+txLwshCp4E24QQQgghxHNN3nKVuKQUqpcqwpw+1Qt0uuAHTb2ISdTyy/5bjFt7Aasn0y1zUoX0RRlp1Pi627H/ehjHbkXkOdh2yD+c95edJio+GbUK+td15+MWZQ0WoDwefJzg2OD/s3ffYU1fXQDHv0nYe4k4UNyoIOLCva3buuqoo7VWW0eXHdYO27fW7tqlraO1ra171721Thy4FQRRlrL3hiTvH5chlU0woPfjw5OfyS83JyIQTs49B0sjS3rV61WhtZ5r8Rz/3P6HOwl3eOvoW/jF+RGbHosCBROaT+AVz1f0s62ykvSo2wM7Ezti02MBGNVkFB92/BBVRRJbFXVZbNPF/Rnw2yMST6d+gq6vl289rRb2zBX90Vo8DQ2KGWZhaApN+4uPId9DaM52z5s7RSVcbCAoDaFJP5FgazpAVLvlGvId/DMbjn0JDk3BfXTB9TUaOPg/cdxhGtg4PxyDQgFDf4D7VyDGH7ZMEwMUdPk5yUyB3weLpOaYVeLfRZIkqYLkgARJkiRJkiSpWD7BcWy5GAbAR0Nb6r0vl0KhYN5AV8Z3qIdWC0kZ2ZgZqejRVPe94kD0bQPwvhNboXX+PhPE5JVnSUjLwr2ONf/M7spHQ1uWKdGm0WoeGg7xoNzBCIMaDKrwtkdDlSEfeH0AwOn7p4lNj6WRdSP+GvQXczvMfawSbSCe77hm4wCRaPyo00f6TbQlR0HAQXHc/W0Y8IU4PvypqMQqjxvbRCWZgQk89Wnp76dUiS2sT30Kr16EmWfE9tO3/WH8WpFIezDRBtBmEnR+RRxvmwmh5wvefn0LhF8BYyvoOqfoxza2FEkwA1O4fRj+/ab0cZfGrX351YNbXoJ7l3S7viRJTySZbJMkSZIkSZKKpNFo+d+OG4DomebhbKPfgHIoFAo+He7GUI/aAPRv6YSJYeUkRnKTbWfvxBab6CpKtlrDR9uv8cG2a6g1WkZ41mHjy51wq2NdpnUC4gLovLYzfTb24cOTH7L37l4ScpMEQFJmEoeCDgEwvPHwMsdZmA61OvCs67MYq4x52eNlNgzdgEcND52sXRW95PESR8Yc4a32b+k9qcy1zaDJFoMGajQDz4nQbJAYaLDlJcjOKNt6mamwTyRP6fI62NQrX1wKBTg2F73bTG2LP7fv/0TFmzpDTDtNCBXXq7NE0hCg86tgXkKfx5otRKUcwIlF4rnoyvWcCbSGZpCdJuJMCtfd+pIkPZHkNlJJkiRJkiSpSFsvhnE5JB5zIxXv9G9W8h0eIZVSwXdjPBjdti6e9Wwq7XFa1bXBxFBJTEomAZHJNKlpWer7JqRlMXuND8f9owF4u38zZvZsVK5EzoqrK0jJSiElK4VtAdvYFrANlUJFqxqt6FqnK+nZ6aSr02lk3YiW9i3LvH5R3u3wLu+0f0e/VV6PiFKhxMG0EnqClcflteLSI2cCqUIBQ3+EkLMQeV0kq55aUPr1Tv4gpoRaO0OX13Qfb2GUKhj1K/zWX8S8Zhy8sFf0fYu7A+Y1oOOM0q3lMQ6OfiYGNwQeEcm+ispIBv/94vjZDbDrTYj2Ewm3KbvFVlpJkqRykJVtkiRJkiRJUqGSM7L5cq8vAK/0aYKjVdWbNmmgUtKjaQ2sKnEog5GBkjb1RAXPmTJsJb0bncLIn09y3D8aU0MVSye2ZVavxuVKtN1Pvs++u/sA+LjTxzzX4jkaWTdCrVVzMfIiP138iRVXVwDwdOOndVqVpVAonohEW5USeRPuXwKlgeiHlsuiBgz7URyf+gnunizdenFBcPJ7cfzUAjB6hFuAjS3h2XUisRZxFTa/KPq4AXR/5+Htp0VRKERlH4Dfbt3EdmsvZKeDXUNw6SriNLUVk1S3zxI97iRJkspBJtskSZIkSZKkQv18JIDIpAzq25sxpYuLvsPRK68GYpubd2BMqc4/dTua4T+f5HZUCrWsTdj4cicGuDmV+/H/vvk3aq0aLycvRjUdxVvt32Lb8G3sG7WPDzt+SC/nXpgZmGFnYsfQRkPL/ThSFZE7GKFJ/4e3WLoOFltK0cK2lyE9seT19n8gkkou3aDFcF1HWzKbejB2NaiMxITV5AhxXdvny7ZOs4Hi0m8vaNQVjyt3C2nLESKZZ9cQxvwlkpzXNsO/X1f8MSRJeiLJZJskSZIkSZL0kOCYVH49fgeADwa3wNjgya5s8mqYPyShpL5tx/2jmPzbWeJTs2jtbMP2WV3K3J/tQUmZSWz23wzAcy2fK3BbbYvajGk2hh97/8iJ8Sc4+MzBqrMNUiofjRqubBDHHmMLP6f/5yJZFR8M++YVv17gMbj5DyiUMPBLkVTSh3peMGxx/t97fQAGRmVbo34XMLaG1OiHBy6UVUYS+B8Qxy1H5F/foBsM/lYcH1kI17dV7HEkSXoiyZ5tkiRJkiRJ0kMW7r5BplpDtyYO9G3uqO9w9K61sw1GKiVRSRncjUmlgYN5oedlZmuYv/062Rotg9ydWDSmdYUHN2y+tZmUrBQaWTeia52uRZ5nqKy8rbTSI3T3OCTdAxNrMVygMCZWMGIZ/D4ILv4NNvXBuQPYNgDruqJXGoA6G/a+K47bTYWauuvlVy4eY0GdCYn3wP2Zst9fZQhN+sG1TeC3SyTwystvrxjcYN8YaroVvK3t8xDlB2d+hq0vg219qO0pbtNqRWVeXBDE3YWEYLCsBY36gFWt8scjSdJjRSbbJEmSJEmSpAJOBkSz73oEKqWCD4e00P9UxirAxFBFa2cbzt6NxTswpshk299ngrgTnYKDhRFfjfaocKItS5PF3zf/BkRVm/xcPAFyt5C6jQID46LPq98ZOr8Cp34UFVi5lIYiOWTXEBQqiLwh+pD1eq9y4y6tNpMqdn/XQTnJtj3Q75Pyr/PfLaT/1W8BRN+CgIOwZizU8hDJtfhgsSW3MDXdoHEfkXir17H4z58kSY81mWyTJEmSJEmS8mSrNXyy4wYAkzrWp2kZJm8+7rwa2olk251YxnWo99Dt8amZ/HDIH4A3n2qGhXHFX2rvu7uPiNQI7E3sGdxQB9MXpaotIxlu/COOc6eQFqf3h2BqA8HeYrpn3F1RORYTID7yzvsAzOwqI+JHr3FfkVCMvgXRAeDQuOxrpCdCQCFbSB+kMoDRK+HXfmJCae7UUhBbcq3qiqSmdV0RS5gPRFwTHyd/AENzaNBd9Jlr/ayoypMk6Ykhk22SJEmSJElSnj9O3cUvIgkbM0Ne79tE3+FUKV4N7PmJALwDY9BqtQ9VmX1/0J+EtCxcnSwZ0865wo+n1Wr58/qfAExoPgEjVRn7W0nVj+9OyEoRVWl125d8voERdHsz/+8aNSSGQewdiA0UCThjK2g7pfJiftRMrMXk0MAjYiqpw6tlX8Nvj0hKOjQFxxbFP9bkbXBxtZgEa1M/J8Hm/HDyLCVGxBRwEAIOQUqkGAZxaw+cXSGmyNZpU/ZYJUmqlmSyTZIkSZIkSUKr1fLbiTss3H8CA6tg5vScgI2ZTO48qE19GwyUCu4lpBMal4aznVnebbejkvn7TBAgBkqolBXf7ukd7o1vrC+mBqaMaTamwutJ1cDlteLSY3z5BhkoVWJwgk09aNhDt7FVJc0G5SfbupQj2VbSFtIHWdWGHm+XvKa5PbiPFh8ajahw898Hp5dAxFX4tQ90mgU93wMjs5LXkySpWnsk00iXLFmCi4sLJiYmeHl5cfbs2WLP37hxI66urpiYmODu7s7u3bsL3K5QKAr9+Prr/NHMLi4uD93+xRdfVMrzkyRJkiRJqs7UGi3/23GDT3fdwLTuX5jWWY9dzetlWkOr1fLKoVcYt3MciZmJlRSpfpkZGdCqrpgqeiYwpsBtn+++SbZGSx9XR7o20c000NyqtuGNh2NtXP5pplI1kRAmJocCtJLJ1WI1GyguQ7whJbps902Lh9uHxHFRW0grSqmEWq2g+9sw6xy4jQatBk79BL90yv88S5L02Kr0ZNv69euZM2cOH330ET4+Pnh4eNC/f38iIyMLPf/UqVOMHz+eqVOncvHiRYYPH87w4cO5du1a3jn3798v8LFy5UoUCgWjRo0qsNYnn3xS4LxXXnmlUp+rJEmSJElSdZOepWbm6gv8ceouStNgVCbhAOy5u6dM61yLvsbR0KNcj7nOV2e/qoxQqwSvhvYAeN+JzbvuZEA0B29GYqBU8N7g5jp5HP84f06EnUCpUDKpeQUbykvVw9WNgBbqdQZbF31HU7XZOIOTu0hgPdhLrTRyt5DWcAVH3Xy9FsuiBoz+DcavB6s6oq/eqmGwfTakxVX+40uSpBeVnmxbtGgR06ZNY8qUKbRo0YKlS5diZmbGypUrCz3/hx9+YMCAAbz99ts0b96cBQsW0KZNGxYvXpx3jpOTU4GP7du306tXLxo2bFhgLUtLywLnmZsXPjVKkiRJkiTpSRSbksmzK86w73oERiol3dvczrvtVNgpEjISSr3W7jv5OxG2397OsZDHs3KjQwPRZN77jqhsU2u0LNgpBkpM7FifRjUsdPI4q26sAqBPvT44W1W8/5tUxWm1D2whHaffWKqLZjkDQ3x3le1+D24hfZSaDYCZZ6D9i+LvF/+CJV4QdPrRxiFJ0iNRqcm2zMxMLly4QN++ffMfUKmkb9++nD5d+DeV06dPFzgfoH///kWeHxERwa5du5g6depDt33xxRfY29vj6enJ119/TXZ2dpGxZmRkkJiYWOBDkiRJkiTpcRUUk8KoX07hExyPlYkBy59ryY3E4wBYGVmRrc3mYNDBUq2l1qjZc0dUwrVyaAXAx6c/Jj49vlJi16d29W1RKiAkNo178WlsPB+Cb3gS1qa6GygRlRrFrkCRQHiu5XM6WVOq4u5fhihfUBlDy+H6jqZ6yN1KevswZKWX7j5pceJ8gBbDKyWsYplYweBvYcpesG8CyRGw9SXIznj0sUiSVKkqNdkWHR2NWq2mZs2aBa6vWbMm4eHhhd4nPDy8TOf/+eefWFpaMnLkyALXv/rqq6xbt44jR47w0ksv8dlnn/HOO+8UGevnn3+OtbV13oezs3wHUZIkSZKkx9OlkHhG/nyKO9Ep1LExZfOMzkRqz5CWnUZD64ZMcROTC0u7lfRs+Fli0mOwMbZhWb9lNLBuQHRaNJ+d/awyn4ZeWJoY4lZH9E877BvJN/tvAfBqnyY6Gyix1nctWZosPB098ajhoZM1pSru8jpx6TpYTMCUSlbLQ2zLzEqFO6WspPXdDZosMYHU0bVy4ytO/U4w/ShYOEF8EJwvfNeXJEnV1yMZkFCZVq5cyYQJEzAxMSlw/Zw5c+jZsyetWrXi5Zdf5ttvv+Wnn34iI6Pwdw3mzZtHQkJC3kdISMijCF+SJEmSJOmRCohM5tkVZ4hJyaRlbSu2zuxMk5qWbLq1CYBRTUYxsIGoGDkXfo7otJKbj+duIX2q/lNYGFmwsMtClAole+7s4UDQgcp7MnrilbOV9LPdN4lOzqCBgzmTOtbXydqpWams91sPyKq2J0akL1zJSbZ5jNdvLNWJQpFf3ea3u/hzc+lrC2lhjC2g1zxxfOwrMbihtBLvg/dySAitlNAkSaq4Sk22OTg4oFKpiIiIKHB9REQETk5Ohd7Hycmp1OcfP34cPz8/XnzxxRJj8fLyIjs7m7t37xZ6u7GxMVZWVgU+JEmSJEmSKiIjW83Ti0/w7IozJGcU3c7iUclSa5iz4RKpmWo6uNix/qVOOFqZcCPmBjdjb2KoNGRoo6HUsahDqxqt0Gg17Lu7r9g1M9QZedtNBzUcBIB7DXemuokWHwtOLyAmLabI+1dHXg3EkITUTDUA7w1qjpGBbl5Wb7y1kcTMROpZ1qNn3Z46WVOqwnx3wa99xPZGh6bQqLe+I6pemonvOfjtAY2m+HNTYyHwiDjWxxbSwrSeCA7NIC0WTnxXuvtkpcHfI2HP2/CDB2ybBdH+lRunJEllVqnJNiMjI9q2bcuhQ4fyrtNoNBw6dIhOnToVep9OnToVOB/gwIEDhZ7/22+/0bZtWzw8Si6vv3TpEkqlEkdHxzI+C0mSJEmSpPI5dyeOy6EJnLodw8t/XSAzu4RfBivZz0ducyU0ASsTA34Y3xoLYwMANt/aDEDfen2xNbEFYKCLqBjZe2dvsWv+G/ovyVnJOJk74enomXf9yx4v08S2CXEZcSz0XohWq62Mp6QX7V3sUCjEcaeG9vRtrpvXl9sCtrHowiJAVLWplCqdrCtVQRoNHP0S1j0Lmcng0g2m7AGVgb4jq15cuoKRpeh9du9i8ef67gJNNtR0gxpNH018JVEZQL//ieMzv0B8KXZXHfgIIm+Aykg8n0t/w+L2sOE50ftPkqQqodK3kc6ZM4cVK1bw559/cvPmTWbMmEFKSgpTpoheIJMnT2bevHl557/22mvs3buXb7/9Fl9fXz7++GPOnz/P7NmzC6ybmJjIxo0bC61qO336NN9//z2XL18mMDCQ1atX88YbbzBx4kRsbW0r9wlLkiRJkiTlOBEQXeD4zY2X0Wj0k3S6GprAT4dF9cOC4W7UsjYFxLbFXXdEM/5RTUflnf+Uy1MoUHAp6hL3ku8Vue7uQLF9a2CDgSgV+S8tjVRGfNb1MwwUBhwIOpA3QOFxYG1mSLcmNTAzUvHhkBYocjNvFbD65mo+PPkhGq2GUU1GMarJqJLvJFVPGUmwYRIczelp2OElmLQVzB30G1d1ZGAMjfuIY78SppLmbSEdXqkhlVnTAVC/C6gz4EgJfS5v7Yezy8TxuDUw9WBOdZ8WbmyDZd3h79EQdKqyo5YkqQSVnmwbO3Ys33zzDfPnz6d169ZcunSJvXv35g1BCA4O5v79+3nnd+7cmTVr1rB8+XI8PDzYtGkT27Ztw83NrcC669atQ6vVMn78w30NjI2NWbduHT169KBly5YsXLiQN954g+XLl1fuk5UkSZIkSXrAyZxk2zNt62KgVLDj8j0W7LpR6iqvxPQsnVSEpWepeWPDJbI1Wga5OzHMo3bebfvu7iMlKwVnS2faO7XPu97RzJF2Tu3yzik0vsxE/g39F4DBDQY/dLurnSvTPaYDsNB7IVGpURV+LlXFisltOTm3Ny1qV6z1iFarZdnlZXxx9gsAJreYzEedPpJVbY+r2ED4tR/47hSVScMWw6CvQGWo78iqL9ec7z1+xST0U6Ih8Kg4blEF+rU9SKGAfgvE8eW1EH618POSI2H7THHs9TI06QfO7WH8WphxCtyfAYUSAg7A7wNhz9xHE78kSYVSaB+nmn4dSkxMxNramoSEBNm/TZIkSZKkMotLyaTNpwfQauHse304HRjDa+suATB3gCszejYq8r6xKZl8s9+PdWeD8axny88T2lDTyqTI80vy6c4b/HriDg4Wxux/ozt25vlTMyfunsjlqMu81uY1XnQvuGNgg98GFpxZQHO75mwYuuGhdbf6b2X+qfk0tmnMlmFbCq3wytJkMXH3RG7E3KBH3R781PsnnVSCPQ60Wi2LLizij+t/ADCz9UxebvWy/Pd5XN0+DBunQHq8mEI59m+RLJEqJjUWvm4MWjW8egnsGuTfps4Cnz/FAILkCKjpDjNO6C3UYm18XlTfNeoDk7YUvE2rhdXPiESaY0uYdhgMC/mZEBsIJ3+AC3+IxNsb18Gq9sPnSZJULmXJE1X7aaSSJEmSJElV0enAGLRaaFrTAkcrE55uXYcPh7QA4Mu9vmw4/3Bvniy1ht9P3qHn10dY4x2MRgsXguIY/OMJzt2NLVccZwJj+O3kHfG4o9wLJNr84/y5HHUZA4UBwxsPf+i+/er3Q6VQcTP2JncT7j50e+4U0kENBhWZIDJUGrKwy0IMlYYcCz3Gr1d/faz6t5WXWqPmkzOf5CXa3mn/DjM8ZshE2+PKbw/8PUok2uq0g+lHZaJNV8zsoH5ncZxb3abRwJWNopfZrjdFos2mHgwp5RACfegzH5SGcPuQSMw+yHuZSLQZmMDo3wpPtAHYNYShP0C9TqDVwKU1lR+3JEmFksk2SZIkSZKkSpDbr61r4xp5103t2oCXejQEYN6Wqxy6mT+B/YR/NIN+OM7/dtwgMT2b5rWsWPysJ65OlkQnZzB++RlWnb5bpkRVUnoWb228jFYL49o706d5zQK3b/YXgxF6OPfAwfThflG2JrZ0rN0RgL13Cw5KiEqN4mz4WUD0aytOY9vGvN7mdQB+vPgj357/Fo1Wv8Mi9ClLk8W8E/PYdGsTSoWSTzp/wqQWk/QdllQaQacgIaxs90mNhX9eEckP9zHw/C6wqlU58T2p8qaS7hZ9zZZ1hy0vQtwdMK8BA7+G2ReqdoLTriG0F1OcOTA/f7pq+DU48KE4fupTcGxe8lptJovLi3+VPKVVkqRKIZNtkiRJkiRJlSC3X1vXJvYFrn93gCuj2tRFrdEya40PO6/cY/qq80z8zRv/yGRszQxZOMKNna90ZUir2myZ2ZkhrWqRrdEyf/t13tp4hfQsdali+HTnTULj0qhra8oHOVV1uTLUGey4vQOg2Gb8uVNJ99zZUyDRt+/uPjRaDR41PKhrWbfEWCa3nMxb7d4C4M8bf/LBiQ/I0mSV6nk8Tvzj/Jl1cBZ77uzBQGHAl92/ZESTKtZDSipc+FXRC2t5D0gsemjIQ/bMhZQoqOEKTy8uuipJKr9mOQn/u8dhzTMQcRWMraD3B2Jrqdd0MDAqdokqofvbIu7wq3B1I2SlweapoM4UgxTaPzwcsFAtnhbrxN2Fu/9WasiSJBVOJtskSZIkSZJ0LCQ2laCYVAyUCjo0KJhsUygUfDHKnV7NapCepWH2movsvxGBSqng+c4uHH2rFxO86qNSiu2EZkYG/DTek/cGuaJUwGafUEYvPUVoXGqxMRy8EcH68yEoFPDtMx5YGBsUuP1A0AESMxOpZV6LzrU7F7lO73q9MVIaEZgQiH+8f971uwLF5L9BDQaV+t/luZbPsbDrQlQKFTsCd/Da4ddIzSr+eTwursdc5/UjrzPyn5Gcvn8aY5UxP/T+gQEuA/QdmlRad46Ly5Qo0V8rO7Pk+/juhqsbRP+sp38W0zMl3bNrADVzBuoZmEDnV+C1yznJKwv9xlYW5g7Q9XVxfHiBSNRG+YK5oximUdpt5kbm4D5aHPusqpRQJUkqnkHJp0iSJEmSJEllkbuF1LOezUNJLgBDlZIlE9ow8VdvfILj6drYgflDW9C0pmWh6ykUCqZ3b0TL2tbMXuPDtbBEhi0+yQeDm2NjZkiWWotaoyVLrUGt0ZKt1vLVPj8AXuzaAK+G9g+tufmW2EI6ovGIYidfWhpZ0q1uNw4FH2LPnT00tW1KUGIQ12KuoVKo6O/Sv0z/NsMaDcPG2IY3j77J8bDjTD8wnSV9lmBtbF2mdaqLS5GXWH5lOcfDRKJGgYK+9fsyw2MGTWyb6Dk6qUxCz+Yfh3iLrX0Dvyz6/LQ42PmGOO78CtRtW7nxPelGLgf/A2Iqp3UdfUdTfl4z4OyvkBAihjsAjPgFLGoUf7//avMcnF8JN3eIrcxmdrqPVZKkIslkmyRJkiRJko7lJtu6NH64D1ouMyMD1k3vRFBMCo0dLUrVGL9LYwd2vNKVl/66wPV7iczZcLnY85s4WvDmU80euv5uwl3OR5xHqVCWagvjgAYD8pJtr3q+mjcYoWPtjtibPpzIK0n3ut1Z8dQKZh2axeWoyzy35zmW9luKk7lTmdeqarLUWSRkJnAr7hYrr67EO9wbAKVCyaAGg3jR/UUa2RQ9iVaqwkLPi8uOM+HMz+C9FOq2z68g+q+970FyONg3gZ7vPbo4n1Q1W4qP6s7IDHq/D9tnib93nAWN+5Z9ndqtwakVhF+By+ug00ydhilJUvFksk2SJEmSJEmHNBotp/KGIxSdbAMwMlDSpIhqtqLUtTVj84zOfLnXF+/AWAxVClRKBQYqJQY5l4ZKBaZGKl7v2wQTw4er1rb4bwGgS+0upUpwda/THVMDU8KSw7gafZXdgSLZNrjB4DLF/qDWjq35c8CfvHTwJW4n3GbSnkks67eMhtYNy73mo3Qo+BC7A3eTkJlAQkb+R2p2wW2xBgoDhjUexlS3qdSzqqenaKUKS7wvKo0USuj1vtiqeGIR/POq2L7o6Frw/Fv74fIaQAHDf5Z92qSy8RgvqvTUWdD3o/Kv02Yy7H5LbCXtOKP021AlSaowmWyTJEmSJEnSoRv3E4lLzcLcSIWHs02lPIaJoYqPhpavgiNTncn229sBGNW06MEIDzIzNKOnc0/23NnD9z7fczfxLsYqY3rX612uGHI1tm3MXwP/4qUDL3E38S7T909n87DNVX5LaVhyGHP/nUuGOqPQ2xUosDWxpV/9frzg9gK1LWo/4gglnQs9Jy4dW4oeYL0/gLALcOcYrJ8I0w6DiZU4Jz0BdrwmjjvNAucO+olZqr6UKhjzZ8XXcX8G9n8IUTdFZWZVnsYqSY8ZmWyTJEmSJEnSodwppB0b2mOoqnqzqFbdWEVseiyOpo50r9u91Pcb6DKQPXf2cC5cJB16OvfE3NC8wvHUtqjNqoGrmLRnEkGJQXxy+hO+6fFNqbbV6svX574mQ51BqxqtGO86Hmsja2yMbbA2tsba2BpLI0uUiqr3uZcqILdfW9124lKpglG/wbLuEOMP/8yGZ/4UlUP73oeke2DXUFTBSZK+mNpAy+Fwea3o/yaTbZL0yMhXAZIkSZIkSTpUmn5t+hKeEs7yK8sBeK3taxgqDUt93y51umBpmL/ltSxTSEtia2LLF92+wEBhwP6g/fxz+x+drV2crf5bmbxnMteir5X6PqfuneJQ8CFUChUfd/qYIQ2H0K1uN9xruFPPqh7WxtYy0fY4CsmpbHuwSs2iBoxZBUpDuLEdTi+BgENw8S9AAU8vEf23JEmf2kwWl9e2QEaSfmORpCeIfCUgSZIkSZKkI+lZas7djQWga5Oql2z76txXpGWn0caxDUMbDi3TfY1URvSp3wcAKyMrutXpptPY3BzcmOUpGoJ/5v0ZIYkhOl3/vzb4bWD+qflcjLzI60deJy49rsT7ZKmz+OLsFwCMcx0np4k+KbIz4f4lcVz3P5VBzu2h/2fi+MB82PqSOPZ6Cep3fmQhSlKR6nUSQzqyUuDaZn1HI0lPDJlskyRJkiRJ0hGf4DjSszTUsDSmiaOFvsMp4NS9UxwIOoBSoeQ9r/fKtU1zvOt4zAzMmNhiIoaq0lfFldaUllNoW7MtqdmpvHv8XbI0WTp/DIDNtzaz4MwCAMwMzIhIjWDeiXlotJpi77fGdw13Eu5gZ2LHzNZyst8TI+IqZKeDqS3YN3749g7TRG8srRpSosDWBfrMf+RhSlKhFIr86jafVfqNRaqatFpQZ+s7iseOTLZJkiRJkiTpyMkHppBWpZ5jWeosPvf+HBAJs2Z2zcq1Tgv7FnhP8GaGxwxdhpdHpVTxedfPsTS05Er0lbwtr7q0LWAb/zv9PwAmNp/IqoGrMFYZczLsJCuvrSzyflGpUfxy+RcAXmvzGlZGVjqPTaqiQs+Ly7rtC5/mqFDA0B+gpjsoDWDYYjCqeD9DSdIZj/Hi/2bYBQgv/bZ56QmQEAp/DIbPaothL9e3QmZqyfeTSiSTbZIkSZIkSTpyIiAGqHr92lbdWMXdxLvVoiKrlkUt5ncSVUHLryznYuRFna294/YO5p+cjxYt413H8077d2hm14z3vUQT+58u/pQ3AOK/vvf5npSsFNzs3RjeeLjOYpKqgZDc4QjFNJc3Moep++C1y9BAt1usJanCLGpAs5w+m7K6TcrltweWdoWgk6DOgJs7YOPz8HVj2PyiuD278KnbUslksk2SJEmSJEkHElKzuBoaD0CXxvb6DeYB4SnhLLuyDIA3271ZLSqyBjQYwLBGw9BoNcw7Po+kzIo39d4duJsPTn6AFi1jm41lXod5edWHwxsPz3u8uf/OJTotusB9L0VeyhvaMM9rnhyA8KQJzUnAFpdsA5Fws65b+fFIUnm0fU5cXlkHWWn6jUXSr+xMMTV57ThIi4PabWDSVuj6BtjUE/39rm4Ut3/TBLbPguQofUdd7chXCpIkSZIkSTpwOjAGjRYa1TCnlrWpvsPJ8/W5r0nLTsPT0bPMQxH0aV6HedSxqENYchifeX9WobX23t2b15NtVJNRD/WsUygUvO/1Po2sGxGVFsW7x99FrVEDoNao8x5/eOPhtKrRqkKxSNVMciTEBwEKqNNW39FIUvk17AXWzpCeADd36jsaSV/i7sLvA+D0YvH3jrPghX3QqDf0/RheuwIvHoKOM8HCSfx/ufi3SLhJZSKTbZIkSZIkSTrwYL+2quL0vdPsD9qPUqHkfa/3q1QfuZJYGFnwRbcvUClU7Azcya7AXWVeQ6vVsufOHt799100Wg3DGw9nfqf5hVammRmasajnIkwNTPG+751XDbglYAs3Y29iYWjBa21eq/DzkqqZ3Ko2x+ZgUvWrQiWpSEoVeE4Uxz5/6jcWST9u/ANLu4vefSY2MG4tDPgMDIzyz1EooG47GPA5zLkBz24U1wccEP3dpFKTyTZJkiRJkiQdyEu2Namh50iELHVWXkXWuGbjyj0UQZ9aO7bmpVYvAbDgzAKWXFpCWHJYiffL0mSxK3AX43aN451/30GtVTOs0TA+7vRxsVtAG9o05MOOHwKw9PJS9t3dx48+PwIws/VMHEyrTiJVekTy+rW1028ckqQLrScACrh7HGJu6zsa6VHQauHeJdjxGmyYBBkJULcDvHwCXAcVf1+lCpo+BS7dQKuBi6sfSciPCwN9ByBJkiRJklTdhcWnERidgkqpwKuhnb7DAeCvm3/lDUWY5Vl9t39MazWNs+FnOR9xnqWXl7Ls8jK8ankxovEI+tTvg7HKOO/cxMxENt/azOqbq4lIjQDAWGXMeNfxvN7mdVRKVYmPN7TRUC5EXGCz/2beOvYWAI2sGzHOdVzlPEGpasvr19ZBv3FIki7YOEPDnhB4REyd7P6WviOSKkN2hkio+u0RH4kPvEnV5TXo/SGoDEu/XpvJYr2Lf4n/M6X4WSrJZJskSZIkSVKFnfQXVW0eda2xMinDC9hyyFRncivuFteirxGVFoVSoUSJEqVCiUqpQoEChULB0stLAZjTdk61GIpQFAOlAcv7Ledg8EG2+m/lzP0zeR9W3lYMbjiY3vV6cyzkGFv8t5CanQqAvYk9413HM6bZGGxNbMv0mO92eJdr0dfwi/MDxFAEQ2Xlfl6lKkidDWE+4rik4QiSVF20HCGSbTe2yWTb4yIrTfRiu38F/HZDwCF4cLCQoTk07g3tXxTJ1rJqPhRMrCEhRPzfadxXV5E/1mSyTZIkSZKkKmXzhVC878Tw4ZAWWFZy4kpXTlRSvza1Rs3dxLtci77G1eirXI++jl+cH1marFLd39PRk6GNqs9QhKIYqgwZ2GAgAxsMJCw5jO0B29kWsI37KfdZ67uWtb5r885tbNOYyS0mM7jhYIxURsWsWjQTAxO+7fktsw/NplPtTnjV8tLVU5Gqk4hrkJ0mfsl0aKrvaCRJN5oPhZ1vQPhVsZXUvpG+I5JKKzlSVJjF3hEfcTmXSfcePtfCCZoNgGaDoUF3MDQp/+MamkKrcXB2Gfisksm2UpLJNkmSJEmSqozkjGw+3H6N1EwxCfKr0R56jqhkGo02r19bFx0m2/xi/Zh5aCaRqZEP3WZtbI2bvRvOls5o0aLValFr1WjRotaISwOlAS+6v1hsj7LqqI5FHWa2nslLrV7C+743WwO2cureKdwc3HiuxXN0qt1JJ4Mg6lvVZ8eIHTqIWKq2creQ1mkHysfr60h6gpnZQcMecPuw3Epandy/DH8MFT3XCmNsLRKnjXqJBFttT91+32ozWSTbfHdDchRYVI3+tFWZTLZJkiRJklRl7Lx8Ly/RtuF8KH2b1+Splk56jqp4fhFJxKRkYmqowrNe2bYrFiUlK4U3j71JZGokpgamNLdrjpuDW95HXYu61WqyaGVQKVV0rtOZznU66zsU6XGV169NbiGVHjMtR+Qk27bJZFt1EHUL/hohEm32jUUPSbsGYNsA7BqKY1NbMUm0sji5Qe02cM8HLq+FLq9W3mM9JmSyTZIkSZKkKmPduRAA6tmZERybyrwtV2lT3xYHC+MS7qk/J3L6tXk1tMPIoOLvImu1Wj498ylBiUE4mTuxaegmrI2tK7yuJElllJtsc5bJNukx4zoEdrwOEVchOgAcGus7Iqko8cHw13BIjYFareG5HWCipz6sbZ8TyTafVdD5lcpN7j0GZD20JEmSJElVgl94EpdC4jFQKlg7vSPNaloSk5LJ+1uvotVq9R1ekQ75iqmX3ZvoZkvFP7f/YWfgTlQKFV91/0om2iRJH1KiITZQHNdpq99YJEnXzOzyG+Xf2KrXUKRiJEfCqqfFNFGHZjBxi/4SbQBuo8SwhRh/CD6jvziqCZlskyRJkiSpSlifU9XWp7kjdWxMWTTWA0OVgn3XI9jiE1bCvfUjITWLc3fjAOjbvGaF1wtMCGSh90IAZraeiaejZ4XXlCSpHELPi0uHZmJ7liQ9bloOF5fXt+kzCqkoaXFi62hsINjUg8nbwNxevzEZW4LbCHHs86d+Y6kGZLJNkiRJkqq5pcdu4/G//RzOqbCqjjKy1Wy9GArA2PbOALSsbc3rfcUEwI//uU5YfJre4ivK0VuRqDVamta0oJ692UO3J2cms/DMQhZdWERKVkqxa6Vnp/P2sbdJy07Dq5YXU92mVlbYkiSVJPSsuJT92qTHlesQUBqIqbvR/vqORnpQRjKsHiM+NxY1YfJ2sKqt76iENs+Ly+vbIC1ej4FUfTLZJkmSJEnVlFarZdGBW3yxx5eEtCwWHw7Qd0jlduBGBHGpWThZmRTYjvlS94a0qWdDUkY2b2+8jEZTtbaTHrwpJoX2KaSqLSQxhIm7J7LObx2/X/ud4duHczz0eJFrfXP+G27F3cLOxI4vun2BSqmqtLglSSqB7NcmPe4e3Eoqq9uqjuwMWD9BJPxNbGDSNjEEoaqo2w5qNIfsNLi2Sd/RVGky2SZJkiRJ1ZBWq+WrfX78eEi8G61QgE9wPLcikvQcWfnkbiEd3bYuBqr8lycGKiXfjmmNqaGKU7dj+OPU3SLXCIlN5dfjgey8cq+ywwUgS63hqJ9Itv13C+npe6cZt2sctxNuU8O0BnUs6hCeEs7MQzN57/h7xKfHFzj/QNAB1vutB+Czrp/hYOrwSJ6DJEmF0KghzEcc1+2g31gkqTK1GC4ub2zTZxRSrsxU2PQCBB4VvdEmboaaLfQdVUEKBbSZLI4vyK2kxZHJNkmSJEmqZrRaLQt33eSXo7cB+GBwc/rlJHtyk1bVSWhcKicCxETPMe2cH7q9gYM57w1uDsCXe30JiMxPKEYmpfP7yTuM/Pkk3b46wqe7bjJ7zUWO+EZWetzn7sSSlJ6NvbkRrZ1tAPG5WX1zNTMOziAxMxF3B3fWDVnHlmFbmNh8IgoU7AjcwdPbn2bf3X1otVrCksP46ORHALzg9gJd6nSp9NglSSpG5A3ITAYjS6jRTN/RSFLlcR2cv5U06pa+o3lypcXDv9/A9+7guxNUxjB+ragiq4o8xoHKCMKvwL1L+o6mypLJNkmSJEmqRrRaLR//c51fT9wB4JOnW/Jit4aM6yCSVFt8QsnIVuszxDLbeD4UrRa6NLYvtO8ZwESvenRvWoOMbA1vrL/M2rPBPLviDB0/O8T/dtzAJzgehQLq2Yn7v73pMlFJGZUad+4W0l6ujqiUCjLVmXx06iO+OPsFaq2aYY2G8fuA33E0c8TM0Iy5Heby16C/aGTdiNj0WN469havH3mdd469Q1JWEq1qtGK25+xKjVmSpFLI3UJaty3I7dzS46zAVNJt+ozkyZQcCQc/Fkm2wwsgNVoMQxi/Bhr20Hd0RTOzg+ZDxbHPKv3GUoUZ6DsASZIkSZJKR6PR8v62a6w9G4xCAZ+NcGd8h3oAdG9SAycrE8IT0zlwI4IhrapII90SqDVaNp4X1XiFVbXlUigUfDWqFU99d4yrYQnM23I17zbPejYMbVWbwa1qYW1qyPAlJ/ENT+LtTZf5/fn2KBSKcsen0Wo4EnKElddWcjfhLg2sG9DYpjFNbJqwJyAZhcqWvs1rEp0WzRtH3uBS1CWUCiVz2s5hcovJDz22Rw0PNgzdwPIry/nt6m8cDjkMgKWRJV91/wpDpWG5Y5UkSUdCcpNtsl+b9ARoOQICDoq+bT3e0Xc0T4b4YDj5I1z8C7LTxXU1mkO3OdByJKiqQZqmzWS4thmuboSnFoCRub4jqnKqwWdRkiRJkiS1RsvczVfYdCEUhQK+GtWKZx5IThmolDzTri4/HQ5g/bmQapNsOxEQzb2EdKxNDenf0qnYc52sTfhqdCteWXuRJo6WDPWozZBWtXC2K1gN98M4T4YuPsFRvyj+OHWXKV0alDmubE02e+/u5bervxEQnz944nLUZS5HXRZ/sQMLO/j8uj3aaxriMuKwNLLk6+5fF7sV1EhlxGzP2fSr34+PTn3ErbhbLOiygDoWdcocpyRJlSCvsk32a5OeAK6DYcfrEHldbCWt0VTfET3eTv4Ahz4BTbb4e5120O1NaDoAlNVo46FLd7CpD/FBcGM7tH5W3xFVOTLZJkmSJElVnEaj5e2Nl9lyMQylAhaNac1wz4cTM2PaOfPT4QCO+0cTEpv6UBKqKlp/LhiAEZ51MDEsebvWALda+C1wQqksulqtmZMl7w9qzkf/XOfzPb50amSPq5NVqeLJVGey/fZ2Vl5dSWhyKAAWhhaMdx1Pn3p9CE4Kxj/On4MBl7mdEIDSKJbY9BgAXKxc+Kn3T7hYu5TqsZrZNWPt4LWkZqdibijfEZakKiE1FmLE4Jkq2y9JknTJ1FZsJQ04ILaSFlXdlhAGe94BcwcY8r1olC+VTdgFsW1UqxH/5t3eBJdu1fPfUqkU1W2HF4hBCTLZ9hCZbJMkSZKkKkyr1bJg1w22XAxDpVTww7jWRVatOduZ0a2JA8f9o9lwPoQ3n6rajb1jkjM4cCMCgLHti95C+l/FJdpyTe5Un2O3ojjsG8mray/yz+yuxSbzMtWZrPdbzx/X/iAyTfRiszW2ZVKLSYx1HYuVkUjWtXRoycAGAznmfYqUoDg+HNYIr6bZxKTF0N6pPWaGZUtwKhQKmWiTpKok7IK4tG8s+hJJ0pOg5QiRbLu+tfBk292TsPE5SIkSf2/xNDTq/WhjrO7UWfDPqyLR5v4MjPpV3xFVXOsJcOQzCDkjJjjXaaPviKqUalSnKEmSJElPnqXHAvn95F0AvnmmVYnbQ3OTVhvPh5Kt1lR2eBWy9WIYWWotrepa07xW6SrPSkuhUPDV6FY4WBhzKyKZz3ffLPQ8rVbL4eDDDN8+nK/OfUVkWiSOZo7MbT+XfaP3Ma3VtLxEW66Y5Ax8guMAGNiiPm4ObvRw7lHmRJskSVVQyFlxKfu1SU8S10GgNBSTeKP88q/XauHMUlg1TCTaDEzE9Sd/0E+c1dmpn8TUV1Nb6P+5vqPRDataInEIcGKRfmOpgmSyTZIkSZKqqA3nQ/hyry8AHwxuzgjPuiXep1+LmtiaGRKemM6//lGVHWK5abVa1p8TgxHKUtVWFg4WxnzzTCsA/jwdxGHfiAK3B8QF8NKBl3jtyGuEJIVQw7QGH3X6iD0j9zCxxURMDUwLXfeIXxRaLbSsbUVtm8LPkSSpGtJqRWUPQP2i+y5K0mPH1BYa9RLH17eJy8xU2PoS7J0r+ou5PwMv/QsKFQQehfuX9RVt9RNzG45+IY77fw4WNfQbjy51fUNc3twBkb76jaWKkck2SZIkSaqCDt2MyJu4+VL3hrzYrWGp7mdsoGJkG5GUW3c2pNLiqyif4Hj8I5MxNVQxzKPyhjn0bObIlC4uALy98QqRSekkZCTwuffnjN4xmtP3T2OoNGSa+zR2jtjJ6KajMVIZFbvmwZytr32a16y0uCVJ0oMQb9GvzdBMbJOTpCdJyxHi8vpWiAuClf3hynqRXOv/GYxcATWagdtIcd7JH/UXa3Wi1cKO10CdAQ17gcc4fUekW46u4DpEHJ/8Xq+hVDUy2SZJkiRJVcyFoDhmrfFBrdEysk0d5g5wLdP9cyvFDvlGEpmYXhkhVtiGnKq2Qe61sDQxrNTHmjvAFVcnS2JS0nh+0/cM3jKYNb5rUGvV9K3Xl+3Dt/Nqm1dLtQ00I1vN8ZyKwb7NHSs1bkmSHjGfVeKy5Ugw0e3Wdkmq8prlbCWNuglLu0H4FTCzh8nboNOs/Cb+nV8Vl9e3QtxdfUWrX+osuH0EMlNKPvfi33D3OBiYwpDvqucwhJJ0myMur2wQiVoJkMk2SZIkSapS/COSeOGPc6RnaejZrAZfjmpVqoEAD2pa05I29WxQa7Rs8gmtpEjLLzkjmx1X7gEwrkPlbCF9kImhii+eaYp5/T8IUa4mITMBa5UzH7T9gUU9F+FsWfoYzgTGkpKppqaVMW61rSsxakmSHqn0xPwtpG0m6zcWSdIHU5v8oQcZCVCrNUw/Bg26FzyvVitRoaVVw+mfH3WUVcOBj+Cv4bC0a36fx8IkRcD+98Vxr/fArsEjCe+Rq9M2///EKVnxmEsm2yRJkiTpEQmLT2Pt2WD2Xw/nSmg8kUnpaDTavNvvxacxeeVZEtKyaO1sw88T2mCoKt+P6nEd6gGw/lwIWq22hLMfrTXeQaRmqmlYw5x29W0r/fGi06L5zOdVlGb+oDEiPfxpQq+9zNy/0+j//b8sORJAaFxqqdbK3ULa27VmmZOgkiRVYdc2Q1YqODQF5w76jkaS9KPDNFAZgedEeGEv2BTxZlSX18Tlxb8gNfbRxVcVZKaK5w0QGyi22x76BLIzHz5371xIT4BaHtBx5qON81Hr9qa49PlLJBklDPQdgCRJkiQ9CSKT0nnml1PcSyi4rdNAqaCmlQk1rYyJSMzgfkI6jWqY8/vz7TEzKv+P6cHutfhkxw2CYlI5ExhLp0b2FX0KOpGWqWb5v4EAzOjRCEUlb6cISgzipQMvEZYchp2JHd/3XExElAPbLt7jsG8ktyKS+XqfH1/v86O9iy0zezaml2vh20O1Wi2HbooXkHILqSQ9ZnK3kLaZ/Hhu85Kk0mjSD967D6oSXn807AlOrcRW03O/Qo93Hkl4VcL1rZCRCDb1oV4nuLIOjn8Lt/bDyGVQs6U4z2+vOFehgqE/lvxvWt25dIW6HSD0LJxZAv0+0XdEevdIKtuWLFmCi4sLJiYmeHl5cfZsMaWWwMaNG3F1dcXExAR3d3d2795d4Pbnn38ehUJR4GPAgAEFzomNjWXChAlYWVlhY2PD1KlTSU5O1vlzkyRJkqSSpGWqmfbnee4lpONkZYJHXWtqWhmjUEC2RktYfBo+wfGExafhZGXCqqle2JoX36S/JObGBgzNGTyw/lywLp6GTqz2DiI6ORNnO1OGe9ap1Me6Fn2NSbsnEZYchrOlM38N/AvPmu4McKvF0kltOfdBX74c5U6nhvYoFHDubhxT/jjHN/v8UGserga8cT+RewnpmBgq6dLYoVJjlyTpEQq/Bvd8RL+qVo9Z83JJKqvSJIUUivzqNu9lkJVWuTFVJRf+EJdtnxPJtTGrwNQOIq7C8p5w8gdIi4ddOX3MOs2C2q31E+ujpFDkV7ed+w3S4vQbTxVQ6enV9evXM2fOHJYuXYqXlxfff/89/fv3x8/PD0fHh98VPnXqFOPHj+fzzz9nyJAhrFmzhuHDh+Pj44Obm1veeQMGDOD333/P+7uxsXGBdSZMmMD9+/c5cOAAWVlZTJkyhenTp7NmzZrKe7KSJEmS9B8ajZY5Gy5xOTQBWzND1k3viIuDOQDZag1RyaKaLSIhnejkDHo2c6SOjalOHntce2fWng1m97VwPk7NxMYsP4GXpdYQGJWCb3gimdkaBreqVaFKutJIz1KzLKeqbXavxuXeIlsaJ8JOMOfoHNKy02hh34IlfZbgYFowQWZtasjY9vUY274e9xPS+OXobVadDmLxkQCuhiXww7jWBf7NDt2MBKBr4xqYGKoqLXZJkh6x3C1hzQaCRQ39xiJJ1UWL4XDwf5AQDJfWQPup+o6o8kXcEJVbChW0niCua/E0OHeEHa/Crb1wYL5IuKXGgK0L9Jyn15Afqab9oaYbRFyDsyuerIrHQii0ldzIxcvLi/bt27N48WIANBoNzs7OvPLKK7z77rsPnT927FhSUlLYuXNn3nUdO3akdevWLF26FBCVbfHx8Wzbtq3Qx7x58yYtWrTg3LlztGvXDoC9e/cyaNAgQkNDqV27dolxJyYmYm1tTUJCAlZWchqRJEmSVD5f7vXll6O3MVIp+ftFLzo0sHtkj63Vahn4w3F8w5OY0sWFOjam3LyfxM37iQREJpOp1uSd62BhzKt9GjOufT2MDConCfbHyTt8vOMGdWxMOfJWz0p7nB23dzD/5Hyytdl0qtWJ73p9h7mheanuu/ViKPO2XCU9S0M9OzOWTmxLi9ridcDTi09wOTSBL0a65/XEkySpmstKh2+bQXo8TNgMTfrqOyJJqj68l8Ged8C2AbxyAZSP+RtRe+aC91JwHQLjVhe8TasVifu98yAzZ0fdpK35QyeeFFc3weapotrvjWtgVLrXX9VFWfJElbqNNDMzkwsXLtC3b/4PLaVSSd++fTl9+nSh9zl9+nSB8wH69+//0PlHjx7F0dGRZs2aMWPGDGJiYgqsYWNjk5doA+jbty9KpRJvb+9CHzcjI4PExMQCH5IkSZJUERvOh/DL0dsAfDna/ZEm2gAUCgXjc5JCv5+8y6e7brLZJ5Qb9xPJVGuwMDagXX1bnO1MiU7OYP726/RZdJStF0ML3UZZEelZan45Jv4tZvZqVGmJtj+v/8l7J94jW5vN4IaDWdJnSakTbQAjPOuyeUZnnO1MCY5NZeQvJ9l+KYyIxHQuhyYA0Fv2a5Mk/UsIhRvbC29KXha+O0WizaouNOqlk9Ak6YnhORFMbSHuDtzcoe9oKldWGlxeJ47bTnn4doVC9HyccRJajYV+C568RBtAyxFg1xDSYuHCn/qORq8qdb9IdHQ0arWamjVrFri+Zs2a+Pr6Fnqf8PDwQs8PDw/P+/uAAQMYOXIkDRo04Pbt27z33nsMHDiQ06dPo1KpCA8Pf2iLqoGBAXZ2dgXWedDnn3/O//73v/I8TUmSJEl6yKnb0by35SoAr/ZuzAjPunqJY0SbOmzxCSU+LYvmTlY0r2WFay1LWtSyoo6NKUqlgsxsDevPh/DjIX9CYtN4Y/1llh0L5K2nmtGnuaNOhhhsPB9CRGIGta1NGN22cv4tDgUd4pvz3wDwXIvnmNNuDkpF2ZN6LWtbs2N2V15Ze5Hj/tG8tu4SHnWtAfBwtsHR0kSncUuSVEbqbFj1NMQEgJM7DP9FXJZH7mAEzwmPf1WOJOmakTm0nwb/fiW2TrZ4+vEdMHLjH5GYt3YuPjFv6wIjlz+qqKoepQq6vC621Z76SWwvNjAu8W6Po0cyIEHXxo0bx7Bhw3B3d2f48OHs3LmTc+fOcfTo0XKvOW/ePBISEvI+QkJCdBewJEmS9ES5HZXMjL99yNZoGepRmzf6NdVbLFYmhmyf3ZVjb/di6aS2vNa3Cf1bOuFsZ4ZSKV4QGxkomdSxPsfe7sk7A5phZWKAb3gSL646z+ilp7kWllChGDKy1fycU+E3o2cjjA10/wttaFIoH578EIDJLSbzVvu3ypVoy2VjZsQfUzowq1cjgLyqtn6yqk2S9O/aJpFoAwjPaUp+7CtQZ5Vtndg7cOcYoMjvvyRJUtl0mA4GJmLISNBJfUdTeXxyqrTaTJaJ+ZJ4jAPL2pB0L78a8AlUqck2BwcHVCoVERERBa6PiIjAycmp0Ps4OTmV6XyAhg0b4uDgQEBAQN4akZGRBc7Jzs4mNja2yHWMjY2xsrIq8CFJkiRJZRWbkskLf5wjIS0Lz3o2fD26lU4qwx4FMyMDZvZszPF3ejOjZyNMDJVcCIpj5C+nKjTRdNOFUO4npFPTyphn2jnrMGIhS53F28feJikrCY8aHrze9nWdrKtSKni7vytLJ7bF3EiFUgED3Ip+PSJJ0iOgzoZ/vxbHnWaL3kmabDiyEH7tAxHXS7/Wxb/FZaNeYFtf97FK0pPAoga0flYcn/xBv7FUlqhbIpGoUIqts1LxDIyh8yvi+MR34vv2E6hSk21GRka0bduWQ4cO5V2n0Wg4dOgQnTp1KvQ+nTp1KnA+wIEDB4o8HyA0NJSYmBhq1aqVt0Z8fDwXLlzIO+fw4cNoNBq8vLwq8pQkSZIkqUhxKZlMX3WeoJhU6tqasnxSu2o5tdLazJC5A1z59+1e9G3uSGa2hrmbrzJvyxXSs9RlWiszW8PPR3Kq2no0qpR/j0UXFnEt5hrWxtZ83f1rDJWGOl1/gJsTh97syT+zu9LY0VKna0uSVEbXNouqNlNb6PkujP0bRv4KJjZw/zIs6yGScSX9cqfOFhMUATwnVXrYkvRY6zQbUID/fjGx83GTW9XWpD9YlTxsUQLaPieGJMTdgRvb9B2NXlT6NtI5c+awYsUK/vzzT27evMmMGTNISUlhyhTRVHDy5MnMm5c/Dve1115j7969fPvtt/j6+vLxxx9z/vx5Zs+eDUBycjJvv/02Z86c4e7duxw6dIinn36axo0b079/fwCaN2/OgAEDmDZtGmfPnuXkyZPMnj2bcePGlWoSqSRJkiSV1dk7sQz68Tjng+KwNDZg5fPtqWFZvXtUOFqZsHxSO956qikKBaw9G8KYZacJi08r9RpbL4YSFp9GDUvjSpngeTDoIH/fFNUpC7sspJZFLZ0/BoCTtQludawrZW1JkkpJoy5Y1WZsKfpDtXoGZp2FZoNBkwWHPxVVbmEXil7r9iGxxcnUDlwHP5r4JelxZd8Img8Rx7l9EB8X2Rn5ifm2z+s1lGrFyBw6zgRja0ivWDuS6qrSk21jx47lm2++Yf78+bRu3ZpLly6xd+/evCEIwcHB3L9/P+/8zp07s2bNGpYvX46HhwebNm1i27ZtuLm5AaBSqbhy5QrDhg2jadOmTJ06lbZt23L8+HGMjfN/qVm9ejWurq706dOHQYMG0bVrV5Yvf4IbFUqSJEmVQq3R8uMhf8YtP839hHQaOpiz7qWONK35eFRAKZUKZvduwh9TOmBjZsiV0ASG/HicE/7RJd43S61h8RHR4uGl7g11XtUWkhTC/JPzAZjScgo9nHvodH1JkqqYa1sgxl9UsXWYXvA2y5owbjWMWJ5T5XYJVvSGv0bA3UL6SOUmBDzGP7HNuyVJp3IrRK9uLHv/xKrs5g4xWdOyNjTuq+9oqpeOL8MbV8WQhCeQQqvVavUdRFWUmJiItbU1CQkJsn+bJEmSVKjIxHReX3+JU7djABjZpg4LnnbD3LhSh33rTUhsKjNWX+BaWCJKBbzVvxkzejQqsifdxvMhvL3pCg4WRhx/pzemRrpLtmWqM5m8ZzLXY67TukZrVg5YqfPto5IkVSEaNfzcEaJvQe8PoPvbRZ+bFA4HP4YrG0Cbs/W9Xmfo/iY06gPJkfBdC9HrbeYZcGz+SJ6CJD3W1FmwqDmkRMGzG6Bpf31HpBt/DoU7/0KPudDrPX1HI+lZWfJEj+dvA5IkSZJUyY76RfLmhsvEpGRiZqRiwdNujGpbV99hVSpnOzM2vdyZD7ddY+OFUL7a68f+6xG41bGivp059ezNqGcnPowNlCzJqWqb3r3hQ4m21KxUkjKTANDy8Pt+pgamWBsXvW1z0YVFXI+5Lvq09dB9nzZJkqqY61tFos3EBjq8VPy5lk4wYqn45fjkD3BpNQSfgr9PQW1PsGskEm1128tEmyTpisoQ3EaD9y9wee3jkWyLuS0SbShkb0epzGSyTZIkSZLKIEut4Zt9fiz7NxCA5rWsWPysJ41qWOg5skfDxFDFV6Nb0aa+LR9tv86lkHguhcQ/dJ6ViQGJ6dnYmRsxsWP+lL/I1Eh+u/obm25tIlOTWexjOZk74WrnSnO75uLDvjk1zWpyMPggq2+uBuCzrp/hZC4nhErSY61Ar7ZZYFLKXSd2DWDo99DjHTi1GM6vhHsXxQdAm8mVEq4kPbE8xolkm+9uSIsHUxt9R1QxudvNG/cFG91PU5cebzLZJkmSJEmllJ6lZuZqHw77RgLwXKf6zBvUvFpOHK0IhULB+A716NTQHu87MQTHphIUk0pIbCpBsanEp2aRmJ6NAdnM6NEcMyMDolKjWHltJRtvbSRDnQGAgcIAHtiBqnjgL1maLMJTwglPCedoyNG8622MbfLuP8VtCt3rdn8UT1mSJH26sQ2ifMHEGrxKqGorjFVtGPAZdJsDZ36GsytEEqDlCF1HKklPtloeUMNVfL3e2C4mUuqDRgMRVyHwKGSmQPsXwcKxbGtkZ4qqWJCDEaRykck2SZIkSSqF1Mxspq+6wImAaEwMlXw3pjUD3Stn8mV14eJgjouD+UPXJ6RlkXb4G2qe/5po80V8dW4TG/w25CXJWtdozczWM+lYq2OR/d6SM5PxjfXFN9aXm7E38Y315Xb8beIz4gHwdPTkFc9XKu25SZJURWg0cCynqq3jTJFwKy9zB+gzH3q8C1oNGJroJkZJkgSFQlS3HfwYrqx/tMm2+GC4fUQk2O4cg9SY/Nt8VsGYVeDcofTr3doj+s9ZOD0eW2KlR04m2yRJkiSpBMkZ2bzw+znO3o3FzEjFyufb07Ghvb7DqpK0Wi0Zib4E3/iZv20tWX/lG9KVIqHWqkYrZnnMolPtTkUm2XJZGFnQzqkd7Zza5V2Xoc4gIC6A4KRgutbpKvu0SdKT4OZ2iLoJxtbg9bJu1jQw0s06kiQ9zH0MHPwfBJ2EuCCwrV/yfcpLo4ZjX4oJqLGBBW8zsgCXruL66Fvw+yAY8LmocivhNQhJ4XDyR3HsOUH0o5OkMpLJNqly3Tku3mVo/WzJ39QkSZKqoITULJ77/SyXQuKxNDHgjykdaFvfVt9hVQnRadF43/cmKDGIuwl3uZt4l6DEIFKzU8HRJu88d4yZ2ec7utTpWmKSrTjGKmNaOrSkpUNLHUQvSVKVp9HAsa/EcccZ1b//kyQ9CazrQIPuorrsygboUczk4Io6shCOfyuOFSqo2w4a9oKGPcWxyhAykmH7LLEdffdbEHoehnwHRmYPr5cWJ4aqnFkK2WmgMpa9HaVyk8k2qfJotbDxeUiNBlNbcB2ku7WzM8Q3Q0vZFFuSpMoTk5zBpN/OcuN+IrZmhvw11Qu3OhXYwvQYCUkMYdyucSRmJj50m0qrpU52Ng3tmvNM4Hm6JSeiCA+Aut30EKkkSdWW7w6IvAHGVtBRR1VtkiRVPo9xItl2eS10f6tyii6ubc5PtA34UhR3FDY8xdgCnvkDTi+GAx/BlXUQcR3GrgK7huKczFTwXgonv4f0BHFd3fbw1Kdg66L72KUngky2SZUnOUIk2gCOfQHNBuruG+2ed8DnL3hhHzi3182akiRJD4hMTGfCr974RybjYGHE6hc70szJUt9hVQnp2enMOTaHxMxEnC2d6eDUgfpW9XGxcqH+icU43zqIYbNBMHItnF4C+96D/R9Ao17yRaskSQ/LShe9kVKiICU6/zh3EqDXy+KNW0mSqofmQ2HXmxB7G8IuiCozXbp/GbbNEsddXis5Ga9QQOdXoFZr2DRFDE9Y3hOG/yK2jB77CpLDxbmOLaD3h7r93VV6Islkm1R5onzzj+9fBr89uqtuCzgMWjVc3yKTbZIk6dy9+DQm/OrNnegUnKxMWD3Ni0Y1LPQdVpXx+dnP8Y31xc7Ejt/7/05N85rihmBvuHUQFEro85G4zmsG3NwJwafEC+PndoBSqb/gJUnSP40aAo/ApbUQcCC/kqQwRpZiC6kkSdWHsSW4DoGrG0R1my6TbclRsG6C2ObZuG/+643SaNANph+Djc9B6DlY92z+bTb1oNf74P4MKJ+sKfNS5ZDJNqnyRN3KOVAAWjj6uW7eIchMgYRgcXz7cMXWkiRJ+o/kjGwm5iTa6tqasubFjtSzL6SvxxNqq/9WtvhvQalQ8mX3L/MTbVotHMx5wdt6Aji6imOlEoYvgV+6QNAJOLtcbgeTpCdVpC9cXiP6OCXdL3ib0hAsHMXEUPMaOR8O4hd2Mzv9xCtJUvl5jBPJtmubof/nuhlMkp0JGyZDQgjYNYJRv5U9MWZdB57fDfvmwblfwdwRur8NbZ+Xw1MknZLJNqnyRPuJyzaT4NoWCL+im+q2aP/84yhfSLwHVrUrtqYkSRJikuYHW68SGJ1CLWsTNrzUido2pvoOq8rwjfVlofdCAGa1nkXHWh3zb7y1F4JPg4EJ9JxX8I52DaHfJ6Ix8cGPxTvRDo0fXeCSJOlPWpxIrl1aA/cv5V9vagtuo6HVGHBoCibWcsuWJD1OGvYECyexPdN/PzQfUvE1974rKuWNrWD8uvIPTTEwgsHfiup7q9qFD0uQpAqS+zikyhOVk2yr1wk6TBfHRz8X1Q8VEX2r4N9vH6nYepIkSTk2XQhl26V7qJQKfhrvKRNtD0jMTGTO0TlkqDPoVqcbL7q/mH+jOlsk0UD0VrKu8/AC7aZCgx5i28e2GWIbmSRJj7f4YFHVuucdkWhTGkCzQTDmL3jTDwZ/A84dxC/MMtEmSY8XpQpaPSOOr6yr+HrnV8L53wAFjFwBNZpWfE2HxjLRJlUamWyTKk9usq1GM9GQ0sgip7ptdwXXzekFp8j57xsok23lcmkt/NwZrm/TdySSVCUERCYxf/t1AOb0a0o7F7ltKZdWq+XDEx8SkhRCbfPafN7tc5SKB15CXF4rvjeb2EDXNwpfRKmEp5eI/kuhZ8VUMEmSHl8pMfDXSEgMA5v6Ylrgm34wfi20GAYGxvqOUJKkytZqnLj02wupseVfJ+gU7H5bHPf5EJoNqHhsklTJZLJNqhxpcZASKY4dmopeG14vib9XtLotN4nnOlhc3j4CGk3513vSZKXDP6/Ctpch8jrsmgMZyfqOSpL0Kj1Lzew1F0nLUtO1sQMzejTS6fpZmiz8Yv3Y6r+V1TdXk6nO1On6le3P639yOOQwhkpDvu35LdbG1vk3ZqXBkc/Ecfe3it/SYeMMA3LOPbxQ9G+SJOnxk5EMa56BGH+wqgtT9ohejeYO+o5MkqRHyckNarqDJksMtiuPhFDRp02TDS1HQtc5uo1RkiqJ7NkmVY7c4QhWdcQ0GoBOs8F7GYRfFdVtucmyssrdRtp6ophKmhoNEdegVquKx/24i7srfljdvwwoRH+U1Bg4uwy6vanv6KRHYMWVFWy8tZFsTTZqrZpsTTYarSbvWKlQMqftHJ5t/mzJiz1GFuy8gW94Eg4WRiwa64FSWf7tTFmaLALiArgRc4MbMTe4GXsTv1g/MjX5CTb/OH8+7vyxDiKvfOfDz/O9z/cAzG0/FzcHt4IneC+FpHtg7Qztp5W8oOckuLlD9G/55xWYul9uH5Okx0luA/OwC6Iv26SthW8tlyTpyeAxFvZfhcvrof2LJZ//X0c+g5QocHKHpxfL1wxStSGTbVLlyN3qWaNZ/nW51W3Hv82ZTDqo7N8s1VkQGyiOndzApSv47xNTSWWyrXi39sGWaZCeAKZ2MGqFKOfeMg1O/ih++JlYl7yOpH/pCeC7C9xGlWkbzu342yy+tBiNtvhK0J8u/sSgBoOwMbGpYKDVw64r91ntLSYcLxrTGkdLk3KvFZgQyMsHXuZ+yv2HbrMwtKCpbVMuRl5ks/9mWtVoxcgmI8v9WLpyPvw8OwJ3oNao0aJFq9WiRYtGq0GLFu/73qi1agY3HMyYZmMK3jk1Fo5/J457vQ+Gpfi3Uyhg6A/wU1uxndR/PzTtr/snJknSo6fRwPaZcPsQGJrBhE266askSVL15f4MHJgvfubH3Ab7MuweSI6EqxvF8eDvwMi8cmKUpEogk21S5citPnNoVvD6TrPBe7mobvPdVfapNLGBooTYyEJUzTXqnZ9s6/q6TkJ/7GjUcGShSHIC1GkHz/whtnNp1OL6KF84/TP0mlfsUqWSFC6qVWq1hh5zQSW/zejctpngu1N83vp9Uuq7fe/zPRqthu51u/Oq56uoFCqUSiUGCgNUShUqhYrZh2bjF+fHH9f/4PW2r1fec6giQmJTeXfLFQBm9GxE96Y1yr9WYgjT9k0jMi0SC0MLWtq3pIV9i7yPupZ1USqULL+ynJ8u/sTCMwtpZteMlvYtdfV0yixLncU7/75DVFpUsec1sm7E/I7zUfz3DZITiyAjARxbiomCpWVVGzpMg5M/iO9PTZ6S71RLUnWn1cL+98UvxkoDMQShbjt9RyVJkr5ZOonf2QIOwpX10Ou90t/3/O+gzhS/vzi3r7wYJakSyN+CpcqRNxzhP+9m5lW3fQPHvhBbScvyC1buug5Nxf0a9RJ/Dz4Dmalymsx/JUfB5hfgzr/i7x2mw1MLxbhrEFOCes6Djc/B6SXic2NWwabwu94UlSr+++GeD4xeKSvmdOneRZFoAzj/B3R/B4wtSrybT4QPR0OOolKoeLPdmzS0bljoebNaz+LVI6+yxncNk1pMwt7UXnexVzFZag2vrL1IUno2berZMKdf+asv7iff58X9LxKZFkljm8as7L8SWxPbQs990f1FrkZd5WjoUeYcmcP6Iev1VkW4L2gfUWlROJg6MKnFJBS5fxT5l8YqY/q79MfM8D/fX9MT4OwKcdz3Y/H9pCw6vwbnfhPb2n13QvOhOnlOkiTpycnv4czP4vjpn6FJX72GI0lSFeIxXiTbzv8OXV4rXYVadgac+1Ucd5xRufFJUiWQAxKkyhGdmxRr9vBtnWaJaXThV/OTBqX14IRTEEk3qzqgzoDgU+WP93G17lmRaDM0h1G/waCv8xNtuZoPE41LM5Pg1I8Ve7wb/4jPqdIADEzFD9Vf+4qS8ZJotWJS0bZZ+Z9n6WFHv8g/zkgQUyBLoNVq+faCqGwc0WREkYk2gJ7OPXGzdyMtO43frv1W4XCrsm/2+XEpJB4rEwN+HO+Joap8PxIjUyN5cf+L3Eu5h4uVCyueWlFkog1AqVCysNtC6lnW417KPeYen4taoy7v0yg3rVbL3zf+BmC863hecHuBKW5TeN7teZ5r+RyTW05mUotJjGk2puBAhFw3d0J2uvg+36Rf2QMwtwevl8Xxkc/koBtJqs4u/g0HPxbHTy0UPZokSZJyNR8Gti5igJ730tLd5/pWcb5lLWjxdKWGJ0mVQSbbJN3LTIF40f+IGq4P315gMumXZfsFK/qByjYoWN12+0j54n1c3bskeiOojGDaIXAfXfh5SiX0fl8cey8TvRHKIy0+fyR3l9fhhb0iERp9C1b0hsCjRd835Cz8PgjWjoVLf8OO18oXQ3Wi1YpBIuqs0t8n9Dzc2gsKZX6S4swvJX4NHQo+xJWoK5gamDLTY2ax5yoUCmZ7zgZgg98GIlIiSh9fNfLP5Xss+1f0f/xqdCvq2pavKjY2PZZp+6cRnBRMHYs6rHhqBQ6mJU/bszKy4rte32GiMuHUvVP8fPnncj1+RVyOusz1mOsYKY0Y3bSI7w/FubZJXLqPLv8W0M6zwdgaIm/Aja3lW0OSJP26f0VMOQfo/Kr4upYkSXqQgZHo7Qpw4gfR87U4Wm1+pWz7F0FlWLnxSVIlkMk2Sfei/cWlmb2oXChMbnVbxFXw21X6tf9b2QbQUCbbCnXxL3HpOgQcmxd/btMBUKctZKXCie/L93gHP4LkcLBvDN3fhtqtYdoRqNse0uPhr5H5W85yRfnBugnwWz9RmWhgIqrigk+LBNzjKCkcji+Cn9rAkvbi+Zc24XzkM3HpMR56fyiSFLG3IeBAkXfJ0mTxg88PAExqMYkaZiX3JOtcuzOejp5kqDNYcXVFiedXN4d9I5iz/hIAL3RpwAC3WuVaJyEjgen7pxOYEEhNs5r8+tSvOJk7lfr+TW2b8lHnjwBYfmU5R0OOliuO8vrrhvgeMaTREOxMyrh9PDkKAo+JY7dR5Q/C1Fb8PABRtamHCj9JkirIbzdo1dCoT5n6iEqS9IRxGy16vGYkiJ6txQk+I9pMGJhA2ymPJj5J0jGZbJN0r6jhCA8ys4N2Od84b2wv3boaTX4iz+G/yTYFRF4XiQwJstLgSs7knjaTSj5foch/t+ncr5B4r2yPd/cEXPhDHA/9MX8ioWVNeG4ntBonXojvfgt2zoG4INg+G37uKLadKpTQZjK84iPOhZJ/CFcn6mzw2wNrx8OiFnDof/lTdf33la6cPviMmO6mUEH3t8kyNIa2k8VtZ4quitrqv5W7iXexNbZlSsvSvVhRKBS84vkKAJv9N3MvuYz/H3QsJSMbrVark7VO345hxt8+ZGu0jPCswweDS0hEFyE5M5mXD7yMX5wf9ib2/PrUr9S1rFvmdYY0HMKzrs8C8N7x9whODC5XPGV1P/k+h4IPATCh+YSyL3Bjm/iaru1Ztqlihek4QyTdom/lTxyTJKn6CD4jLpsNlINOJEkqmlIJfT4Ux97Liv+9zfsXcdlqTNHFG5JUxclkm6R7Ub7iskYxyTYQU2kAQrxLt25CCGSniW2Rti7515vbQ61W4ri4rYpPkhv/iHeNrOtBg56lu0+j3lCvk+h/lzu5tDSy0vO3j7SdAi5dCt5uaAIjlkLf/wEKOP8b/NBKVN5pNaLybuYZGPYTWNeBziLJg+8uiA4ofRyPWnKk6Fl18GOxfXb/B3D4U/j3azj1k6ji8/lL3P5dS1g7Lv/d/7odYNhi0dcGRFXg/SsFlg+NSyUwKjn/ipyqtpTWY3n/xq90WN2B78wNyVYoxf/7iBsAZGZr+HzPTf65fI/UrFR+viQScS95vISFUcmDFHK1d2qPl5MX2Zpsll9ZXt5/pXKLTcnkz1N3eXrxCVp+tI8eXx/l8z03uRQSX+7E2+WQeF788xwZ2Rr6tajJ16NboVSW/RfDpMwkZh2axbWYa9gY27DiqRW4WLuUKyaAt9q9ResarUnKSuL1o6+Tlp1W7rVKa63fWtRaNV5OXjS1LcdgiGubxaVbObaf/peJldh6BqK6rSxbqyVJ0i+NWrQ4AKjXUb+xSJJU9TUdIF4HZ6fBsa8KPyc+GG7uEMdecjCCVH3JaaSS7hW21bMwdduJiqb4YEi8D1YlbOXKXde+Maj+81+3UW9Ranz7CHiMK1/cj5PcLaSeE8W7SKWhUEDvD+CPwXDhTzEpyKZeyff79yuxldHCCfr9r+i1u74u/k9sfhEyk6FeZ3G+c4eC5zq6ih/Et/bC6cUw9PvSxV+Z1FkQcQ1Czok+eCFnIT6obGuY2Yvtn56TxHME0Y8i6KRIwm1+EaYfBSMzopIyGPzjCdKz1Bx4owf1knzgzjFuGpvydlYgQbdPALAyYBPXG7ry9R1fbL1/gWE/sf58CMuOBWJiqGTG8EBi0mNwtnRmTNMxZX7asz1n473Hm20B23jB7QXqWZXi/0MFZGZrOOwbyRafUI74RZKlzk+qBcemsuxYIMuOBVLHxpT+LZ0Y5O5Em3q2pUqY3YpI4rnfz5KSqaZzI3t+Gu+JQRkHImSqM9l4ayPLLi8jLiMOS0NLlvdbThPbJmV+rg8yVBnybc9vGbNjDP5x/nx17is+6vRRhdYsTmpWKptuiX5rE1tMLPsC8SFiqzcKcBupm6A6TBcTkePuiKEfbSbrZl1JkipX5A0xYMnIEhxb6DsaSZKqOoVCTDD/YxD4/Cl6PNr9Z3DX2RXiDfkGPaCm/L4iVV8y2SbpXt420hKqJYwtoWZLMZU0xBtaDi9h3f8MR3hQw15w4jsIPCISGE/yNoaY23D3OKCA1s+W7b4uXcUPtjvHxLtNTy8u/vzwa/nbPQd/AyaFTCx8ULOBooot8Z5IshX1eer8qki2XVoDvd4DC8eyPQ9d8tuTnyAsQCF64dVtL+LLThcjyh+8zEoHQ1ORkGg68OFJsAqFqHD7pbP4/73/fRjyHd/s8yMhTVT3LD58iy+TF7LGyoJv7e3JSrlHTbOajG02lhVXV+CdnczYOk58d3MzTXp8yC9HRDVghiaRVTf+BOBVz1cxLEdj2daOrelapysnwk6w9PJSPuv2WZnXKI3QuFSW/xvIP5fvEZ+aX9XkVseKkZ516deiJldCE9h97T5HfCMJi09j5ck7rDx5B0dLY/q3dGKguxMdXOwKTaAFx6Qy8Vdv4lOzaO1sw4rJ7TAxVJU6Po1Ww547e/jp4k+EJYcB4GLlwufdPqe5ffm2of6Xo5kjX3X/iqn7p7LFfwuTWkwqdmpsRey4vYOkzCScLZ3pXrd72Re4vkVc1u8MVrV1E5SxBXR9Q3wNHPtabCf/79eLJElVT+4W0rrtQFn676uSJD3BXLpA474QcFDs3Bj1a/5tmSkiCQfQsfihXpJU1clkm6Rb2Zki2QMlV7YBOHvlJNvOlpxsK65irl5HMDCF5AjxLmvNlmUK+7Fy8W9x2bgP2DiX/f69P4DfjolEV9c3iu7HpFHDP6+AJhuaDxUfpWHjXHJc9TtDnXYQdh7OLhcx6YNWC4cXikSbibVIrNXtAM7txUCJkpKLpWFuL7bZ/jUczq8kyKYTGy6Y590ccmUHr9e6xWF7O0BLz7o9WdBlATYmNvR07skbR94gKCmIyY52PLfjTe4liMpOI4dDZGrSaGHfgqdcnip3eLNbz+ZE2Al2Bu7kRfcXaWij2wRQtlrDhF+9CYpJBcDR0pgRnnUY2aYuzZws885ztjNjcKtapGepOXYrir3Xwjl4I4LIpAz+OhPEX2eCsDM3on/Lmgxwq0XnRvYYqpSEJ6Qz4bczRCZl4OpkyR9T2mNuXPoffafuneL7C99zM/YmAA6mDsxsPZMRjUdgoNTtj9AOtTrQ07knR0OOsvjiYhb1XKTT9UEkDlf7rgZErzalohzdJK7mTCGtyGCEwrSfKrZgJwTDxVVi+pgkSVVb7jAjuYVUkqSy6DNfJNuuboIur4OTm7j+8lpITxDVbk3K//pVkqoC2bNN0q3YQNGTysgCrOqUfL6zl7gsTd+24irmDIxFVRbA7cOli/VxpM4WSTIQ2xXLw7kDNOkvPo+73gTf3eLz+t+Jmd7L4J6PmIg58OuKxf1fCgV0yenhdHYFZPy3quwRCfEWE3MNTODVSzBxM/ScK7Yt6yLRlqtRr7xedXaH5uCojWV469q0bRLP/fobOGxuhgEK5rafy4+9f8TGxAaAJrZNWDtkLT2tm5KpVLBCfQlTp02M72qEoa34mhrb8OXyJVRytHRoSW/n3mjR8vPlogcxlNch30iCYlKxNTPkzxc6cHpeH+YNal4g0fYgE0MV/Vs68d3Y1pz/sC+/P9+eMe3qYmNmSGxKJmvPhvDcyrO0+/Qgb264zMTfvAmJTaO+vRmrpnbAxqx01VK34m4xbf80XjrwEjdjb2JuaM4rnq+wa8Qunmn6jM4Tbble9XwVBQoOBB3gesx1na9/6t4p7iTcwdzQnKcbPV32BaL9IfyKmBrcYrhugzM0hW5viuN/vxWVoZIkVW0hOZVt/20JIUmSVJxaHtByBKCFwwvEdRoNnMkZGub1culb4UhSFSX/B0sly0rLnwJakge3epZmK2fui7P7l8XjFEWrLbkXXKNe4vL2kdLF+jgKOADJ4aI/WLNB5V+n13viMvAIrBsPP3rCZ7VhWXfY8pLY5pX7g7Hf/0rut1cerkPEu1rp8fnVeo/a2RXi0n20mKBbmXrPJ966BZbaJL43XkqzJpcIMPiKGEOom5XNN22/YWKLiSj+83VlaWTJD4NX83JiFgqtFgPb8+yPexeFQkN2clN87zpVOLSZrUUZ/767+/CL9avweg/667TofTe2fT16NK2BqgwDC4wNVPRydeSr0R6ce78vf0/14lmvejhYGJGQlsVmn1ACIpOpZW3C31O9cLQ0KdW6IUkhPL/nec7cP4OB0oCJzSeyZ+QepreajpmhWbmeZ2k1sW3C4IaDAfjJ5yedr//3DfG1NKLxiDINzMiTOxihYa/KmQ7WZrJ4oybpHlz4XffrS5KkO4n3Rd9dhVJUo0uSJJVFrw9AoRKtY4LPiIKJGH8wtip7KxxJqoJksk0q2b73YXE7uLWv5HOjcqrPSrOFFMCmPljUBE0W3LtU9HkpUSLpolCKAQmFyZ1uGnTyya2I8MkZjOAxvmL9jmq3hvHrwf0ZqOkOKmMxNej+ZbiyDo58ClmpUL8LtHlOJ6E/RKmCTrPE8ZklomqvOPcuwumfdfe5T4qAG9vFcftpulmzGKkaJdNTZ5CqNaauoS+/XFuEBg2DklMYHOzOrutF963TKI0wTuzNzxFRWGogU5OJAgUZkQPZ4hNKllpT5H1Lo5ldM/q79AfgB58fUGvUFVov1+2oZE4ERKNQwASvig1fMFQp6drEgc9GuOP9Xl/WTe/I851d6NWsBn9N9cLZrnRJsgx1Bm8efZOkrCTc7N3YMXwHczvMxdbEtkLxlcVMj5kYKAw4ee8k58LP6WzdwPhATt47iQIFzzYvx4tYrTZ/C6m7DqaQFsbQBLq/JY6PLxK9WyRJqrjMVFGR/tdIOPg/0XO1nJOd8+TuSnBsKaYKS5IklYVDY/CcII4P/g/O5Oyg8JwkentLUjUnk21SyXKTbLnbE4sT5SsuS5tsUyjyq9uK20qau65NfbHVqDA1XMGylmhMn7ut4UmSFC7eGYLybyF9ULMBomHpjBPw/n14xQfGrhb909xGiz4Kw3+u3BLv1hPAzEG8c35jW9HnXfgDfu0H++bB0c9189g+f4okcN32IvlYyX4+cpuzSfb8ZDyVVdaWaIEuqWl8FpfKH5nD2XYpjNtRhW+n3XHlHj8ndaNdmoYNYWE8VaMtc9q+hZ2hC9HJmRzxjaxwfDM9ZqJUKDkedpzpB6YTmVrxNXOr2vq41ix1Mqw0VEoFHRva8/Gwlvw+pQONHUtfwfXl2S+5GXsTG2Mbvuv1HXUt6+osrtJytnJmVFPRD+1Hnx/RVvQX4hyrb4pebT2de+JsWY5+juFXxDvOBibgOlgnMRWq9UTxvT4lEg59UnmPI0nVUeI9WN4L/h4FVzaUnJBOT4Dj38L37rDnHbh9CE4sgqVdYImXGIaU22u3rHJft9XzKt/9JUmSerwr3tQPPiW+P6GADpX/JrckPQoy2SYVL/EeJIaK44CDJVcN5W0jLWWyDcA5p6luscm2EraQgkjcNczdSvoE9m27vFb0WavbARxdydZks+P2DhZfXMyFiAsVq0ZSqsSghOZDoPvbMPo3mLARbF10Fn6hDE2hw3RxfOrHh9+FV2eJvnI7XhOJMRDvisXdLdPDJKZncTU0Af+IJELjUolJSEZzfqW4MffxK1FwTCrLjwcC0Hjg82yzEhUCLyQkovKaTuvmTdFo4cdDD2/nVmu0/HQ4gFisCKw1iLrZar5NUvO822RGtRF9EzecD61wjA1tGvJZ188wNTDlbPhZRv0zimMhx8q9XkpGNpsviLgmd6pf4fh0YcftHWy8tREFCr7o9gVO5hXfglte01tNx0RlwqWoS/wb+m+F10vISOCf2/8AMKlFOZPxuVVtTftX7jvOBkYw5Dtx7L0U7p6ovMeSpOrmzM+iX2rAQdgyDb5uAlumi78/WAGeEgOHFsB37iJpnRoNNvXEG2auQ0BlJF6zHVkIP7URbSJO/ggp0aWPJXcSqbMcjiBJUjlZ1ymYXHMdDHYN9BePJOmQTLZJxQt9YAtTZjLcKeaXPo06v7dbaSvboOCQhKIqOIobjvCgJ7Vvm1abt4VU7TmBnYE7Gb59OO+deI9lV5bx/N7n6bupLwtOL+D0vdNk5SamqoP2L4pJs/cvw50HkjvJUbDqaTj3K6AQv0A06AHqTDj4camXz8zW8PTikwxdfIJ+3/1L1y+P8MGXX6FMuk+01gr3jSZ0/fIwh30jdP7Uci3cfYPMbA1dGtsTrTxKOlqaZ0N7Axvo/Bqv920CwD+X7xEQmVTgvruu3icwKgUbM0NcBudsv/PdCXFBPNNOVGUd8YskMqni22sHNxzM+iHraW7XnPiMeGYfns0XZ78gU51Z5rW2XgwjKSObBg7mdG3sUK540rLT+PTMp3xz7hvSsyv2/Pzj/FlwRvQhfNnjZbrU6VKh9SrK0cyR8c3HA/DjxR/RaCu2FXiz/2bS1ek0tW1Ku5rl6K2k0cC1LeJY11NIC9O4D7R9Xhxvm6m/ISmSVJVkpcNFUaGK+xiwbQBZKXBlvah0W9Qc9s6DPe/C925w/BvISBBvgI5YJirUu78N41bD2wEw/Bdo3Ff0TLp/GQ58CL8PLLltA4htqeFXxLEcjiBJUkV0nSP6tAF0nKHfWCRJh2SyTSpeXrItp2m5746iz40PFls4VcZiC1Bp1Wol7pMaI6ZeFqY0lW0ADXuKy/ArIhnzpAg6hSb2NvusbBkVvIV5x+cRlBiErbEtT9V/CksjS6LTotlwawPTD0yn14ZefHDiA/4N/VdnW9Qqjbk9tMmpxDn5o7i8fxlW9BL9+YwsYfw68QtE/4WAAq5vheBSTLgFtl0M4050CkYqJbZmhhgbKJmsOgDAWnVvkrJUhMal8cqaiw8lunThZEA0+65HoFIqmDeoMWt8xXbt57svQPHqRTC3x62ONU+1qIlWCz8cCsi7r0aj5aecarepXRpgVtddfA1oNXB2OY0dLWlTzwa1RstWnzCdxNvAugF/D/qbic0nAmJr4oTdE7iTcKfUa2i12rwtpBM71kdZhqEIuRIyEpi+fzrr/dbz540/eW7vc4SnhJd5HYCUrBTmHJ1DWnYaHWt15KVWL5VrHV17oeULWBhacCvuFnvv7C33OgkZCaz1XQvAxOYPD9koldCzosrZyFJsIX8UnvoUrOtBfBAcmP9oHlOSqrIb2yAtFqzqwoil8OpFmHpQ9BU1tRNbr8/8DN6/iL6qtTxgzF8w8wx4jAOVYf5aJtaiAfnEzfDWLRi8SFwXfavgG1tFuecDmmzRvsOmYj03JUl6wpnbw3P/iHY1Ll31HY0k6YxMtknFCz0vLluNFZd+e0QFW2Fyq8/sG4PKoPSPYWAMtT3FcVFbSfMq20pItlk4csOpBQPq1mbhsbk6a+RelWm1Wg6f/Z4xtZ14y96S24l3sDKy4lXPV9kzag/f9vyWY2OOsbTvUkY1GYWdiR0JGQlsv72dWYdm8ePFH/X9FErWaZYYjnH7kOgv81t/SAgBu0Yw7ZDoLwfg5J6fmNs3T1TjFCNbreHnoyJ59Xb/Zlyc/xR+r7rQSXUDrULJhJkfcfydXnRqaE9KppqX/rpAckYp3vEvpWy1hv/tuA7ApI718Us+Rmx6LLXMa9Gv0WAwMs879/W+oqpz55V73IoQSb+918Pxj0zG0sSA57q4iBM7ismhnF0BkTcZ00705tpwPkRniVUjlRFzO8xlSZ8l2Brb4hvry9idY9nqv7VUj+F9Jxa/iCRMDVWMblv2nmiRqZE8v/d5LkVdwtLIEhtjG27E3GDszrGcDz9fprW0Wi0fn/qYu4l3cTRz5MvuX6JSqsocU2WwMbHh+ZbPA7Dk0pJyVaSeDDvJyO0jCU8Jx97EnkENyzmlOHcLafMhRffN1DVjS3h6sTg+/9uTV7EsSf+V296g7fOivYNCAc7tYfA3ImE2fr3oqdpssEiiTT8GLYaV3FvV3AHaTxVDkUBUypUkbwupV+mmz0uSJBWntqd4jSFJjxGZbJOKps4SEx4Burwm3vFMiYKQs4Wfn1d9VsJWz8IUNyQhPQGS7pdqbY1WwydWBoQZGrAu+hzvnXiPbI3ukiNVTXx6PJN2Pctrab74GRthoTJlhscM9o7ay7RW0zA3FMkaQ5UhXep04ePOH3P4mcOs7L+SUU3EVrDVN1eTmJmoz6dRMlsXaPG0OD6yUExGbdwXph1+uNqx1wdgaA5hF+Da5mKX3XX1PndjUrE1M+TZ3GmYZ1cAoGg2CLvaDXG2M+PH8Z44WZlwOyqFuZuulDpp9e+tKOZvv8aSIwHsuHyPq6EJJKTlJ0xWewdzKyIZWzNDXu3TiD9v/AmI6iNDpWGBtVrUtmKgm5Oobjvoj0ajzevh9kKXBliZ5Jzf5Clo3A/UGbBpKoNb2GJqqOJ2VAo+wfGliru0utftzqZhm/By8iItO435p+bz27XfSrxfblXbcM86WJsalnB2QcGJwUzeM5mA+ABqmNbgjwF/sG7IOprZNiM2PZZp+6ex1ndtqT9H6/zWsffuXgwUBnzT4xvsTOzKFE9lm9hiInYmdgQnBbMtYFup75ealcqnZz7l5YMvE5kWiYuVC0v6LsFYZVz2INTZ+QNK3CppCmlRGvbInwb8zyuQXsW/V0lSZQm/Jl4jKQ2gzeSHb1cZijeeRv8G49fkbA8tYxKs1ThxeXNHyVu3c18LOsvhCJIkSZJUGJlsk4oWflVsCzWxEZM+m+ZUD/nuLPz8qHIMR8hVL3dIQiGJvKicqjbLWiLhV4xtAdu4nhWPqUaDgVbL7ju7eeffd6pXj7JS0mq1zD81n8sx1zDVaHgx04C9o/czs/VMLI2Kbl6uUqpo79Sejzp9RGObxqRlp7HVf+sjjLycOr+af9zldXh2A5jaPHyeZU3o9oY4PvgxZKUVupxGo2XJEVHV9kKXBpgbG4hf5HPf0X9gMEINS2OWTGiDoUrBrqv3+e1E8VsmtVqx9uSVZ1l1Ooiv9/nxytqLDF18Ao//7cfzk/08veQk3+wTXzNznmrG1Vhv7iTcwdLQMm8S5X+9ltO7bdfV+yw+EoBveBIWxga80OWBRrIKhZgSa+YAkdexPPEZg9xrAbDxfEixcZeHo5kjy/otY4aH6LHxo8+PHA4uekBJeEI6+66L7Z5lHYzgG+vL5D2TCUsOw9nSmVUDV9HUtil1LOrw16C/GOgykGxtNp95f8ZHpz4qsZfc1airfHXuKwBeb/s6no6eZYrnUTA3NGeau0g2Lb28tFS96S5FXmL0jtGs9xP/l591fZYNQzfQ0r5l+YK4c0y80WJmL5Jfj1rfj0XCPSEE9r//6B9fkqqC8zlvZLgOET/nKkPddmDXUGxB9d1V9HkajZxEKkmSJEklkMk2qWi5W0jrthdbEFxzSnt9dxU+yCC6lH3VClM3p7It8iakxRe+bgnDERIzE/nB5wcAZiemsSgyGgOFigNBB3jz6JvlauJela33W8+RkCMYaOGP+xG81nIq1iY2pb6/QqFgQvMJAKz1XVv1t9zWaSO2xTy/C/r9T2yhKUqn2aKnTWIonF5S6CkHbkZwKyIZS2MDJnd2EVdeXicGgTg0gwbdC5zftr4t84e0AODzPb6cCYwpdN30LDVzNlzm65xE2pBWtRjVpi7t6tviYCGqiuJSs7gcEk9SRjauTpY826Eef1z/A4DRzUbnVST+l6uTFYNzEmeLDogk9POdXbA2+091mIWjSLgBnPmZabVEL8Qdl++Rmqn7Sk+VUsXM1jMZ22wsWrS8e/xd/GL9Cj13zdlgsjVaOrjY0byWVakf43z4eabsnUJMegzNbJuxauAq6lrmb0E1NTDly+5f8mbbN1EqlGwN2MqUvVOISIlAo9UQmRrJpchL7L2zl5XXVvLpmU957chrZGuy6VOvD5NbFFIpUkWMaTYGJ3MnIlMj8xJohclUZ/L9he95bu9zhCSFUNOsJiueWsE8r3mYGlRg62duhWiL4QV7Pj0qxhbw9M+AAnxWgf+BRx+DJOlTRhJc2SCO20+tvMdRKPLbhhS3lTT6FqTHi+FFTq0qLx5JkiRJqsbK0FhLeuLkDkeo215cNu4DBiYQdwcib0DNB6oktNr8CrTyJNssaoh3U2MDRZKvSd/820o5HOGXS78Qmx5LQ+uGjDcwwdB/Lz/W7MXrkf9yJOQIrx95ne96fVe+bVRVzK24W3x97msA5sTG0SIb0fy4jAY3HMz3Pt8TlhzGsdBj9K7XW8eR6ljjviWfA6KnVN+PYcuLcOI78JxUoBJAq9Wy+LCoapvcub7YyqjVwjmxhZQO0wrdfjOxY30uBsez5WIYs9f4sPOVbjhZm+St6Rd1jze2HMA/LggTx1jcXbKwdrRlVutZ1LbwACAlI5ugmFSCY1O4n5BOvxY1uRl7nfMR5zFQGDDBdUKxT+21vk3Yfe0+Wi2YGamY2rWI8ehN+4vtd+dW0Mx7Lh52X3E5FnZfDS9Xn7TSmNthLncT7uId7s2rh19lzeA12Jva592ema1h7dlgACaVoartaMhR3jr2FhnqDNrWbMtPvX8qtHpToVDwvNvzNLVtytv/vs2V6CsM3TaUbE12kdWtzpbOLOiyoHxDAx4RI5URMz1mMv/UfL49/y3LrizD0tAScyNzLAwtMDc0x9LQEv94fwLixf/rYY2GMbfDXKyMSp/QLFR2hthSBo9mCmlRXLqICWVnfhbbSWeeBlNb/cUjSY/SlfXijSD7JuDSrXIfq9UYOPo5BB6BpHCwdHr4nNyqtjpt9ZOAlyRJkqRqQFa2SUXLS7a1E5dG5tAoJxnz3+0FyRFivLxCKQYklEdu34//9m3LG45QdGVbQFxA3rS9uR3mYpiTrOsW5sviPosxUZlwPOw4sw/NJi278G2F1UVadhrvHHuHTE0m3cycmZiYJCZQmjuUeS1TA9O83m1rbq4p9f2uR1/nM+/PqnavN7dR4heBzGQ48mmBm/71j+ZqWAKmhqr8LZh3jon/a0YW+e/s/4dCoWDhCHdcnSyJTs5k5ppzfHLqU0b+M5L2f3fgmT0DCDX9FtPamzC0P4xv0nH+uf0PE3ZP4HqMGIRgbmxAi9pWDHCrxZQuDahra8af10WvtoENBlLTvPjtQU1rWjLMozYgqtpszY2KPvmpBVDDFUVyBN+b/gZo2VAJW0lzGSoN+bbnt9SzrMe9lHvMOTqnQEXpvuvhRCVlUMPSmP4tC/kF7j/CU8L53PtzXj/yOhnqDHrW7cnSvkuL3SYN0LlOZ9YNXpe3TTpLk4VSoaSWeS3aOLZhcMPBTHOfxkedPmLt4LUlrlcVDG00FDd7N7RoScpM4l7KPfzj/LkYeZETYSfYc3cPAfEB2JnY8X3P71nYdWHFE20gqsgyEsGqDtTrVPH1KqL3h2IoStJ92PuefmORpEdFq4VzOYMR2r1Q+cMI7BqK3QZaTdF9T+UWUkmSJEkqkaxskwqXHCUq2EAkLHK5Dga/3aLSocc7+ddH+YpL2wZiumh5OHeAy2sfTrblVba5Fno3rVbLF+e+QK1V06deHzrX7gymIhlBiDed7Frwc9+fmXVoFmfun2HWoVks7r0YM0OzAmtkajJJz05HoVDo5pfUSvL1ua+5nXAbB1MHPk03RAHQqFe51xvXbBx/Xv8T73BvbsXdoqlt8dt1kzKTePXwq0SmRWJtbM2s1rPK/diVSqmE/p/Byv7g85fowebkDsDiw2KwwLNe9bDP2dqZOxgBj3FgUvTn39RIxbJJbRny0wmuxB/G339T3m1arQKVxg73mo1wtW+As6Uz229vxz/Onyl7p/BNj2/oXrfg9tSw5DD2B+0H4LmWz5XqqX0+0p2BbrXo29yx+BMNTWHUr7CiNw1i/mWiqjF/3+nLnegUGjgUvlW1RFptsb/sWRtb81Ofn5i4ayI+kT4sOLOATzp/gkKhyBuMML5DPYwMin6vJyQxhN+u/cb229vzBpw83ehpPu78MQbK0v3YcrZyZsOQDdyKv4WtsS2OZo6lvm9VZKA0YPXg1USlRpGSnUJKZgrJWcniIzOZlKwUQCRsH6wmrLCrOVvX3EaWPNGwshmZwfBfxNf05TXQfCi4lnO6qiRVFyFnIfK62LLZevyjeUyPsRB6VrRW6FTIz/i8SaQdH008kiRJklQNVd/fPKTKFZbTr82hWcEm9E0HiOq18CsQHww2ORMcK7KFNFduZVvYBTH9TmUgmtvH3S127YPBB/G+742R0oi32r0lrrR1EdstYvwh8CjtWzwtmrgfnMG58HMM3joYY5UxadlppGWnkZ6djpb8PnQjm4xkbvu5BRJyVcGBoANsvLURBQo+7/wJdn+OETc0KH/T8loWtehdrzcHgg6w5uYaPu78cbHnf3/heyLTIgE4GHSw6ibbQAzeaDkCrm+Ffe/D5O1434nl3N04jFRKpndvKM6LDxFJZMiffFiM+vbmfPuMG2+c+gyAzJjuZMZ1wKteI5ZO6ICNWX612cgmI3nz6Jucvn+aVw6/wvte7zOm2Zi82/++8TcarYZOtTrRzK50Xz9mRgYMcCu5MgwQCca+H8O+9/jQaDWn05uz8XwI7wwoPHldKI1a9As69gUYWcJz/4BZ0VM7G1o35KseXzHr0Cy2BWyjiU0T2tsN5+zdWAyUCibkTn79j9vxt/n16q/svrMbjVYDQHun9kxvNR0vJ68yb/U0VBmWfyhAFaRUKEusfNSp9ATw2yuO3ccUf+6jUs8LOs+GUz/B9llQ6wRY19F3VNKDzv8uBmp0e0v/CdrHQe5gBLdRj27rdMuRsOdd8Vov8iY4Ns+/LSUaYm+L49ydD5IkSZIkPUS+CpIKl7uF1Ll9wevNHaBeZ3Hsuzv/+lIOMShWjeZgbCW2/UXeENfFBABaMRHVvMZDd0nLTsvrXTbFbUqBhuk06Scuc5ppezp6sqLfCiyNLIlOiyYsOYzY9FjSstMKJNoAtvhvYczOMVyPvl7+56Nj95Lv8dGpjwB4we0FOmYrxMQw8xrg2KJCa09sPhGAnYE7iU+PL/I8nwgfNtwSlS4qhYqA+ADuJBQ/mVPv+n4MKiOxTfTAfPz++YaJqgN82eAiNW9vFkmkwwvElhmXbuBYuiRUspE3SqNYNNkWZET1ZXybNvz1QucCiTYASyNLlvRdwvDGw9FoNSw4s4DvLnyHRqshISOBzf5im87zLZ/X8RN/gNcMaNQbY20GPxou5p8Ld8hWa0q+n1Yrvn6WdYdtL4vEd8RV2DAZ1MVP+O1ap2te8vvbC9/yzfHtAPRv6URNK9HnLlOdSWB8IIeCDzHn6BxGbB/BzsCdaLQautbpyqqBq1jZfyUda3Ws0j3VHls3/gF1hqgqzqkKrRJ6fwi1PCAtFja/KN6ckaqG2Duw83U4shBO/aDvaKq/lBjxZhFA+xce3eOa2UGTp8Txfwcl5O4+qOFa7JsukiRJkvSkeyTJtiVLluDi4oKJiQleXl6cPXu22PM3btyIq6srJiYmuLu7s3t3flInKyuLuXPn4u7ujrm5ObVr12by5Mncu3evwBouLi4oFIoCH1988UWlPL/H0n+HIzzIdbC49N2Zf10phxgUS6nMf7zcF3MPrlvIL9u/X/ud+yn3cTJ3YhjkYWUAAHI+SURBVKr7fyZ05TbTDziUNz3VvYY7e0buYWX/lawZtIYtw7awe+Rujow5wunxp7k46SIr+6+kpllNghKDmLh7IiuvrcyrstGXbE027x5/l6TMJFo5tGKW5ywIPCZubNCjwtUDno6eNLdrToY6Iy/581+Z6kw+Pv0xIKq1OtYS20cOBh2s0GNXOlsX0Vgd4NSPTI7/mU8Nf2dE6JewfSZsmZb/y0SHkqvaALLUWSy/shyA7o7P8MPYDiwc7oahqvDPg6HSkE86f5JXBbjy2kre/fdd1viuIS07jSa2TehUuxL7YSmVMPwXtKZ2tFQGMSXtT/ZdDS7+PmE+8OdQWD0aIq6BsTV0nSN62t09DvtK7pk1sflERjYZiUarwTv5RwztjmPkuIMZB2cwaMsg2q9uz9Pbn+b1I69zIOgAWrT0rdeXdUPW8UvfX/B09NTRP4BULrlbSFuNqfw+UWVhYAyjfxdVlsGn4NiX+o5IynXhj/zjQwsg6LTeQnksXPob1JlQq3XBlh6PQqucatYrG0HzwGugvC2ksl+bJEmSJBWn0pNt69evZ86cOXz00Uf4+Pjg4eFB//79iYyMLPT8U6dOMX78eKZOncrFixcZPnw4w4cP59q1awCkpqbi4+PDhx9+iI+PD1u2bMHPz49hw4Y9tNYnn3zC/fv38z5eeeWVSn2ujw2NWvyiDcUn24JOQmqsONZFsg0eHpJQzHCEsOQwVl4TTYPfavcWpgamBU+o30X0OEm6l18ph+gp1d6pPe413Gli2wRnS2ccTB2wMLLAQGlAe6f2bB62mX71+5Gtzea7C98xff90IlIiKvbcKmDZlWVcjLyIhaEFX3b/EkOlIQQeFTc27Fnh9RUKBROaiymY6/zW5fXJetCKqyu4k3AHexN75rSdQ9/6Ipl5IOhAhR+/0vWYC51mc8a8DzvVHbli2R2aDoTG/cS/n0s30Xi62eBSLbf99nbCksOwN7Fn0cAZPN26TomVVwqFgpc9XmZh14UYKAzYc3cPP1/6GRBVbZVeuWXphOLpJQBMNdjDgG2eaH9sA2vGwYH5cPFv0Rso/BpsfB5W9BJJNZURdJoNr12Cvh/ByBWAAs4uF9vFSnjOH3h9QD0zNxSqdExq7uLgvU2cCDtBSFIIGq0GMwMzWti3YFSTUWwdtpXven33WG39rLYS78Gd4+LY/Rn9xlIY+0Yw9Htx/O/X+d8PS5KeAMHeeW/ASDqUnSm+j4CoetKqYdMLojpLKjuNJv97bPupxZ9bGZoOEG+yJIaK13u5QnLeMJfJNkmSJEkqVqX3bFu0aBHTpk1jypQpACxdupRdu3axcuVK3n333YfO/+GHHxgwYABvv/02AAsWLODAgQMsXryYpUuXYm1tzYEDBX+5X7x4MR06dCA4OJh69fJ7AVlaWuLkVMq+RlK+yJtiK6eRReFDCWzriy1F4VfBb49oUJ2SkzytyDZSEEMSoPDKtv/49vy3ZKgz6ODUgafqP/XwWoYm0KAb+O8XW+Fqlv4XeGtja77t8S1bA7byxdkv8A73ZtSOUfyv0//oU79PWZ9VhZwPP59XRfVhxw/FVtn0BNHbDnSSbAMY0GAAiy4sIjwlnMPBh3nKJf/fNCAugF+v/grAe17vYW1sTe96vVlwZgE3Y28SmhRacAtvVWNkzs1Wcxl35DgKBRyc3ANqWJRrqQer2qa6T304yVuCYY2GUdOsJm8ceYOkrCQcTR0Z6DKwXLGUmesgMnq8T+ax77AkVfTdib0Nt/YUcrJCTGXt/X5+b8acNej9gdh6u/st8TXv0qXIhzRUGeKYOo3bSctoVMOMXg1bUt+6Pi5WLrhYueBg6iC3iFZFVzcBWjGB1KbwHnt65z5abA/3WQVbpsPLJ8CimKEhIedEIjkxFPp/Dp1mPrJQnwi+OyA1GixrwQv74Ne+om/qtpdh/HrZv62sAg+LQVXG1qJf26NmaAItnxZfX1fWiddT2Rlw76K4vZ4cjiBJkiRJxanUVz6ZmZlcuHCBvn375j+gUknfvn05fbrwrQWnT58ucD5A//79izwfICEhAYVCgY2NTYHrv/jiC+zt7fH09OTrr78mO7vovi4ZGRkkJiYW+Hhi5W4hrdMGlKrCz3EdIi59d+UPR7CqA8aWFXvsOm3FAIb4YEi8X+Qk0jP3z3Ag6AAqhYq5HeYW/ct645y+bQFl3+qoUCgY2WQk64esp7ldcxIy/t/eXcdXWfd/HH+ds2YsWI9uGN0wREJSsbGxAwxsvW9Rbzvun3GLgd0CBiq2KBIq3Tm6Rm3EYIP1dq7fH9+djblgcbazsffz8djjHM+54nvw2nb2OZ9I5p559/DaytfKfayKSs9J57GFj+GwHFzQ6gLOaZk3eW/XApM1ENIKgpu45Fw+Hj5c2tZksEzdODX/8VxHLo8vepwcRw6DmwxmeDPzbxriG0LPSFPWMjt+tkvWUJUmz90GwDmdo2lVwUAbwIxtMziQeoBwv/D8f6/y6hvdl0/P/pSzm5/Ns2c+i5eHV4XXU14+Q/7Fl2fNp0/GZCZ4PUn2yBfNpNaWg833sM1uSrBv/RsufqdQoCU710Guw4Iz7zcNtB058NU15vu1BOlZuSzalknG3mt54cxJPND7AS5teym9o3oTXi9cgbaayllCWhOz2k426v9Mv88TiSbg5iim5N+yYNFk+GiUCbSBGfbhzMwW13BmYfW41gxWuvRj8PQ1H3gtrL7fm6cN579n1yvAu4LToyuryxXmNu4HM7Bq/2rTx7FeGIS0dM+aREREaokqDbYdPnyY3NxcIiMLT0+LjIwkISGh2H0SEhLKtX1GRgb//ve/ufLKKwkMDMx//K677uKLL75g7ty5jB8/nueee45//etfJa71+eefJygoKP+rSRPXBDBqpb15k0gb9yl5G2ewbfucgk85K1tCCuAbCBF5GWjxC/MGJFAoY86yLP63/H8AXN7ucto2KCWbrk1e4DZ+EWRULIDaIqgFU8+Zyg2dTHbm++veZ/ux7RU6Vnm9teYt9hzfQ2S9SCb2nVjwhAtLSE92WbvL8LR5svLgSuKOmNLbLzd/ydpDa/H38ueRvo8UCo4Ma1o7Skm3JB7n53UHAJgwpHWFj5OVm1Uoq83X07fCx2rdoDUvDHohv/dddbo6tjkeQdH8dLwNn+YOh3NehGu/h/vi4D+H4epvijTEP3IikxGv/MXw//1JZq4DLphsmtSnHYHPr4TME8Wea/62w2RkO2gU7EeH6MBit5Ea5uAmk7ls9zTTfGsy73p5QR0/2DEXFrxS+Pn0Y/Dl1abHoCPHvJ6IjiY7+O+X3bHi09Phrabs3GY3wTaAqE5wdl4/vdlPFfT6klNL3lcwIbtXNQ5G+KemsRDUFDJTTCXDnpP6temDEhERkVLV6pz+7OxsLrvsMizL4q233ir03H333cfgwYPp0qULt956Ky+//DKvv/46mZmZxR5r4sSJJCcn53/t2bOnOl5CzVTacASnyI4Q3Axy0mHpO+axMBcE2wCa5vUBWfsVOLLBqx4EFQQ//973NxuTNuLn6cdtXW8r/VghLc2XI8eUG1WQl4cX9/W8j6FNh2Jh5QdcysqyLFYdXEV6TnqZ94k7EsenGz4FTPmov9dJn2xXUbAtol5Efvno1I1TSUhN4NWVZqLcPT3uIcq/cFn20KampHbNoTVu7WlXmsycXO75YjWWBSM6RBJTiYDPt1u/JTEtkYh6EVzS9hIXrrJ6+Xp5cPfQNoDJ+DuReVLWbzHZrLkOi3u+XM3Ow6nsOJzK3E0HTZDjimngH2EGKHx3W7FZRb9vMB+UjOgYqSy22sKZ1dZmRO2YNhjRHka/ZO7PebYgqLN/lZmku+kn03vwnJfMYIXhT5nnl75rJuxK5TkHI7QZCUEntRTocZ3JjnT2b1M2Ydms/MRMyG42oMwTsquE3Q5d8rJb135Z0K+tqfq1iYiInEqVBtvCwsLw8PAgMbHwH+GJiYkl9lKLiooq0/bOQNvu3buZNWtWoay24vTt25ecnBx27dpV7PM+Pj4EBgYW+qqT0o/B4bzSzca9St7OZoOY88z9pB3mNryS/dqcnE13t/5ubkNb5/d6sSyL99a+B8BlbS8j2Df41MerRCnpP43vMh6AmbtmsjN5Z5n3m7ZpGtf+ei03zryRtOy0U26f48jhiYVPkGvlcnbzsxnUZFDBkyn78/4f2UwPFRe7OuZqAH7d+SsPz3+YtJw0uoV347J2lxXZNtI/kq7hXQGYs2eOy9fiCi/O3EzcgRQa1PPi6Qs7Vfg4mbmZvLfOXHs3d74ZHw8fVy3RLS7p2ZiWYf4kpWbxwd+lX8uvz9nK31sP5//31yv2mTtBjeGKqSaQsfEH+OuFQvvl5Dr4Y6P5eT68Q+GM5UJWTYWZE+FE8YNzpBo5HGb6INT8EtKTdRtregw6gzoLX4cPRsCx3aYU+sbfzLRhmw1aDzUfVORmwZxn3L3y2i87HVbntR7odUPh52w2OPcV83s8ZR/MuLX4Ul8pkHncBILBPYMR/qnL5eZ22x8mexE0HEFERKQMqjTY5u3tTc+ePZk9u6Cfk8PhYPbs2cTGxha7T2xsbKHtAWbNmlVoe2egbevWrfzxxx+Ehoaeci2rV6/GbrcTEVFK82SBfXklpA1agH9Y6du2/8fkxuKGKVSEc0iClfeG/KTy1OWJy1l9aDXedm+u63hd2Y7XJi/YtvWPSk+giwmNYXDjwTgsR/7AgFM5nH6YN1a9AcD6I+u5b959ZDuyS93ns7jP2Ji0kSCfIP7d59+Fn9yRl6HXsDv4NSj3aziVzuGd6RLWhWxHNssSluFp9+SJ/k9gtxX/48LZw+2P3ZUPZrran1sO8f58E0h64ZKuRAZWvOzzmy3fcDDtIJH1IhnTxg3Nql3M08POfSNMgPy9v3eQlJpV7Hbztx7m1dlbAbhtcCsA5m0+yJETeVnCTfqYP6YB5j1vemPlfZ+t2H2Uo2nZBPl50ad5MRlSDocJsn1/Oyx+Eyb3hfXfaFKkO+1ZAsnx4B0A7appcIcr2Gww+mXTxzJlH/z+qAmmtRsN4/8yPUhP3nb4U4AN1k0vmL4tFRP3PaQfNRnorYcVfd4nwJT6evjA1t9g0evVvsRaZdkH5t8ztDV0uMDdqzHvwaK7mQqBjGTz4Up0N3evSkREpMar8jLS++67j/fee49PPvmEjRs3ctttt5Gampo/nfTaa69l4sSCXlR33303M2fO5OWXX2bTpk088cQTLF++nAkTJgAm0HbJJZewfPlypk6dSm5uLgkJCSQkJJCVZf5YXLRoEZMmTWLNmjXs2LGDqVOncu+993L11VfToIHrgxOnlfx+baWUkDo16Wua5Dq5qow0uBnUPykL5qTjOss3L2pzEeH1wst2vOYDTJPmlL1waFOll3dr11sB+HnHz8SnlNwY3mnSikmcyD5Bi6AW+Hn6sWD/Ah5f8DgOq/hP9+NT4pm8ejIAD/Z6kFC/fwSTq6iE9GRjY8bm3x/XeRytgluVuK2zlHR54nKSMmpOidDhE5nc/9UaAK7p16z0zKpTyMjJ4IN1HwBwS+db8Pbwdska3e2cTtF0bBjIicwc3pq3rcjzCckZ3P3FKiwLrujdhH+Pak/nRkHkOCx+XLO/YMPuV0P/O8393x6Gb2+BrDRmxZmstqExEXh6/OPXTXYGfH29CbKB+b5PTzJZSV9dCycOVcErllNylpDGnAde5Zu063bOoI6nr+k3N/I5k3lZ3IcS0V0LMnZmPaYAb2XkD0a4ruShSlGdC/q3/fGkApwlyUqDRebDOc68v+R/z+rW9YqC+w27m0mlIiIiUqoqD7ZdfvnlvPTSSzz22GN069aN1atXM3PmzPwhCPHx8Rw4cCB/+/79+zNt2jTeffddunbtytdff813331Hp06m/Gvfvn388MMP7N27l27duhEdHZ3/tXDhQsCUhH7xxRcMGjSIjh078uyzz3Lvvffy7rvl67NVJzn7tTUpZTiCk92jIPOhXij4nzrDsExstsLnz8tsW3doHYsPLMbD5pE/rKBMvPxMwA1ga+Ub+XcM68iZjc4k18rNLyssyZpDa/h++/cAPHPGM7w86GU8bB78uONHJq2YVGR7y7J4ctGTZOZm0i+6H+e3Ov+fG1RLsG14s+H0jupN3+i+3NS59DKWxgGNiQmJwWE5mBs/t8rWVB6WZfHg9DUcPpFJ28j6PDI6plLH+3rL1xxMP0iUfxQXtanhDePLwW638eBI8/31yaLdHEgu6CmYnevgzs9XciQ1i5joQJ443wwuubhHIwC+XbWv8MGGPw1nv2CCHOumY30wjHXrTbBzRId/tA1IS4JPLzAZMXYvuPh9mLAcBk80+2/8ASb3gfXfVtErl2LlZMGGGeZ+l6Jl47VCdBe4fZG5nmLvKL2J+1mPmGyrXX8XtC2Q8kmMM03zbR7Q45rSt+15vRmuZOWanqxS1IqPIfWQKX2uSWXcncaY/8dQtveHIiIiUj0DEiZMmMDu3bvJzMxkyZIl9O1b0Oth3rx5fPzxx4W2v/TSS9m8eTOZmZmsX7+ec845J/+55s2bY1lWsV+DBw8GoEePHixevJhjx46Rnp5OXFwcEydOxMendvdYqnIOx0mZbaX0azuZ8w+ypsWXBVdYk5MmNOYF295dZ4Klo1uOplH9RuU7nrO0ZZtrpmaO72p6t/24/Uf2Ht9b7Da5jlyeW/IcABe2vpAu4V04s/GZPHWGac790YaP+GTDJ4X2+W7bdyxNWIqvhy+P9XusaEP5Q5vhRILJ3KjCnileHl58OPJD3h/xfpmyuJylpLPia8ZU0o8X7mLu5kN4e9p57cru+HpVPDsgIyeDD9afflltToPahtOnRQhZOQ5eyysXBXjp980s23WU+j6evDm2R/6/4fldG+Jpt7F2bzJbE48XHMhmg77j4dofwD8cW+IG3k2/j+FeaxjY9qQM2KO7TC+tPYvBJwiumWEacHt6w+CH4Ja5ENk5L8vtBmW5Vadtf5jytfpR0GKgu1dTcSEtIaTFqbcLbmquWTDZbbk5pW8vRa3Iy2prfw4EFN+LN5/NBu3y3s8lrq/addVG2RmwwAwkYsB94OHl3vWcrH4EdMj78K9tLSovFxERcaNaPY1UXOzINsg4ZgI5kWVsJN9iIIz7E853cQ8WZyDJ7gkhLdlydAvz9szDho2bO99c/uM5hyTsXgSZJyq9vK7hXenfsD+5Vm6JvdtmbJtB3JE46nvV5+4ed+c/fn6r87m3570AvLT8JX7a8RNgeru9uPxFAO7odgdNApsUPagzq61pbI0q4xjazJSSLjmwhJSsFLeuZeOBFJ7/xZQLP3JODO2jKjfsZNqmaRxOP0xD/4Zc1Pr0yWpzstls/Csvu+2r5XvZcegEs+ISeedPM/jkhUu60CKsYBJuaH0fBrczvS+/Wbmv6AGbnwHj/+JAQGeCbGm84/EC9Ra9YoL5+1bC+8PgyFYIbAw3/VZ0yEd0F7hlDgx6yHz/x30Pb/aF/aur5PXLSZwlpJ3G1Jzytap25v2mzPTQpoIm/yWxLMgtvd9mnZKVCmu+MPd73Vi2faI6m9uEtSrd/adVn5kP0wIbQber3L2aoi5402SMNj/D3SsRERGpFRRskwLOEtKG3cv3iWrDblCvmObnldGoJ/S7w/Tc8fDi/bUmoDW82XBaBJUhY+GfQltBg+bgyIadf7lkic7ebd9v/579J/YXei45M5lXV5pPqO/odgdhHvVg9TQ4bnpY3dDxhvypn/+Z/x8W7lvI80ue53jWcWJCYri6w9XFn7QaSkgromVQS1oFtSLHkcOfe/502zrSs3K56/NVZOU6GNo+gmtjm1XqeHuP7+XtNW8DcFu32/CqSZkGLtSreQhntY8g12Hx2PcbuP+r1QBc378553SOLrL9mLxS0u9W7SPXUcwfzIENuc3rKabmDMWOBXOfgc8ugI9HmxKpyM5w8x8QUUJ5r6c3DJlogm4RHSHtCCyY5KJXK8XKSIHNv5r7XWpQ+VpV8wuGgQ+a+3OfMwGkf8pINoM/XusGT4fDpC4wZQz8+hAse9/8Tkk5UPeCR+u/hcwU87u1xeCy7RPe3pSOZyTDsVP3PK0zcrIKstrOuAc8a2Alhnc9CGvj7lWIiIjUGgq2SQFnsK2sJaRVyW6HUc9B3/HsTtnNb7t/A+CWLrdU7Hg2W0F2W2mlpLk5cGhLmf5o6h7Rnb5Rfclx5PDh+g8LPffGqjc4lnmM1sGtubzl+TDtMvjuNvj0fMjOwGaz8WDvBzm7xdnkWDncOedOft/9Ox42D57s/ySeds9i1pYNu+ab+zUs2AYwrJkp1Z21232lpM/+EsfWgycID/DhhUu6FC3DLQfLsnh68dOk56TTK7IXF7SqAVPhqtADI0x22/xth0nJyKFbk2AePqf4YNhZMREE+nqSkJLBou1Hijy//1g6q/en82juTRwf+YqZXrfzL8hOg5ZD4IZfILBoEK+I6K4w/ElzPzGuwq9NymDTT5CTAWFt696kwd43mwEdJxJMUM3p8Fb4+QF4OcYM/ji6C7Dg2G5TcrvkLfj5fvjkPPhfe3ihJWypQ73fluf93ut5g/mdXRae3hCRN7k8YV3VrKs2WvsFJO8xw6FO1ftOREREagUF26RAfr+2mtX89oN1H+CwHAxsPJD2Ie0rfiBn37atfxQfTNv2B7x9BkzuDT9MMGVvp+Ds3fbt1m9JSE0AYHPSZr7aYsqxJva8D6+vrjENuMGUKs17HgC7zc6zZzxLv+h+ZDnMJN3rOl5HTGgJ2T77VkLWcVPyFNWlrK+62jj7ti3cv5C07LRqP/+suESmLDaZEv+7rCuh9SuXGfDTjp9YuH8h3nZvHo99vFKBu9qgQ8NAzu/aEIDgel5MHtsDb8/if0X4eHpwXt62364s2rPQOYW0V7MGBMTeCDfMhIY9oM94GDsdfMtR2hvRwdwe2QY5meV4RVIua780t50vK32owOnI0weGPmbuL3gV1n9jMtfe6AXL3oPsVAiPgXMnwT3r4fpf4LxXIXYCtBlpesTZ7KbP4A93Fp8dd7rZvxr2rzRZat1LyMQuifP3l4JtRm4O/P2yud//rto3BVhERESKpWCbGJkn4OAGc79xb/eu5SQHThzgx+0/AqY5faW0ONNk2CTHm4wFp0NbYOql5o+rQ6bXF6umwMx/nzLDrXdUb3pG9iTbkc1H6z/CsiyeW/IcDsvBiKbD6DP3FVP66V0fBv7L7LTwNdhjsgi9PLyYNGQSQ5oMYUCjAdzW9baST+YsIW0xqOxZBNWobYO2NAloQmZuJn/tc02pbnm88+d2AG4e0IIz24RX6lhHM47y4jLTP2981/E0D2pe2eXVCo+eG8OlPRvzwXW9aBRc+h98Y3o2BuDX9QmkZhZuLP97nAk8508hbdwTxs2Fc14of9PvwIbgG2QmGB7eUr59pWyOJxSU13e+xL1rcZeOF5uAcNYJ+PpG8+ELeQ39r/3eTDjtdQMENzE9q3peDyOfhbFfwV2rYOK+guy4xW+5+9VUPedghA7ng39Y6dv+U37fNgXbAFj/tcmarBdqrjERERE5LdS8v9jFPfavBMthmpaXpbyrmny04SNyrBz6RPWhW0S3yh3M2x+a5TX23TYL0pLg13/DW7Gw9XfTjD12Aoz+H2CDpe/CH0+cMuDm7N329Zav+TTuU1YeXImfhy8P7NsJ22eDVz2TzXPWI9DlcvPv/N1tkJ0OgL+XP6+d9RpvDXsLX89Shh7szOuFVgNLSME02neWkv6x+49qPXdWjoO1+5IBuKpv00of78VlL3I08yitg1tzQ8e688dPRIAvL17alZ7NTt2DsXuTYFqE+ZOencuv6xPyH09Oy2bxjiQAhneIrPyibLaC7DaVklaN9d+Yn0uN+5RtiufpyG43PULtXuATaHqG3rUSrvzc/Mw9Vbafdz046z/m/oJXIbVoefVpIyMF1k4398s6GOFk+Zlta123ptrKkQt/vWTux04w71NERETktKBgmxhu6NeWkJrAf5f+l082fMLKxJVk5GQUev5w+mG+3fotUIlebf/UJq9v29J34fUesORtcOSY7IXbl5hMhd43wbn/M9stmFTwRrgEfaP60i28G1mOLF5abra92Qokettc8PSDq76CZv3NxqP+C/WjzDTGOc+Ufd2ZJ2DPUnO/hgbbAIY3Nf++f+39q8j/z6oUdyCFrBwHDep5FZqcWREL9y3kxx0/YsPGk/2fPG2HIlSWzWbj4u5mUMLJpaRzNx8k12HRNrI+zSv5/yKfM9h2UME2l7MsM7wFoMtl7l2LuzWLhXvXw/2bTM/QkJbl27/TGBNIykyBv0v/vVGrLX7TlNaGtS34AKs8ovKmnSfvMR961WVx35n3A77BpnegiIiInDYUbBMjv19b9ZWQ/m/5/5i6cSovLX+J62ZeR+y0WC778TKeWfwM32/7nsmrJ5OZm0mXsC70jerrmpM6hyQc3QXpR80f8dd8Z7IXwloXbNfrRpPlAGaS4slNs//BZrPlZ7cBNMab63YsB09fuOoLU77qVC8Ezn/N3F80GeIXl23d8YvMJNXgpjU686RTWCei/KNIz0ln4f6F1XbelbuPAtC9aYNK9VZLy07jqcVPAXBl+yvpEl7zeuPVJBfmBdsW7TjCvmMmU7NICakrRCrYVmXiF0HievPBQKcx7l6N+wVEVTy7yG4vGOix9L28gQqnmcS4gg+gBv27Yv39fINMyS2Ya6+ucjgK/i373Va+XpYiIiJS4ynYJiazwZnZ1qR6hiMcTj/MrHgztfKMhmcQ5hdGjpXDxqSNfLn5Sx5d8Chfb/kaMFltLmtOH9bGDEoIbATnvgLj/4ZWQ4rfNvYOGPKIuf/bw7D8oxIP279hf3pEdMcGTEzYi4/dB66YVnwWWtuR0O1qwDLlpFllGCbg7NdWg7PaIK+UtGn1l5KujDfBth5Ngyt1nLfWvMW+E/uI8o/irh53uWBlp7cmIfXo2yIEy4LvVu0jIzuXeZsPATCiowtKSJ1URlp1lr5rbrtcaj4MkMppdZaZuOvIhjnPuns1rpWbA9/fYV5bu3MqF5xV3zbY/LP5AME7APqOd/dqRERExMU83b0AqQES1kLqIfDwqbYplzO2ziDHkUOXsC68PfxtLMviQOoB1h5ey7pD61h3eB0bj2ykW0Q3BjUe5LoT22xw9Tdl337gg2ay3IJJ8NO9pv9a18vNcxnJcGAN7F+Fbf9q3ty3ikMn9tPcssPlU6H10JKPO/JZ2DEXknbA7Kfg7P+Wvo5aEmwDGNZsGFM2TmHennlk5Wbh7eFd5edcFX8MMJltFRV3JI5P4z4F4NG+j+Lvpd45ZTGmZ2OW7Ezi25V7iYkOIC0rl6hAXzo3CnLdSSLyJvSm7DXfd74uPHZtkptjyt69SuntWB4p+yHuB3O/j/7Yd5lhT8C7c2HdV9B/AkR3dfeKXGPxZNPf1SfI9DatzIdg0V1h009woIb0bcvNAY9qfEtsWfCXGcJD33FmyriIiIicVhRsE1g11dy2P8d1f8SVIteRy/QtprnyZe1MjyCbzUbD+g1pWL8ho5qPAsCyLNdltFWUzWb+cMpOMxkg390GG38wn0Yn7Si0qT/g7+kHl30EbUeUfly/YFNOOmUMLHkLYs6F5gOK3/bEwYJSmxYuDDxWke4R3Qn3C+dQ+iEW7V/EoCZVu+bElAz2HUvHboOuTYIrdIwcRw5PLHwCh+VgZPORVb7m08nZnaJ47Pv1bD+UyiuzzJTf4R0iXfu969fAZKOm7IODG6FpP9cdu7bYsxS+uQlysuDO5eATUPljLv/QTHltdkZBHy2pvIbdoPOlsG66GbJzzQx3r6jyDm+DuXmtFUY+W/lBSjUps+33/5g+dGO/LjnT3dWO7jQf1nl4m2EcIiIictpRsK2uy8k0n74DdL+6Wk45f998DqQeINA7kJHNR5a4ndsDbU42G4z6P1PuuXqK+TTeKbgpRHczf1xFd4NGPcr+CXXrYdDjOlj5iSnNuXUB+NQveD47HZL3QVzeH2pRncE/zEUvqurYbXZGNB/B1I1T+W3Xb1UeuFqVV0LaNjKA+j7F/0jLzM3kvbXvkZyZjKfdEw+bBx52DzztnnjaPIk/Hs/GpI0EeAfwUJ+HqnS9p5sAXy9Gdozi+9X7WZc3EdalJaROETEm2Ja4oW4F2xwOWPgqzH7aBMYAdi805eiVkZMJKz429/u4aACNFDjrUdjwHWyfA9vnVl8Qpyo4HPDDnZCTYUpkXfFewRlsO7wZsjOq5YO+Yi3/CBY6+6i+UX3/n5xBxogO4B9aPecUERGRaqVgW123+RczKCCwkXkTXQ2+2PwFABe1vghfTze9wS4vu91kokXEQG4mNOxugmuV7XE04hnzx9jRXfD5FSbj7dgeSN4LaYcLb1sLstqcRjQzwba5e+ZWeSnpyrwS0h7NSg5yfrT+I95Z+84pj/VArwcI86v5Ac2a5uIejfl+9X4AAnw96duiCv54jOgA2/4wmW11xYlDMGM8bJ9t/tuvgfl5vWt+5YNtG74z7QMCGkL7cyu9VPmHBs3NdMklb8Gsx6DFn+b3SG20/AOIXwhe/nDeq5UrH3UKbFRwPR/aZD6wqm67FsAvDxT89/Y5prQ6sGHVn9tZPhutITwiIiKnKwXb6jpnCWnXK8DuUeWn23t8Lwv2LQDg0naXVvn5XMruYfrvuJJvIJz/Onx2Iez6u+jzXv4Q3ARCW0PfW4s+X0N1i+hGhF8EB9MPsnD/QgY3GVxl53JOIu1RQr+25MxkPtnwCWACvKF+oeQ6csmxcshx5JDryCXXyqVpYFMuan1Rla3zdDagdRgRAT4cPJ7JkHYReHtWQVAhsqO5rSsTSXf+Dd/cDCcSzGTjs18wJWff3Qq7F1T++M7BCL1vBA+vyh9Pihr4AKyaYvqibvgWOl/i7hWV39HdMOtxc3/4k9CgmWuOa7OZHrE7/zT/PtUdbDu6G766xvRA7HgRpByAPYthzRdw5n1Vf35nZls19ckVERGR6qdgW12Wsr8gY6Lb2Go55fQt07Gw6N+wP80CXfSmvbZrNQQufh8S10FQEwhqXPDlG+yaLIJq5iwlnbJxCr/t+q3Kgm1ZOQ7W5pUuljSJ9JMNn3Ai+wRtGrThif5PYLfV0uySGszDbuP2wa34v5mbuSa2ir6vnUMSEjeY5uK18PuiTBy5pnH6n/8HlgPC2sGlH0NkBzgWb7bZvxoyTxQuOy+PfStg33ITvOtxvYsWLkX4h8GAu2HOM2YQTsx54Onj7lWVnWXBj3dDdio07Q+9bnLt8aM65wXbqrlvW+YJ+OIqSDtiBjVc8Cas/9oE21ZPgwH3Vv3Pl4S8zDYF20RERE5b+quzLlvzufljrml/CG1V5afLys1ixlbTf8w5GEHydLkUhj9leie1O9v8EeLXoNQ3/Dm5Dn5cs58bPlrKZ4t2Vd9ay8jZj2/unrlk5mZWyTniDqSQleMguJ4XLcKKTg89kn6EKRunADCh2wQF2qrQ9We0YOPTo+jdvJKl1SUJawc2D8g4BscTquYc7pZ5Aj69AOY9b342d7saxs01gTYwPSKDmprebXuWVPw8S/Ky2jpeDPXDK79uKVm/26F+FBzbbfqD1Sarp5qp2Z6+JgPb1WWwzkBTdQbbHA5Tmp24Hvwj4Ipp4F3PZLd51YMjW2Hvsqpdw4lDcPwAYCvI2BUREZHTjv7yrKssy5S3AHSveFZbWnYaTy96mtnxs0+57e+7f+do5lEi60UyqHHt6T9W06Rl5fDxgp0MeXked36+irmbD/HsLxtJzcxx99IK6RLehch6kaRmp7Jw38IqOYdzOEL3JsHFDtT4cP2HpOek0zG0I0Oa1OIG5WIaqDs/FDi4wb1rqSrLPzTl5F7+cNG7cOFk8P5HELlZf3O7u4LfUycOmZJGgL7jKr5WKRtvfxicN3Rl3vMmo7w2SDkAvz1s7g95GMJau/4czn5lCetNEKw6/PlfM+TIwxuumGoyyMFM940539xfPbVq1+DMagttVfHsVBEREanxFGyrq+IXQ9IO80ddhwsrfJhpm6bx1ZavuG/efacMuH212Uw9vaTtJXjaVcFcXoeOZ/Ly75vp/985PPFjHHuS0gnx9ybE35uMbAd/bEx09xILsdvsDG82HIDfdv9WJefIH45QTL+2xNREvtz8JQB3dr+z5ky3lYrLLyU9Tfu2rc2bDD3iaeh6efHbND/D3Fa0b9vKjyE3Cxr1NF9S9bpfYwbqZByDb24xpcI13cyHICPZDAPqd0fVnCO0DXj4QNZxOLqzas5xsg0zTHk2wLmToEmfws87P3hc/62ZPl5V8vu1da66c4iIiIjbKdhWV63Oy2rreFGFP1nNceTwxSYzWdRhOfjXn/9iWULx5Rebkzaz6uAqPG2ejGkzpkLnq6uSUrOY+O06zvi/Obw+ZxvH0rJpFlqPpy/sxIJ/n8XYvk0B+HHNATevtChnKem8PfOqpJQ0fzhCMZNI31v3Hpm5mfSI6EH/hv1dfm5xg4jTeEhCYpzp22j3Mj+XS9IsL9i2bwVkp5fvHLnZsOxDc7/P+IqtU8rPwxMu+RC868Pu+fD3y+5eUen2rYS47wAbnP+GWX9V8PAsKJGu6lLSA2tgxm3mfuyE4jP6mw0wpdqZKbDp56pbi/q1iYiI1AkKttVFmSdgvemdVpkS0jnxc0hMSyTEN4QhTYaQ5cjizjl3Enek6B/Czqy2s5qeRXg99Qgqj3u+XM3nS+PJynHQrUkwb43twZz7B3NNv2b4eXtwXteGAPy55SDJadluXm1hXcK7EOUfRWp2av4U2oqyLIu/9v7FmkNrADiYksG+Y+nYbdC1SXChbfce38s3W78BlNV2WnH+YX46BtvW5WW1tRkB9UrpexfS0vQAy82CvcvLd45NP8Px/eAfDh0vrPBSpQJCW8HovCDbvOdh9yL3rqc0c542t12vgKhOVXsuZ3ZXVQbbUg/D51dBTjq0GgrDnix+O7sdul5l7ldlKakmkYqIiNQJCrbVRXHfm+liIS2haWyFDzN1o3kzeknbS3hx0Iv0jupNanYqt/1xG7uSd+Vvl5qdyk87fgLg8nYllEad5tbuPUbs87P5cH75SmUWbjvMX1sO4eVhY9rNfZlxe3/O7hyNh70geNQ2MoB2kQFk51r8FlezGsfbbXZGNBsBwG+7Kl5KuuHwBq759RrumH0H1/xyDZ9s+IQVu5MA8/rr+xTOvHhn7TvkOHKIjY6lV1Svir8AqVki8oJthzbXjlK8snI4YO10c7/LKYbH2GwVLyVdmjcYoef1tWsq5umi6xXQ5Qoz/OKbmyEtyd0rKmrn37B9jsmwdPaaq0pVPSTBkQtf3wgpeyGkFVzyQemZet2uNLc75sGxPa5fT1YqHN5q7kcr2CYiInI6U7CtLnIORug2tsLj7Tce2cjKgyvxtHlyWdvL8PHw4bUhrxETEkNSRhLjZ40nMdX0EPtp+0+k5aTRIqgFvaN6u+pV1CrP/bKRA8kZ/PfXTew8nFqmfSzL4r8zNwEwtm8z+rcOKzFD67yu0QD8tLbmlZKOaG6CbfP2zCMjJ6Nc+x5OP8xjCx7jyp+vZM2hNXjaPbGweGn5S7wd918gh+7/6Ne2K3kXP2z/AYAJ3Se44iVITdGgOXj6QU6G6Tl5uohfaIIBPoHQdtSpt88fklCOYFvCerO9zQN63VixdUrljX7JfNCVshd+uNMMK6opLKsgq63ndeb7rapVdbBtzjOw808zafTyKWbKd2kaNIfmZwIWrP3C9etJjDPHrh8J9SNcf3wRERGpMRRsq2uObDd/2Nns0PXKCh9m2qZpAAxvNpxI/0gA6nvX561hb9EssBn7U/czftZ4jmUc44vN5g3r5e0ur5PlfIt3HGHxDpPBkJXr4PEfNmCV4Q+sX9YlsHZvMv7eHkw4q/RJcOd2MaWkC7Yd5sgJ1/dGq4wuYV2I9o8mLSetzKWk2bnZfLz+Y86dcS4zts3AwuLclucy8+KZ/Lv3v7Hb7OzImItf0w9o37DwNfXmmjdxWA4GNx5Ml3BlDpxW7B4Q0d7cP51KSZ2DETqcb6aunkqzAeZ2zzLIySrbOZxZbTHnQWDD8q9RXMMnwPRvs3uZqZjLP3D3igps/R32LDEB7YEPVs85IzsANlPenHrYtcfe9DPM/5+5f/7rBWXop9LNWUo6zfXBUPVrExERqTMUbKtrVpsgGa3OgqBGFTpEUkYSv+z4BYCrYq4q9FyoXyjvDn+XiHoRbE/ezlW/XMW2Y9vw8/TjvFbnVWrptdWrf5iSkSHtwvH2sPPXlkP8tqH0yaHZuQ5e+n0zALcMbElY/dJLvpqH+dO5URC5Dotf19esUlKbzVZQSlqGqaR/7/2bi3+4mJdXvExqdiodQjvw2dmf8fyZzxPpH8nVHa5m0qDXsXJ98fTfyWd7HmD7se0AbDm6hZk7ZwJwR/cqmqAn7uUcknC6TCTNycxrRg90KWOZfXg7qBdqelDtX3nq7dOPFgT0+mowgts17A7D8/qGzXwYEje4dz1gSpln52W19R0HAVHVc16fAJPpBwWBqJLsWwkL34CMlFMf98h2mHGrud/3Vuh8SdnXFHO+mdSetMNMbnel/GCbJpGKiIic7hRsq0scuQXBtm4VH4zwzZZvyHJk0TG0I13DuxZ5vmH9hrwz7B0CvQPZc9z0PDmnxTkEegdW+Jy11dKdSSzacQQvDxvPXNSZcQPNHxVP/xRHWlZOift9uWwPOw+nEurvzc1ntizTuZylpD+u2V/5hbvYyVNJSyolzXHk8MziZ7h99u3sStlFiG8IT/V/is9Hf063iG6Ftg2xdyFt122QHUpi2j6u/uVq/t77N2+ufhMLixHNRtA+pH0Vvypxi4gYc3uwBgQoXGHr75CRDIGNCjLWTsVmK18p6cpPTWAusnOl+nSKC/W9DVoPh9xMmH4DZKW5dz0bvjXTcH0C4Yx7qvfc0WUoJU0/ClMuht8fgbf6w/a5JW+blQpfXmOmijbpB8OfLt96fOoXTAR29aCE/OEICraJiIic7hRsq0t2zDWlGr7B0O6cCh0i25HNl5u/BExWW0lloa0btObNYW/i5+mHDVudHYzw6uwtAFzaqwmNgv24Y0hrGgX7se9YOpPnbit2n7SsHF6dbbLh7hrapkjz/5KMzislXboricSU8vVGq2qdwjrR0L8h6TnpzN83v8jzqdmp3DnnTr7c/CU2bFzb4Vp+uugnLmpzEXZb0R9TK+OP4siKpKvHf+gR0YMT2SeYMGcCs+NnY7fZuaObstpOW/kTSTe6dx2ustb8PKXTGDMNsaycgbldpwi25ebA0vfM/b7jK9ynU1zMbocL3zK9uw5vhpnVMIygJLnZMPdZc7//XaVPw60KZZlI+ucLJuAGkLwHPrsQfroXMo8X3s6y4Md7TDDePwIu/Rg8vcu/Jmcp6YYZJnjnCrk5BVmM0UU/qBQREZHTi4JtdcmqvE9ou1xWtr5AxZgTP4fEtERCfEMY1bz0Rt5dw7vy+ejP+WDkB8SExlTofLXZsl1JLNhmstpuH9wKAD9vDx47zwQL3v1rBzsOnSiy34fzd3LoeCZNQ+pxZZ+mZT5fo2A/ejVrgGXBzzVsUILNZssflPD7rt8LPZeQmsC1v17L/H3z8fXw5ZXBr/Bg7wcJ8A4o8Xgr448B0LdpU94f8T4Xtb4Ih+UAYHSL0bQMLls2oNRCzjLSpB2Qne7etVRW+lHYkldaXdYSUidnZtueJeaP+JJs/tkEJ+qFQudLK7ZOqRr1w+HidwEbrPwE5j5nyjldwZFrMhqnjDEBo9J6j62eZr6f6oVBv1tdc/7ycPYvO1BCGenhrQU9By+fAr1vMfeXf2iy3Hb+VbDtsvdh3VdmEMilH0FgdMXW1Ky/GZaQdQI2/lixY/zTkW1muIt3fWjQwjXHFBERkRpLwba6Ii3JNGOGSpWQTttoylAvbXsp3h6n/rS4VXCrOjuB1Nmr7ZKeTWjcoF7+4yM6RDKkXTjZuVaRYQlJqVm886eZsnj/iLZ4e5bvW/TcLnmlpGtrcCnp3nmk55ggSdyROMb+PJYtR7cQ6hvKR6M+Ymizoac81qp4k+HQvWkDvDy8eLL/kzzS9xGGNR3GPT3vqbLXIDVA/QjwCwHLAYc2uXs1lRP3A+RmmQBiVKfy7RvZEXyDTDAgYU3J2y1+29z2vKHCH7JIFWo5GM56xNz/8//gq2uKZmuVh2XB1lnw9gAz7XTbHzD9evjyakgp5kOY7AxzXoAz7zc91KqbM7PtyNbiy2l/fxQcOWZSb8x5ZqLrtT9AUFM4Fg+fnAe/PGhKS2dONPsMfxKal7Esuzg2W8F7JecE98py9muL7FS+LFYRERGplfTbvq5Y/435oy6yU4XLF+KOxLHy4Eo8bZ5c1u4yFy/w9LJidxLztx3G016Q1eZks9l44vyOeHva+XvrYWaeNNBg8txtHM/MoWPDQM7rUv6Jged0icZug1Xxx9iT5OYeQP/QMbQjjeo3yi8lnbdnHtfPvJ6D6QdpHdyaaaOn0Sns1AGHg8cz2Hs0HZsNujYJAsy/6RXtr+CVIa8QUS+iil+JuJXNZgJNUPtLSZ1DC7pUIOPM7gFNnX3bFha/zYE1Zvq03RN631yxNUrVG/ggXPAmeHibD8U+GAFJO8t/nANrTXnl1EvMtF7fYOh6lfn/v+knmNwXVnxSOMtt+YeQss/0DOx1o6teUfkERJmST8tR9Ht6+xzYMtO8hhHPFDzechDcvtAEkcFkvn12ITiyocMFEDuh8uvqeiVgg11/w9HdlT+ehiOIiIjUKQq21RW52eDXALpfXeGePc6stuHNhyugcQqT8rPaGtMkpF6R55uF+nPrIBOEeypvWMLeo2l8tsi8of/3qPbY7eX//xQR4Eu/lqEA/Lyu5paSvrLiFe6eezfpOenERsfy6dmf0rB+2YKLK3cfA6BdZAABvl5VtVypySLy+rbVhCmOFXVsD+zO619Y0fJOZylpSX3bnFltHS6seDmdVI/uY+H6X0wPt4Nx8N4Q2DGvbPsm7zWTN98ZaPbx8DbBprtWwUVvwfi/oFFPyEyGH+8ymWBHtpsMur9fMscY9G/3Zj7m9207qZQ0Nwd+y8v66zMOwtoU3scnAM6bBFd/C4GNzWNhbeGCya7pTRjcBFoMNPeXvV96KW5ZOMtknQMhRERE5LSmYFtdEXs73L8ZelxXod2PpB/hl52/ADA2puJlqHXBit1H+XuryWq7Y0jrEre7fXArGjfw40ByBq/P2cb/Zm0hK9dB/1ahnNkmrMLnP6+rCVrV5Kmke47vwWE5GNNmDJOHTc7vz5aYksGTP25g9Z5jJR7j5BJSqaPyJ5LGuXcdlbH+a3PbbAAENa7YMZqfYW7jF5oeXSc7cbDgHP1uq9jxpXo16Q3j5pnAWPpR+OxiWPxW0SBPbg4krDdZat/dAa/3hDWfA5YZtDFhGYx8tmDQQWRHuGkWjHwOvOqZTK23zoAvroK0IxDSqlLtJVyiuGDbyk/M97hfAxj0r5L3bT3UZLmd/zpc95NrS2G7X21uF74Gb/SGRZNNW47ysixNIhUREaljyjbmUE4Pnj4V3vWbrd+Q7cimU2gnuoTpU9nSOCeJjulRfFabk6+XB0+c15GbP13Oe3/tIDfvD6p/j2pf4pTXshjVMYr/fLeeDftT2H7oBK3C61f4WK7WIaQDrYNbs+3YNu7teS83dLyh0Gt94ocN/Lo+galL4nn50q75gcOTrcwLtvVoGlxdy5aa5nQoI80vIa1ESX5UV9NsPSPZBCVO/iN++UemdUCjXtC4V+XWKtUnsKHJcPvxblj7hZlSmrAO2gyHvcth30o4sBqy/9EmoGl/U2bZuGfxx7V7QOwdZhL5j3fDzj8LBguc9Qh4uPnt4D8nkqYfK5iQOvhhE3ArjW8Q9LjW9evqNMYEAJd/ZHrK/fYwzH4KOl5kym4b9y5bFl3KPkhPMuWw4XVvYJSIiEhdpMw2OaVsRzZfbvoSgKtirqpUIOh0tyr+KH9tOYTHKbLanIZ1iGRo+whyHBaWBaM7R9O1SXCl1tDA35sBeZlxP62peaWkH4z8gO8v+J4bO91Y6FradTiVmRtM/7qsHAd3fr6KyXO3FRogkZ3rYO3eZAB6NFNmW50V3t7cHj9QepZJ+lHY/Gvly79cLWG9CY55eJv+UhXl4QlN+pr7J5eS5mTB8g/MfWW11T5evnDR2yYTzWaH1VPNkINFb5gsxuw08A4wJY4D7oVrZsANv5QcaDtZSAu49ntTalkvzAxo6HBRVb+iU3P2kk3cYLI0/3rRZN2FtYNeN7hvXXYPE8S8fxOc+wpEdjYTRdd8Dh8MN4Mo1nxx6uM4g4hh7TSoREREpI5QZpuc0uzdszmYfpBQ39D8MkApXkFWWyOahpac1Xayx8/ryILth8l1WNw/oq1L1nFel4bM23yIH9fu566hrWtUgDTEN4QQ35Aij78/fweWBQPbhtMmoj4fzN/Ji79tZtfhVJ69qDPennY2HkghM8dBkJ8XLUL93bB6qRF8A80kwuR4E7QqbupgdgZ8fB4kroOL36tcBpmrrTUfXtB2JPgFV+5Yzc+A7bNh9wLod6t5bMMMOJEIAdGVC+aJ+9hsJhMtIgZ+/48J+jTqZUpMG/U0/cvsHhU/dverTemoZdWMyZghLU2Ja3aamaa65B3z+MjnwKMG9Ob0CTCZbD1vgH0rzGCJ9d9A4nqYMR7qhZrsw5KoX5uIiEido2CbnNKUjWbs/aXtLsXbw9vNq6m5Vu85xrzNJqttwpA2p94hT9PQenx/xwByHA5auqjkc3jHSLxn2Nl28ASbE4/TPirQJcetKkdOZDJ9+V4Abh3Ukv6twmgWWo8nftjA9BV72Xcsnbeu7snK3c5+bcEVGiAhp5HIDnnBto3FB9t+f8QE2gA2/1Jzgm2OXFiX10uty+WVP16zvL5tuxcWZPAtecvc9r6pZgQqpOJanQW3nVU1x7bZXDNIwBXsHqY8fO8y+O42M1W09XBoM8zdKyvMZjNl2Y17mb54v/7bBM///l/pwTZNIhUREalzasDHmVKTrT20ljWH1uBp9+Tydi74w/A0NnnuNgAu6l72rDandlEBdGwY5LK1BPp6MaRdOFAzByX802eLd5OZ46BzoyBi86apXhvbnA+u642/twcLtx/h4jcX8NuGRAB6aDiClDaRNO57Mz3Qace8ogME3GX3Aji+3/SYajOi8sdr2AM8fSHtMBzaDHuWwv5V4OFjsnBEagtnICo9CWweJphVk/k1gGFPmnLw+IUQv7jkbfOHIyizTUREpK5QsE1K5cxqO6fFOYT5VXxC5unueEY28zYfBGDcwJZuXo1xbhfnVNIDhfqe1TTpWbl8umg3YP7tTi55HdI+gum39icq0Jfth1JZtOMIoGCbUBBs++dE0qO74Ps7zf3YCeATZHq37V9dnasrmXMwQocLKzW0Jp+nt2nSDiaQ58xq63wp+OtnttQiJweiet8M4e3ct5ayCoyGrleY+/MnFb9N+jE4Zn7HEdWpOlYlIiIiNYCCbVKihNQEZu2aBcDYmLFuXk3NNm/zIbJzLVqG+9M2MsDdywFgaEwEfl4exCel8cWyPe5eTom+XrmXpNQsGjfw4+xOUUWe79AwkO/uOIOODU0prM0GXZu4LgtQaqlIZ7BtY0H5ZG42fH0TZCZD4z4w7AloOdA8t32OW5ZZiGXBtj/M/Y4ubErvLKNd9zXE/WDuO/u3idQWzqm5vsEw+CG3LqVc+t8N2GDLr5AYV/T5xPXmNrjpqaeqioiIyGlDwTYp0ZebvyTHyqFnZE86hHZw93JqtN/jTHnjiA5Fg0XuUs/bk1vysuwenrGuRpaT5jos3v97BwA3D2iBp0fxP5Kignz5anws18U245FzYgjwVR+qOi+0Ddg9ITMFkk2/P2Y/BfuWmxLNSz4w/cpa5fW7qgnBtiPbzQRVD29o2s91x3X2bYtfCFYuNBug3lBS+0R1hiummcmq9YoO0amxwlpDh/PN/QWvFn3eORxBJaQiIiJ1ioJtUqz0nHSmb5kOwDUdrnHzamq2zJxc5m4yJaQjO0a6eTWF3TusDVf2aYplwb1frmbOpkR3L6mQ3zcksPtIGkF+Xlzaq0mp2/r7ePLkBZ24+cyaUaYrbubpbQJuYEpJt86Cha+Z/77gTZNFAgXBtr1LISOl+td5sp1/mtvGfcDLz3XHbdzLBPCclNUmtVX70WZQQm1zxj3mdt10OLq78HP5/doUABcREalLFGyTYv24/UeSM5NpVL8RgxsPdvdyarTFO5I4kZlDRIAPXRsHu3s5hdhsNp65sBMXdGtIjsPi1ikrWbj9sLuXBYBlWbzzl8lqu6ZfM/x9NBxZyslZSrptNswYb+73GQ8x5xZs06A5hLQERw7s+rval1iI8/wtBrr2uF5+0KinuR/cFNqd49rji0jpGvWAloNNZumiNwo/l6DMNhERkbpIwTYpwmE58gcjjI0Zi4fdw80rqtl+25AAwPAOkdjttlNsXf087DZeurQrwztEkpXj4JZPlrMq/qi7l8Xy3UdZvecY3p52ruvf3N3LkdrIOSRh6TuQdsT8MTvi6aLbtRpqbl1VSmpZcHBT+SacWhbsmm/utzjTNes4Wacx5nbgg6Cf2SLVb8C95nblp3DikLmfkwmHNpn7ymwTERGpU6ol2DZ58mSaN2+Or68vffv2ZenSpaVuP336dNq3b4+vry+dO3fml19+KfS8ZVk89thjREdH4+fnx7Bhw9i6dWuhbZKSkhg7diyBgYEEBwdz0003ceLECZe/ttPRov2L2Jm8E38vfy5q7cIm3qchh8NilrNfW8ea06/tn7w87Lx+ZXcGtA4jNSuX6z9axsYD7i2pe+dPk9U2pkcjwgNcMJVR6p6Ik3pJeteHSz8ufsKnq/u2/fYwvNm3+P5MJTm0CVIPgedJWWiu1PtmeHA79LjW9ccWkVNrMQgadoecDPMBAJgBLo4cMxghqLF71yciIiLVqsqDbV9++SX33Xcfjz/+OCtXrqRr166MHDmSgwcPFrv9woULufLKK7nppptYtWoVF154IRdeeCHr16/P3+aFF17gtdde4+2332bJkiX4+/szcuRIMjIy8rcZO3YsGzZsYNasWfz000/89ddfjBs3rqpf7mnhs7jPALio9UXU967v5tXUbKv3HuPQ8UwCfDyJbRnq7uWUytfLg3ev7UnPZg1ITs/mmg+WsONQxQLQK+OP8sQPG7j/qzXcMXUlN3y0lMvfWcT5b8xn2P/+5MwX5nDX56tYvy+52P23HTzBHxsTsdlQDzapuKhOBffPnQShrYrfrvkAM0whaQck7azcOeO+h8Vvmvtrvij7fjv/MrdN+xYfEKwsmw38w1x/XBEpG5utILtt6buQebxwvzZbzct8FxERkapjsyzLqsoT9O3bl969e/PGG6aHhcPhoEmTJtx555089FDR0e6XX345qamp/PTTT/mP9evXj27duvH2229jWRYNGzbk/vvv54EHHgAgOTmZyMhIPv74Y6644go2btxIhw4dWLZsGb16mVHyM2fO5JxzzmHv3r00bNjwlOtOSUkhKCiI5ORkAgMDXfFPUStsP7adC7+/EBs2fr74Z5oElN60vq7776+bePvP7ZzftSGvXdnd3cspk+T0bK58dzFxB1JoGOTLJzf2oU1kQJn3fWHmJqYtjaesPzkGtA5j3MCWnNkmDFveHxsPfbOWL5btYXiHSN67tldFX4oILJpshgP0uaX07T4820zrHP0/6H1Txc6VtBPeGWgmoDrdsQzC25563y/GwqafYOhjcOb9FTu/iNRsjlyY3AeObIMRz8CxeBN4i50AI5919+pERESkksoTJ6rSjuRZWVmsWLGCiRMn5j9mt9sZNmwYixYtKnafRYsWcd999xV6bOTIkXz33XcA7Ny5k4SEBIYNG5b/fFBQEH379mXRokVcccUVLFq0iODg4PxAG8CwYcOw2+0sWbKEiy4qWhqZmZlJZmZm/n+npLh5ap2bTN04FYCzmp6lQFsZ/B5n+rWNqGFTSEsT5OfFZzf14bJ3FrH9UCojJv3FOZ2iuW1wKzo1Cip2H8uy+GntAZ78MY7DJ8z3yQXdGtI+KpB63h74eXng5+1h7nt7YFnw1fI9/LT2APO3HWb+tsPERAdy66CW9GkRwrcr9wEwbqCy2qSSYu8o23atzzLBtu1zKhZsy8mE6debQFuTvuDpa6aLbvoRwk8RPHM4YPcCc7+5i4cjiEjNYfeAM+6GH+40HwQERJvHNRxBRESkzqnSYNvhw4fJzc0lMrJwICIyMpJNmzYVu09CQkKx2yckJOQ/73ystG0iIiIKPe/p6UlISEj+Nv/0/PPP8+STT5bxlZ2ejmUc48ftPwJwdczVbl5Nzbft4HF2HErF28POoLbh7l5OuYTW92HaLf149Lv1zIpL5Od1B/h53QEGtQ3njiGt6dMiJH/bPUlpPPrdev7cYho+twz357mLOtPvFGWzZ7QO44ER7fhwwU6+WLqHjQdSuPuL1fh42snKddC9aTC9mjWo0tcpkq/VWTDnGVPOmZsDHuX89ff7f+DAatN76ZIPYessE2zb+OOpM9US10P6UdNXrmG3ir4CEakNulwOc5+H4/vh+AHzWLSCbSIiInWNppHmmThxIsnJyflfe/bscfeSXGrHsR08Mv8RViSuoKTK4a+3fk1GbgYxITH0jKyCBt6nmd82mMEI/VuHEuDr5ebVlF9koC/vXduL3+4ZyIXdGmK3wZ9bDnHZO4u49O2FzNmUyNt/bmf4K3/y55ZDeHvYuXdYW369+8xTBtqcmoTU4/HzOrJo4lk8MKItYfW9ycxxADB+YMv8slKRKhfdzQTKMlNg3/Ly7Rv3fUHD84veMY3O248GbLB/FSTvLX1/Z7+2Zv3Bo/b9rBCRcvD0KZxx6+kLoW3ctx4RERFxiyrNbAsLC8PDw4PExMRCjycmJhIVVfzkxqioqFK3d94mJiYSHR1daJtu3brlb/PPAQw5OTkkJSWVeF4fHx98fE7fiYjfbP2GH7b/wA/bf6B5YHPGtBnDea3OI9TPBE2yHdl8vvFzAK7pcI2CIGXwu3MKaYeaO4W0LNpFBTDpiu7cO7wt7/y1g6+X72XZrqMs+7ggIBHbMpRnLupEq/CKDcwIrufNhLPacPOZLfl+9T6ychyMrMHTW+U0ZPeAloNhwwxTStq0X9n2S9oJ308w98+4G9qONPfrR5hjxC+CTT9D3/ElH2PX3+a2+ZkVXr6I1CI9r4O/XoSMY2ZqcnkzaUVERKTWq9LMNm9vb3r27Mns2bPzH3M4HMyePZvY2Nhi94mNjS20PcCsWbPyt2/RogVRUVGFtklJSWHJkiX528TGxnLs2DFWrFiRv82cOXNwOBz07dvXZa+vNjmnxTlc3OZi/Dz92JWyi5dXvMyw6cO4b959zN83n5k7Z3Iw/SBhfmGMbD7S3cut8RKSM1iz5xg2GwzrEHHqHWqBZqGmPPTvfw9h3MCW1PP2oEE9L166tCvTbulb4UDbyXy9PLi8d1OuiW2ugK5Uv1ZDze32OWXb/p992s76T+Hn259rbjf+WPIxcnNg90Jzv4WCbSJ1gk8A9Lvd3G/W371rEREREbeo8o/a7rvvPq677jp69epFnz59mDRpEqmpqdxwww0AXHvttTRq1Ijnn38egLvvvptBgwbx8ssvM3r0aL744guWL1/Ou+++C4DNZuOee+7hmWeeoU2bNrRo0YL//Oc/NGzYkAsvvBCAmJgYRo0axS233MLbb79NdnY2EyZM4IorrijTJNLTUcewjjwZ9iT/6v0vZu6cybdbv2Xt4bXM2j2LWbtnYcMEPi5vdzneHt5uXm3NN2ujyWrr0bQBEQG+bl6Na0UG+vLwOTHcM6wNdpsNXy8Pdy9JxDVaDTG3+1aYHmp+p+gZ+M8+bf8sAY05F35/xAw/SD0C/sWUVyesMcE63yA1SRepSwY+CI16lj2LVkRERE4rVR5su/zyyzl06BCPPfYYCQkJdOvWjZkzZ+YPOIiPj8duL0iw69+/P9OmTePRRx/l4Ycfpk2bNnz33Xd06tQpf5t//etfpKamMm7cOI4dO8aAAQOYOXMmvr4FQY+pU6cyYcIEhg4dit1uZ8yYMbz22mtV/XJrPH8vf8a0HcOYtmPYnLSZGdtm8OP2H0nJSsHXw5dL217q7iXWCr9vyJtC2qH2TCEtr3reKnuR00xQYwhrB4c3w44/oeOFJW9bXJ+2f2rQHKI6Q8I62PwL9Lim6Db5/doGmFJWEakb7HZoM8zdqxARERE3sVkldcuv41JSUggKCiI5OZnAwEB3L6dKZeZmMn/ffKLqRdExrKO7l1PjJadn0/PpWeQ4LOY+MJgWYf7uXpKIlNWvD8GSt6DHdXB+CR/A7F0On5wP2ammT9vwp0o+3rz/g3nPQdtRcNWXRZ//7GLYPhtG/Rf63eaa1yAiIiIiItWuPHEiTSMVfDx8GNp0qAJtZTRv80FyHBZtIuor0CZS27R29m2bC8V91pQYB1PGmEBbyyFF+7T9U8x5BcfLPF74udxsiF9s7ms4goiIiIhInaFgm0g5/b4hbwppx9O3hFTktNWsP3h4Q3I8HNle+LmknfDZRWaCYOPecPmUon3a/ikiBkJaQm4mbJ1V+Ll9K03Qzi/ETCQUEREREZE6QcE2Oe0kpmRw1+ermLv5oMuPnZGdy7y8447oEOXy44tIFfP2L2hYvv2kydfHE+CzC+FEggmMXfUV+JRhAq/NVpDdtumnws85+7W1ONP0bxIRERERkTpB7/7ltGJZFvd/tYYf1uznv79sKvf+G/Ync8uny/nfrC2s25vMP1saLtp+hNSsXKICfenSOMhVyxaR6tTqLHO7fY65TUsyGW1Hd5mhB9fMgHohZT9e+7xg25bfISez4PFdecE2lZCKiIiIiNQpCrbJaWXK4t3M33YYgM2JxzmYklGu/V+bvZVZcYm8Nnsr570xn9jn5/DIjHXM3XSQjOxcfnNOIe0Yic1mc/n6RaQatMrr27bzb0g/CtMug4NxUD8Krv0eAsqZtdqop9k367iZcgom6LZnqbnfYqDr1i4iIiIiIjWegm11RFaOgw/n7yQzJ9fdS6kyuw6n8lxeNpu3p7m0nYG3ssjOdbBw2xEAzmgdSj1vDxJSMpi6JJ4bPl5Gj6dn8d3qfYBKSEVqtchO4B9u+qm9Pwz2LgO/BnDtdyazrbzsdog519zf+IO53bsMcjKgfiSEtXXVykVEREREpBZQsK2OeGPuNp76KY7zX1/A2r3H3L0cl8t1WDwwfQ3p2bn0axnCDWc0B8oXbFsVf4zjmTmE+Hvz2Y19Wfmf4Xx8Q2+u7teUqEBf0rJyych2EOTnRd+W5SgxE5GaxW43k0YBjmwDL38Y+7UZdlBR7fOCbZt/BUeuyZoDU0KqLFgRERERkTrF090LkOrRITqQsPrebE48zkVvLmT8wJbcPawNPp4e7l6aS3wwfwfLdx+lvo8nL17SlfikNN75cwfztx7GsqwylXz+teUQAANah2G32/C1ezC4XQSD20Xw9AUWG/anMH/bYXo0bYCXh+LUIrVa66Gw7iszmfTKadC4V+WO13wA+AZD2mGIX1x4OIKIiIiIiNQpCrbVEaM6RdGnRQhP/LCBH9bs581525kVl8iLl3alW5Ngdy+vUrYkHuel37YA8J9zY2gSUo/wAB98PO0cPJ7J1oMnaBsZcMrj/JkXbBvUNrzIczabjU6NgujUSEMRRE4LncbAke3QcpAJlFWWhxe0OxvWfG6CeHuXmcc1HEFEREREpM5Rek4dEuLvzWtXduftq3sSVt+HrQdPcPGbC/jvr5vIyK6dvdyycx3c99VqsnIdDGkXzmW9mgDg6+VBnxam1PPvracuJT18IpN1+5IBOLNtWNUtWERqBg8vOOsR1wTanGLyppKu/Awc2RDYGEJauu74IiIiIiJSKyjYVgeN6hTFrHsHcmG3hjgsePvP7Yx+7W9W7znm7qWV2+S521i/L4UgPy/+O6ZLoXLRM9uYoNn8rYdOeZz5eQG5DtGBRAT4Vs1iReT01uos8KoHVt6HFy3Ur01EREREpC5SsK2OauDvzaQruvPuNT0JD/Bh+6FUrnpvMfFH0ty9tDJbtzeZN+ZsA+DpCzsRGVg4SDagtSkHXbIziawcR6nHcvZrG1hMCamISJl4+ZlecE4qIRURERERqZMUbKvjRnQ0WW69mjUgLSuXf32zBofDcveyTikjO5f7vlpNjsNidOdozusSXWSb9lEBhPp7k5aVy6r4oyUey+Gw+Gtryf3aRETKLOb8gvsajiAiIiIiUicp2CYE1/Pm5cu64uflweIdSUxdGu/uJZ3S5Lnb2HrwBGH1fXj6wk7FThu1222c0TqvlHRbyX3b4g6kcPhEFv7eHvRs1qDK1iwidUDbUaZPW6uhENzU3asRERERERE3ULBNAGgW6s+/R7UD4PlfNrInqWaXk/6y7gAAj46OIcTfu8TtBuT1bSttSIIzqy22VSjenvqWEJFK8A2Eu1bBNd+6eyUiIiIiIuImiixIvmtjm9OneQhpWbn8+5u1WFbNLCdNy8phx+FUAPq3Di11W+eQhLV7j5Gcll3sNn9uVgmpiIiIiIiIiLiGgm2Sz2638cIlXfD1srNw+xGm1dBy0o0HjmNZEBHgc8rJodFBfrQK98dhwaIdRbPbTmTmsGK36eem4QgiIiIiIiIiUlkKtkkhzcP8+dfI9gA893PNLCeN258MQMeGgWXafkApfdsWbjtMjsOieWg9moX6u26RIiIiIiIiIlInKdgmRVzfvzm9mzcgNSuXh74tvZzUsiz2HUsnIzu32ta3fl8KAB0bBpVp+wFtTMba/GL6tjn7tSmrTURERERERERcQcE2KcKUk3bF18vOgm1H+HzpniLbnMjMYcri3Yx+bT5n/HcO//5mbbWtb8OB8mW29WsZgofdxq4jaYUy9SzL4s8t6tcmIiIiIiIiIq6jYJsUq0WYPw/mlZM++3Mce4+aINX6fclM/HYdfZ/9g0e/W0/cAZNlNnN9QrVkt2XnOtiScAKATo3KltkW4OtF9ybBQOFSUhN8S8fLw0a/lqUPWhARERERERERKQsF26RE1/dvTq9mppz09qkrueCN+Zz7+nw+XxpPalYuLcP8eXR0DBEBPmTmOPIHDVSlrYknyMp1EOjrSeMGfmXeb0DeVNKTS0n/ystq69UsBH8fT9cuVERERERERETqJAXbpEQeedNJfTztrN2bzJq9yXh52Diva0M+v6Ufs+8fxM1ntswfQPB3MT3RXG193nCEDg0DsdlsZd7vzLxg24Lth3E4TA+6/BLSdiohFRERERERERHXUDqPlKpleH1euKQLny3azbAOkVzSszFh9X0KbTOgTRjfrtrH/G2HgPZVup64/eUbjuDUpXEw9X08OZaWzYb9KbSNqs+i7UcAGNhGwTYRERERERERcQ0F2+SULujWiAu6NSrxeWdm24b9KSSlZhHi711la9mQl9nWqVHZhiM4eXnY6dcylD82JvL3tkOkZGSTnp1LeIAPMdEBVbFUEREREREREamDVEYqlRYR6EvbyPpYFizcXnWlpA6HVeHMNigoJZ2/9XB+v7aBbcLLVY4qIiIiIiIiIlIaBdvEJQa0NqWY86uwb9uuI6mkZuXi42mnZZh/ufd3DklYvusos+ISARjYNsylaxQRERERERGRuk3BNnEJZ9bY31sPY1lWlZxjQ15WW/voQDw9yn/ptgzzp2GQL1m5DnYcTsVmgzPVr01EREREREREXEjBNnGJPi1C8PKwse9YOruPpFXJOTbkl5CWr1+bk81m44zWBZlsXRoFVWl/ORERERERERGpexRsE5fw9/Gke9MGAPy9rWpKSfOHI1SgX5uTs5QUYFBbZbWJiIiIiIiIiGsp2CYuc2Ze1tiCKujbZllWpTPbgEKZbQMVbBMRERERERERF1OwTVzmjLyssYXbD5PrcG3ftoSUDJJSs/Cw22gXFVDh44TV9+HBke24LrYZPfIy8UREREREREREXMXT3QuQ00eXRkEE+HqSkpHD2r3H8stKXWHDPpPV1jq8Pr5eHpU61h1DWrtiSSIiIiIiIiIiRSizTVzG08NO/1ahACxwcd+2/BLSRhUvIRURERERERERqWoKtolLDcjrifa3i/u2rc8bjtCxEsMRRERERERERESqmoJt4lID2pihAyvjj5KameOy48a5YDiCiIiIiIiIiEhVU7BNXKp5aD0aBfuRnWuxdFeSS455NDWLfcfSAeigYJuIiIiIiIiI1GAKtolL2Wy2/FLS+S4qJY07YLLamoXWI9DXyyXHFBERERERERGpCgq2icsNaGOCba4akrB+n7Nfm7LaRERERERERKRmU7BNXO6M1mHYbLAp4TgHj2dU+nj5k0g1HEFEREREREREajgF28TlQvy987PQXJHdtiFvEqn6tYmIiIiIiIhITadgm1SJM/L7th2p1HHSsnLYcTgVUBmpiIiIiIiIiNR8VRZsS0pKYuzYsQQGBhIcHMxNN93EiRMnSt0nIyODO+64g9DQUOrXr8+YMWNITEzMf37NmjVceeWVNGnSBD8/P2JiYnj11VcLHWPevHnYbLYiXwkJCVXyOqV4Z7YOB2D+tkNYllXh42w8kIJlQUSADxEBvq5anoiIiIiIiIhIlfCsqgOPHTuWAwcOMGvWLLKzs7nhhhsYN24c06ZNK3Gfe++9l59//pnp06cTFBTEhAkTuPjii1mwYAEAK1asICIigilTptCkSRMWLlzIuHHj8PDwYMKECYWOtXnzZgIDCzKhIiIiquaFSrF6NW+Aj6edxJRMth08QZvIgAodp6Bfm7LaRERERERERKTmq5Jg28aNG5k5cybLli2jV69eALz++uucc845vPTSSzRs2LDIPsnJyXzwwQdMmzaNs846C4CPPvqImJgYFi9eTL9+/bjxxhsL7dOyZUsWLVrEt99+WyTYFhERQXBwcFW8PCkDXy8PejcPYf62w8zfdrjiwbZ9Go4gIiIiIiIiIrVHlZSRLlq0iODg4PxAG8CwYcOw2+0sWbKk2H1WrFhBdnY2w4YNy3+sffv2NG3alEWLFpV4ruTkZEJCQoo83q1bN6Kjoxk+fHh+ZlxpMjMzSUlJKfQllTOgjbNvW8WHJGw4YIYjKLNNRERERERERGqDKgm2JSQkFCnb9PT0JCQkpMTeaQkJCXh7exfJRouMjCxxn4ULF/Lll18ybty4/Meio6N5++23+eabb/jmm29o0qQJgwcPZuXKlaWu+fnnnycoKCj/q0mTJmV4pVKaAXlDEhbvOEJWjqPc+2flONiSYPr8dWqkzDYRERERERERqfnKFWx76KGHih0+cPLXpk2bqmqthaxfv54LLriAxx9/nBEjRuQ/3q5dO8aPH0/Pnj3p378/H374If379+eVV14p9XgTJ04kOTk5/2vPnj1V/RJOex2iAwmr70NqVi5Tl+wu9/5bDx4nK9dBoK8njRv4VcEKRURERERERERcq1w92+6//36uv/76Urdp2bIlUVFRHDx4sNDjOTk5JCUlERUVVex+UVFRZGVlcezYsULZbYmJiUX2iYuLY+jQoYwbN45HH330lOvu06cP8+fPL3UbHx8ffHx8TnksKTu73ca9w9vwyIz1vPTbZkZ1iiI6qOxBM+dwhA4NA7HZbFW1TBERERERERERlylXsC08PJzw8PBTbhcbG8uxY8dYsWIFPXv2BGDOnDk4HA769u1b7D49e/bEy8uL2bNnM2bMGMBMFI2Pjyc2NjZ/uw0bNnDWWWdx3XXX8eyzz5Zp3atXryY6OrpM24prXdm7Kd+s2MvK+GM8+UMcb1/Ts8z7xu3XcAQRERERERERqV2qpGdbTEwMo0aN4pZbbmHp0qUsWLCACRMmcMUVV+RPIt23bx/t27dn6dKlAAQFBXHTTTdx3333MXfuXFasWMENN9xAbGws/fr1A0zp6JAhQxgxYgT33XcfCQkJJCQkcOjQofxzT5o0ie+//55t27axfv167rnnHubMmcMdd9xRFS9VTsFut/HcxZ3xtNuYuSGBWXGJZd53w34zHKFTIw1HEBEREREREZHaoUqCbQBTp06lffv2DB06lHPOOYcBAwbw7rvv5j+fnZ3N5s2bSUtLy3/slVde4dxzz2XMmDEMHDiQqKgovv322/znv/76aw4dOsSUKVOIjo7O/+rdu3f+NllZWdx///107tyZQYMGsWbNGv744w+GDh1aVS9VTqF9VCA3n9kSgMe/X09qZs4p93E4LGW2iYiIiIiIiEitY7Msy3L3ImqilJQUgoKCSE5OJjBQmVWVlZ6Vy/BX/mTv0XRuObMFj4zuUOK2uQ6Lx39Yz5TF8dTz9mDt4yPw9KiyuLCIiIiIiIiISKnKEydSBEOqhZ+3B09f2AmADxfsYv2+5GK3y8jO5Y6pK5myOB6bDR47t4MCbSIiIiIiIiJSayiKIdVmSLsIRneJJtdh8ciMdeQ6CidVJqdnc92HS5m5IQFvDzuTr+rBFX2aumm1IiIiIiIiIiLlp2CbVKvHz+1AgI8na/YmM2Xx7vzHE1MyuPydRSzZmUSAjyef3NiHczprgqyIiIiIiIiI1C4Ktkm1igj05V9ntwfgxd82k5iSwfZDJ7j4zYVsSjhOeIAPX46PJbZVqJtXKiIiIiIiIiJSfp7uXoDUPWP7NOWbFXtZvecYd36+iq2Jxzmalk2LMH8+vbEPTULquXuJIiIiIiIiIiIVosw2qXZ2u43nLuqMh93G0p1JHE3LpmvjIL6+NVaBNhERERERERGp1RRsE7fo0DCQ2wa1AmBg23Cm3dKP0Po+bl6ViIiIiIiIiEjlqIxU3Ob+EW25uEcjmof6Y7fb3L0cEREREREREZFKU7BN3MZms9EyvL67lyEiIiIiIiIi4jIqIxUREREREREREXERBdtERERERERERERcRME2ERERERERERERF1GwTURERERERERExEUUbBMREREREREREXERBdtERERERERERERcRME2ERERERERERERF1GwTURERERERERExEUUbBMREREREREREXERBdtERERERERERERcRME2ERERERERERERF1GwTURERERERERExEUUbBMREREREREREXERBdtERERERERERERcxNPdC6ipLMsCICUlxc0rERERERERERERd3LGh5zxotIo2FaC48ePA9CkSRM3r0RERERERERERGqC48ePExQUVOo2NqssIbk6yOFwsH//fgICArDZbO5ejkukpKTQpEkT9uzZQ2BgoLuXI7WYriVxJV1P4kq6nsSVdD2JK+l6ElfRtSSupOup7CzL4vjx4zRs2BC7vfSubMpsK4Hdbqdx48buXkaVCAwM1DeRuISuJXElXU/iSrqexJV0PYkr6XoSV9G1JK6k66lsTpXR5qQBCSIiIiIiIiIiIi6iYJuIiIiIiIiIiIiLKNhWh/j4+PD444/j4+Pj7qVILadrSVxJ15O4kq4ncSVdT+JKup7EVXQtiSvpeqoaGpAgIiIiIiIiIiLiIspsExERERERERERcREF20RERERERERERFxEwTYREREREREREREXUbBNRERERERERETERRRsq4UmT55M8+bN8fX1pW/fvixdujT/uYyMDO644w5CQ0OpX78+Y8aMITExsdD+8fHxjB49mnr16hEREcGDDz5ITk5O/vMHDhzgqquuom3bttjtdu65557qemniBpW9npYtW8bQoUMJDg6mQYMGjBw5kjVr1hQ6xvXXX0/nzp3x9PTkwgsvrK6XJtWstGvp3XffZfDgwQQGBmKz2Th27Fixx/j555/p27cvfn5+NGjQoMj1ctddd9GzZ098fHzo1q1b1b0YcbuSrqekpCTuvPNO2rVrh5+fH02bNuWuu+4iOTm52OMcOXKExo0bF7nu9Luubint59P48eNp1aoVfn5+hIeHc8EFF7Bp06b859esWcOVV15JkyZN8PPzIyYmhldffbXQ8XU91S2lXU9OlmVx9tlnY7PZ+O677wo9p/ficrLKXk96Ly5OpV1LgwcPxmazFfq69dZbixzj448/pkuXLvj6+hIREcEdd9yR/5yupfJRsK2W+fLLL7nvvvt4/PHHWblyJV27dmXkyJEcPHgQgHvvvZcff/yR6dOn8+eff7J//34uvvji/P1zc3MZPXo0WVlZLFy4kE8++YSPP/6Yxx57LH+bzMxMwsPDefTRR+natWu1v0apPpW9nk6cOMGoUaNo2rQpS5YsYf78+QQEBDBy5Eiys7MBc835+flx1113MWzYMLe8Tql6p7qW0tLSGDVqFA8//HCJx/jmm2+45ppruOGGG1izZg0LFizgqquuKrLdjTfeyOWXX15lr0Xcr7Traf/+/ezfv5+XXnqJ9evX8/HHHzNz5kxuuummYo9100030aVLlyKP63dd3XGqn089e/bko48+YuPGjfz2229YlsWIESPIzc0FYMWKFURERDBlyhQ2bNjAI488wsSJE3njjTfyz6Hrqe441fXkNGnSJGw2W5H99V5cTlbZ60nvxcWpLNfSLbfcwoEDB/K/XnjhhULH+N///scjjzzCQw89xIYNG/jjjz8YOXJk/vO6lsrJklqlT58+1h133JH/37m5uVbDhg2t559/3jp27Jjl5eVlTZ8+Pf/5jRs3WoC1aNEiy7Is65dffrHsdruVkJCQv81bb71lBQYGWpmZmUXON2jQIOvuu++uuhckblXZ62nZsmUWYMXHx+dvs3btWguwtm7dWuR81113nXXBBRdU3QsStyntWjrZ3LlzLcA6evRoocezs7OtRo0aWe+//36Zzvf4449bXbt2reyypYYq6/Xk9NVXX1ne3t5WdnZ2ocfffPNNa9CgQdbs2bOLve6c9Lvu9Fbe62nNmjUWYG3btq3EY95+++3WkCFDin1O19PprSzX06pVq6xGjRpZBw4csABrxowZ+c/pvbicrLLXk96Li9OprqVT/SxJSkqy/Pz8rD/++KNM59O1dGrKbKtFsrKyWLFiRaEost1uZ9iwYSxatIgVK1aQnZ1d6Pn27dvTtGlTFi1aBMCiRYvo3LkzkZGR+duMHDmSlJQUNmzYUH0vRtzOFddTu3btCA0N5YMPPiArK4v09HQ++OADYmJiaN68eXW/JHGTU11LZbFy5Ur27duH3W6ne/fuREdHc/bZZ7N+/fqqWrbUUBW5npKTkwkMDMTT0zP/sbi4OJ566ik+/fRT7Ha93amryns9paam8tFHH9GiRQuaNGlS4nGTk5MJCQmpkjVLzVWW6yktLY2rrrqKyZMnExUVVeQYei8uTq64nvReXKDsv+umTp1KWFgYnTp1YuLEiaSlpeU/N2vWLBwOB/v27SMmJobGjRtz2WWXsWfPnmp9LacTvfusRQ4fPkxubm6hX84AkZGRJCQkkJCQgLe3N8HBwcU+D5CQkFDs/s7npO5wxfUUEBDAvHnzmDJlCn5+ftSvX5+ZM2fy66+/FvqjV05vp7qWymLHjh0APPHEEzz66KP89NNPNGjQgMGDB5OUlOTyNUvNVd7r6fDhwzz99NOMGzcu/7HMzEyuvPJKXnzxRZo2bVrla5aaq6zX05tvvkn9+vWpX78+v/76K7NmzcLb27vYYy5cuJAvv/yy0DUndUNZrqd7772X/v37c8EFFxR7DL0XFydXXE96Ly5QtmvpqquuYsqUKcydO5eJEyfy2WefcfXVV+dvu2PHDhwOB8899xyTJk3i66+/JikpieHDh5OVlVWtr+d0oWCbiFRYeno6N910E2eccQaLFy9mwYIFdOrUidGjR5Oenu7u5Ukt4nA4AHjkkUcYM2ZMfg8lm83G9OnT3bw6qalSUlIYPXo0HTp04Iknnsh/fOLEicTExBR6EylSmrFjx7Jq1Sr+/PNP2rZty2WXXUZGRkaR7davX88FF1zA448/zogRI9ywUqnJfvjhB+bMmcOkSZPcvRQ5DZTletJ7cSmrcePGMXLkSDp37szYsWP59NNPmTFjBtu3bwfMe/Hs7Gxee+01Ro4cSb9+/fj888/ZunUrc+fOdfPqaycF22qRsLAwPDw8ikyDTExMJCoqiqioKLKysopM+XM+DxAVFVXs/s7npO5wxfU0bdo0du3axUcffUTv3r3p168f06ZNY+fOnXz//ffV9VLEzU51LZVFdHQ0AB06dMh/zMfHh5YtWxIfH++6xUqNV9br6fjx44waNYqAgABmzJiBl5dX/nNz5sxh+vTpeHp64unpydChQ/OP/fjjj1fPC5EaoazXU1BQEG3atGHgwIF8/fXXbNq0iRkzZhTaJy4ujqFDhzJu3DgeffTRalm/1Cynup7mzJnD9u3bCQ4Ozv/5AzBmzBgGDx4M6L24FHDF9aT34gIVey/et29fALZt2wYU/148PDycsLAwvRevIAXbahFvb2969uzJ7Nmz8x9zOBzMnj2b2NhYevbsiZeXV6HnN2/eTHx8PLGxsQDExsaybt26QlNJZs2aRWBgYKFvLDn9ueJ6SktLw263F5qO5PxvZ6aSnP5OdS2VRc+ePfHx8WHz5s35j2VnZ7Nr1y6aNWvm8jVLzVWW6yklJYURI0bg7e3NDz/8gK+vb6FjfPPNN6xZs4bVq1ezevVq3n//fQD+/vvvQiPs5fRXkZ9PlmVhWRaZmZn5j23YsIEhQ4Zw3XXX8eyzz1b5uqVmOtX19NBDD7F27dr8nz2rV68G4JVXXuGjjz4C9F5cCrjietJ7cYGK/a5zXk/OINsZZ5wBUOi9eFJSEocPH9Z78Ypy94QGKZ8vvvjC8vHxsT7++GMrLi7OGjdunBUcHJw/0ejWW2+1mjZtas2ZM8davny5FRsba8XGxubvn5OTY3Xq1MkaMWKEtXr1amvmzJlWeHi4NXHixELnWbVqlbVq1SqrZ8+e1lVXXWWtWrXK2rBhQ7W+Vql6lb2eNm7caPn4+Fi33XabFRcXZ61fv966+uqrraCgIGv//v35223YsMFatWqVdd5551mDBw/Ov77k9HGqa+nAgQPWqlWrrPfee88CrL/++statWqVdeTIkfxj3H333VajRo2s3377zdq0aZN10003WREREVZSUlL+Nlu3brVWrVpljR8/3mrbtm3+tVTcBDepvUq7npKTk62+fftanTt3trZt22YdOHAg/ysnJ6fY45U0BVe/6+qG0q6n7du3W88995y1fPlya/fu3daCBQus8847zwoJCbESExMty7KsdevWWeHh4dbVV19d6Ho7ePBgofPoeqobTvX77p/4x/RIvReXk1X2etJ7cXEq7Vratm2b9dRTT1nLly+3du7caX3//fdWy5YtrYEDBxY6xgUXXGB17NjRWrBggbVu3Trr3HPPtTp06GBlZWXlb6NrqewUbKuFXn/9datp06aWt7e31adPH2vx4sX5z6Wnp1u333671aBBA6tevXrWRRddZB04cKDQ/rt27bLOPvtsy8/PzwoLC7Puv/9+Kzs7u9A2QJGvZs2aVcfLk2pW2evp999/t8444wwrKCjIatCggXXWWWdZixYtKrRNs2bNir2m5PRS2rX0+OOPF3sNfPTRR/nbZGVlWffff78VERFhBQQEWMOGDbPWr19f6ByDBg0q9jg7d+6splcp1aWk68kZOCvPdVBSsE2/6+qOkq6nffv2WWeffbYVERFheXl5WY0bN7auuuoqa9OmTfn7lvTz65/Xiq6nuqO033f/9M/giGXpvbgUVtnrSe/Fxamkayk+Pt4aOHCgFRISYvn4+FitW7e2HnzwQSs5ObnQ/snJydaNN95oBQcHWyEhIdZFF11kxcfHF9pG11LZ2SzLslyQICciIiIiIiIiIlLnqWebiIiIiIiIiIiIiyjYJiIiIiIiIiIi4iIKtomIiIiIiIiIiLiIgm0iIiIiIiIiIiIuomCbiIiIiIiIiIiIiyjYJiIiIiIiIiIi4iIKtomIiIiIiIiIiLiIgm0iIiIiIiIiIiIuomCbiIiIiIiIiIiIiyjYJiIiIlJHXX/99dhstiJfo0aNcvfSRERERGotT3cvQERERETcZ9SoUXz00UeFHvPx8Sl22+zsbLy8vKpjWSIiIiK1ljLbREREROowHx8foqKiCn01aNAAAJvNxltvvcX555+Pv78/zz77LLm5udx00020aNECPz8/2rVrx6uvvurmVyEiIiJScyizTURERERK9MQTT/Df//6XSZMm4enpicPhoHHjxkyfPp3Q0FAWLlzIuHHjiI6O5rLLLnP3ckVERETczmZZluXuRYiIiIhI9bv++uuZMmUKvr6+hR5/+OGHefjhh7HZbNxzzz288sorpR5nwoQJJCQk8PXXX1flckVERERqBWW2iYiIiNRhQ4YM4a233ir0WEhISP79Xr16Fdln8uTJfPjhh8THx5Oenk5WVhbdunWr6qWKiIiI1AoKtomIiIjUYf7+/rRu3brU50/2xRdf8MADD/Dyyy8TGxtLQEAAL774IkuWLKnqpYqIiIjUCgq2iYiIiEiZLViwgP79+3P77bfnP7Z9+3Y3rkhERESkZlGwTURERKQOy8zMJCEhodBjnp6ehIWFFbt9mzZt+PTTT/ntt99o0aIFn332GcuWLaNFixbVsVwRERGRGs/u7gWIiIiIiPvMnDmT6OjoQl8DBgwocfvx48dz8cUXc/nll9O3b1+OHDlSKMtNREREpK7TNFIREREREREREREXUWabiIiIiIiIiIiIiyjYJiIiIiIiIiIi4iIKtomIiIiIiIiIiLiIgm0iIiIiIiIiIiIuomCbiIiIiIiIiIiIiyjYJiIiIiIiIiIi4iIKtomIiIiIiIiIiLiIgm0iIiIiIiIiIiIuomCbiIiIiIiIiIiIiyjYJiIiIiIiIiIi4iIKtomIiIiIiIiIiLjI/wPJYFVyOeA5SgAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# install numerai-tools\n",
+        "!pip install -q --no-deps numerai-tools\n",
+        "\n",
+        "# import numerai_corr, you can read the source code here:\n",
+        "# https://github.com/numerai/numerai-tools/blob/master/numerai_tools/scoring.py\n",
+        "from numerai_tools.scoring import numerai_corr\n",
+        "import numpy as np\n",
+        "\n",
+        "# Compute the per-era correlation of each serenity feature to the target\n",
+        "per_era_corr = train.groupby(\"era\").apply(\n",
+        "    lambda d: numerai_corr(d[med_serenity_feats], d[\"target\"])\n",
+        ")\n",
+        "\n",
+        "# Flip sign for negative mean correlation since we only care about magnitude\n",
+        "per_era_corr *= np.sign(per_era_corr.mean())\n",
+        "\n",
+        "# Plot the per-era correlations\n",
+        "per_era_corr.cumsum().plot(\n",
+        "    title=\"Cumulative Absolute Value CORR of Features and the Target\",\n",
+        "    figsize=(15, 5),\n",
+        "    legend=False,\n",
+        "    xlabel=\"Era\"\n",
+        "  )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_tMfPJ4Gyu-Q"
+      },
+      "source": [
+        "Let's compute some summary performance metrics as we did in the previous notebook for our model predictions.\n",
+        "\n",
+        "Notice above that some features can perform extremely differently at different times over the validation period. To measure how much the performance of the feature changes over this period, we introduce a new metric:\n",
+        "\n",
+        "- `delta` is the absolute difference in `mean` correlation between the first and second half of the analysis period."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "88KzLZGCyu-R",
+        "outputId": "9dfa901f-4da3-4d03-a70a-751f08f05537"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                 mean       std    sharpe  \\\n",
+              "feature_glandered_unimproved_peafowl         0.000899  0.004457  0.201743   \n",
+              "feature_elusive_vapoury_accomplice           0.000619  0.006414  0.096574   \n",
+              "feature_unsystematized_subcardinal_malaysia  0.000334  0.007446  0.044894   \n",
+              "\n",
+              "                                             max_drawdown     delta  \n",
+              "feature_glandered_unimproved_peafowl            -0.024903  0.000329  \n",
+              "feature_elusive_vapoury_accomplice              -0.033173  0.000904  \n",
+              "feature_unsystematized_subcardinal_malaysia     -0.054091  0.000301  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-937416d0-06bb-49f6-a0e4-209e92d792d1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sharpe</th>\n",
+              "      <th>max_drawdown</th>\n",
+              "      <th>delta</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>feature_glandered_unimproved_peafowl</th>\n",
+              "      <td>0.000899</td>\n",
+              "      <td>0.004457</td>\n",
+              "      <td>0.201743</td>\n",
+              "      <td>-0.024903</td>\n",
+              "      <td>0.000329</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>feature_elusive_vapoury_accomplice</th>\n",
+              "      <td>0.000619</td>\n",
+              "      <td>0.006414</td>\n",
+              "      <td>0.096574</td>\n",
+              "      <td>-0.033173</td>\n",
+              "      <td>0.000904</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>feature_unsystematized_subcardinal_malaysia</th>\n",
+              "      <td>0.000334</td>\n",
+              "      <td>0.007446</td>\n",
+              "      <td>0.044894</td>\n",
+              "      <td>-0.054091</td>\n",
+              "      <td>0.000301</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-937416d0-06bb-49f6-a0e4-209e92d792d1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-937416d0-06bb-49f6-a0e4-209e92d792d1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-937416d0-06bb-49f6-a0e4-209e92d792d1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6218c080-5a0a-4dea-9a14-2e48e8cde338\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6218c080-5a0a-4dea-9a14-2e48e8cde338')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6218c080-5a0a-4dea-9a14-2e48e8cde338 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_5ac9f0f8-603c-40fc-a2ed-8624defe8ce0\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feature_metrics')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_5ac9f0f8-603c-40fc-a2ed-8624defe8ce0 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('feature_metrics');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "feature_metrics",
+              "summary": "{\n  \"name\": \"feature_metrics\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00028243417447662453,\n        \"min\": 0.0003342739840022657,\n        \"max\": 0.0008991334332094645,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0008991334332094645,\n          0.0006194497453649387,\n          0.0003342739840022657\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0015181950301400144,\n        \"min\": 0.004456826743448467,\n        \"max\": 0.00744579363504462,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.004456826743448467,\n          0.006414242300178635,\n          0.00744579363504462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07992994555831012,\n        \"min\": 0.044894339057284724,\n        \"max\": 0.20174296309166384,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.20174296309166384,\n          0.09657411060815199,\n          0.044894339057284724\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01504391826935727,\n        \"min\": -0.05409122670456076,\n        \"max\": -0.024903067833911087,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.024903067833911087,\n          -0.03317283173381311,\n          -0.05409122670456076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00034012680617547246,\n        \"min\": 0.00030122394260529854,\n        \"max\": 0.0009038638947642461,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.00032927198656627183,\n          0.0009038638947642461,\n          0.00030122394260529854\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "def metrics(corr):\n",
+        "    corr_mean = corr.mean()\n",
+        "    corr_std = corr.std(ddof=0)\n",
+        "    corr_sharpe = corr_mean / corr_std\n",
+        "    max_drawdown = -(corr.cumsum().expanding(min_periods=1).max() - corr.cumsum()).max()\n",
+        "\n",
+        "    eras = train.era.unique()\n",
+        "    halfway_era = len(eras)//2\n",
+        "    corr_mean_first_half = corr.loc[eras[:halfway_era]].mean()\n",
+        "    corr_mean_second_half = corr.loc[eras[halfway_era:]].mean()\n",
+        "    delta = abs(corr_mean_first_half - corr_mean_second_half)\n",
+        "\n",
+        "    return {\n",
+        "      \"mean\": corr_mean,\n",
+        "      \"std\": corr_std,\n",
+        "      \"sharpe\": corr_sharpe,\n",
+        "      \"max_drawdown\": max_drawdown,\n",
+        "      \"delta\": delta\n",
+        "    }\n",
+        "\n",
+        "# compute performance metrics for each feature\n",
+        "feature_metrics = [\n",
+        "    metrics(per_era_corr[feature_name])\n",
+        "    for feature_name in med_serenity_feats\n",
+        "]\n",
+        "\n",
+        "# convert to numeric DataFrame and sort\n",
+        "feature_metrics = (\n",
+        "    pd.DataFrame(feature_metrics, index=med_serenity_feats)\n",
+        "    .apply(pd.to_numeric)\n",
+        "    .sort_values(\"mean\", ascending=False)\n",
+        ")\n",
+        "\n",
+        "feature_metrics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "odM2_6-Hyu-R"
+      },
+      "source": [
+        "Looking at the summary visualizations below, the most obvious observation is that `mean` and `sharpe` seem strongly correlated. This should not be suprising given that `sharpe` is just `mean` divided by `std`.\n",
+        "\n",
+        "A more interesting obvservation is that `mean` does not seem to be strongly correlated with `std`, `max_drawdown`, or `delta`. This tells us very clearly that just because a feature has high `mean` does not mean that it is consistent or low risk.\n",
+        "\n",
+        "In the next section we more closely examine `std`, `max_drawdown`, and `delta` to better understand feature risk."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "id": "mqqdKda_yu-R",
+        "outputId": "7473f9b4-57b6-4988-94c8-7ed843248358"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<Axes: title={'center': 'mean'}>,\n",
+              "        <Axes: title={'center': 'std'}>,\n",
+              "        <Axes: title={'center': 'sharpe'}>],\n",
+              "       [<Axes: title={'center': 'max_drawdown'}>,\n",
+              "        <Axes: title={'center': 'delta'}>, <Axes: >]], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x600 with 6 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# plot the performance metrics of the features as bar charts sorted by mean\n",
+        "feature_metrics.sort_values(\"mean\", ascending=False).plot.bar(\n",
+        "    title=\"Performance Metrics of Features Sorted by Mean\",\n",
+        "    subplots=True,\n",
+        "    figsize=(15, 6),\n",
+        "    layout=(2, 3),\n",
+        "    sharex=False,\n",
+        "    xticks=[],\n",
+        "    snap=False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QgrZJXRnyu-S"
+      },
+      "source": [
+        "### Comparing feature risk\n",
+        "\n",
+        "Below is a performance comparison of the highest and lowest `std` features. Which one looks more risky to you and why?\n",
+        "\n",
+        "One might argue that the orange line looks more risky given its more sudden and violent reversals. Extrapolating forward, we may expect this volatility to continue out of sample."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 505
+        },
+        "id": "XVQe5lnAyu-S",
+        "outputId": "8d3f8818-ed7e-4f7a-bdc0-96540bbb71b2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fca1fbc0590>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# plot the per era correlation of the feature with the highest vs lowest std\n",
+        "per_era_corr[[feature_metrics[\"std\"].idxmin(), feature_metrics[\"std\"].idxmax()]].plot(\n",
+        "    figsize=(15, 5), title=\"Per-era Correlation of Features to the Target\", xlabel=\"Era\"\n",
+        ")\n",
+        "plt.legend([\"lowest std\", \"highest std\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uv6H5CnWyu-S"
+      },
+      "source": [
+        "Below is a comparison of the highest and lowest `delta` features. Which one looks more risky to you and why?\n",
+        "\n",
+        "One might argue that the orange line looks more risky given the complete reversal in performance between the first and second half, despite both ending up in a similar spot. Extraoploating forward, we may expect this feature to stop working completely out-of-sample."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 505
+        },
+        "id": "5hgFAmOOyu-S",
+        "outputId": "5d9b15a5-c027-4cdd-e349-93e39965b8f1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fc855dfdcd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# plot the cumulative per era correlation of the feature with the highest vs lowest delta\n",
+        "per_era_corr[[feature_metrics[\"delta\"].idxmin(), feature_metrics[\"delta\"].idxmax()]].cumsum().plot(\n",
+        "    figsize=(15, 5), title=\"Cumulative Correlation of Features to the Target\", xlabel=\"Era\"\n",
+        ")\n",
+        "plt.legend([\"lowest delta\", \"highest delta\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D6XD2knayu-S"
+      },
+      "source": [
+        "Below is a comparison of the highest and lowest `max_drawdown` features. Which one looks more risky to you and why?\n",
+        "\n",
+        "One might argue that the orange line is more risky given the huge drawdown in the middle, despite both ending up in a similar spot. Extrapolating forward, we may expect it to have another big drawdown out of sample."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 505
+        },
+        "id": "xlFsPKNzyu-T",
+        "outputId": "418e6341-930b-49c7-bb99-8303712d819f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fc855d5f690>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# plot the cumulative per era correlation of the feature with the highest vs lowest max_drawdown\n",
+        "per_era_corr[[feature_metrics[\"max_drawdown\"].idxmax(), feature_metrics[\"max_drawdown\"].idxmin()]].cumsum().plot(\n",
+        "    figsize=(15, 5), title=\"Cumulative Correlation of Features to the Target\", xlabel=\"Era\"\n",
+        ")\n",
+        "plt.legend([\"lowest max_drawdown\", \"highest max_drawdown\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AMBFbIvNyu-T"
+      },
+      "source": [
+        "The metrics analyzed above are only a few of many different ways you can quantify feature risk.\n",
+        "\n",
+        "What are some other ways you can think of?\n",
+        "\n",
+        "Think about this while we train a model on the entire small feature set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 180
+        },
+        "id": "0nm5VBXy4UBK",
+        "outputId": "fe8deacb-6e34-42ed-ba13-bd079fe06c01"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096787 seconds.\n",
+            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Total Bins 210\n",
+            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
+            "[LightGBM] [Info] Start training from score 0.500008\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000, num_leaves=15)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000, num_leaves=15)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('boosting_type',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">boosting_type&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('num_leaves',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">num_leaves&nbsp;</td>\n",
+              "            <td class=\"value\">15</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_depth',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_depth&nbsp;</td>\n",
+              "            <td class=\"value\">5</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('learning_rate',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+              "            <td class=\"value\">0.01</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_estimators',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+              "            <td class=\"value\">2000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
+              "            <td class=\"value\">200000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('objective',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">objective&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('class_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">class_weight&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_split_gain',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_child_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
+              "            <td class=\"value\">0.001</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_child_samples',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
+              "            <td class=\"value\">20</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample&nbsp;</td>\n",
+              "            <td class=\"value\">1.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample_freq',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
+              "            <td class=\"value\">0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
+              "            <td class=\"value\">0.1</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_alpha',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_lambda',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">random_state&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('importance_type',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">importance_type&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;split&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "import lightgbm as lgb\n",
+        "\n",
+        "model = lgb.LGBMRegressor(\n",
+        "    n_estimators=2000,\n",
+        "    learning_rate=0.01,\n",
+        "    max_depth=5,\n",
+        "    num_leaves=2**4-1,\n",
+        "    colsample_bytree=0.1\n",
+        ")\n",
+        "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
+        "# model = lgb.LGBMRegressor(\n",
+        "#     n_estimators=30_000,\n",
+        "#     learning_rate=0.001,\n",
+        "#     max_depth=10,\n",
+        "#     num_leaves=2**10,\n",
+        "#     colsample_bytree=0.1\n",
+        "#     min_data_in_leaf=10000,\n",
+        "# )\n",
+        "model.fit(\n",
+        "    train[small_features],\n",
+        "    train[\"target\"]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7DXXnuUPyu-T"
+      },
+      "source": [
+        "## 2. Feature Exposure\n",
+        "\n",
+        "`Feature exposure` is a measure of a model's exposure to the risk of individual features, given by the Pearson correlation between a model's predictions and each feature. Let's load up and predict on the validation data for our small feature set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1fZmZVFuyu-T",
+        "outputId": "c7a4fee8-e158-4184-91bb-7f77a07a9ccf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/validation.parquet: 3.45GB [01:16, 44.8MB/s]                            \n"
+          ]
+        }
+      ],
+      "source": [
+        "# Download validation data\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
+        "\n",
+        "# Load the validation data, filtering for data_type == \"validation\"\n",
+        "validation = pd.read_parquet(\n",
+        "    f\"{DATA_VERSION}/validation.parquet\",\n",
+        "    columns=[\"era\", \"data_type\", \"target\"] + small_features\n",
+        ")\n",
+        "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
+        "del validation[\"data_type\"]\n",
+        "\n",
+        "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n",
+        "# Comment out the line below to use all the data\n",
+        "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
+        "\n",
+        "# Embargo overlapping eras from training data\n",
+        "last_train_era = int(train[\"era\"].unique()[-1])\n",
+        "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
+        "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n",
+        "\n",
+        "# Generate predictions against the small feature set of the validation data\n",
+        "validation[\"prediction\"] = model.predict(validation[small_features])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "48t8e3Huyu-U"
+      },
+      "source": [
+        "### Visualizing feature exposures\n",
+        "\n",
+        "As seen in the chart below, our model seems to be consistently correlated to a few features. If these features suddenly reverse or stop working, then our model predictions will likely exhibit the same risky characteristics we saw above.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 280
+        },
+        "id": "mExyr3VSyu-U",
+        "outputId": "09689cdb-2349-4d75-c015-4e6b6ef1567b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-14-2475583133.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  feature_exposures = validation.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 0.98, 'Feature Exposures')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x1000 with 35 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Compute the Peason correlation of the predictions with each of the\n",
+        "# serenity features of the small feature set\n",
+        "feature_exposures = validation.groupby(\"era\").apply(\n",
+        "    lambda d: d[med_serenity_feats].corrwith(d[\"prediction\"])\n",
+        ")\n",
+        "\n",
+        "# Plot the feature exposures as bar charts\n",
+        "feature_exposures.plot.bar(\n",
+        "    title=\"Feature Exposures\",\n",
+        "    figsize=(16, 10),\n",
+        "    layout=(7,5),\n",
+        "    xticks=[],\n",
+        "    subplots=True,\n",
+        "    sharex=False,\n",
+        "    legend=False,\n",
+        "    snap=False\n",
+        ")\n",
+        "for ax in plt.gcf().axes:\n",
+        "    ax.set_xlabel(\"\")\n",
+        "    ax.title.set_fontsize(10)\n",
+        "plt.tight_layout(pad=1.5)\n",
+        "plt.gcf().suptitle(\"Feature Exposures\", fontsize=15)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b7d9TTlIyu-U"
+      },
+      "source": [
+        "### Max feature exposure\n",
+        "\n",
+        "When reviewing the visualizations above, the scale and consistency of exposure changes feature-to-feature.\n",
+        "\n",
+        "Can you think of a better way to visualize this?\n",
+        "\n",
+        "A more useful way to visualize the overall feature exposure of our model might be to look at the maximum feature exposure each era. This is a simple way for us to estimate the maximum exposure the model has to any one feature at any given time.\n",
+        "\n",
+        "Note that we are only measuring the feature exposures of the subset of features we chose to analyze."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 481
+        },
+        "id": "9_rmsQRSyu-U",
+        "outputId": "a03250c5-a4c2-4b8c-bc15-8f55647847bd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean of max feature exposure 0.05044495998668359\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot the max feature exposure per era\n",
+        "max_feature_exposure = feature_exposures.max(axis=1)\n",
+        "max_feature_exposure.plot(\n",
+        "  title=\"Max Feature Exposure\",\n",
+        "  kind=\"bar\",\n",
+        "  figsize=(10, 5),\n",
+        "  xticks=[],\n",
+        "  snap=False\n",
+        ")\n",
+        "# Mean max feature exposure across eras\n",
+        "print(\"Mean of max feature exposure\", max_feature_exposure.mean())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zQangXyGyu-U"
+      },
+      "source": [
+        "## 3. Feature Neutralization\n",
+        "\n",
+        "Clearly the model has some consistent exposure to the features on which it was trained.\n",
+        "\n",
+        "`Feature Neutralization` is a way to reduce these feature exposures.\n",
+        "\n",
+        "At a high level, neutralizing to a feature means removing the component of your predictions (or \"signal\") that is correlated with that feature, leaving only the residual unique component of the signal.\n",
+        "\n",
+        "Read these forum posts if you want to learn more about the math behind the feature neutralization:\n",
+        "- https://forum.numer.ai/t/model-diagnostics-feature-exposure/899\n",
+        "- https://forum.numer.ai/t/an-introduction-to-feature-neutralization-exposure/4955"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fBtfEPPLyu-V"
+      },
+      "source": [
+        "### Applying feature neutralization\n",
+        "\n",
+        "Let's apply feature neutralization to our predictions at different porportions and see how that impacts max feature exposure."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 618
+        },
+        "id": "rt2YbOPxyu-V",
+        "outputId": "cf6b400e-118f-4324-f17e-1f81034f1f57"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-16-2245945915.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-16-2245945915.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-16-2245945915.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-16-2245945915.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   era  target  prediction  neutralized_25  neutralized_50  \\\n",
+              "id                                                                           \n",
+              "n000c290e4364875  0579    0.50    0.495167        0.370184        0.245201   \n",
+              "n002a15bc5575bbb  0579    0.25    0.516067        0.390981        0.265896   \n",
+              "n00309caaa0f955e  0579    0.75    0.513778        0.388530        0.263283   \n",
+              "n0039cbdcf835708  0579    0.50    0.507834        0.382948        0.258063   \n",
+              "n004143458984f89  0579    0.50    0.484917        0.360013        0.235109   \n",
+              "...                ...     ...         ...             ...             ...   \n",
+              "nffc5b7319b4b998  1167    0.75    0.497589        0.372700        0.247811   \n",
+              "nffd7ad35b86d121  1167    0.50    0.509668        0.384632        0.259596   \n",
+              "nffdb1a3a768a420  1167    0.50    0.498573        0.373387        0.248200   \n",
+              "nffdc129924fae18  1167    0.50    0.493419        0.368194        0.242969   \n",
+              "nfff193e9bccc4f1  1167    0.25    0.494057        0.368843        0.243629   \n",
+              "\n",
+              "                  neutralized_75  neutralized_100  \n",
+              "id                                                 \n",
+              "n000c290e4364875        0.120218        -0.004765  \n",
+              "n002a15bc5575bbb        0.140810         0.015725  \n",
+              "n00309caaa0f955e        0.138035         0.012787  \n",
+              "n0039cbdcf835708        0.133178         0.008293  \n",
+              "n004143458984f89        0.110204        -0.014700  \n",
+              "...                          ...              ...  \n",
+              "nffc5b7319b4b998        0.122922        -0.001967  \n",
+              "nffd7ad35b86d121        0.134560         0.009524  \n",
+              "nffdb1a3a768a420        0.123014        -0.002172  \n",
+              "nffdc129924fae18        0.117744        -0.007481  \n",
+              "nfff193e9bccc4f1        0.118415        -0.006799  \n",
+              "\n",
+              "[916263 rows x 7 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c32c4c22-6da7-43ef-b1a3-5ccd18b56699\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>era</th>\n",
+              "      <th>target</th>\n",
+              "      <th>prediction</th>\n",
+              "      <th>neutralized_25</th>\n",
+              "      <th>neutralized_50</th>\n",
+              "      <th>neutralized_75</th>\n",
+              "      <th>neutralized_100</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n000c290e4364875</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.495167</td>\n",
+              "      <td>0.370184</td>\n",
+              "      <td>0.245201</td>\n",
+              "      <td>0.120218</td>\n",
+              "      <td>-0.004765</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002a15bc5575bbb</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.516067</td>\n",
+              "      <td>0.390981</td>\n",
+              "      <td>0.265896</td>\n",
+              "      <td>0.140810</td>\n",
+              "      <td>0.015725</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n00309caaa0f955e</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.513778</td>\n",
+              "      <td>0.388530</td>\n",
+              "      <td>0.263283</td>\n",
+              "      <td>0.138035</td>\n",
+              "      <td>0.012787</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0039cbdcf835708</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.507834</td>\n",
+              "      <td>0.382948</td>\n",
+              "      <td>0.258063</td>\n",
+              "      <td>0.133178</td>\n",
+              "      <td>0.008293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n004143458984f89</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.484917</td>\n",
+              "      <td>0.360013</td>\n",
+              "      <td>0.235109</td>\n",
+              "      <td>0.110204</td>\n",
+              "      <td>-0.014700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc5b7319b4b998</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.497589</td>\n",
+              "      <td>0.372700</td>\n",
+              "      <td>0.247811</td>\n",
+              "      <td>0.122922</td>\n",
+              "      <td>-0.001967</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd7ad35b86d121</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.509668</td>\n",
+              "      <td>0.384632</td>\n",
+              "      <td>0.259596</td>\n",
+              "      <td>0.134560</td>\n",
+              "      <td>0.009524</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdb1a3a768a420</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.498573</td>\n",
+              "      <td>0.373387</td>\n",
+              "      <td>0.248200</td>\n",
+              "      <td>0.123014</td>\n",
+              "      <td>-0.002172</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdc129924fae18</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.493419</td>\n",
+              "      <td>0.368194</td>\n",
+              "      <td>0.242969</td>\n",
+              "      <td>0.117744</td>\n",
+              "      <td>-0.007481</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff193e9bccc4f1</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.494057</td>\n",
+              "      <td>0.368843</td>\n",
+              "      <td>0.243629</td>\n",
+              "      <td>0.118415</td>\n",
+              "      <td>-0.006799</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>916263 rows × 7 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c32c4c22-6da7-43ef-b1a3-5ccd18b56699')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c32c4c22-6da7-43ef-b1a3-5ccd18b56699 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c32c4c22-6da7-43ef-b1a3-5ccd18b56699');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-7d524d59-8f5b-44ae-a6ac-b08fe67059dd\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7d524d59-8f5b-44ae-a6ac-b08fe67059dd')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-7d524d59-8f5b-44ae-a6ac-b08fe67059dd button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "# import neutralization from numerai-tools\n",
+        "from numerai_tools.scoring import neutralize\n",
+        "\n",
+        "# Neutralize predictions per-era against features at different proportions\n",
+        "proportions = [0.25, 0.5, 0.75, 1.0]\n",
+        "for proportion in proportions:\n",
+        "    neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+        "        lambda d: neutralize(\n",
+        "          d[[\"prediction\"]],\n",
+        "          d[med_serenity_feats],\n",
+        "          proportion=proportion\n",
+        "        )\n",
+        "    ).reset_index().set_index(\"id\")\n",
+        "    validation[f\"neutralized_{proportion*100:.0f}\"] = neutralized[\"prediction\"]\n",
+        "\n",
+        "# Align the neutralized predictions with the validation data\n",
+        "prediction_cols = [\"prediction\"] + [f for f in validation.columns if \"neutralized\" in f]\n",
+        "validation[[\"era\", \"target\"] + prediction_cols]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l0tMOf9Jyu-V"
+      },
+      "source": [
+        "We can see below that, as neutralization proportion reaches 1, feature exposure reaches 0."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 804
+        },
+        "id": "x-qSdjNQyu-V",
+        "outputId": "d246081c-ba90-4bb1-c431-b016fd534728"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-17-2597986011.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-17-2597986011.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-17-2597986011.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-17-2597986011.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-17-2597986011.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  validation.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "mean feature exposures:\n",
+            "prediction         0.056\n",
+            "neutralized_25     0.042\n",
+            "neutralized_50     0.028\n",
+            "neutralized_75     0.014\n",
+            "neutralized_100    0.000\n",
+            "dtype: float64\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Max Feature Exposures'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Compute max feature exposure for each set of predictions\n",
+        "max_feature_exposures = pd.concat([\n",
+        "    validation.groupby(\"era\").apply(\n",
+        "        lambda d: d[med_serenity_feats].corrwith(d[col]).abs().max()\n",
+        "    ).rename(col)\n",
+        "    for col in prediction_cols\n",
+        "], axis=1)\n",
+        "\n",
+        "# print mean feature exposure of each proportion\n",
+        "print('mean feature exposures:')\n",
+        "print(round(max_feature_exposures.mean(), 3))\n",
+        "\n",
+        "# Plot max feature exposures\n",
+        "max_feature_exposures.plot.bar(\n",
+        "  title=\"Max Feature Exposures\",\n",
+        "  figsize=(10, 5),\n",
+        "  xticks=[],\n",
+        "  snap=False\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GcgsFazTyu-W"
+      },
+      "source": [
+        "### Performance impact of neutralization\n",
+        "\n",
+        "Looking at the performance below, we see that there is a marginal performance improvement as we increase the porportion of neutralization applied, but the overall shape of the line remains largely the same.\n",
+        "\n",
+        "You might see below that sometimes the optimal neutralization proportion is not 1.0 over the validation period - seeming to imply that a small amount of feature exposure can sometimes be helpful. After completing this tutorial, continue experimenting with neutralizing at different proportions and analyze the tradeoff between reducing exposure and improving performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 614
+        },
+        "id": "zW4f961lyu-W",
+        "outputId": "af9d9c85-02c1-46aa-92ed-4e0b272d9148"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-18-1183669635.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  correlations = validation.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative Correlation of Neutralized Predictions'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# calculate per-era CORR for each set of predictions\n",
+        "correlations = validation.groupby(\"era\").apply(\n",
+        "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
+        ")\n",
+        "\n",
+        "# calculate the cumulative corr across eras for each neutralization proportion\n",
+        "cumulative_correlations = correlations.cumsum().sort_index()\n",
+        "\n",
+        "# Show the cumulative correlations\n",
+        "pd.DataFrame(cumulative_correlations).plot(\n",
+        "    title=\"Cumulative Correlation of Neutralized Predictions\",\n",
+        "    figsize=(10, 6),\n",
+        "    xticks=[]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2eAuFO8pyu-W"
+      },
+      "source": [
+        "Let's look at some other aggregate metrics like `mean`, `std`, `sharpe`, and `max_drawdown`.\n",
+        "\n",
+        "What kind of relationship do you see between neutralization proportion and overall performance?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "P3YxoLZByu-W",
+        "outputId": "c70954c0-e762-4aec-9f4d-27cf29323a5c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                    mean      std   sharpe  max_drawdown\n",
+              "prediction      0.017011 0.018569 0.916098      0.040911\n",
+              "neutralized_25  0.017012 0.018587 0.915301      0.041098\n",
+              "neutralized_50  0.017022 0.018579 0.916202      0.041238\n",
+              "neutralized_75  0.017005 0.018562 0.916151      0.041385\n",
+              "neutralized_100 0.017003 0.018548 0.916709      0.041532"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b234ba99-6228-4879-a237-178a60c27cb1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sharpe</th>\n",
+              "      <th>max_drawdown</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction</th>\n",
+              "      <td>0.017011</td>\n",
+              "      <td>0.018569</td>\n",
+              "      <td>0.916098</td>\n",
+              "      <td>0.040911</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_25</th>\n",
+              "      <td>0.017012</td>\n",
+              "      <td>0.018587</td>\n",
+              "      <td>0.915301</td>\n",
+              "      <td>0.041098</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_50</th>\n",
+              "      <td>0.017022</td>\n",
+              "      <td>0.018579</td>\n",
+              "      <td>0.916202</td>\n",
+              "      <td>0.041238</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_75</th>\n",
+              "      <td>0.017005</td>\n",
+              "      <td>0.018562</td>\n",
+              "      <td>0.916151</td>\n",
+              "      <td>0.041385</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_100</th>\n",
+              "      <td>0.017003</td>\n",
+              "      <td>0.018548</td>\n",
+              "      <td>0.916709</td>\n",
+              "      <td>0.041532</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b234ba99-6228-4879-a237-178a60c27cb1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b234ba99-6228-4879-a237-178a60c27cb1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b234ba99-6228-4879-a237-178a60c27cb1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-dd5937d6-37a0-4139-9bb2-e26680a3db49\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd5937d6-37a0-4139-9bb2-e26680a3db49')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-dd5937d6-37a0-4139-9bb2-e26680a3db49 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.3982623854293426e-06,\n        \"min\": 0.01700274755562367,\n        \"max\": 0.017021800310878205,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.017012323440644992,\n          0.01700274755562367,\n          0.017021800310878205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5127285713469576e-05,\n        \"min\": 0.018547590396466314,\n        \"max\": 0.01858659580978255,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01858659580978255,\n          0.018547590396466314,\n          0.018578647846191246\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005057005409990061,\n        \"min\": 0.9153006615493848,\n        \"max\": 0.9167092432051461,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9153006615493848,\n          0.9167092432051461,\n          0.9162023227846366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00024204444390256514,\n        \"min\": 0.04091138900274505,\n        \"max\": 0.04153225842367525,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.04109786186340458,\n          0.04153225842367525,\n          0.041238108151321784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "summary_metrics = {}\n",
+        "for col in prediction_cols:\n",
+        "    mean = correlations[col].mean()\n",
+        "    std = correlations[col].std(ddof=0)\n",
+        "    sharpe = mean / std\n",
+        "    rolling_max = cumulative_correlations[col].expanding(min_periods=1).max()\n",
+        "    max_drawdown = (rolling_max - cumulative_correlations[col]).max()\n",
+        "    summary_metrics[col] = {\n",
+        "        \"mean\": mean,\n",
+        "        \"std\": std,\n",
+        "        \"sharpe\": sharpe,\n",
+        "        \"max_drawdown\": max_drawdown,\n",
+        "    }\n",
+        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
+        "pd.DataFrame(summary_metrics).T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t1o7PnCLyu-b"
+      },
+      "source": [
+        "### Neutralizing different groups\n",
+        "Given that we trained our model on the entire `small` set of features, it is not surprising that neutralizing just a small subset of 34 features will have a small impact on performance. So let's re-run this experiment but this time try to neutralize the each group within `small` while holding porportion constant at 100%.\n",
+        "\n",
+        "As we can see in the performance chart below, neutralizing against the different groups gives a much more pronounced impact on performance, which makes sense since these groups are fundamentally different from one another."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 935
+        },
+        "id": "NKiNDWygyu-b",
+        "outputId": "3c04f392-2988-4921-a47f-f487bb88e785"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+            "/tmp/ipython-input-20-2089295116.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  group_neutral_corr = validation.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative Correlation of Neutralized Predictions'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# neutralize preds against each group\n",
+        "for group in groups:\n",
+        "    neutral_feature_subset = list(subgroups[\"small\"][group])\n",
+        "    neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
+        "        lambda d: neutralize(d[[\"prediction\"]], d[neutral_feature_subset])\n",
+        "    ).reset_index().set_index(\"id\")\n",
+        "    validation[f\"neutralized_{group}\"] = neutralized[\"prediction\"]\n",
+        "\n",
+        "group_neutral_cols = [\"prediction\"] + [f\"neutralized_{group}\" for group in groups]\n",
+        "group_neutral_corr = validation.groupby(\"era\").apply(\n",
+        "    lambda d: numerai_corr(d[group_neutral_cols], d[\"target\"])\n",
+        ")\n",
+        "group_neutral_cumsum = group_neutral_corr.cumsum()\n",
+        "\n",
+        "group_neutral_cumsum.plot(\n",
+        "  title=\"Cumulative Correlation of Neutralized Predictions\",\n",
+        "  figsize=(10, 6),\n",
+        "  xticks=[]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yDsU5ifkpykA"
+      },
+      "source": [
+        "We see that neutralizing against some groups help with CORR while others seem to hurt. Can you think of why this might be the case?\n",
+        "\n",
+        "Let's see if this same characteristic applies to MMC:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 631
+        },
+        "id": "76IGP1UGnzNW",
+        "outputId": "9e282da6-94fa-410e-c832-19d322aba7df"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v4.3/meta_model.parquet: 29.0MB [00:00, 39.6MB/s]                            \n",
+            "/tmp/ipython-input-21-13882294.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative MMC of Neutralized Predictions'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from numerai_tools.scoring import correlation_contribution\n",
+        "\n",
+        "# Download and join in the meta_model for the validation eras\n",
+        "napi.download_dataset(f\"v4.3/meta_model.parquet\", round_num=842)\n",
+        "validation[\"meta_model\"] = pd.read_parquet(\n",
+        "    f\"v4.3/meta_model.parquet\"\n",
+        ")[\"numerai_meta_model\"]\n",
+        "\n",
+        "# Compute the per-era mmc between our predictions, the meta model, and the target values\n",
+        "per_era_mmc = validation.dropna().groupby(\"era\").apply(\n",
+        "    lambda x: correlation_contribution(\n",
+        "        x[group_neutral_cols], x[\"meta_model\"], x[\"target\"]\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "cumsum_mmc = per_era_mmc.cumsum()\n",
+        "\n",
+        "cumsum_mmc.plot(\n",
+        "  title=\"Cumulative MMC of Neutralized Predictions\",\n",
+        "  figsize=(10, 6),\n",
+        "  xticks=[]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4eFNUepsyu-b"
+      },
+      "source": [
+        "Perhaps the most interesting observation is that neutralizing against `all` of the groups within `small` performs by far the worst in terms of `mean` and `sharpe` of CORR or MMC. Can you think of why this might be the case?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "YPJcnqWyyu-b",
+        "outputId": "67f1eca5-807e-40bf-c0c4-0b8e35a8708d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                          corr_mean  mmc_mean  corr_std  mmc_std  corr_sharpe  \\\n",
+              "prediction                 0.017011  0.006914  0.018632 0.016378     0.912998   \n",
+              "neutralized_intelligence   0.017220  0.006981  0.018583 0.016106     0.926692   \n",
+              "neutralized_wisdom         0.017109  0.006811  0.018323 0.015608     0.933731   \n",
+              "neutralized_charisma       0.016819  0.006577  0.018429 0.016334     0.912675   \n",
+              "neutralized_dexterity      0.016630  0.007240  0.018588 0.015867     0.894663   \n",
+              "neutralized_strength       0.017092  0.007191  0.018618 0.016378     0.918055   \n",
+              "neutralized_constitution   0.016807  0.006826  0.018464 0.016325     0.910256   \n",
+              "neutralized_agility        0.017176  0.006010  0.018581 0.016761     0.924417   \n",
+              "neutralized_serenity       0.017003  0.006763  0.018611 0.016328     0.913607   \n",
+              "neutralized_all            0.011036  0.000367  0.017061 0.015585     0.646881   \n",
+              "\n",
+              "                          mmc_sharpe  corr_max_drawdown  mmc_max_drawdown  \n",
+              "prediction                  0.422153           0.040911          1.949824  \n",
+              "neutralized_intelligence    0.433412           0.038507          1.944338  \n",
+              "neutralized_wisdom          0.436368           0.045409          1.955404  \n",
+              "neutralized_charisma        0.402639           0.034907          1.967068  \n",
+              "neutralized_dexterity       0.456288           0.043867          1.927256  \n",
+              "neutralized_strength        0.439084           0.040946          1.932065  \n",
+              "neutralized_constitution    0.418166           0.040313          1.958392  \n",
+              "neutralized_agility         0.358560           0.037780          2.007267  \n",
+              "neutralized_serenity        0.414211           0.041532          1.957557  \n",
+              "neutralized_all             0.023564           0.076985          2.339628  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-03c0524e-b23e-4bd8-8f84-7370f318245d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>corr_mean</th>\n",
+              "      <th>mmc_mean</th>\n",
+              "      <th>corr_std</th>\n",
+              "      <th>mmc_std</th>\n",
+              "      <th>corr_sharpe</th>\n",
+              "      <th>mmc_sharpe</th>\n",
+              "      <th>corr_max_drawdown</th>\n",
+              "      <th>mmc_max_drawdown</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction</th>\n",
+              "      <td>0.017011</td>\n",
+              "      <td>0.006914</td>\n",
+              "      <td>0.018632</td>\n",
+              "      <td>0.016378</td>\n",
+              "      <td>0.912998</td>\n",
+              "      <td>0.422153</td>\n",
+              "      <td>0.040911</td>\n",
+              "      <td>1.949824</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_intelligence</th>\n",
+              "      <td>0.017220</td>\n",
+              "      <td>0.006981</td>\n",
+              "      <td>0.018583</td>\n",
+              "      <td>0.016106</td>\n",
+              "      <td>0.926692</td>\n",
+              "      <td>0.433412</td>\n",
+              "      <td>0.038507</td>\n",
+              "      <td>1.944338</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_wisdom</th>\n",
+              "      <td>0.017109</td>\n",
+              "      <td>0.006811</td>\n",
+              "      <td>0.018323</td>\n",
+              "      <td>0.015608</td>\n",
+              "      <td>0.933731</td>\n",
+              "      <td>0.436368</td>\n",
+              "      <td>0.045409</td>\n",
+              "      <td>1.955404</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_charisma</th>\n",
+              "      <td>0.016819</td>\n",
+              "      <td>0.006577</td>\n",
+              "      <td>0.018429</td>\n",
+              "      <td>0.016334</td>\n",
+              "      <td>0.912675</td>\n",
+              "      <td>0.402639</td>\n",
+              "      <td>0.034907</td>\n",
+              "      <td>1.967068</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_dexterity</th>\n",
+              "      <td>0.016630</td>\n",
+              "      <td>0.007240</td>\n",
+              "      <td>0.018588</td>\n",
+              "      <td>0.015867</td>\n",
+              "      <td>0.894663</td>\n",
+              "      <td>0.456288</td>\n",
+              "      <td>0.043867</td>\n",
+              "      <td>1.927256</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_strength</th>\n",
+              "      <td>0.017092</td>\n",
+              "      <td>0.007191</td>\n",
+              "      <td>0.018618</td>\n",
+              "      <td>0.016378</td>\n",
+              "      <td>0.918055</td>\n",
+              "      <td>0.439084</td>\n",
+              "      <td>0.040946</td>\n",
+              "      <td>1.932065</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_constitution</th>\n",
+              "      <td>0.016807</td>\n",
+              "      <td>0.006826</td>\n",
+              "      <td>0.018464</td>\n",
+              "      <td>0.016325</td>\n",
+              "      <td>0.910256</td>\n",
+              "      <td>0.418166</td>\n",
+              "      <td>0.040313</td>\n",
+              "      <td>1.958392</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_agility</th>\n",
+              "      <td>0.017176</td>\n",
+              "      <td>0.006010</td>\n",
+              "      <td>0.018581</td>\n",
+              "      <td>0.016761</td>\n",
+              "      <td>0.924417</td>\n",
+              "      <td>0.358560</td>\n",
+              "      <td>0.037780</td>\n",
+              "      <td>2.007267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_serenity</th>\n",
+              "      <td>0.017003</td>\n",
+              "      <td>0.006763</td>\n",
+              "      <td>0.018611</td>\n",
+              "      <td>0.016328</td>\n",
+              "      <td>0.913607</td>\n",
+              "      <td>0.414211</td>\n",
+              "      <td>0.041532</td>\n",
+              "      <td>1.957557</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>neutralized_all</th>\n",
+              "      <td>0.011036</td>\n",
+              "      <td>0.000367</td>\n",
+              "      <td>0.017061</td>\n",
+              "      <td>0.015585</td>\n",
+              "      <td>0.646881</td>\n",
+              "      <td>0.023564</td>\n",
+              "      <td>0.076985</td>\n",
+              "      <td>2.339628</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03c0524e-b23e-4bd8-8f84-7370f318245d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-03c0524e-b23e-4bd8-8f84-7370f318245d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-03c0524e-b23e-4bd8-8f84-7370f318245d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5f30bfab-320b-4b2e-80a0-f5d1790306b4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f30bfab-320b-4b2e-80a0-f5d1790306b4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5f30bfab-320b-4b2e-80a0-f5d1790306b4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"corr_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018902414024967978,\n        \"min\": 0.011036178216675846,\n        \"max\": 0.01722027165108786,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.01700274755562367,\n          0.01722027165108786,\n          0.017092066231184002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mmc_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0020670841872304584,\n        \"min\": 0.0003672493214399956,\n        \"max\": 0.007240110853680583,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.006763250270212257,\n          0.006980560783520258,\n          0.007191370793629398\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"corr_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0004772920323001692,\n        \"min\": 0.017060606882669964,\n        \"max\": 0.01863158016168851,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.018610570511133878,\n          0.018582526476994255,\n          0.018617702843973804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mmc_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00037481709711927074,\n        \"min\": 0.015585494594304505,\n        \"max\": 0.016761121265777387,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.016328034433062324,\n          0.016106044549135876,\n          0.016378124567024782\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"corr_sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0858720233601104,\n        \"min\": 0.6468807524008019,\n        \"max\": 0.9337314008611669,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.9136070033668061,\n          0.9266916246510984,\n          0.9180545191006945\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mmc_sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1281327775437267,\n        \"min\": 0.0235635333365809,\n        \"max\": 0.45628808707736324,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.4142109264858899,\n          0.43341248450071995,\n          0.4390838990264053\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"corr_max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011925624610928501,\n        \"min\": 0.03490707585556874,\n        \"max\": 0.07698524426446984,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.04153225842367525,\n          0.03850726798518922,\n          0.04094637347650165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mmc_max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12344779297508088,\n        \"min\": 1.9272564647711312,\n        \"max\": 2.3396281567055666,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.957556603009172,\n          1.9443378520846206,\n          1.9320647139148264\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "group_neutral_summary_metrics = {}\n",
+        "for col in group_neutral_cols:\n",
+        "    corr_mean = group_neutral_corr[col].mean()\n",
+        "    corr_std = group_neutral_corr[col].std()\n",
+        "    corr_sharpe = corr_mean / corr_std\n",
+        "    corr_rolling_max = group_neutral_cumsum[col].expanding(min_periods=1).max()\n",
+        "    corr_max_drawdown = (corr_rolling_max - group_neutral_cumsum[col]).max()\n",
+        "    mmc_mean = per_era_mmc[col].mean()\n",
+        "    mmc_std = per_era_mmc[col].std()\n",
+        "    mmc_sharpe = mmc_mean / mmc_std\n",
+        "    mmc_rolling_max = cumsum_mmc[col].expanding(min_periods=1).max()\n",
+        "    mmc_max_drawdown = (rolling_max - cumsum_mmc[col]).max()\n",
+        "    group_neutral_summary_metrics[col] = {\n",
+        "        \"corr_mean\": corr_mean,\n",
+        "        \"mmc_mean\": mmc_mean,\n",
+        "        \"corr_std\": corr_std,\n",
+        "        \"mmc_std\": mmc_std,\n",
+        "        \"corr_sharpe\": corr_sharpe,\n",
+        "        \"mmc_sharpe\": mmc_sharpe,\n",
+        "        \"corr_max_drawdown\": corr_max_drawdown,\n",
+        "        \"mmc_max_drawdown\": mmc_max_drawdown,\n",
+        "    }\n",
+        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
+        "pd.DataFrame(group_neutral_summary_metrics).T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GjDZ4rP6yu-c"
+      },
+      "source": [
+        "**Based** on our simple analysis above, it seems like neutralizing `serenity` group within `small` at porportion of 1 may be the best choice for performance. What do you think?\n",
+        "\n",
+        "In your research, you may want to experiment with neutralizing different subsets of features at different porportions and make your own judgement on how to balance the risk reward benefits of neutralization. You may even consider incorporating neutralization into the objective function of your training instead of applying it to predictions like we do here.\n",
+        "\n",
+        "Lastly, whether you want to apply feature neutralization to your model or not is completely up to you. In fact, many great performing models have no feature neutralization at all!   \n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_mqza1fTyu-c"
+      },
+      "source": [
+        "## 4. Building a feature-neutral model\n",
+        "\n",
+        "To wrap up this notebook, let's build and upload our new feature neutral model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "ds3HPim6M-s5"
+      },
+      "outputs": [],
+      "source": [
+        "# We copy this neutralization code here because Numerai's model upload framework\n",
+        "# does not currently include numerai-tools\n",
+        "\n",
+        "def neutralize(\n",
+        "    df: pd.DataFrame,\n",
+        "    neutralizers: np.ndarray,\n",
+        "    proportion: float = 1.0,\n",
+        ") -> pd.DataFrame:\n",
+        "    \"\"\"Neutralize each column of a given DataFrame by each feature in a given\n",
+        "    neutralizers DataFrame. Neutralization uses least-squares regression to\n",
+        "    find the orthogonal projection of each column onto the neutralizers, then\n",
+        "    subtracts the result from the original predictions.\n",
+        "\n",
+        "    Arguments:\n",
+        "        df: pd.DataFrame - the data with columns to neutralize\n",
+        "        neutralizers: pd.DataFrame - the neutralizer data with features as columns\n",
+        "        proportion: float - the degree to which neutralization occurs\n",
+        "\n",
+        "    Returns:\n",
+        "        pd.DataFrame - the neutralized data\n",
+        "    \"\"\"\n",
+        "    assert not neutralizers.isna().any().any(), \"Neutralizers contain NaNs\"\n",
+        "    assert len(df.index) == len(neutralizers.index), \"Indices don't match\"\n",
+        "    assert (df.index == neutralizers.index).all(), \"Indices don't match\"\n",
+        "    df[df.columns[df.std() == 0]] = np.nan\n",
+        "    df_arr = df.values\n",
+        "    neutralizer_arr = neutralizers.values\n",
+        "    neutralizer_arr = np.hstack(\n",
+        "        # add a column of 1s to the neutralizer array in case neutralizer_arr is a single column\n",
+        "        (neutralizer_arr, np.array([1] * len(neutralizer_arr)).reshape(-1, 1))\n",
+        "    )\n",
+        "    inverse_neutralizers = np.linalg.pinv(neutralizer_arr, rcond=1e-6)\n",
+        "    adjustments = proportion * neutralizer_arr.dot(inverse_neutralizers.dot(df_arr))\n",
+        "    neutral = df_arr - adjustments\n",
+        "    return pd.DataFrame(neutral, index=df.index, columns=df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "PMGoxkbuyu-c"
+      },
+      "outputs": [],
+      "source": [
+        "def predict_neutral(live_features: pd.DataFrame) -> pd.DataFrame:\n",
+        "    # make predictions using all features\n",
+        "    predictions = pd.DataFrame(\n",
+        "        model.predict(live_features[small_features]),\n",
+        "        index=live_features.index,\n",
+        "        columns=[\"prediction\"]\n",
+        "    )\n",
+        "    # neutralize predictions to a subset of features\n",
+        "    neutralized = neutralize(predictions, live_features[med_serenity_feats])\n",
+        "    return neutralized.rank(pct=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 473
+        },
+        "id": "JfJd0vL4yu-c",
+        "outputId": "7f47c616-27ad-43b6-98fe-19a54eadd3b2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/live.parquet: 8.27MB [00:00, 19.7MB/s]                            \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  prediction\n",
+              "id                          \n",
+              "n001ba451c4cf24f    0.895136\n",
+              "n00208b1df989b47    0.345679\n",
+              "n002115a1e41ac5c    0.100997\n",
+              "n0021e1e026d7e47    0.325004\n",
+              "n002ddf4912dda8d    0.739253\n",
+              "...                      ...\n",
+              "nffcf4d74ac07190    0.656255\n",
+              "nffd30a4ec0c8662    0.327979\n",
+              "nffe131b7e72bc81    0.153205\n",
+              "nfff66b587bd248f    0.737171\n",
+              "nfff8eb83b5e7585    0.728395\n",
+              "\n",
+              "[6723 rows x 1 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-edfebdd1-fe16-4503-81a3-67c4963f6d30\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>prediction</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n001ba451c4cf24f</th>\n",
+              "      <td>0.895136</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n00208b1df989b47</th>\n",
+              "      <td>0.345679</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002115a1e41ac5c</th>\n",
+              "      <td>0.100997</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0021e1e026d7e47</th>\n",
+              "      <td>0.325004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002ddf4912dda8d</th>\n",
+              "      <td>0.739253</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffcf4d74ac07190</th>\n",
+              "      <td>0.656255</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd30a4ec0c8662</th>\n",
+              "      <td>0.327979</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffe131b7e72bc81</th>\n",
+              "      <td>0.153205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff66b587bd248f</th>\n",
+              "      <td>0.737171</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff8eb83b5e7585</th>\n",
+              "      <td>0.728395</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>6723 rows × 1 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edfebdd1-fe16-4503-81a3-67c4963f6d30')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-edfebdd1-fe16-4503-81a3-67c4963f6d30 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-edfebdd1-fe16-4503-81a3-67c4963f6d30');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-8a20e05f-c607-47be-ba54-f40aa92cb1e7\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a20e05f-c607-47be-ba54-f40aa92cb1e7')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8a20e05f-c607-47be-ba54-f40aa92cb1e7 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"predict_neutral(live_features)\",\n  \"rows\": 6723,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          \"nd9351550a167617\",\n          \"n83f3a823c3d3ce0\",\n          \"n998459b1b948928\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28869660301670663,\n        \"min\": 0.00014874312063067083,\n        \"max\": 1.0,\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          0.9525509445188161,\n          0.6900193366056819,\n          0.286925479696564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "# Quick test\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
+        "live_features = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\", columns=small_features)\n",
+        "predict_neutral(live_features)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "g5A5r1ZFyu-c"
+      },
+      "outputs": [],
+      "source": [
+        "# Use the cloudpickle library to serialize your function and its dependencies\n",
+        "import cloudpickle\n",
+        "p = cloudpickle.dumps(predict_neutral)\n",
+        "with open(\"feature_neutralization.pkl\", \"wb\") as f:\n",
+        "    f.write(p)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "J813OXJ6yu-d",
+        "outputId": "dd687ffc-2dc4-41f3-cf61-764862809432"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_626ff704-5abd-4196-b403-196b9b7dbda8\", \"feature_neutralization.pkl\", 3317855)"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Download file if running in Google Colab\n",
+        "try:\n",
+        "    from google.colab import files\n",
+        "    files.download('feature_neutralization.pkl')\n",
+        "except:\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wqBNYzHpyu-d"
+      },
+      "source": [
+        "That's it! Now head back to [numer.ai](numer.ai) to upload your model!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "V28"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    },
+    "orig_nbformat": 4,
+    "accelerator": "TPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/numerai/hello_numerai.ipynb b/numerai/hello_numerai.ipynb
new file mode 100644
index 0000000..e83e6ce
--- /dev/null
+++ b/numerai/hello_numerai.ipynb
@@ -0,0 +1,3700 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "suBex3vnr5GO"
+      },
+      "source": [
+        "# Hello, Numerai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k7VRkpptr5GR"
+      },
+      "source": [
+        "Hello and welcome to the Numerai Data Science Tournament!\n",
+        "\n",
+        "This notebook is designed to help you build your first machine learning model and start competing the tournament.\n",
+        "\n",
+        "In this notebook we will\n",
+        "1. Download and explore the Numerai dataset\n",
+        "2. Train and evaluate your first machine learning model\n",
+        "3. Deploy your model to start making live submissions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python --version"
+      ],
+      "metadata": {
+        "id": "gRGkuracAkoj",
+        "outputId": "912511ae-5456-4faa-8fdb-1f731b077c3e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Python 3.11.13\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "THMEU_T4r5GS"
+      },
+      "outputs": [],
+      "source": [
+        "# Install dependencies\n",
+        "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
+        "\n",
+        "# Inline plots\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YoeLNZTSr5GT"
+      },
+      "source": [
+        "## 1. Dataset  \n",
+        "\n",
+        "At a high level, the Numerai dataset is a tabular dataset that describes the stock market over time. It is compiled from high-quality (and expensive) data that might be difficult for individuals to obtain.\n",
+        "\n",
+        "The unique thing about Numerai's dataset is that it is `obfuscated`, which means that the underlying stock ids, feature names, and target definitions are anonymized. This makes it so that we can give this data out for free and so that it can be modeled without any financial domain knowledge (or bias!)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4ECU5uszr5GU"
+      },
+      "source": [
+        "### Listing the datasets\n",
+        "Firstly, take a look at the files Numerai offers below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4B4bbH07r5GU",
+        "outputId": "5a959b9d-4edc-4660-df06-27e633ceb48d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Available versions:\n",
+            " ['v5.0']\n",
+            "Available v5.0 files:\n",
+            " ['v5.0/features.json', 'v5.0/live.parquet', 'v5.0/live_benchmark_models.parquet', 'v5.0/live_example_preds.csv', 'v5.0/live_example_preds.parquet', 'v5.0/meta_model.parquet', 'v5.0/train.parquet', 'v5.0/train_benchmark_models.parquet', 'v5.0/validation.parquet', 'v5.0/validation_benchmark_models.parquet', 'v5.0/validation_example_preds.csv', 'v5.0/validation_example_preds.parquet']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize NumerAPI - the official Python API client for Numerai\n",
+        "from numerapi import NumerAPI\n",
+        "napi = NumerAPI()\n",
+        "\n",
+        "# list the datasets and available versions\n",
+        "all_datasets = napi.list_datasets()\n",
+        "dataset_versions = list(set(d.split('/')[0] for d in all_datasets))\n",
+        "print(\"Available versions:\\n\", dataset_versions)\n",
+        "\n",
+        "# Set data version to one of the latest datasets\n",
+        "DATA_VERSION = \"v5.0\"\n",
+        "\n",
+        "# Print all files available for download for our version\n",
+        "current_version_files = [f for f in all_datasets if f.startswith(DATA_VERSION)]\n",
+        "print(\"Available\", DATA_VERSION, \"files:\\n\", current_version_files)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "avIeXI5T9CC-"
+      },
+      "source": [
+        "### Downloading datasets\n",
+        "\n",
+        "The `features.json` file contains metadata about features in the dataset including:\n",
+        "- statistics on each feature\n",
+        "- helpful sets of features\n",
+        "- the targets available for training\n",
+        "\n",
+        "Let's download it and take a look:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_Mldufeo9BKS",
+        "outputId": "0f97b4ed-ccb3-482c-cf49-b7226a49f526"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/features.json: 291kB [00:00, 2.14MB/s]                           "
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "feature_sets 17\n",
+            "targets 37\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "import json\n",
+        "\n",
+        "# download the feature metadata file\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n",
+        "\n",
+        "# read the metadata and display\n",
+        "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
+        "for metadata in feature_metadata:\n",
+        "  print(metadata, len(feature_metadata[metadata]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sESS7Wfs_pqz"
+      },
+      "source": [
+        "### Feature Sets & Groups\n",
+        "As you can see there are many features and targets to choose from.\n",
+        "\n",
+        "Instead of training a model on all 2000+ features, let's pick a subset of features to analyze.\n",
+        "\n",
+        "Here are a few starter sets Numerai offers:\n",
+        "\n",
+        "- `small` contains a minimal subset of features that have the highest [feature importance](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)\n",
+        "\n",
+        "- `medium` contains all the \"basic\" features, each unique in some way (e.g. P/E ratios vs analyst ratings)\n",
+        "\n",
+        "- `all` contains all features in `medium` and their variants (e.g. P/E by country vs P/E by sector)\n",
+        "\n",
+        "Let's take a look at the medium feature set:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TeAzyU9q_dwR",
+        "outputId": "ddb255d1-29f4-4d45-9b82-4477613a8089"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "small 42\n",
+            "medium 705\n",
+            "all 2376\n"
+          ]
+        }
+      ],
+      "source": [
+        "feature_sets = feature_metadata[\"feature_sets\"]\n",
+        "for feature_set in [\"small\", \"medium\", \"all\"]:\n",
+        "  print(feature_set, len(feature_sets[feature_set]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h0-iMpdJAubs"
+      },
+      "source": [
+        "\n",
+        "The `medium` set seems much more reasonable.\n",
+        "\n",
+        "Using it will speed up model training and reduce memory usage (required for Colab free tier).\n",
+        "\n",
+        "Let's load the training data for just the medium feature set:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UC5YkX1xr5GV",
+        "outputId": "04c9de0c-778d-4f1c-edff-c58d040685eb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/train.parquet: 2.37GB [00:59, 40.0MB/s]                            \n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Define our feature set\n",
+        "feature_set = feature_sets[\"small\"]\n",
+        "# use \"medium\" or \"all\" for better performance. Requires more RAM.\n",
+        "# features = feature_metadata[\"feature_sets\"][\"medium\"]\n",
+        "# features = feature_metadata[\"feature_sets\"][\"all\"]\n",
+        "\n",
+        "# Download the training data - this will take a few minutes\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
+        "\n",
+        "# Load only the \"medium\" feature set to\n",
+        "# Use the \"all\" feature set to use all features\n",
+        "train = pd.read_parquet(\n",
+        "    f\"{DATA_VERSION}/train.parquet\",\n",
+        "    columns=[\"era\", \"target\"] + feature_set\n",
+        ")\n",
+        "\n",
+        "# Downsample to every 4th era to reduce memory usage and speedup model training (suggested for Colab free tier)\n",
+        "# Comment out the line below to use all the data\n",
+        "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jBcMKMX6FoNl"
+      },
+      "source": [
+        "\n",
+        "### Training data\n",
+        "\n",
+        "Each row represents a stock at a specific point in time:\n",
+        "- `id` is the stock id\n",
+        "- `era` is the date\n",
+        "- `target` is a measure of future returns for that stock\n",
+        "- `features` describe the attributes of the stock (eg. P/E ratio) for that date"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 475
+        },
+        "id": "o9JOOMqpFscM",
+        "outputId": "e7dd7980-053d-4f42-a861-7e871379492c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   era  target  feature_antistrophic_striate_conscriptionist  \\\n",
+              "id                                                                             \n",
+              "n0007b5abb0c3a25  0001    0.25                                             2   \n",
+              "n003bba8a98662e4  0001    0.25                                             2   \n",
+              "n003bee128c2fcfc  0001    0.75                                             2   \n",
+              "n0048ac83aff7194  0001    0.25                                             2   \n",
+              "n0055a2401ba6480  0001    0.25                                             2   \n",
+              "...                ...     ...                                           ...   \n",
+              "nffc2d5e4b79a7ae  0573    0.00                                             1   \n",
+              "nffc9844c1c7a6a9  0573    0.25                                             2   \n",
+              "nffd79773f4109bb  0573    0.50                                             3   \n",
+              "nfff6ab9d6dc0b32  0573    0.25                                             2   \n",
+              "nfff87b21e4db902  0573    0.50                                             3   \n",
+              "\n",
+              "                  feature_bicameral_showery_wallaba  \\\n",
+              "id                                                    \n",
+              "n0007b5abb0c3a25                                  2   \n",
+              "n003bba8a98662e4                                  2   \n",
+              "n003bee128c2fcfc                                  2   \n",
+              "n0048ac83aff7194                                  2   \n",
+              "n0055a2401ba6480                                  2   \n",
+              "...                                             ...   \n",
+              "nffc2d5e4b79a7ae                                  2   \n",
+              "nffc9844c1c7a6a9                                  1   \n",
+              "nffd79773f4109bb                                  4   \n",
+              "nfff6ab9d6dc0b32                                  0   \n",
+              "nfff87b21e4db902                                  0   \n",
+              "\n",
+              "                  feature_bridal_fingered_pensioner  \\\n",
+              "id                                                    \n",
+              "n0007b5abb0c3a25                                  2   \n",
+              "n003bba8a98662e4                                  2   \n",
+              "n003bee128c2fcfc                                  2   \n",
+              "n0048ac83aff7194                                  2   \n",
+              "n0055a2401ba6480                                  2   \n",
+              "...                                             ...   \n",
+              "nffc2d5e4b79a7ae                                  1   \n",
+              "nffc9844c1c7a6a9                                  2   \n",
+              "nffd79773f4109bb                                  0   \n",
+              "nfff6ab9d6dc0b32                                  3   \n",
+              "nfff87b21e4db902                                  4   \n",
+              "\n",
+              "                  feature_collectivist_flaxen_gueux  \\\n",
+              "id                                                    \n",
+              "n0007b5abb0c3a25                                  2   \n",
+              "n003bba8a98662e4                                  2   \n",
+              "n003bee128c2fcfc                                  2   \n",
+              "n0048ac83aff7194                                  2   \n",
+              "n0055a2401ba6480                                  2   \n",
+              "...                                             ...   \n",
+              "nffc2d5e4b79a7ae                                  0   \n",
+              "nffc9844c1c7a6a9                                  1   \n",
+              "nffd79773f4109bb                                  3   \n",
+              "nfff6ab9d6dc0b32                                  1   \n",
+              "nfff87b21e4db902                                  3   \n",
+              "\n",
+              "                  feature_concurring_fabled_adapter  \\\n",
+              "id                                                    \n",
+              "n0007b5abb0c3a25                                  2   \n",
+              "n003bba8a98662e4                                  2   \n",
+              "n003bee128c2fcfc                                  2   \n",
+              "n0048ac83aff7194                                  2   \n",
+              "n0055a2401ba6480                                  2   \n",
+              "...                                             ...   \n",
+              "nffc2d5e4b79a7ae                                  3   \n",
+              "nffc9844c1c7a6a9                                  4   \n",
+              "nffd79773f4109bb                                  4   \n",
+              "nfff6ab9d6dc0b32                                  2   \n",
+              "nfff87b21e4db902                                  0   \n",
+              "\n",
+              "                  feature_crosscut_whilom_ataxy  \\\n",
+              "id                                                \n",
+              "n0007b5abb0c3a25                              0   \n",
+              "n003bba8a98662e4                              1   \n",
+              "n003bee128c2fcfc                              2   \n",
+              "n0048ac83aff7194                              1   \n",
+              "n0055a2401ba6480                              0   \n",
+              "...                                         ...   \n",
+              "nffc2d5e4b79a7ae                              2   \n",
+              "nffc9844c1c7a6a9                              2   \n",
+              "nffd79773f4109bb                              1   \n",
+              "nfff6ab9d6dc0b32                              2   \n",
+              "nfff87b21e4db902                              2   \n",
+              "\n",
+              "                  feature_departmental_inimitable_sentencer  \\\n",
+              "id                                                            \n",
+              "n0007b5abb0c3a25                                          1   \n",
+              "n003bba8a98662e4                                          4   \n",
+              "n003bee128c2fcfc                                          2   \n",
+              "n0048ac83aff7194                                          4   \n",
+              "n0055a2401ba6480                                          0   \n",
+              "...                                                     ...   \n",
+              "nffc2d5e4b79a7ae                                          1   \n",
+              "nffc9844c1c7a6a9                                          2   \n",
+              "nffd79773f4109bb                                          0   \n",
+              "nfff6ab9d6dc0b32                                          1   \n",
+              "nfff87b21e4db902                                          2   \n",
+              "\n",
+              "                  feature_dialectal_homely_cambodia  ...  \\\n",
+              "id                                                   ...   \n",
+              "n0007b5abb0c3a25                                  2  ...   \n",
+              "n003bba8a98662e4                                  2  ...   \n",
+              "n003bee128c2fcfc                                  2  ...   \n",
+              "n0048ac83aff7194                                  2  ...   \n",
+              "n0055a2401ba6480                                  2  ...   \n",
+              "...                                             ...  ...   \n",
+              "nffc2d5e4b79a7ae                                  1  ...   \n",
+              "nffc9844c1c7a6a9                                  4  ...   \n",
+              "nffd79773f4109bb                                  1  ...   \n",
+              "nfff6ab9d6dc0b32                                  2  ...   \n",
+              "nfff87b21e4db902                                  3  ...   \n",
+              "\n",
+              "                  feature_tridactyl_immoral_snorting  \\\n",
+              "id                                                     \n",
+              "n0007b5abb0c3a25                                   4   \n",
+              "n003bba8a98662e4                                   4   \n",
+              "n003bee128c2fcfc                                   3   \n",
+              "n0048ac83aff7194                                   1   \n",
+              "n0055a2401ba6480                                   1   \n",
+              "...                                              ...   \n",
+              "nffc2d5e4b79a7ae                                   2   \n",
+              "nffc9844c1c7a6a9                                   4   \n",
+              "nffd79773f4109bb                                   3   \n",
+              "nfff6ab9d6dc0b32                                   4   \n",
+              "nfff87b21e4db902                                   4   \n",
+              "\n",
+              "                  feature_trimeter_soggy_greatest  \\\n",
+              "id                                                  \n",
+              "n0007b5abb0c3a25                                1   \n",
+              "n003bba8a98662e4                                2   \n",
+              "n003bee128c2fcfc                                1   \n",
+              "n0048ac83aff7194                                3   \n",
+              "n0055a2401ba6480                                0   \n",
+              "...                                           ...   \n",
+              "nffc2d5e4b79a7ae                                2   \n",
+              "nffc9844c1c7a6a9                                0   \n",
+              "nffd79773f4109bb                                0   \n",
+              "nfff6ab9d6dc0b32                                1   \n",
+              "nfff87b21e4db902                                0   \n",
+              "\n",
+              "                  feature_unanalyzable_excusable_whirlwind  \\\n",
+              "id                                                           \n",
+              "n0007b5abb0c3a25                                         1   \n",
+              "n003bba8a98662e4                                         0   \n",
+              "n003bee128c2fcfc                                         1   \n",
+              "n0048ac83aff7194                                         4   \n",
+              "n0055a2401ba6480                                         1   \n",
+              "...                                                    ...   \n",
+              "nffc2d5e4b79a7ae                                         1   \n",
+              "nffc9844c1c7a6a9                                         0   \n",
+              "nffd79773f4109bb                                         0   \n",
+              "nfff6ab9d6dc0b32                                         1   \n",
+              "nfff87b21e4db902                                         0   \n",
+              "\n",
+              "                  feature_unbreakable_constraining_hegelianism  \\\n",
+              "id                                                               \n",
+              "n0007b5abb0c3a25                                             3   \n",
+              "n003bba8a98662e4                                             0   \n",
+              "n003bee128c2fcfc                                             0   \n",
+              "n0048ac83aff7194                                             1   \n",
+              "n0055a2401ba6480                                             0   \n",
+              "...                                                        ...   \n",
+              "nffc2d5e4b79a7ae                                             2   \n",
+              "nffc9844c1c7a6a9                                             3   \n",
+              "nffd79773f4109bb                                             0   \n",
+              "nfff6ab9d6dc0b32                                             1   \n",
+              "nfff87b21e4db902                                             0   \n",
+              "\n",
+              "                  feature_unformed_bent_smatch  \\\n",
+              "id                                               \n",
+              "n0007b5abb0c3a25                             0   \n",
+              "n003bba8a98662e4                             0   \n",
+              "n003bee128c2fcfc                             1   \n",
+              "n0048ac83aff7194                             2   \n",
+              "n0055a2401ba6480                             0   \n",
+              "...                                        ...   \n",
+              "nffc2d5e4b79a7ae                             3   \n",
+              "nffc9844c1c7a6a9                             1   \n",
+              "nffd79773f4109bb                             1   \n",
+              "nfff6ab9d6dc0b32                             2   \n",
+              "nfff87b21e4db902                             0   \n",
+              "\n",
+              "                  feature_unministerial_unextenuated_teleostean  \\\n",
+              "id                                                                \n",
+              "n0007b5abb0c3a25                                              2   \n",
+              "n003bba8a98662e4                                              2   \n",
+              "n003bee128c2fcfc                                              2   \n",
+              "n0048ac83aff7194                                              2   \n",
+              "n0055a2401ba6480                                              2   \n",
+              "...                                                         ...   \n",
+              "nffc2d5e4b79a7ae                                              3   \n",
+              "nffc9844c1c7a6a9                                              3   \n",
+              "nffd79773f4109bb                                              1   \n",
+              "nfff6ab9d6dc0b32                                              2   \n",
+              "nfff87b21e4db902                                              1   \n",
+              "\n",
+              "                  feature_unmodish_zymogenic_rousing  \\\n",
+              "id                                                     \n",
+              "n0007b5abb0c3a25                                   2   \n",
+              "n003bba8a98662e4                                   2   \n",
+              "n003bee128c2fcfc                                   2   \n",
+              "n0048ac83aff7194                                   2   \n",
+              "n0055a2401ba6480                                   2   \n",
+              "...                                              ...   \n",
+              "nffc2d5e4b79a7ae                                   3   \n",
+              "nffc9844c1c7a6a9                                   3   \n",
+              "nffd79773f4109bb                                   4   \n",
+              "nfff6ab9d6dc0b32                                   1   \n",
+              "nfff87b21e4db902                                   0   \n",
+              "\n",
+              "                  feature_unsystematized_subcardinal_malaysia  \\\n",
+              "id                                                              \n",
+              "n0007b5abb0c3a25                                            3   \n",
+              "n003bba8a98662e4                                            4   \n",
+              "n003bee128c2fcfc                                            0   \n",
+              "n0048ac83aff7194                                            2   \n",
+              "n0055a2401ba6480                                            1   \n",
+              "...                                                       ...   \n",
+              "nffc2d5e4b79a7ae                                            2   \n",
+              "nffc9844c1c7a6a9                                            2   \n",
+              "nffd79773f4109bb                                            0   \n",
+              "nfff6ab9d6dc0b32                                            0   \n",
+              "nfff87b21e4db902                                            0   \n",
+              "\n",
+              "                  feature_willful_sere_chronobiology  \\\n",
+              "id                                                     \n",
+              "n0007b5abb0c3a25                                   3   \n",
+              "n003bba8a98662e4                                   4   \n",
+              "n003bee128c2fcfc                                   3   \n",
+              "n0048ac83aff7194                                   0   \n",
+              "n0055a2401ba6480                                   4   \n",
+              "...                                              ...   \n",
+              "nffc2d5e4b79a7ae                                   2   \n",
+              "nffc9844c1c7a6a9                                   3   \n",
+              "nffd79773f4109bb                                   1   \n",
+              "nfff6ab9d6dc0b32                                   0   \n",
+              "nfff87b21e4db902                                   4   \n",
+              "\n",
+              "                  feature_zoological_peristomial_scute  \n",
+              "id                                                      \n",
+              "n0007b5abb0c3a25                                     2  \n",
+              "n003bba8a98662e4                                     2  \n",
+              "n003bee128c2fcfc                                     2  \n",
+              "n0048ac83aff7194                                     2  \n",
+              "n0055a2401ba6480                                     2  \n",
+              "...                                                ...  \n",
+              "nffc2d5e4b79a7ae                                     1  \n",
+              "nffc9844c1c7a6a9                                     2  \n",
+              "nffd79773f4109bb                                     2  \n",
+              "nfff6ab9d6dc0b32                                     2  \n",
+              "nfff87b21e4db902                                     3  \n",
+              "\n",
+              "[688184 rows x 44 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-01c6f263-309d-4be0-95de-ca34a686157e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>era</th>\n",
+              "      <th>target</th>\n",
+              "      <th>feature_antistrophic_striate_conscriptionist</th>\n",
+              "      <th>feature_bicameral_showery_wallaba</th>\n",
+              "      <th>feature_bridal_fingered_pensioner</th>\n",
+              "      <th>feature_collectivist_flaxen_gueux</th>\n",
+              "      <th>feature_concurring_fabled_adapter</th>\n",
+              "      <th>feature_crosscut_whilom_ataxy</th>\n",
+              "      <th>feature_departmental_inimitable_sentencer</th>\n",
+              "      <th>feature_dialectal_homely_cambodia</th>\n",
+              "      <th>...</th>\n",
+              "      <th>feature_tridactyl_immoral_snorting</th>\n",
+              "      <th>feature_trimeter_soggy_greatest</th>\n",
+              "      <th>feature_unanalyzable_excusable_whirlwind</th>\n",
+              "      <th>feature_unbreakable_constraining_hegelianism</th>\n",
+              "      <th>feature_unformed_bent_smatch</th>\n",
+              "      <th>feature_unministerial_unextenuated_teleostean</th>\n",
+              "      <th>feature_unmodish_zymogenic_rousing</th>\n",
+              "      <th>feature_unsystematized_subcardinal_malaysia</th>\n",
+              "      <th>feature_willful_sere_chronobiology</th>\n",
+              "      <th>feature_zoological_peristomial_scute</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n0007b5abb0c3a25</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n003bba8a98662e4</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n003bee128c2fcfc</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0048ac83aff7194</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0055a2401ba6480</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc2d5e4b79a7ae</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>...</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc9844c1c7a6a9</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd79773f4109bb</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff6ab9d6dc0b32</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff87b21e4db902</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>688184 rows × 44 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01c6f263-309d-4be0-95de-ca34a686157e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-01c6f263-309d-4be0-95de-ca34a686157e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-01c6f263-309d-4be0-95de-ca34a686157e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f95a7a62-833b-4fe2-86bd-7d2be186ba69\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f95a7a62-833b-4fe2-86bd-7d2be186ba69')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f95a7a62-833b-4fe2-86bd-7d2be186ba69 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_06aefb72-6258-4a39-9079-6efa70f76e3b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_06aefb72-6258-4a39-9079-6efa70f76e3b button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('train');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "train"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "train"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lSvdym9wr5GW"
+      },
+      "source": [
+        "### Eras\n",
+        "As mentioned above, each `era` corresponds to a different date. Each era is exactly 1 week apart.\n",
+        "\n",
+        "It is helpful to think about rows of stocks within the same `era` as a single example. You will notice that throughout this notebook and other examples, we often talk about things \"per era\". For example, the number of rows per era represents the number of stocks in Numerai's investable universe on that date."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 351
+        },
+        "id": "7JX0Bs95r5GX",
+        "outputId": "475e99c8-577d-401f-c0cf-bd4c46b80016"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Number of rows per era'}, xlabel='Era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x300 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot the number of rows per era\n",
+        "train.groupby(\"era\").size().plot(\n",
+        "    title=\"Number of rows per era\",\n",
+        "    figsize=(5, 3),\n",
+        "    xlabel=\"Era\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FxQCUEEPr5GZ"
+      },
+      "source": [
+        "### Target\n",
+        "The `target` is a measure of stock market returns over the next 20 (business) days. Specifically, it is a measure of \"stock-specific\" returns that are not explained by well-known \"factors\" or broader trends in the market, country, or sector. For example, if Apple went up and the tech sector also went up, we only want to know if Apple went up more or less than the tech sector.\n",
+        "\n",
+        "Target values are binned into 5 unequal bins: `0`, `0.25`, `0.5`, `0.75`, `1.0`. Again, this heavy regularization of target values is to avoid overfitting as the underlying values are extremely noisy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 351
+        },
+        "id": "8ALp0YQ6r5GZ",
+        "outputId": "0afccf3e-13a6-4d30-bc7d-a3e14cdb6253"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Target'}, xlabel='Value', ylabel='Frequency'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x300 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot density histogram of the target\n",
+        "train[\"target\"].plot(\n",
+        "  kind=\"hist\",\n",
+        "  title=\"Target\",\n",
+        "  figsize=(5, 3),\n",
+        "  xlabel=\"Value\",\n",
+        "  density=True,\n",
+        "  bins=50\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rhj9RZGNr5GX"
+      },
+      "source": [
+        "### Features\n",
+        "The `features` are quantitative attributes of each stock: fundamentals like P/E ratio, technical signals like RSI, market data like short interest, secondary data like analyst ratings, and much more.\n",
+        "\n",
+        "The underlying definition of each feature is not important, just know that Numerai has included these features in the dataset because we believe they are predictive of the `target` either by themselves or in combination with other features.\n",
+        "\n",
+        "Feature values are binned into 5 equal bins: `0`, `1`, `2`, `3`, `4`. This heavy regularization of feature values is to avoid overfitting as the underlying values are extremely noisy. Unlike the target, these are integers instead of floats to reduce the storage needs of the overall dataset.\n",
+        "\n",
+        "If data for a particular feature is missing for that era (more common in early `eras`), then all values will be set to `2`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 332
+        },
+        "id": "CHlSJccVr5GY",
+        "outputId": "e59bb818-a976-47af-bc71-5bdbd0fa4ea1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'missing data'}, ylabel='Frequency'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x300 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3))\n",
+        "first_era = train[train[\"era\"] == train[\"era\"].unique()[0]]\n",
+        "last_era = train[train[\"era\"] == train[\"era\"].unique()[-1]]\n",
+        "last_era[feature_set[-1]].plot(\n",
+        "   title=\"5 equal bins\",\n",
+        "   kind=\"hist\",\n",
+        "   density=True,\n",
+        "   bins=50,\n",
+        "   ax=ax1\n",
+        ")\n",
+        "first_era[feature_set[-1]].plot(\n",
+        "   title=\"missing data\",\n",
+        "   kind=\"hist\",\n",
+        "   density=True,\n",
+        "   bins=50,\n",
+        "   ax=ax2\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Eyn-0Or3r5GZ"
+      },
+      "source": [
+        "## 2. Modeling\n",
+        "At a high level, our task is to model and predict the `target` using the `features`.\n",
+        "\n",
+        "### Model training\n",
+        "\n",
+        "You are free to use any tool or framework, but here we will be using LGBMRegressor, a popular choice amongst tournament participants. While you wait for the model to train, watch this [video](https://www.youtube.com/watch?v=w8Y7hY05z7k) to learn why tree-based models work so well on tabular datasets from our Chief Scientist MDO."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 198
+        },
+        "id": "prHdeg5Nr5GZ",
+        "outputId": "02a58e7b-b32e-424c-818f-100bbc5b95f5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010521 seconds.\n",
+            "You can set `force_row_wise=true` to remove the overhead.\n",
+            "And if memory is not enough, you can set `force_col_wise=true`.\n",
+            "[LightGBM] [Info] Total Bins 210\n",
+            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
+            "[LightGBM] [Info] Start training from score 0.500008\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('boosting_type',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">boosting_type&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('num_leaves',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">num_leaves&nbsp;</td>\n",
+              "            <td class=\"value\">31</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_depth',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_depth&nbsp;</td>\n",
+              "            <td class=\"value\">5</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('learning_rate',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+              "            <td class=\"value\">0.01</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_estimators',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+              "            <td class=\"value\">2000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
+              "            <td class=\"value\">200000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('objective',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">objective&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('class_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">class_weight&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_split_gain',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_child_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
+              "            <td class=\"value\">0.001</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_child_samples',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
+              "            <td class=\"value\">20</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample&nbsp;</td>\n",
+              "            <td class=\"value\">1.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample_freq',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
+              "            <td class=\"value\">0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
+              "            <td class=\"value\">0.1</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_alpha',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_lambda',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">random_state&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('importance_type',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">importance_type&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;split&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "# https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html\n",
+        "import lightgbm as lgb\n",
+        "\n",
+        "# https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html\n",
+        "model = lgb.LGBMRegressor(\n",
+        "  n_estimators=2000,\n",
+        "  learning_rate=0.01,\n",
+        "  max_depth=5,\n",
+        "  num_leaves=2**5-1,\n",
+        "  colsample_bytree=0.1\n",
+        ")\n",
+        "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
+        "# model = lgb.LGBMRegressor(\n",
+        "#     n_estimators=30_000,\n",
+        "#     learning_rate=0.001,\n",
+        "#     max_depth=10,\n",
+        "#     num_leaves=2**10,\n",
+        "#     colsample_bytree=0.1\n",
+        "#     min_data_in_leaf=10000,\n",
+        "# )\n",
+        "\n",
+        "# This will take a few minutes 🍵\n",
+        "model.fit(\n",
+        "  train[feature_set],\n",
+        "  train[\"target\"]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jTqgml-br5Ga"
+      },
+      "source": [
+        "### Validation predictions\n",
+        "\n",
+        "Now let's make some out-of-sample predictions on the validation dataset to evaluate our model's performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 473
+        },
+        "id": "ImonnvQYr5Ga",
+        "outputId": "e4fa1d3e-003a-4824-ad6f-a73798830533"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/validation.parquet: 3.45GB [01:27, 39.5MB/s]                            \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   era  prediction  target\n",
+              "id                                        \n",
+              "n000c290e4364875  0579    0.494547    0.50\n",
+              "n002a15bc5575bbb  0579    0.517560    0.25\n",
+              "n00309caaa0f955e  0579    0.515683    0.75\n",
+              "n0039cbdcf835708  0579    0.509313    0.50\n",
+              "n004143458984f89  0579    0.483896    0.50\n",
+              "...                ...         ...     ...\n",
+              "nffc5b7319b4b998  1167    0.497411    0.75\n",
+              "nffd7ad35b86d121  1167    0.509441    0.50\n",
+              "nffdb1a3a768a420  1167    0.497954    0.50\n",
+              "nffdc129924fae18  1167    0.492830    0.50\n",
+              "nfff193e9bccc4f1  1167    0.495584    0.25\n",
+              "\n",
+              "[916263 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fd8a0d3b-c8b4-472b-b62a-61c77545ae95\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>era</th>\n",
+              "      <th>prediction</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n000c290e4364875</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.494547</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002a15bc5575bbb</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.517560</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n00309caaa0f955e</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.515683</td>\n",
+              "      <td>0.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0039cbdcf835708</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.509313</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n004143458984f89</th>\n",
+              "      <td>0579</td>\n",
+              "      <td>0.483896</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc5b7319b4b998</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.497411</td>\n",
+              "      <td>0.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd7ad35b86d121</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.509441</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdb1a3a768a420</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.497954</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdc129924fae18</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.492830</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff193e9bccc4f1</th>\n",
+              "      <td>1167</td>\n",
+              "      <td>0.495584</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>916263 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd8a0d3b-c8b4-472b-b62a-61c77545ae95')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fd8a0d3b-c8b4-472b-b62a-61c77545ae95 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fd8a0d3b-c8b4-472b-b62a-61c77545ae95');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-624a91e3-4e37-420f-9b32-bbd7753adf16\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-624a91e3-4e37-420f-9b32-bbd7753adf16')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-624a91e3-4e37-420f-9b32-bbd7753adf16 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "# Download validation data - this will take a few minutes\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
+        "\n",
+        "# Load the validation data and filter for data_type == \"validation\"\n",
+        "validation = pd.read_parquet(\n",
+        "    f\"{DATA_VERSION}/validation.parquet\",\n",
+        "    columns=[\"era\", \"data_type\", \"target\"] + feature_set\n",
+        ")\n",
+        "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
+        "del validation[\"data_type\"]\n",
+        "\n",
+        "# Downsample to every 4th era to reduce memory usage and speedup evaluation (suggested for Colab free tier)\n",
+        "# Comment out the line below to use all the data (slower and higher memory usage, but more accurate evaluation)\n",
+        "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
+        "\n",
+        "# Eras are 1 week apart, but targets look 20 days (o 4 weeks/eras) into the future,\n",
+        "# so we need to \"embargo\" the first 4 eras following our last train era to avoid \"data leakage\"\n",
+        "last_train_era = int(train[\"era\"].unique()[-1])\n",
+        "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
+        "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n",
+        "\n",
+        "# Generate predictions against the out-of-sample validation features\n",
+        "# This will take a few minutes 🍵\n",
+        "validation[\"prediction\"] = model.predict(validation[feature_set])\n",
+        "validation[[\"era\", \"prediction\", \"target\"]]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "toGRSHN9r5Ga"
+      },
+      "source": [
+        "### Performance evaluation\n",
+        "\n",
+        "Numerai calculates scores designed to \"align incentives\" between your model and the hedge fund - a model with good scores should help the hedge fund make good returns. The primary scoring metrics in Numerai are:\n",
+        "\n",
+        "- `CORR` (or \"Correlation\") which is calculated by the function `numerai_corr` - a Numerai specific variant of the Pearson Correlation between your model and the target.\n",
+        "\n",
+        "- `MMC` (or \"Meta Model Contribution\") which is a calculated by the function `correlation_contribution` - a measure of how uniquely additive your model is to the Numerai Meta Model.\n",
+        "\n",
+        "On the Numerai website you will see `CORR` referred to as `CORR20V2`, where the \"20\" refers to the 20-day return target and \"v2\" specifies that we are using the 2nd version of the scoring function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "lTdo3r_Kr5Ga",
+        "outputId": "85d7e416-dc88-4062-9782-4c9d82ff652a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v4.3/meta_model.parquet: 29.0MB [00:00, 32.2MB/s]                            \n"
+          ]
+        }
+      ],
+      "source": [
+        "# install Numerai's open-source scoring tools\n",
+        "!pip install -q --no-deps numerai-tools\n",
+        "\n",
+        "# import the 2 scoring functions\n",
+        "from numerai_tools.scoring import numerai_corr, correlation_contribution\n",
+        "\n",
+        "# Download and join in the meta_model for the validation eras\n",
+        "napi.download_dataset(f\"v4.3/meta_model.parquet\", round_num=842)\n",
+        "validation[\"meta_model\"] = pd.read_parquet(\n",
+        "    f\"v4.3/meta_model.parquet\"\n",
+        ")[\"numerai_meta_model\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BX49Z_Lnr5Gb"
+      },
+      "source": [
+        "As mentioned above, it is important for us to score each historical `era` independantly. So when evaluating the performance of our model, we should be looking at the \"per era\" metrics.\n",
+        "\n",
+        "One thing you may notice here is how low the scores are (in the range of +/- 0.05). This is very normal in the domain of quantitative finance and is part of the reason why we say Numerai is the \"hardest data science tournament\" in the world."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 864
+        },
+        "id": "u_qnP9QVr5Gb",
+        "outputId": "9e168f8b-4865-40b8-ad30-e84c7f7e39cf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-17-295801370.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  per_era_corr = validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-17-295801370.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Validation MMC'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Compute the per-era corr between our predictions and the target values\n",
+        "per_era_corr = validation.groupby(\"era\").apply(\n",
+        "    lambda x: numerai_corr(x[[\"prediction\"]].dropna(), x[\"target\"].dropna())\n",
+        ")\n",
+        "\n",
+        "# Compute the per-era mmc between our predictions, the meta model, and the target values\n",
+        "per_era_mmc = validation.dropna().groupby(\"era\").apply(\n",
+        "    lambda x: correlation_contribution(x[[\"prediction\"]], x[\"meta_model\"], x[\"target\"])\n",
+        ")\n",
+        "\n",
+        "\n",
+        "# Plot the per-era correlation\n",
+        "per_era_corr.plot(\n",
+        "  title=\"Validation CORR\",\n",
+        "  kind=\"bar\",\n",
+        "  figsize=(8, 4),\n",
+        "  xticks=[],\n",
+        "  legend=False,\n",
+        "  snap=False\n",
+        ")\n",
+        "per_era_mmc.plot(\n",
+        "  title=\"Validation MMC\",\n",
+        "  kind=\"bar\",\n",
+        "  figsize=(8, 4),\n",
+        "  xticks=[],\n",
+        "  legend=False,\n",
+        "  snap=False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bAMEtbTFr5Gb"
+      },
+      "source": [
+        "Instead of looking at the raw score for each era, it is helpful to look at the cumulative scores.\n",
+        "\n",
+        "If you are familiar with \"backtesting\" in quant finance where people simulate the historical performance of their investment strategies, you can roughly think of this plot as a backtest of your model performance over the historical validation period.\n",
+        "\n",
+        "Notice a few things below:\n",
+        "\n",
+        "- CORR gradually increases over many eras of the validation data even with this simple model on modern data.\n",
+        "\n",
+        "- MMC is generated over a smaller set of recent eras - this is because the validation time range pre-dates the Meta Model.\n",
+        "\n",
+        "- MMC is very high early on in the Meta Model's existence, MMC - this is because the newest datasets were not available and models trained on the newest data are could have been very additive in the past.\n",
+        "\n",
+        "- MMC is flat and decreasing recently because the Meta Model has started catching up to modern data sets and getting correlation has been difficult in recent eras."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 821
+        },
+        "id": "T62k0nGpr5Gb",
+        "outputId": "1c0db1d3-f518-4c7c-93b2-2e352c13e53d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative Validation MMC'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot the cumulative per-era correlation\n",
+        "per_era_corr.cumsum().plot(\n",
+        "  title=\"Cumulative Validation CORR\",\n",
+        "  kind=\"line\",\n",
+        "  figsize=(8, 4),\n",
+        "  legend=False\n",
+        ")\n",
+        "per_era_mmc.cumsum().plot(\n",
+        "  title=\"Cumulative Validation MMC\",\n",
+        "  kind=\"line\",\n",
+        "  figsize=(8, 4),\n",
+        "  legend=False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VtW4n1jpr5Gc"
+      },
+      "source": [
+        "### Performance metrics\n",
+        "\n",
+        "To evaluate the performance of our model, it is also helpful to compute some summary metrics over the entire validation period:\n",
+        "\n",
+        "- `Mean` is the primary measure of your model's long-term performance.\n",
+        "\n",
+        "- `Sharpe` is a measure of your model's consistency. In finance, the Sharpe ratio of an investment strategy measures risk adjusted returns. In Numerai, we compute sharpe as the mean divided by the standard deviation.\n",
+        "\n",
+        "- `Max drawdown` is a measure of your model's risk. In finance, the max drawdown of an investment strategy is the largest loss suffered. In Numerai, we compute max drawdown as the maximum peak to trough drop in a cumulative score."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "0EXHy3M0r5Gc",
+        "outputId": "d152681f-bd4e-40fe-c7c2-b8ab1572760f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                               CORR  \\\n",
+              "mean          prediction    0.017393\n",
+              "dtype: float64   \n",
+              "std            prediction    0.01883\n",
+              "dtype: float64   \n",
+              "sharpe        prediction    0.923659\n",
+              "dtype: float64   \n",
+              "max_drawdown  prediction    0.041581\n",
+              "dtype: float64   \n",
+              "\n",
+              "                                                MMC  \n",
+              "mean          prediction    0.006881\n",
+              "dtype: float64  \n",
+              "std           prediction    0.016548\n",
+              "dtype: float64  \n",
+              "sharpe        prediction    0.415804\n",
+              "dtype: float64  \n",
+              "max_drawdown  prediction    0.067466\n",
+              "dtype: float64  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c4715481-7493-491e-bf6f-0597cd4b402d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>CORR</th>\n",
+              "      <th>MMC</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>prediction    0.017393\n",
+              "dtype: float64</td>\n",
+              "      <td>prediction    0.006881\n",
+              "dtype: float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>prediction    0.01883\n",
+              "dtype: float64</td>\n",
+              "      <td>prediction    0.016548\n",
+              "dtype: float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sharpe</th>\n",
+              "      <td>prediction    0.923659\n",
+              "dtype: float64</td>\n",
+              "      <td>prediction    0.415804\n",
+              "dtype: float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max_drawdown</th>\n",
+              "      <td>prediction    0.041581\n",
+              "dtype: float64</td>\n",
+              "      <td>prediction    0.067466\n",
+              "dtype: float64</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4715481-7493-491e-bf6f-0597cd4b402d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c4715481-7493-491e-bf6f-0597cd4b402d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c4715481-7493-491e-bf6f-0597cd4b402d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-17e2a450-b2fb-474b-9a13-dad193da59c3\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-17e2a450-b2fb-474b-9a13-dad193da59c3')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-17e2a450-b2fb-474b-9a13-dad193da59c3 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"}, index=[\\\"CORR\\\", \\\"MMC\\\"])\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"CORR\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MMC\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "# Compute performance metrics\n",
+        "corr_mean = per_era_corr.mean()\n",
+        "corr_std = per_era_corr.std(ddof=0)\n",
+        "corr_sharpe = corr_mean / corr_std\n",
+        "corr_max_drawdown = (per_era_corr.cumsum().expanding(min_periods=1).max() - per_era_corr.cumsum()).max()\n",
+        "\n",
+        "mmc_mean = per_era_mmc.mean()\n",
+        "mmc_std = per_era_mmc.std(ddof=0)\n",
+        "mmc_sharpe = mmc_mean / mmc_std\n",
+        "mmc_max_drawdown = (per_era_mmc.cumsum().expanding(min_periods=1).max() - per_era_mmc.cumsum()).max()\n",
+        "\n",
+        "pd.DataFrame({\n",
+        "    \"mean\": [corr_mean, mmc_mean],\n",
+        "    \"std\": [corr_std, mmc_std],\n",
+        "    \"sharpe\": [corr_sharpe, mmc_sharpe],\n",
+        "    \"max_drawdown\": [corr_max_drawdown, mmc_max_drawdown]\n",
+        "}, index=[\"CORR\", \"MMC\"]).T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7k973wHgr5Gc"
+      },
+      "source": [
+        "These performance metrics above are not amazing but that's ok, we are just getting started. In the next few tutorials, you will learn how to improve our model performance."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5gC06mZ-r5Gc"
+      },
+      "source": [
+        "## 3. Submissions\n",
+        "\n",
+        "Unlike Kaggle competitions that evalute models based on <ins>test</ins> performance, Numerai evaluates models based based on <ins>live</ins> performance.\n",
+        "\n",
+        "### Live predictions\n",
+        "\n",
+        "Every Tuesday-Saturday, new `live features` are released, which represent the current state of the stock market.\n",
+        "\n",
+        "Your task is to generate `live predictions` on the unknown target values, which represent stock market returns 20 days into the future."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 473
+        },
+        "id": "yUEWmrdnr5Gc",
+        "outputId": "2621bd44-8ae2-4ea2-caa9-6bc719697661"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/live.parquet: 8.27MB [00:00, 21.7MB/s]                            \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  prediction\n",
+              "id                          \n",
+              "n001ba451c4cf24f    0.510206\n",
+              "n00208b1df989b47    0.499164\n",
+              "n002115a1e41ac5c    0.490775\n",
+              "n0021e1e026d7e47    0.496782\n",
+              "n002ddf4912dda8d    0.507223\n",
+              "...                      ...\n",
+              "nffcf4d74ac07190    0.506064\n",
+              "nffd30a4ec0c8662    0.495681\n",
+              "nffe131b7e72bc81    0.490468\n",
+              "nfff66b587bd248f    0.505397\n",
+              "nfff8eb83b5e7585    0.504471\n",
+              "\n",
+              "[6723 rows x 1 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-186eef8a-4263-4c72-a4ee-bca92df428c5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>prediction</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n001ba451c4cf24f</th>\n",
+              "      <td>0.510206</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n00208b1df989b47</th>\n",
+              "      <td>0.499164</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002115a1e41ac5c</th>\n",
+              "      <td>0.490775</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0021e1e026d7e47</th>\n",
+              "      <td>0.496782</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002ddf4912dda8d</th>\n",
+              "      <td>0.507223</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffcf4d74ac07190</th>\n",
+              "      <td>0.506064</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd30a4ec0c8662</th>\n",
+              "      <td>0.495681</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffe131b7e72bc81</th>\n",
+              "      <td>0.490468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff66b587bd248f</th>\n",
+              "      <td>0.505397</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff8eb83b5e7585</th>\n",
+              "      <td>0.504471</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>6723 rows × 1 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-186eef8a-4263-4c72-a4ee-bca92df428c5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-186eef8a-4263-4c72-a4ee-bca92df428c5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-186eef8a-4263-4c72-a4ee-bca92df428c5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c3041da1-33f5-4167-9660-584c9c376466\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3041da1-33f5-4167-9660-584c9c376466')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c3041da1-33f5-4167-9660-584c9c376466 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 6723,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          \"nd9351550a167617\",\n          \"n83f3a823c3d3ce0\",\n          \"n998459b1b948928\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007638615831525296,\n        \"min\": 0.47617992360728667,\n        \"max\": 0.5289348481080441,\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          0.5136618224846665,\n          0.502684214643171,\n          0.49472510218735094\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "# Download latest live features\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
+        "\n",
+        "# Load live features\n",
+        "live_features = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\", columns=feature_set)\n",
+        "\n",
+        "# Generate live predictions\n",
+        "live_predictions = model.predict(live_features[feature_set])\n",
+        "\n",
+        "# Format submission\n",
+        "pd.Series(live_predictions, index=live_features.index).to_frame(\"prediction\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JAsl2z7mr5Gd"
+      },
+      "source": [
+        "### Model upload\n",
+        "\n",
+        "To participate in the tournament, you must submit live predictions every Tuesday-Saturday.\n",
+        "\n",
+        "To automate this process, you can simply:\n",
+        "- Define your prediction pipeline as a function\n",
+        "- Serialize your function using the `cloudpickle` library\n",
+        "- Upload your model pickle file to Numerai\n",
+        "- Let Numerai run your model to submit live predictions every day\n",
+        "\n",
+        "Read more about Model Uploads and other self-hosted automation options in our [docs](https://docs.numer.ai/numerai-tournament/submissions#automation).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "U4bHP_BGr5Gd"
+      },
+      "outputs": [],
+      "source": [
+        "# Define your prediction pipeline as a function\n",
+        "def predict(live_features: pd.DataFrame) -> pd.DataFrame:\n",
+        "    live_predictions = model.predict(live_features[feature_set])\n",
+        "    submission = pd.Series(live_predictions, index=live_features.index)\n",
+        "    return submission.to_frame(\"prediction\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "l442TN44r5Gd"
+      },
+      "outputs": [],
+      "source": [
+        "# Use the cloudpickle library to serialize your function\n",
+        "import cloudpickle\n",
+        "p = cloudpickle.dumps(predict)\n",
+        "with open(\"hello_numerai.pkl\", \"wb\") as f:\n",
+        "    f.write(p)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "IFUq_XpDr5Gd",
+        "outputId": "393d8652-639c-433e-b48c-f4bcc605726f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_27763597-ebab-42c4-8da4-4b054627cc6e\", \"hello_numerai.pkl\", 6513436)"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Download file if running in Google Colab\n",
+        "try:\n",
+        "    from google.colab import files\n",
+        "    files.download('hello_numerai.pkl')\n",
+        "except:\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iplRaPPLr5Gd"
+      },
+      "source": [
+        "That's it! You now have a pickle file that is ready for upload.\n",
+        "\n",
+        "Head back to the [Hello Numerai Tutorial](https://numer.ai/tutorial/hello-numerai) to upload your model!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/numerai/target_ensemble.ipynb b/numerai/target_ensemble.ipynb
new file mode 100644
index 0000000..b3cbc4b
--- /dev/null
+++ b/numerai/target_ensemble.ipynb
@@ -0,0 +1,6082 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOUvgVp3xnNW"
+      },
+      "source": [
+        "# Target Ensemble\n",
+        "\n",
+        "Apart from the main target, there are actually many auxilliary targets in the dataset.\n",
+        "\n",
+        "These targets are fundamentally related to the main target which make them potentially helpful to model. And because these targets have a wide range of correlations to the main targets, it means that we could potentially build some nice ensembles to boost our performance.\n",
+        "\n",
+        "In this notebook, we will\n",
+        "1. Explore the auxilliary targets\n",
+        "2. Select our favorite targets to include in the ensemble\n",
+        "3. Create an ensemble of models trained on different targets\n",
+        "4. Pickle and upload our ensemble model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python --version"
+      ],
+      "metadata": {
+        "id": "Ej4poji3G1Df",
+        "outputId": "171d8edf-bd43-406a-e782-64ed09624904",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Python 3.11.13\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KD826S8uxnNY",
+        "outputId": "2d93c23b-7937-4dba-8dde-1f51df9884c0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.9/61.9 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m125.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m128.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m92.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.9/12.9 MB\u001b[0m \u001b[31m114.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.3/35.3 MB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.1 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "# Install dependencies\n",
+        "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
+        "\n",
+        "# Inline plots\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VwChLrKexnNa"
+      },
+      "source": [
+        "## 1. Auxilliary Targets\n",
+        "\n",
+        "Let's start by taking a look at the different targets in the training data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 511
+        },
+        "id": "R1I_xkY4xnNa",
+        "outputId": "62fdbb5e-df86-4e4e-f648-fe52d726c949"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/train.parquet: 2.37GB [04:12, 9.39MB/s]                            \n",
+            "v5.0/features.json: 291kB [00:00, 1.77MB/s]                           \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   era  target_agnes_20  target_agnes_60  target_alpha_20  \\\n",
+              "id                                                                          \n",
+              "n0007b5abb0c3a25  0001             0.25             0.00             0.25   \n",
+              "n003bba8a98662e4  0001             0.25             0.25             0.25   \n",
+              "n003bee128c2fcfc  0001             1.00             1.00             1.00   \n",
+              "n0048ac83aff7194  0001             0.25             0.25             0.25   \n",
+              "n0055a2401ba6480  0001             0.25             0.50             0.25   \n",
+              "...                ...              ...              ...              ...   \n",
+              "nffc2d5e4b79a7ae  0573             0.00             0.25             0.00   \n",
+              "nffc9844c1c7a6a9  0573             0.50             0.50             0.25   \n",
+              "nffd79773f4109bb  0573             0.50             0.50             0.75   \n",
+              "nfff6ab9d6dc0b32  0573             0.50             0.50             0.25   \n",
+              "nfff87b21e4db902  0573             0.75             0.75             0.50   \n",
+              "\n",
+              "                  target_alpha_60  target_bravo_20  target_bravo_60  \\\n",
+              "id                                                                    \n",
+              "n0007b5abb0c3a25             0.25             0.00             0.00   \n",
+              "n003bba8a98662e4             0.00             0.25             0.00   \n",
+              "n003bee128c2fcfc             1.00             0.75             1.00   \n",
+              "n0048ac83aff7194             0.25             0.50             0.25   \n",
+              "n0055a2401ba6480             0.50             0.25             0.50   \n",
+              "...                           ...              ...              ...   \n",
+              "nffc2d5e4b79a7ae             0.25             0.00             0.25   \n",
+              "nffc9844c1c7a6a9             0.50             0.50             0.50   \n",
+              "nffd79773f4109bb             0.50             0.75             0.50   \n",
+              "nfff6ab9d6dc0b32             0.50             0.50             0.50   \n",
+              "nfff87b21e4db902             0.75             0.50             0.75   \n",
+              "\n",
+              "                  target_caroline_20  target_caroline_60  target_charlie_20  \\\n",
+              "id                                                                            \n",
+              "n0007b5abb0c3a25                0.25                0.00               0.25   \n",
+              "n003bba8a98662e4                0.25                0.25               0.25   \n",
+              "n003bee128c2fcfc                0.75                0.75               0.75   \n",
+              "n0048ac83aff7194                0.50                0.25               0.50   \n",
+              "n0055a2401ba6480                0.25                0.50               0.25   \n",
+              "...                              ...                 ...                ...   \n",
+              "nffc2d5e4b79a7ae                0.25                0.50               0.00   \n",
+              "nffc9844c1c7a6a9                0.50                0.50               0.50   \n",
+              "nffd79773f4109bb                0.50                0.50               0.75   \n",
+              "nfff6ab9d6dc0b32                0.25                0.50               0.25   \n",
+              "nfff87b21e4db902                0.50                0.50               0.50   \n",
+              "\n",
+              "                  ...  target_teager2b_60  target_tyler_20  target_tyler_60  \\\n",
+              "id                ...                                                         \n",
+              "n0007b5abb0c3a25  ...                0.50             0.25             0.25   \n",
+              "n003bba8a98662e4  ...                0.50             0.25             0.25   \n",
+              "n003bee128c2fcfc  ...                1.00             1.00             0.75   \n",
+              "n0048ac83aff7194  ...                0.25             0.25             0.25   \n",
+              "n0055a2401ba6480  ...                0.50             0.25             0.50   \n",
+              "...               ...                 ...              ...              ...   \n",
+              "nffc2d5e4b79a7ae  ...                0.50             0.25             0.50   \n",
+              "nffc9844c1c7a6a9  ...                0.75             0.50             0.50   \n",
+              "nffd79773f4109bb  ...                0.75             0.50             0.50   \n",
+              "nfff6ab9d6dc0b32  ...                0.50             0.50             0.25   \n",
+              "nfff87b21e4db902  ...                0.75             0.75             0.75   \n",
+              "\n",
+              "                  target_victor_20  target_victor_60  target_waldo_20  \\\n",
+              "id                                                                      \n",
+              "n0007b5abb0c3a25              0.25              0.25             0.25   \n",
+              "n003bba8a98662e4              0.25              0.00             0.25   \n",
+              "n003bee128c2fcfc              0.75              0.75             0.75   \n",
+              "n0048ac83aff7194              0.50              0.25             0.25   \n",
+              "n0055a2401ba6480              0.25              0.50             0.25   \n",
+              "...                            ...               ...              ...   \n",
+              "nffc2d5e4b79a7ae              0.25              0.50             0.00   \n",
+              "nffc9844c1c7a6a9              0.50              0.50             0.50   \n",
+              "nffd79773f4109bb              0.50              0.50             0.50   \n",
+              "nfff6ab9d6dc0b32              0.25              0.50             0.50   \n",
+              "nfff87b21e4db902              0.50              0.75             0.50   \n",
+              "\n",
+              "                  target_waldo_60  target_xerxes_20  target_xerxes_60  target  \n",
+              "id                                                                             \n",
+              "n0007b5abb0c3a25             0.00              0.25              0.00    0.25  \n",
+              "n003bba8a98662e4             0.25              0.25              0.25    0.25  \n",
+              "n003bee128c2fcfc             1.00              0.75              0.75    0.75  \n",
+              "n0048ac83aff7194             0.25              0.25              0.25    0.25  \n",
+              "n0055a2401ba6480             0.50              0.25              0.50    0.25  \n",
+              "...                           ...               ...               ...     ...  \n",
+              "nffc2d5e4b79a7ae             0.50              0.00              0.25    0.00  \n",
+              "nffc9844c1c7a6a9             0.50              0.50              0.50    0.25  \n",
+              "nffd79773f4109bb             0.75              0.50              0.50    0.50  \n",
+              "nfff6ab9d6dc0b32             0.50              0.25              0.50    0.25  \n",
+              "nfff87b21e4db902             0.50              0.50              0.50    0.50  \n",
+              "\n",
+              "[688184 rows x 38 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>era</th>\n",
+              "      <th>target_agnes_20</th>\n",
+              "      <th>target_agnes_60</th>\n",
+              "      <th>target_alpha_20</th>\n",
+              "      <th>target_alpha_60</th>\n",
+              "      <th>target_bravo_20</th>\n",
+              "      <th>target_bravo_60</th>\n",
+              "      <th>target_caroline_20</th>\n",
+              "      <th>target_caroline_60</th>\n",
+              "      <th>target_charlie_20</th>\n",
+              "      <th>...</th>\n",
+              "      <th>target_teager2b_60</th>\n",
+              "      <th>target_tyler_20</th>\n",
+              "      <th>target_tyler_60</th>\n",
+              "      <th>target_victor_20</th>\n",
+              "      <th>target_victor_60</th>\n",
+              "      <th>target_waldo_20</th>\n",
+              "      <th>target_waldo_60</th>\n",
+              "      <th>target_xerxes_20</th>\n",
+              "      <th>target_xerxes_60</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n0007b5abb0c3a25</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n003bba8a98662e4</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n003bee128c2fcfc</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0048ac83aff7194</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0055a2401ba6480</th>\n",
+              "      <td>0001</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc2d5e4b79a7ae</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc9844c1c7a6a9</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd79773f4109bb</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff6ab9d6dc0b32</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff87b21e4db902</th>\n",
+              "      <td>0573</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.75</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>688184 rows × 38 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-e77cb780-76c2-488c-afef-011dd93b07a5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e77cb780-76c2-488c-afef-011dd93b07a5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-e77cb780-76c2-488c-afef-011dd93b07a5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import json\n",
+        "from numerapi import NumerAPI\n",
+        "\n",
+        "# Set the data version to one of the most recent versions\n",
+        "DATA_VERSION = \"v5.0\"\n",
+        "MAIN_TARGET = \"target_cyrusd_20\"\n",
+        "TARGET_CANDIDATES = [\n",
+        "  MAIN_TARGET,\n",
+        "  \"target_victor_20\",\n",
+        "  \"target_xerxes_20\",\n",
+        "  \"target_teager2b_20\"\n",
+        "]\n",
+        "FAVORITE_MODEL = \"v5_lgbm_ct_blend\"\n",
+        "\n",
+        "# Download data\n",
+        "napi = NumerAPI()\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n",
+        "\n",
+        "# Load data\n",
+        "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
+        "feature_cols = feature_metadata[\"feature_sets\"][\"small\"]\n",
+        "# use \"medium\" or \"all\" for better performance. Requires more RAM.\n",
+        "# features = feature_metadata[\"feature_sets\"][\"medium\"]\n",
+        "# features = feature_metadata[\"feature_sets\"][\"all\"]\n",
+        "target_cols = feature_metadata[\"targets\"]\n",
+        "train = pd.read_parquet(\n",
+        "    f\"{DATA_VERSION}/train.parquet\",\n",
+        "    columns=[\"era\"] + feature_cols + target_cols\n",
+        ")\n",
+        "\n",
+        "# Downsample to every 4th era to reduce memory usage and speedup model training (suggested for Colab free tier)\n",
+        "# Comment out the line below to use all the data (higher memory usage, slower model training, potentially better performance)\n",
+        "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n",
+        "\n",
+        "# Print target columns\n",
+        "train[[\"era\"] + target_cols]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4YzbRO5uxnNa"
+      },
+      "source": [
+        "### The main target"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R1o6PJcbxnNa"
+      },
+      "source": [
+        "First thing to note is that `target` is just an alias for the `cyrus` target, so we can drop this column for the rest of the notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "pP6LnWcExnNa"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop `target` column\n",
+        "assert train[\"target\"].equals(train[MAIN_TARGET])\n",
+        "targets_df = train[[\"era\"] + target_cols]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d46TQDtrxnNb"
+      },
+      "source": [
+        "### Target names\n",
+        "\n",
+        "At a high level, each target represents a different kind of stock market return\n",
+        "- the `name` represents the type of stock market return (eg. residual to market/country/sector vs market/country/style)\n",
+        "- the `_20` or `_60` suffix denotes the time horizon of the target (ie. 20 vs 60 market days)\n",
+        "\n",
+        "The reason why `cyrus` as our main target is because it most closely matches the type of returns we want for our hedge fund. Just like how we are always in search for better features to include in the dataset, we are also always in search for better targets to make our main target. During our research, we often come up with targets we like but not as much as the main target, and these are instead released as auxilliary targets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 645
+        },
+        "id": "P7uAdarxxnNb",
+        "outputId": "ece63dd6-310d-4b40-c863-c22be9170fad"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                          20                  60\n",
+              "name                                            \n",
+              "agnes        target_agnes_20     target_agnes_60\n",
+              "alpha        target_alpha_20     target_alpha_60\n",
+              "bravo        target_bravo_20     target_bravo_60\n",
+              "caroline  target_caroline_20  target_caroline_60\n",
+              "charlie    target_charlie_20   target_charlie_60\n",
+              "claudia    target_claudia_20   target_claudia_60\n",
+              "cyrusd      target_cyrusd_20    target_cyrusd_60\n",
+              "delta        target_delta_20     target_delta_60\n",
+              "echo          target_echo_20      target_echo_60\n",
+              "jeremy      target_jeremy_20    target_jeremy_60\n",
+              "ralph        target_ralph_20     target_ralph_60\n",
+              "rowan        target_rowan_20     target_rowan_60\n",
+              "sam            target_sam_20       target_sam_60\n",
+              "teager2b  target_teager2b_20  target_teager2b_60\n",
+              "tyler        target_tyler_20     target_tyler_60\n",
+              "victor      target_victor_20    target_victor_60\n",
+              "waldo        target_waldo_20     target_waldo_60\n",
+              "xerxes      target_xerxes_20    target_xerxes_60"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8c17913b-801b-4550-ad3c-c81e6b8f590b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>20</th>\n",
+              "      <th>60</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>name</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>agnes</th>\n",
+              "      <td>target_agnes_20</td>\n",
+              "      <td>target_agnes_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alpha</th>\n",
+              "      <td>target_alpha_20</td>\n",
+              "      <td>target_alpha_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bravo</th>\n",
+              "      <td>target_bravo_20</td>\n",
+              "      <td>target_bravo_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>caroline</th>\n",
+              "      <td>target_caroline_20</td>\n",
+              "      <td>target_caroline_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charlie</th>\n",
+              "      <td>target_charlie_20</td>\n",
+              "      <td>target_charlie_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>claudia</th>\n",
+              "      <td>target_claudia_20</td>\n",
+              "      <td>target_claudia_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cyrusd</th>\n",
+              "      <td>target_cyrusd_20</td>\n",
+              "      <td>target_cyrusd_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>delta</th>\n",
+              "      <td>target_delta_20</td>\n",
+              "      <td>target_delta_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>echo</th>\n",
+              "      <td>target_echo_20</td>\n",
+              "      <td>target_echo_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>jeremy</th>\n",
+              "      <td>target_jeremy_20</td>\n",
+              "      <td>target_jeremy_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ralph</th>\n",
+              "      <td>target_ralph_20</td>\n",
+              "      <td>target_ralph_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rowan</th>\n",
+              "      <td>target_rowan_20</td>\n",
+              "      <td>target_rowan_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sam</th>\n",
+              "      <td>target_sam_20</td>\n",
+              "      <td>target_sam_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>teager2b</th>\n",
+              "      <td>target_teager2b_20</td>\n",
+              "      <td>target_teager2b_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>tyler</th>\n",
+              "      <td>target_tyler_20</td>\n",
+              "      <td>target_tyler_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>victor</th>\n",
+              "      <td>target_victor_20</td>\n",
+              "      <td>target_victor_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>waldo</th>\n",
+              "      <td>target_waldo_20</td>\n",
+              "      <td>target_waldo_60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>xerxes</th>\n",
+              "      <td>target_xerxes_20</td>\n",
+              "      <td>target_xerxes_60</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c17913b-801b-4550-ad3c-c81e6b8f590b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8c17913b-801b-4550-ad3c-c81e6b8f590b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8c17913b-801b-4550-ad3c-c81e6b8f590b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9ff1255c-0697-43f1-ae0c-6252eec64133\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ff1255c-0697-43f1-ae0c-6252eec64133')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9ff1255c-0697-43f1-ae0c-6252eec64133 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"agnes\",\n          \"alpha\",\n          \"echo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"20\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"target_agnes_20\",\n          \"target_alpha_20\",\n          \"target_echo_20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"60\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"target_agnes_60\",\n          \"target_alpha_60\",\n          \"target_echo_60\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "# Print target names grouped by name and time horizon\n",
+        "pd.set_option('display.max_rows', 100)\n",
+        "t20s = [t for t in target_cols if t.endswith(\"_20\")]\n",
+        "t60s = [t for t in target_cols if t.endswith(\"_60\")]\n",
+        "names = [t.replace(\"target_\", \"\").replace(\"_20\", \"\") for t in t20s]\n",
+        "pd.DataFrame({\"name\": names,\"20\": t20s,\"60\": t60s}).set_index(\"name\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PDkTNQMrxnNb"
+      },
+      "source": [
+        "### Target values\n",
+        "\n",
+        "Note that some targets are binned into 5 bins while others are binned into 7 bins.\n",
+        "\n",
+        "Unlike feature values which are integers ranging from 0-4, target values are floats which range from 0-1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "Uw_4oswnxnNb",
+        "outputId": "a208b358-fc87-4423-bf2a-9e8d67007274"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<Axes: >, <Axes: >],\n",
+              "       [<Axes: >, <Axes: >]], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 4 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn8AAAGHCAYAAADBZzQSAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQoxJREFUeJzt3Xl4jPf+//HXZN8TShAJQTWWYyunqptWKY6DLkRRxVGtolpdDqc9qq1jaQ/dVFenljaqVdqqqqW1tajaa2sosYulSBBkmc/vD7/M18g2iSQTcz8f15Xrau77M/f9/ozJu6+573vusRljjAAAAGAJXu4uAAAAAKWH8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AfgmnHnnXfqzjvvLJV92Ww2vfTSS47fX3rpJdlsNp04caJU9h8bG6s+ffqUyr4AWAvhDyhjbDabSz/Lli1zd6lOVq1apZdeekmnT592aXyfPn2c5hMSEqKaNWuqS5cumj17tux2u1vqKk1luTYAnsvH3QUAcPbJJ584/T59+nQtXrw4x/K6deuWZlkFWrVqlV5++WX16dNHERERLj3G399fkydPliSdP39e+/bt07fffqsuXbrozjvv1DfffKOwsDDH+EWLFpVKXdn1+PiUbIvMr7bExER5efH+HEDxI/wBZcxDDz3k9Psvv/yixYsX51heFMYYXbhwQYGBgVe9reLg4+OTY17/+c9/NG7cOP3rX/9S//799fnnnzvW+fn5lWg9drtd6enpCggIUEBAQInuqyD+/v5u3T8Az8XbSuAaNGXKFLVq1UqRkZHy9/dXvXr19N577+UYFxsbq7///e9auHChmjVrpsDAQH3wwQeSpH379qlTp04KDg5WZGSkhg4dqoULF+Z6SnnNmjVq166dwsPDFRQUpJYtW2rlypWO9S+99JKee+45SVKNGjUcp3L37t1bpPkNHz5c99xzj2bNmqWdO3c6lud2zd/EiRNVv359BQUFqVy5cmrWrJlmzJjhUl02m02DBw9WQkKC6tevL39/fy1YsMCx7vJr/rKdOHFC8fHxCgsL03XXXacnn3xSFy5ccKzfu3evbDabpk6dmuOxl2+zoNpyu+Zvz5496tq1q8qXL6+goCDdfPPN+u6775zGLFu2TDabTV988YVGjx6t6OhoBQQE6O6779Yff/zhNHbXrl164IEHVLlyZQUEBCg6OloPPvigUlJSctQOwHNw5A+4Br333nuqX7++OnXqJB8fH3377bcaOHCg7Ha7Bg0a5DQ2MTFR3bt312OPPab+/fsrLi5O586dU6tWrXTkyBE9+eSTqly5smbMmKGlS5fm2NeSJUvUvn17NW3aVCNHjpSXl5cjfP7000+66aabdP/992vnzp367LPP9MYbb6hChQqSpIoVKxZ5jr169dKiRYu0ePFi3XDDDbmO+eijjzRkyBB16dLFEcJ+++03rVmzRj169HCpriVLluiLL77Q4MGDVaFCBcXGxuZbV3x8vGJjYzV27Fj98ssvevvtt3Xq1ClNnz69UPMr7HN29OhR3XLLLUpLS9OQIUN03XXXadq0aerUqZO+/PJL3XfffU7jx40bJy8vLz377LNKSUnRa6+9pp49e2rNmjWSpPT0dLVt21YXL17UE088ocqVK+vQoUOaN2+eTp8+rfDw8ELNB8A1xAAo0wYNGmSu/FNNS0vLMa5t27amZs2aTsuqV69uJJkFCxY4LZ8wYYKRZL7++mvHsvPnz5s6deoYSWbp0qXGGGPsdrupXbu2adu2rbHb7U77r1GjhmnTpo1j2X//+18jySQlJbk0r969e5vg4OA812/cuNFIMkOHDnUsa9mypWnZsqXj986dO5v69evnu5/86pJkvLy8zLZt23JdN3LkSMfvI0eONJJMp06dnMYNHDjQSDKbN282xhiTlJRkJJkpU6YUuM38aqtevbrp3bu34/ennnrKSDI//fSTY9mZM2dMjRo1TGxsrMnKyjLGGLN06VIjydStW9dcvHjRMfatt94yksyWLVuMMf/3/M6aNSvHvgF4Nk77Ategy6/ZS0lJ0YkTJ9SyZUvt2bMnxym7GjVqqG3btk7LFixYoKpVq6pTp06OZQEBAerfv7/TuE2bNmnXrl3q0aOH/vzzT504cUInTpzQuXPndPfdd2vFihXF9qncK4WEhEiSzpw5k+eYiIgIHTx4UGvXri3yflq2bKl69eq5PP7KI6tPPPGEJGn+/PlFrsEV8+fP10033aTbbrvNsSwkJESPPvqo9u7dq+3btzuN79u3r9M1krfffrukS6eOJTmO7C1cuFBpaWklWjuAsoXwB1yDVq5cqdatWys4OFgRERGqWLGinn/+eUnKNfxdad++fapVq5ZsNpvT8uuvv97p9127dkmSevfurYoVKzr9TJ48WRcvXiyx68POnj0rSQoNDc1zzLBhwxQSEqKbbrpJtWvX1qBBg5yuRXRFbs9PfmrXru30e61ateTl5VXk6xtdtW/fPsXFxeVYnv2p73379jktr1atmtPv5cqVkySdOnVK0qV5P/3005o8ebIqVKigtm3batKkSVzvB1gA1/wB15jdu3fr7rvvVp06dfT6668rJiZGfn5+mj9/vt54440cR+Ku5pO92dv673//q8aNG+c6JvsIXXHbunWrpJyB9HJ169ZVYmKi5s2bpwULFmj27Nl699139eKLL+rll192aT9X+8nnKwP0lb9ny8rKuqr9FJa3t3euy40xjv+eMGGC+vTpo2+++UaLFi3SkCFDHNcyRkdHl1apAEoZ4Q+4xnz77be6ePGi5s6d63R0J7cPa+SlevXq2r59u4wxTmHlyk+D1qpVS5IUFham1q1b57vNvEJPUX3yySey2Wxq06ZNvuOCg4PVrVs3devWTenp6br//vs1evRo/etf/1JAQECx17Vr1y6no4V//PGH7Ha744Mi2UfYrrxx85VH5qTCPWfVq1dXYmJijuW///67Y31RNGjQQA0aNNC///1vrVq1Srfeeqvef/99/ec//ynS9gCUfZz2Ba4x2Ud0Lj+Ck5KSoilTpri8jbZt2+rQoUOaO3euY9mFCxf00UcfOY1r2rSpatWqpfHjxztOw17u+PHjjv8ODg6WlDP0FMW4ceO0aNEidevWLcdp1sv9+eefTr/7+fmpXr16MsYoIyOj2OuSpEmTJjn9PnHiRElS+/btJV0KyhUqVNCKFSucxr377rs5tlWY2v72t7/p119/1erVqx3Lzp07pw8//FCxsbGFum5RklJTU5WZmem0rEGDBvLy8tLFixcLtS0A1xaO/AHXmHvuuUd+fn7q2LGjHnvsMZ09e1YfffSRIiMjdeTIEZe28dhjj+mdd95R9+7d9eSTT6pKlSpKSEhw3Ng4+4iUl5eXJk+erPbt26t+/frq27evqlatqkOHDmnp0qUKCwvTt99+K+lSUJSkF154QQ8++KB8fX3VsWNHR8DJTWZmpj799FNJl8Lnvn37NHfuXP3222+666679OGHHxb4XFSuXFm33nqrKlWqpB07duidd95Rhw4dHNcKFqWu/CQlJalTp05q166dVq9erU8//VQ9evRQo0aNHGMeeeQRjRs3To888oiaNWumFStWON2vMFthahs+fLg+++wztW/fXkOGDFH58uU1bdo0JSUlafbs2YX+NpAlS5Zo8ODB6tq1q2644QZlZmbqk08+kbe3tx544IFCPisArinu/bAxgILkdquXuXPnmoYNG5qAgAATGxtrXn31VfPxxx/nuG1I9erVTYcOHXLd7p49e0yHDh1MYGCgqVixonnmmWfM7NmzjSTzyy+/OI3duHGjuf/++811111n/P39TfXq1U18fLz58ccfncaNGjXKVK1a1Xh5eRV425fevXsbSY6foKAgExsbax544AHz5ZdfOm5dcrkrb/XywQcfmDvuuMNRV61atcxzzz1nUlJSXKpLkhk0aFCu9SmPW71s377ddOnSxYSGhppy5cqZwYMHm/Pnzzs9Ni0tzfTr18+Eh4eb0NBQEx8fb44dO5Zjm/nVduWtXowxZvfu3aZLly4mIiLCBAQEmJtuusnMmzfPaUz2rV6uvIXLlbeg2bNnj/nHP/5hatWqZQICAkz58uXNXXfdZX744Ydcnw8AnsNmzGXnjgBY2ptvvqmhQ4fq4MGDqlq1qrvLAQCUAMIfYFHnz593+qTrhQsX1KRJE2VlZeV6ihIA4Bm45g+wqPvvv1/VqlVT48aNlZKSok8//VS///67EhIS3F0aAKAEEf4Ai2rbtq0mT56shIQEZWVlqV69epo5c6a6devm7tIAACWI074AAAAWwn3+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAvxcWWQ3W7X4cOHFRoaKpvNVtI1AbAgY4zOnDmjqKgoeXl53vtS+iiAkuZqH3Up/B0+fFgxMTHFVhwA5OXAgQOKjo52dxnFjj4KoLQU1EddCn+hoaGOjYWFhRVPZQBwmdTUVMXExDj6jaehjwIoaa72UZfCX/YpirCwMJoWgBLlqadE6aMASktBfdTzLqwBAABAngh/AAAAFkL4AwAAsBCXrvnDtSMrK0sZGRnuLgPIwdfXV97e3u4uA7gqdrtd6enp7i4DFlVcfZTw5yGMMUpOTtbp06fdVsPBU+fzXR9dLrCUKkFZFRERocqVK3vshzrg2dLT05WUlCS73V5yOzm9P//1EdVKbt+4JhRHHyX8eYjs4BcZGamgoCC3/M81PTA13/U1KvMJR6syxigtLU3Hjh2TJFWpUsXNFQGFY4zRkSNH5O3trZiYmJK7Efmx/N9EK7JGyewXZV5x9lHCnwfIyspyBL/rrrvObXXYfC7kuz4gIKCUKkFZFBh46cjvsWPHFBkZySlgXFMyMzOVlpamqKgoBQUFldyOfAp4404ftbTi6qN84MMDZF/jV6INCSgG2a9RrkvFtSYrK0uS5Ofn5+ZKYHXF0UcJfx6E66hQ1vEaxbWO1zDcrTheg4Q/AAAACyH8AQAAWAgf+PBwscO/K9X9zR18a6nuD66788471bhxY7355pvuLgXwHC+Fl/L+Ukp3f9cAm82mr776Svfee6+7S7lmcOQPbnXnnXfqqaeecncZDmWtHnfIyMjQsGHD1KBBAwUHBysqKkoPP/ywDh8+7DTu5MmT6tmzp8LCwhQREaF+/frp7NmzbqoaQG7KWk8riXqOHDmi9u3buzT2pZdeUuPGjYt1/3kZO3as/vrXvyo0NFSRkZG69957lZiY6DTmwoULGjRokK677jqFhITogQce0NGjR0u8NsIfrnmedLf9rKyskr2BrAvS0tK0YcMGjRgxQhs2bNCcOXOUmJioTp06OY3r2bOntm3bpsWLF2vevHlasWKFHn30UTdVDaCklPUeW7lyZfn7+5fqPl15TpYvX65Bgwbpl19+0eLFi5WRkaF77rlH586dc4wZOnSovv32W82aNUvLly/X4cOHdf/995dk6ZIIf3CjPn36aPny5Xrrrbdks9lks9m0e/du9evXTzVq1FBgYKDi4uL01ltv5Xjcvffeq9GjRysqKkpxcXGSpFWrVqlx48YKCAhQs2bN9PXXX8tms2nTpk2Ox27dulXt27dXSEiIKlWqpF69eunEiRN51rN3794C57Ft2zb9/e9/V1hYmEJDQ3X77bdr9+7dWrFihXx9fZWcnOw0/qmnntLtt98uSZo6daoiIiI0d+5c1atXT/7+/tq/f3+u747vvfde9enTx/H7u+++q9q1aysgIECVKlVSly5dHOvOnTunhx9+WCEhIapSpYomTJhQ4DyyhYeHa/HixYqPj1dcXJxuvvlmvfPOO1q/fr3277/07QM7duzQggULNHnyZDVv3ly33XabJk6cqJkzZ+Y4QgjAPa71Hmu32xUdHa333nvPafnGjRvl5eWlffv2Sbp02vfrr792rD948KC6d++u8uXLKzg4WM2aNdOaNWs0depUvfzyy9q8ebNj/1OnTpUk7d+/X507d1ZISIjCwsIUHx/vdAQu+4jh5MmTVaNGDZfuW7tgwQL16dNH9evXV6NGjTR16lTt379f69evlySlpKTof//7n15//XW1atVKTZs21ZQpU7Rq1Sr98ssvBW7/ahD+4DZvvfWWWrRoof79++vIkSM6cuSIoqOjFR0drVmzZmn79u168cUX9fzzz+uLL75weuyPP/6oxMREx1Gn1NRUdezYUQ0aNNCGDRs0atQoDRs2zOkxp0+fVqtWrdSkSROtW7dOCxYs0NGjRxUfH59nPTExMfnO4dChQ7rjjjvk7++vJUuWaP369frHP/6hzMxM3XHHHapZs6Y++eQTx/iMjAwlJCToH//4h2NZWlqaXn31VU2ePFnbtm1TZGRkgc/dunXrNGTIEL3yyitKTEzUggULdMcddzjWP/fcc1q+fLm++eYbLVq0SMuWLdOGDRsK3G5eUlJSZLPZFBERIUlavXq1IiIi1KxZM8eY1q1by8vLS2vWrCnyfgAUn2u9x3p5eal79+6aMWOG0/KEhATdeuutql69eo7HnD17Vi1bttShQ4c0d+5cbd68Wf/85z9lt9vVrVs3PfPMM6pfv75j/926dZPdblfnzp118uRJLV++XIsXL9aePXvUrVs3p23/8ccfmj17tubMmeMUeF2VknLpes3y5ctLktavX6+MjAy1bt3aMaZOnTqqVq2aVq9eXejtFwYf+IDbhIeHy8/PT0FBQapcubJj+csvv+z47xo1amj16tX64osvHA1EkoKDgzV58mTHDVfff/992Ww2ffTRRwoICFC9evV06NAh9e/f3/GYd955R02aNNGYMWMcyz7++GPFxMRo586duuGGG3KtJz+TJk1SeHi4Zs6cKV9fX0nSDTfc4Fjfr18/TZkyRc8995wk6dtvv9WFCxec5pKRkaF3331XjRo1cmmf0qV3qcHBwfr73/+u0NBQVa9eXU2aNJF0qfn973//06effqq7775bkjRt2jRFR0e7vP3LXbhwQcOGDVP37t0VFnbpK/qSk5NzhFQfHx+VL18+x5FOAO7hCT22Z8+emjBhgvbv369q1arJbrdr5syZ+ve//53r+BkzZuj48eNau3atI2Rdf/31jvUhISHy8fFx2v/ixYu1ZcsWJSUlOcLo9OnTVb9+fa1du1Z//etfJV061Tt9+nRVrFjRpdovZ7fb9dRTT+nWW2/VX/7yF0mX+qifn5/jTXW2SpUqlXgf5cgfypxJkyapadOmqlixokJCQvThhx86Tjdma9CggdOd9hMTE9WwYUOnQ/E33XST02M2b96spUuXKiQkxPFTp04dSdLu3buLVOumTZt0++23O4Lflfr06aM//vjDcQh/6tSpio+PV3BwsGOMn5+fGjZsWKj9tmnTRtWrV1fNmjXVq1cvJSQkKC0tzTGX9PR0NW/e3DG+fPnyjlM3hZGRkaH4+HgZY3KcegFwbbqWemzjxo1Vt25dx9G/5cuX69ixY+ratWuu4zdt2qQmTZo4gp8rduzYoZiYGKejkPXq1VNERIR27NjhWFa9evUiBT9JGjRokLZu3aqZM2cW6fHFjfCHMmXmzJl69tln1a9fPy1atEibNm1S3759c1xce3l4ctXZs2fVsWNHbdq0yeln165dTqdMCyP7exbzEhkZqY4dO2rKlCk6evSovv/+e6dTvtnbuPKO7V5eXjLGOC27/Kt8QkNDtWHDBn322WeqUqWKXnzxRTVq1EinT58u0jxykx389u3bp8WLFzuO+kmXLrDO/nLxbJmZmTp58qTL7+gBlL5rrcdKl47+ZYe/GTNmqF27dnl+j31BPflqFOU5kaTBgwdr3rx5Wrp0qdMZmMqVKys9PT1H3z569GiJ91HCH9zKz8/P8Z2ZkrRy5UrdcsstGjhwoJo0aaLrr7/epXeMcXFx2rJliy5evOhYtnbtWqcxN954o7Zt26bY2Fhdf/31Tj/Zf9RX1lOQhg0b6qeffsr3OxYfeeQRff755/rwww9Vq1Yt3XprwfdCrFixoo4cOeL4PSsrS1u3bnUa4+Pjo9atW+u1117Tb7/9pr1792rJkiWqVauWfH19na69O3XqlHbu3OnyvLKD365du/TDDz/kaLQtWrTQ6dOnHRcuS9KSJUtkt9udjjgCcK9rvcdKUo8ePbR161atX79eX375pXr27Jnn2IYNG2rTpk06efJkrutz23/dunV14MABHThwwLFs+/btOn36tOrVq1eoWi9njNHgwYP11VdfacmSJapRo4bT+qZNm8rX11c//vijY1liYqL279+vFi1aFHm/riD8wa1iY2O1Zs0a7d27VydOnFDt2rW1bt06LVy4UDt37tSIESNyNJjc9OjRQ3a7XY8++qh27NihhQsXavz48ZL+73sQBw0apJMnT6p79+5au3atdu/erYULF6pv376OZnBlPQXddmXw4MFKTU3Vgw8+qHXr1mnXrl365JNPnO7l1LZtW4WFhek///mP+vbt69Lz0qpVK3333Xf67rvv9Pvvv+vxxx93enc4b948vf3229q0aZP27dun6dOny263Ky4uTiEhIerXr5+ee+45LVmyRFu3blWfPn3k5eXan3tGRoa6dOmidevWKSEhQVlZWUpOTlZycrLj6EDdunXVrl079e/fX7/++qtWrlypwYMH68EHH1RUVJRL+wFQ8q71Hpv9mFtuuUX9+vVTVlZWjttOXa579+6qXLmy7r33Xq1cuVJ79uzR7NmzHR+giI2NVVJSkjZt2qQTJ07o4sWLat26tRo0aKCePXtqw4YN+vXXX/Xwww+rZcuWTh9qK6xBgwbp008/1YwZMxQaGuroo+fPn5d06ZrMfv366emnn9bSpUu1fv169e3bVy1atNDNN99c5P26xLggJSXFSDIpKSmuDEcpO3/+vNm+fbs5f/68W+vYfOBUvj+5SUxMNDfffLMJDAw0kszvv/9u+vTpY8LDw01ERIR5/PHHzfDhw02jRo0cj+ndu7fp3Llzjm2tXLnSNGzY0Pj5+ZmmTZuaGTNmOLaZbefOnea+++4zERERJjAw0NSpU8c89dRTxm6351pPUlJSwfPevNncc889JigoyISGhprbb7/d7N6922nMiBEjjLe3tzl8+LDT8ilTppjw8PAc20xPTzePP/64KV++vImMjDRjx441nTt3Nr179zbGGPPTTz+Zli1bmnLlypnAwEDTsGFD8/nnnzsef+bMGfPQQw+ZoKAgU6lSJfPaa6+Zli1bmieffLLA+SQlJRlJuf4sXbrUMe7PP/803bt3NyEhISYsLMz07dvXnDlzJt9t5/da9fQ+4+nz83Sl1mcPbcj/p5A8occaY8y7775rJJmHH344xzpJ5quvvnL8vnfvXvPAAw+YsLAwExQUZJo1a2bWrFljjDHmwoUL5oEHHjARERFGkpkyZYoxxph9+/aZTp06meDgYBMaGmq6du1qkpOTHdscOXKk03Pkirz6aPY+jbn0uho4cKApV66cCQoKMvfdd585cuRIvtstjj5q+/8F5is1NVXh4eFKSUlxuu4HZcOFCxeUlJTk8r2HSspvB0/nu75hdESp1JEtISFBffv2VUpKSoleB+KKfv366fjx45o7d65b63C3/F6rnt5nPH1+nq7U+uzhjfmvj2pScvsupLLUY62kOPoot3qBx5g+fbpq1qypqlWravPmzRo2bJji4+Pd2pRSUlK0ZcsWzZgxw/LBD8C1rSz2WBQN1/zBYyQnJ+uhhx5S3bp1NXToUHXt2lUffvjhVW1zwIABTrctuPxnwIABBT6+c+fOuueeezRgwAC1adPmqmopLj/99FOecwoJCXF3eQDKqLLYY91l//79+fbRK2+dU9Zw2tcDcNq35Bw7dkypqam5rgsLC3Pp2zjKmvPnz+vQoUN5rr/8hqjFjdO+njs/T8dp35JxrfbYzMzMfL+aLjY2Vj4+JXNyldO+QAmLjIwss82nqAIDA0s04AGAq67VHuvj43NN91FO+3oQVz4yD7gTr1Fc61w4WQaUqOLooxz58wB+fn7y8vLS4cOHVbFiRfn5+eX4xojSYDLT811/4cKFUqoEZY0xRunp6Tp+/Li8vLycvjYKuBb4+vrKZrPp+PHjqlixYsn12MwCwiV91LKKs48S/jyAl5eXatSooSNHjujw4cNuq+PYqfP5rvc7zyfCrC4oKEjVqlVz+YbTQFnh7e2t6OhoHTx4MN9rva7a6eP5rz+XVHL7xjWhOPoo4c9D+Pn5qVq1asrMzCz0V+cUl0fmLMt3/Y/P3FkqdaBs8vb2lo+Pj1uOSgPFISQkRLVr18736xyv2jtd818/eF3J7RtlXnH1UcKfB7HZbPL19ZWvr69b9n/oTP6h052fRAaA4uDt7S1vb++S28HZA/mvp4+iGHDuBQAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICF+Li7AE8QO/y7fNfvHdehlCpBWZDf64HXApCPl8LzWZdSenXA/fJ7LUi8Hq4SR/4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQn5LceOzw7/Jct3dch5LcNYBSwN94KXgpPJ91KaVXB4CS4Ya/cY78AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABbi0nf7GmMkSampqYXauP1iWp7rCrutsiy/eUqeNdf88DxcYpXXvVS8c80en91vPE1R+6gu5vN8eNjryVJzzUt+z4HE85DNk56HYnzdu9pHbcaFTnvw4EHFxMQUqgAAKIoDBw4oOjra3WUUO/oogNJSUB91KfzZ7XYdPnxYoaGhstlsLu04NTVVMTExOnDggMLCwlyvuAxiLmWXJ83H6nMxxujMmTOKioqSl5fnXZFCH2UuZZUnzcfqc3G1j7p02tfLy6vI78TDwsKu+X+AbMyl7PKk+Vh5LuHh4SVYjXvRRy9hLmWXJ83HynNxpY963ttrAAAA5InwBwAAYCElFv78/f01cuRI+fv7l9QuSg1zKbs8aT7MBVfypOeRuZRdnjQf5uIalz7wAQAAAM/AaV8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFuLjyiC73a7Dhw8rNDRUNputpGsCYEHGGJ05c0ZRUVHy8vK896X0UQAlzdU+6lL4O3z4sGJiYoqtOADIy4EDBxQdHe3uMoodfRRAaSmoj7oU/kJDQx0bCwsLK57KAOAyqampiomJcfQbT0MfBVDSXO2jLoW/7FMUYWFhNC0AJcpTT4nSRwGUloL6qOddWAMAAIA8Ef4AAAAshPAHAABgIS5d84dri91uV3p6urvLAJz4+vrK29vb3WXAw9H/4MmKq48S/jxMenq6kpKSZLfbS33fh88eznd9VEhUKVWCsioiIkKVK1f22A91wL3c2f+KS8bh/PuobxR91OqKo48S/jyIMUZHjhyRt7e3YmJiSv1GuZmnMvNdX6NcjVKqBGWNMUZpaWk6duyYJKlKlSpurgiext39r7hcyMjId31ADfqoVRVnHyX8eZDMzEylpaUpKipKQUFBpb5/L9/8m21AQEApVYKyKDAwUJJ07NgxRUZGcgoYxcrd/a+4mAJCK33U2oqrj16bb42Qq6ysLEmSn5+fmysBcpf9P+WMAo5uAIVF/4NVFEcfJfx5IK6nQlnFaxMljdcYPF1xvMYJfwAAABZC+AMAALAQPvBhAQ2mNSjV/c3sMLNU9wcAedlRp26p7q/u7ztKdX8oOXfeeacaN26sN998092lFDuO/MHt7rzzTj311FPuLsOhrNVT2k6ePKknnnhCcXFxCgwMVLVq1TRkyBClpKQ4jdu/f786dOigoKAgRUZG6rnnnlNmZv63+wHgrKz1m8LWs2zZMtlsNp0+fbrEaioJc+bMUZs2bVSxYkWFhYWpRYsWWrhwYbFtPyMjQ8OGDVODBg0UHBysqKgoPfzwwzp8xX0cT548qZ49eyosLEwRERHq16+fzp49W2x15IXwB4/gaXf0d+d8Dh8+rMOHD2v8+PHaunWrpk6dqgULFqhfv36OMVlZWerQoYPS09O1atUqTZs2TVOnTtWLL77otroBq/K0/leSjDHKzMzUihUr1KZNG82fP1/r16/XXXfdpY4dO2rjxo3Fsp+0tDRt2LBBI0aM0IYNGzRnzhwlJiaqU6dOTuN69uypbdu2afHixZo3b55WrFihRx99tFhqyA/hD27Vp08fLV++XG+99ZZsNptsNpt2796tfv36qUaNGgoMDFRcXJzeeuutHI+79957NXr0aEVFRSkuLk6StGrVKjVu3FgBAQFq1qyZvv76a9lsNm3atMnx2K1bt6p9+/YKCQlRpUqV1KtXL504cSLPevbu3ZvvHF555RVFRUXpzz//dCzr0KGD7rrrLsc3Dfz888+6/fbbFRgYqJiYGA0ZMkTnzp1zjI+NjdWoUaP08MMPKywsTP3791fr1q3Vtm1bGWMkXXqHGB0d7RSwJk+erLp16yogIEB16tTRu+++61iXnp6uwYMHq0qVKgoICFD16tU1duzYAv9N/vKXv2j27Nnq2LGjatWqpVatWmn06NH69ttvHUf2Fi1apO3bt+vTTz9V48aN1b59e40aNUqTJk3if0SAi3LrN3sOHNCAF19U3XbtVL5ZMzXq2FGTPv00x+PKQv/bu3ev7rrrLklSuXLlZLPZ1KdPH0mXvmZv7Nixjj7eqFEjffnll47HZmVlFdjnMzMzNWTIEEVEROi6667TsGHD1Lt3b917772OMQXtJ/vI5Pfff6+mTZvK399fP//8s958803985//1F//+lfVrl1bY8aMUe3atfXtt9/mqGHw4MEKDw9XhQoVNGLECEdPzk94eLgWL16s+Ph4xcXF6eabb9Y777yj9evXa//+/ZKkHTt2aMGCBZo8ebKaN2+u2267TRMnTtTMmTNzHCEsboQ/uNVbb72lFi1aqH///jpy5IiOHDmi6OhoRUdHa9asWdq+fbtefPFFPf/88/riiy+cHvvjjz8qMTHR8Y4pNTVVHTt2VIMGDbRhwwaNGjVKw4YNc3rM6dOn1apVKzVp0kTr1q3TggULdPToUcXHx+dZT0xMTL5zeOGFFxQbG6tHHnlEkjRp0iTH0TAvLy/t3r1b7dq10wMPPKDffvtNn3/+uX7++WcNHjzYaTvjx49Xo0aNtHHjRr344ouaNm2a1q5dq7fffluSNGDAAFWtWtUR/hISEvTiiy9q9OjR2rFjh8aMGaMRI0Zo2rRpkqS3335bc+fO1RdffKHExEQlJCQoNja2SP9OKSkpCgsLk4/PpcuEV69erQYNGqhSpUqOMW3btlVqaqq2bdtWpH0AVpNbv6laqZKqVqqkTydM0Iavv9a/HntMI99+W7MXLHB6bFnofzExMZo9e7YkKTExUUeOHHEEuLFjx2r69Ol6//33tW3bNg0dOlQPPfSQli9fLulSaCuoz7/66qtKSEjQlClTtHLlSqWmpurrr792qqGg/WQbPny4xo0bpx07dqhhw4Y55mK323XmzBmVL1/eafm0adPk4+OjX3/9VW+99ZZef/11TZ48Oc/nJD8pKSmy2WyKiIiQdKmPRkREqFmzZo4xrVu3lpeXl9asWVOkfbiKD3zArcLDw+Xn56egoCBVrlzZsfzll192/HeNGjW0evVqffHFF44mJUnBwcGaPHmy46au77//vmw2mz766CMFBASoXr16OnTokPr37+94zDvvvKMmTZpozJgxjmUff/yxYmJitHPnTt1www251pMfb29vxxGw4cOH6+2339bkyZNVrVo1SZeaU8+ePR3X0dSuXVtvv/22WrZsqffee89xx/5WrVrpmWeecdr2Bx98oIcffljJycmaP3++Nm7c6AhgI0eO1IQJE3T//fc7nqft27frgw8+UO/evbV//37Vrl1bt912m2w2m6pXr+7SfK504sQJjRo1yulURHJyslPwk+T4PTk5uUj7Aawmt/53/sQJjRg0yDEmNjpaazZv1uxFi/RAu3aO5WWh/3l7ezvCUmRkpCPUXLx4UWPGjNEPP/ygFi1aSJJq1qypn3/+WR988IFatmwpX1/fAvv8xIkT9a9//Uv33Xefo/758+c7HuPKfrK98soratOmTZ5zGT9+vM6ePev0/xjpUsB94403ZLPZFBcXpy1btuiNN95wel5dceHCBQ0bNkzdu3dXWFiYpEu9MjIy0mmcj4+PypcvX+J9lPCHMmnSpEn6+OOPtX//fp0/f17p6elq3Lix05gGDRo43c0/MTFRDRs2dPr6o5tuusnpMZs3b9bSpUsVEhKSY5+7d+/WDTfcUKR6a9asqfHjx+uxxx5Tt27d1KNHD6d9/vbbb0pISHAsM8bIbrcrKSlJdete+jTi5e/+snXt2lVfffWVxo0bp/fee0+1a9eWJJ07d85xevzyJpSZmanw8HBJl07htGnTRnFxcWrXrp3+/ve/65577inUvFJTU9WhQwfVq1dPL730UqEeC6Bo3v/sM03/6isdTE7W+QsXlJ6RoYZ16jiNKUv970p//PGH0tLScoSt9PR0NWnSxPF7fn0+JSVFR48edZqDt7e3mjZt6ricxtX9SLn312wzZszQyy+/rG+++SZHGLv55pudbqrcokULTZgwQVlZWS5/tVpGRobi4+NljNF7773n0mNKGuEPZc7MmTP17LPPasKECWrRooVCQ0P13//+N8dh8ODg4EJv++zZs+rYsaNeffXVHOuu5kuyJWnFihXy9vbW3r17lZmZ6ThCd/bsWT322GMaMmRIjsdkHx2Ucp9PWlqa1q9fL29vb+3atctpHpL00UcfqXnz5k6PyW5IN954o5KSkvT999/rhx9+UHx8vFq3bu10PUx+zpw5o3bt2ik0NFRfffWVfH19HesqV66sX3/91Wn80aNHHesAFM2s77/X8xMmaOyzz6p5o0YKDQ7WG1OmaO2WLU7jylr/u3I/kvTdd9+patWqTuv8/f0lud7nr3Y/2fJ6vmbOnKlHHnlEs2bNUuvWrV3et6uyg9++ffu0ZMkSx1E/6VKvPHbsmNP4zMxMnTx5ssT7KOEPbufn5+f4Xk5JWrlypW655RYNHDjQsWz37t0FbicuLk6ffvqpLl686PjDX7t2rdOYG2+8UbNnz1ZsbKwjnBVUjys+//xzzZkzR8uWLVN8fLxGjRrlOKVx4403avv27br++usLtU1JeuaZZ+Tl5aXvv/9ef/vb39ShQwe1atVKlSpVUlRUlPbs2aOePXvm+fiwsDB169ZN3bp1U5cuXdSuXTudPHkyx3UtV0pNTVXbtm3l7++vuXPn5vgy+RYtWmj06NGOLxeXpMWLFyssLEz16tUr9DwBq7qy36zeuFE3N26sxx580LEs6cCBArfjrv6XffTx8sfUq1dP/v7+2r9/v9Op18sV1OfDw8NVqVIlrV27VnfccYdjHxs2bHAcHXRlP/n57LPP9I9//EMzZ85Uhw4dch1zZRj95ZdfVLt2bZeO+mUHv127dmnp0qW67rrrnNa3aNFCp0+f1vr169W0aVNJ0pIlS2S323O8qS9ufOADbhcbG6s1a9Zo7969OnHihGrXrq1169Zp4cKF2rlzp0aMGJGjieWmR48estvtevTRR7Vjxw4tXLhQ48ePl/R/34U4aNAgnTx5Ut27d9fatWu1e/duLVy4UH379nU0ryvryT7FkJeDBw/q8ccf16uvvqrbbrtNU6ZM0ZgxY/TLL79IkoYNG6ZVq1Zp8ODB2rRpk3bt2qVvvvkmxwc+rvTdd9/p448/VkJCgtq0aaPnnntOvXv31qlTpyRdui5y7Nixevvtt7Vz505t2bJFU6ZM0euvvy5Jev311/XZZ5/p999/186dOzVr1ixVrlzZcV1OXlJTU3XPPffo3Llz+t///qfU1FQlJycrOTnZ8Rzdc889qlevnnr16qXNmzdr4cKF+ve//61BgwbleMcNIG9X9pvrq1fXhm3btHjlSu3au1cvT5yo9S58iMpd/a969eqy2WyaN2+ejh8/rrNnzyo0NFTPPvushg4dqmnTpmn37t3asGGDJk6c6PhAmit9/oknntDYsWP1zTffKDExUU8++aROnTrlmI8r+8nLjBkz9PDDD2vChAlq3ry5o8fldj/Tp59+WomJifrss880ceJEPfnkkwX+e2RkZKhLly5at26dEhISlJWV5dhH9h0R6tatq3bt2ql///769ddftXLlSg0ePFgPPvigoqKiCtzHVTEuSElJMZJMSkqKK8PhJufPnzfbt28358+fd8v+tx7fmu9PXhITE83NN99sAgMDjSTz+++/mz59+pjw8HATERFhHn/8cTN8+HDTqFEjx2N69+5tOnfunGNbK1euNA0bNjR+fn6madOmZsaMGY5tZtu5c6e57777TEREhAkMDDR16tQxTz31lLHb7bnWk5SUlGftdrvd3H333aZt27aOxxtjzBNPPGFq1aplzpw5Y4wx5tdffzVt2rQxISEhJjg42DRs2NCMHj3aMb569ermjTfecPx+7NgxU6lSJTNmzBjHsvT0dNO0aVMTHx/vWJaQkGAaN25s/Pz8TLly5cwdd9xh5syZY4wx5sMPPzSNGzc2wcHBJiwszNx9991mw4YNec4l29KlS42kXH8ufy727t1r2rdvbwIDA02FChXMM888YzIyMvLddn6vUU/vM54+P3dzd/8rqiv7zaa5c81DnTub8NBQExEaavp362ae7dfPNIiLM2lbthhjyk7/y/bKK6+YypUrG5vNZnr37m2MudQb33zzTRMXF2d8fX1NxYoVTdu2bc3y5cuNMcZcuHChwD6fkZFhBg8ebMLCwky5cuXMsGHDTNeuXc2DDz7oGFPQfrL72alTp5xqbtmyZa49Lrv+7DEDBw40AwYMcNTw/PPPO/X6vCQlJeXZR5cuXeoY9+eff5ru3bubkJAQExYWZvr27ev4/0ZeiqOP2owp+IY1qampCg8Pd9zuAWXThQsXlJSUpBo1auQ4TVcatp3I/91p/Qr1S6mS/5OQkKC+ffsqJSVFgYGBpb5/OMvvNerpfcbT5+du7u5/xeX81q35rg/8y19c3pan9T+73a66des6Lq2xquLoo1zzB48yffp01axZU1WrVtXmzZs1bNgwxcfHe0TjA4D8eFr/27dvnxYtWqSWLVvq4sWLeuedd5SUlOR0NwUUDdf8waMkJyfroYceUt26dTV06FB17dpVH3744VVtc8CAAQoJCcn1Z8CAAcVUeelJSEjIcz7165f+0VkAxcPT+p+Xl5emTp2qv/71r7r11lu1ZcsW/fDDD47bY7nTTz/9lOfzktutdMoaTvt6EHef9iiLp32Lw7Fjx5SamprrurCwsBz3hSrrzpw547gty5V8fX2LfDNoV3Da13Pn527u7n/FpThP+xYHT+t/xeX8+fM6dOhQnuuLcncHV3HaFygFkZGRHtXgQkNDFRoa6u4yAFwDPK3/FZfAwMASDXgljdO+HsiFg7mAWxR02wjgatH/4OmKo49y5M+D+Pr6ymaz6fjx46pYsaLTV9KUBntG/i/ICxculFIlKGuMMUpPT9fx48fl5eXl9LVUQHFwd/8rLhcL+B+7jT5qWcXZRwl/HsTb21vR0dE6ePCg9u7dW+r7P3b2WL7rfU7zcrO6oKAgVatWTV5enHRA8XJ3/ysuGceP57v+8q9ZhDUVRx/l/8YeJiQkRLVr11ZGRkap7/vJr/K/6/nc++aWUiUoi7y9veXj43PNHpFB2efO/ldcdg8clO/6Gt/PL6VKUBYVVx8l/Hkgb29vl753sLgdST+S7/pr+RN4AK4N7up/xcXrCH0UJY9zLwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAsxMfdBXiCBtMa5Lt+S+8tpVQJyoL8Xg+8FoC87ahTN891dX/fUYqVwN3yey1IvB6uFkf+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEJ+S3HiDaQ3yXLel95aS3DWAUsDfeMnbUadunuvq/r6jFCsBUBLc8TfOkT8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQlz6bl9jjCQpNTW1UBvPOp+V57rCbqssy2+ekmfNNT88D5dY5XUvFe9cs8dn9xtPU9Q+ejbLOq8nK801L/k9BxLPQzZPeh6K83Xvah+1GRc67cGDBxUTE1OoAgCgKA4cOKDo6Gh3l1Hs6KMASktBfdSl8Ge323X48GGFhobKZrO5tOPU1FTFxMTowIEDCgsLc73iMoi5lF2eNB+rz8UYozNnzigqKkpeXp53RQp9lLmUVZ40H6vPxdU+6tJpXy8vryK/Ew8LC7vm/wGyMZeyy5PmY+W5hIeHl2A17kUfvYS5lF2eNB8rz8WVPup5b68BAACQJ8IfAACAhZRY+PP399fIkSPl7+9fUrsoNcyl7PKk+TAXXMmTnkfmUnZ50nyYi2tc+sAHAAAAPAOnfQEAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQq4q/E2aNEmxsbEKCAhQ8+bN9euvv+Y7ftasWapTp44CAgLUoEEDzZ8//2p2X6wKM5ePPvpIt99+u8qVK6dy5cqpdevWBc69NBX23yXbzJkzZbPZdO+995ZsgYVQ2LmcPn1agwYNUpUqVeTv768bbrjhmn2dSdKbb76puLg4BQYGKiYmRkOHDtWFCxdKqdq8rVixQh07dlRUVJRsNpu+/vrrAh+zbNky3XjjjfL399f111+vqVOnlnid1wL6KH20NHhSL6WPFkMfNUU0c+ZM4+fnZz7++GOzbds2079/fxMREWGOHj2a6/iVK1cab29v89prr5nt27ebf//738bX19ds2bKlqCUUm8LOpUePHmbSpElm48aNZseOHaZPnz4mPDzcHDx4sJQrz6mwc8mWlJRkqlatam6//XbTuXPn0im2AIWdy8WLF02zZs3M3/72N/Pzzz+bpKQks2zZMrNp06ZSrjx3hZ1PQkKC8ff3NwkJCSYpKcksXLjQVKlSxQwdOrSUK89p/vz55oUXXjBz5swxksxXX32V7/g9e/aYoKAg8/TTT5vt27ebiRMnGm9vb7NgwYLSKbiMoo/SR0uDJ/VS+mjx9NEih7+bbrrJDBo0yPF7VlaWiYqKMmPHjs11fHx8vOnQoYPTsubNm5vHHnusqCUUm8LO5UqZmZkmNDTUTJs2raRKdFlR5pKZmWluueUWM3nyZNO7d+8y07QKO5f33nvP1KxZ06Snp5dWiYVS2PkMGjTItGrVymnZ008/bW699dYSrbOwXGla//znP039+vWdlnXr1s20bdu2BCsr++ij/4c+WnI8qZfSR4unjxbptG96errWr1+v1q1bO5Z5eXmpdevWWr16da6PWb16tdN4SWrbtm2e40tLUeZypbS0NGVkZKh8+fIlVaZLijqXV155RZGRkerXr19plOmSosxl7ty5atGihQYNGqRKlSrpL3/5i8aMGaOsrKzSKjtPRZnPLbfcovXr1ztOaezZs0fz58/X3/72t1KpuTiV1b9/d6KPOqOPlgxP6qX00eL7+/cpSgEnTpxQVlaWKlWq5LS8UqVK+v3333N9THJycq7jk5OTi1JCsSnKXK40bNgwRUVF5fhHKW1FmcvPP/+s//3vf9q0aVMpVOi6osxlz549WrJkiXr27Kn58+frjz/+0MCBA5WRkaGRI0eWRtl5Ksp8evTooRMnTui2226TMUaZmZkaMGCAnn/++dIouVjl9fefmpqq8+fPKzAw0E2VuQ991Bl9tGR4Ui+ljxZfH+XTvldp3Lhxmjlzpr766isFBAS4u5xCOXPmjHr16qWPPvpIFSpUcHc5V81utysyMlIffvihmjZtqm7duumFF17Q+++/7+7SimTZsmUaM2aM3n33XW3YsEFz5szRd999p1GjRrm7NKBY0UfLFk/qpfTR3BXpyF+FChXk7e2to0ePOi0/evSoKleunOtjKleuXKjxpaUoc8k2fvx4jRs3Tj/88IMaNmxYkmW6pLBz2b17t/bu3auOHTs6ltntdkmSj4+PEhMTVatWrZItOg9F+XepUqWKfH195e3t7VhWt25dJScnKz09XX5+fiVac36KMp8RI0aoV69eeuSRRyRJDRo00Llz5/Too4/qhRdekJfXtfPeLa+//7CwMEse9ZPoo9nooyXLk3opfbT4+miRZu3n56emTZvqxx9/dCyz2+368ccf1aJFi1wf06JFC6fxkrR48eI8x5eWosxFkl577TWNGjVKCxYsULNmzUqj1AIVdi516tTRli1btGnTJsdPp06ddNddd2nTpk2KiYkpzfKdFOXf5dZbb9Uff/zhaLyStHPnTlWpUsWtwU8q2nzS0tJyNKbsZnzp+uBrR1n9+3cn+ih9tDR4Ui+ljxbj33+hPyLy/82cOdP4+/ubqVOnmu3bt5tHH33UREREmOTkZGOMMb169TLDhw93jF+5cqXx8fEx48ePNzt27DAjR44sU7coKMxcxo0bZ/z8/MyXX35pjhw54vg5c+aMu6bgUNi5XKksfUqtsHPZv3+/CQ0NNYMHDzaJiYlm3rx5JjIy0vznP/9x1xScFHY+I0eONKGhoeazzz4ze/bsMYsWLTK1atUy8fHx7pqCw5kzZ8zGjRvNxo0bjSTz+uuvm40bN5p9+/YZY4wZPny46dWrl2N89i0KnnvuObNjxw4zadIkbvVi6KP00dLhSb2UPlo8fbTI4c8YYyZOnGiqVatm/Pz8zE033WR++eUXx7qWLVua3r17O43/4osvzA033GD8/PxM/fr1zXfffXc1uy9WhZlL9erVjaQcPyNHjiz9wnNR2H+Xy5W1plXYuaxatco0b97c+Pv7m5o1a5rRo0ebzMzMUq46b4WZT0ZGhnnppZdMrVq1TEBAgImJiTEDBw40p06dKv3Cr7B06dJc/way6+/du7dp2bJljsc0btzY+Pn5mZo1a5opU6aUet1lEX2UPloaPKmX0kevvo/ajLnGjnsCAACgyK6dKx0BAABw1Qh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsJD/B5CU7T+tW0+XAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot target distributions\n",
+        "targets_df[TARGET_CANDIDATES].plot(\n",
+        "  title=\"Target Distributions\",\n",
+        "  kind=\"hist\",\n",
+        "  bins=35,\n",
+        "  density=True,\n",
+        "  figsize=(8, 4),\n",
+        "  subplots=True,\n",
+        "  layout=(2, 2),\n",
+        "  ylabel=\"\",\n",
+        "  yticks=[]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2DM-mHG_xnNc"
+      },
+      "source": [
+        "It is also important to note that the auxilary targets can be `NaN`, but the primary target will never be `NaN`. Since we are using tree-based models here we won't need to do any special pre-processing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "id": "FT3YCXrYxnNc",
+        "outputId": "1dbaa0ec-86b7-43ed-dde8-6be3c37c02e6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-7-1209343005.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Number of NaNs per Era'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# print number of NaNs per era\n",
+        "nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n",
+        "nans_per_era[target_cols].plot(figsize=(8, 4), title=\"Number of NaNs per Era\", legend=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KdPdFvXFxnNc"
+      },
+      "source": [
+        "### Target correlations\n",
+        "\n",
+        "The targets have a wide range of correlations with each other even though they are all fundamentally related, which should allow the construction of diverse models that ensemble together nicely."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "jAvpw-XvxnNc",
+        "outputId": "1c0e3f11-e25a-4df6-a0aa-7c040ddfbd33"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot correlation matrix of targets\n",
+        "import seaborn as sns\n",
+        "sns.heatmap(\n",
+        "  targets_df[target_cols].corr(),\n",
+        "  cmap=\"coolwarm\",\n",
+        "  xticklabels=False,\n",
+        "  yticklabels=False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n4DcPlrixnNd"
+      },
+      "source": [
+        "Since we are ultimately trying to predict the main target, it is perhaps most important to consider each auxilliary target's correlation to it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "sFN8_azMxnNd",
+        "outputId": "4516093b-af13-4ea8-e8c6-efb301a842b6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                    corr_with_cyrus_v4_20\n",
+              "target_cyrusd_20                 1.000000\n",
+              "target                           1.000000\n",
+              "target_xerxes_20                 0.942907\n",
+              "target_caroline_20               0.922111\n",
+              "target_sam_20                    0.911981\n",
+              "target_ralph_20                  0.894999\n",
+              "target_echo_20                   0.851763\n",
+              "target_victor_20                 0.838408\n",
+              "target_waldo_20                  0.833260\n",
+              "target_delta_20                  0.806752\n",
+              "target_bravo_20                  0.801685\n",
+              "target_jeremy_20                 0.790352\n",
+              "target_charlie_20                0.767458\n",
+              "target_alpha_20                  0.765305\n",
+              "target_claudia_20                0.745132\n",
+              "target_teager2b_20               0.717080\n",
+              "target_agnes_20                  0.710171\n",
+              "target_tyler_20                  0.707080\n",
+              "target_rowan_20                  0.704642\n",
+              "target_cyrusd_60                 0.489257\n",
+              "target_xerxes_60                 0.485867\n",
+              "target_caroline_60               0.482128\n",
+              "target_sam_60                    0.479950\n",
+              "target_ralph_60                  0.477175\n",
+              "target_echo_60                   0.461583\n",
+              "target_victor_60                 0.459265\n",
+              "target_waldo_60                  0.455948\n",
+              "target_delta_60                  0.441798\n",
+              "target_bravo_60                  0.441597\n",
+              "target_jeremy_60                 0.437988\n",
+              "target_charlie_60                0.424056\n",
+              "target_alpha_60                  0.423646\n",
+              "target_claudia_60                0.410047\n",
+              "target_teager2b_60               0.398701\n",
+              "target_agnes_60                  0.396759\n",
+              "target_tyler_60                  0.396696\n",
+              "target_rowan_60                  0.391740"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1d57e5aa-4852-4898-bc72-1ffb73037e7d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>corr_with_cyrus_v4_20</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>target_cyrusd_20</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_xerxes_20</th>\n",
+              "      <td>0.942907</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_caroline_20</th>\n",
+              "      <td>0.922111</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_sam_20</th>\n",
+              "      <td>0.911981</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_ralph_20</th>\n",
+              "      <td>0.894999</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_echo_20</th>\n",
+              "      <td>0.851763</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_victor_20</th>\n",
+              "      <td>0.838408</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_waldo_20</th>\n",
+              "      <td>0.833260</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_delta_20</th>\n",
+              "      <td>0.806752</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_bravo_20</th>\n",
+              "      <td>0.801685</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_jeremy_20</th>\n",
+              "      <td>0.790352</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_charlie_20</th>\n",
+              "      <td>0.767458</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_alpha_20</th>\n",
+              "      <td>0.765305</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_claudia_20</th>\n",
+              "      <td>0.745132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_teager2b_20</th>\n",
+              "      <td>0.717080</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_agnes_20</th>\n",
+              "      <td>0.710171</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_tyler_20</th>\n",
+              "      <td>0.707080</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_rowan_20</th>\n",
+              "      <td>0.704642</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_cyrusd_60</th>\n",
+              "      <td>0.489257</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_xerxes_60</th>\n",
+              "      <td>0.485867</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_caroline_60</th>\n",
+              "      <td>0.482128</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_sam_60</th>\n",
+              "      <td>0.479950</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_ralph_60</th>\n",
+              "      <td>0.477175</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_echo_60</th>\n",
+              "      <td>0.461583</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_victor_60</th>\n",
+              "      <td>0.459265</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_waldo_60</th>\n",
+              "      <td>0.455948</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_delta_60</th>\n",
+              "      <td>0.441798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_bravo_60</th>\n",
+              "      <td>0.441597</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_jeremy_60</th>\n",
+              "      <td>0.437988</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_charlie_60</th>\n",
+              "      <td>0.424056</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_alpha_60</th>\n",
+              "      <td>0.423646</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_claudia_60</th>\n",
+              "      <td>0.410047</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_teager2b_60</th>\n",
+              "      <td>0.398701</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_agnes_60</th>\n",
+              "      <td>0.396759</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_tyler_60</th>\n",
+              "      <td>0.396696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target_rowan_60</th>\n",
+              "      <td>0.391740</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d57e5aa-4852-4898-bc72-1ffb73037e7d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1d57e5aa-4852-4898-bc72-1ffb73037e7d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1d57e5aa-4852-4898-bc72-1ffb73037e7d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-569127d1-c0ab-4c2e-987f-9b03e3c27b1e\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-569127d1-c0ab-4c2e-987f-9b03e3c27b1e')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-569127d1-c0ab-4c2e-987f-9b03e3c27b1e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \")\",\n  \"rows\": 37,\n  \"fields\": [\n    {\n      \"column\": \"corr_with_cyrus_v4_20\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20790557337103638,\n        \"min\": 0.39173967763293127,\n        \"max\": 1.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.39173967763293127,\n          0.7451322228078557,\n          0.44179771893050096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "(\n",
+        "    targets_df[target_cols]\n",
+        "    .corrwith(targets_df[MAIN_TARGET])\n",
+        "    .sort_values(ascending=False)\n",
+        "    .to_frame(\"corr_with_cyrus_v4_20\")\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jl7_XIKfxnNd"
+      },
+      "source": [
+        "## 2. Target Selection\n",
+        "\n",
+        "Our goal is to create an ensemble of models trained on different targets. But which targets should we use?\n",
+        "\n",
+        "When deciding which model to ensemble, we should consider a few things:\n",
+        "\n",
+        "- The performance of the predictions of the model trained on the target vs the main target\n",
+        "\n",
+        "- The correlation between the target and the main target\n",
+        "\n",
+        "To keep things simple and fast, let's just arbitrarily pick a few 20-day targets to evaluate."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4YDRiSoPxnNd"
+      },
+      "source": [
+        "### Model training and generating validation predictions\n",
+        "\n",
+        "Like usual we train on the training dataset, but this time we do it for each target.\n",
+        "\n",
+        "Sit back and relax, this will take a while\n",
+        "# ☕"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VqBaYwmqxnNd",
+        "outputId": "6716cbd7-827c-4593-b65d-25d120de0af5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002188 seconds.\n",
+            "You can set `force_row_wise=true` to remove the overhead.\n",
+            "And if memory is not enough, you can set `force_col_wise=true`.\n",
+            "[LightGBM] [Info] Total Bins 210\n",
+            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
+            "[LightGBM] [Info] Start training from score 0.500008\n",
+            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004084 seconds.\n",
+            "You can set `force_row_wise=true` to remove the overhead.\n",
+            "And if memory is not enough, you can set `force_col_wise=true`.\n",
+            "[LightGBM] [Info] Total Bins 210\n",
+            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
+            "[LightGBM] [Info] Start training from score 0.500003\n",
+            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002349 seconds.\n",
+            "You can set `force_row_wise=true` to remove the overhead.\n",
+            "And if memory is not enough, you can set `force_col_wise=true`.\n",
+            "[LightGBM] [Info] Total Bins 210\n",
+            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
+            "[LightGBM] [Info] Start training from score 0.500031\n",
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083061 seconds.\n",
+            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Total Bins 210\n",
+            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
+            "[LightGBM] [Info] Start training from score 0.499948\n"
+          ]
+        }
+      ],
+      "source": [
+        "import lightgbm as lgb\n",
+        "\n",
+        "models = {}\n",
+        "for target in TARGET_CANDIDATES:\n",
+        "    model = lgb.LGBMRegressor(\n",
+        "        n_estimators=2000,\n",
+        "        learning_rate=0.01,\n",
+        "        max_depth=5,\n",
+        "        num_leaves=2**4-1,\n",
+        "        colsample_bytree=0.1\n",
+        "    )\n",
+        "    # We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
+        "    # model = lgb.LGBMRegressor(\n",
+        "    #     n_estimators=30_000,\n",
+        "    #     learning_rate=0.001,\n",
+        "    #     max_depth=10,\n",
+        "    #     num_leaves=2**10,\n",
+        "    #     colsample_bytree=0.1\n",
+        "    #     min_data_in_leaf=10000,\n",
+        "    # )\n",
+        "    model.fit(\n",
+        "        train[feature_cols],\n",
+        "        train[target]\n",
+        "    )\n",
+        "    models[target] = model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9jD6j1JBxnNe"
+      },
+      "source": [
+        "Then we will generate predictions on the validation dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 473
+        },
+        "id": "Ic9eGKxSxnNe",
+        "outputId": "e3522459-1f43-4d45-d20c-a6edfcf6f28a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/validation.parquet: 3.45GB [01:27, 39.5MB/s]                            \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
+              "id                                                                           \n",
+              "n000c290e4364875                     0.495167                     0.491972   \n",
+              "n002a15bc5575bbb                     0.516067                     0.512950   \n",
+              "n00309caaa0f955e                     0.513778                     0.512101   \n",
+              "n0039cbdcf835708                     0.507834                     0.505156   \n",
+              "n004143458984f89                     0.484917                     0.485125   \n",
+              "...                                       ...                          ...   \n",
+              "nffc5b7319b4b998                     0.497589                     0.491416   \n",
+              "nffd7ad35b86d121                     0.509668                     0.504195   \n",
+              "nffdb1a3a768a420                     0.498573                     0.502095   \n",
+              "nffdc129924fae18                     0.493419                     0.489640   \n",
+              "nfff193e9bccc4f1                     0.494057                     0.494121   \n",
+              "\n",
+              "                  prediction_target_xerxes_20  prediction_target_teager2b_20  \n",
+              "id                                                                            \n",
+              "n000c290e4364875                     0.495561                       0.496844  \n",
+              "n002a15bc5575bbb                     0.515098                       0.508923  \n",
+              "n00309caaa0f955e                     0.513682                       0.505769  \n",
+              "n0039cbdcf835708                     0.506836                       0.504405  \n",
+              "n004143458984f89                     0.486912                       0.490126  \n",
+              "...                                       ...                            ...  \n",
+              "nffc5b7319b4b998                     0.497194                       0.498360  \n",
+              "nffd7ad35b86d121                     0.508191                       0.501186  \n",
+              "nffdb1a3a768a420                     0.496591                       0.502693  \n",
+              "nffdc129924fae18                     0.493709                       0.498717  \n",
+              "nfff193e9bccc4f1                     0.493246                       0.495440  \n",
+              "\n",
+              "[916263 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-62468f07-723f-4ee4-87da-205c05aa1bfa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>prediction_target_cyrusd_20</th>\n",
+              "      <th>prediction_target_victor_20</th>\n",
+              "      <th>prediction_target_xerxes_20</th>\n",
+              "      <th>prediction_target_teager2b_20</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n000c290e4364875</th>\n",
+              "      <td>0.495167</td>\n",
+              "      <td>0.491972</td>\n",
+              "      <td>0.495561</td>\n",
+              "      <td>0.496844</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002a15bc5575bbb</th>\n",
+              "      <td>0.516067</td>\n",
+              "      <td>0.512950</td>\n",
+              "      <td>0.515098</td>\n",
+              "      <td>0.508923</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n00309caaa0f955e</th>\n",
+              "      <td>0.513778</td>\n",
+              "      <td>0.512101</td>\n",
+              "      <td>0.513682</td>\n",
+              "      <td>0.505769</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0039cbdcf835708</th>\n",
+              "      <td>0.507834</td>\n",
+              "      <td>0.505156</td>\n",
+              "      <td>0.506836</td>\n",
+              "      <td>0.504405</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n004143458984f89</th>\n",
+              "      <td>0.484917</td>\n",
+              "      <td>0.485125</td>\n",
+              "      <td>0.486912</td>\n",
+              "      <td>0.490126</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc5b7319b4b998</th>\n",
+              "      <td>0.497589</td>\n",
+              "      <td>0.491416</td>\n",
+              "      <td>0.497194</td>\n",
+              "      <td>0.498360</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd7ad35b86d121</th>\n",
+              "      <td>0.509668</td>\n",
+              "      <td>0.504195</td>\n",
+              "      <td>0.508191</td>\n",
+              "      <td>0.501186</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdb1a3a768a420</th>\n",
+              "      <td>0.498573</td>\n",
+              "      <td>0.502095</td>\n",
+              "      <td>0.496591</td>\n",
+              "      <td>0.502693</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdc129924fae18</th>\n",
+              "      <td>0.493419</td>\n",
+              "      <td>0.489640</td>\n",
+              "      <td>0.493709</td>\n",
+              "      <td>0.498717</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff193e9bccc4f1</th>\n",
+              "      <td>0.494057</td>\n",
+              "      <td>0.494121</td>\n",
+              "      <td>0.493246</td>\n",
+              "      <td>0.495440</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>916263 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62468f07-723f-4ee4-87da-205c05aa1bfa')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-62468f07-723f-4ee4-87da-205c05aa1bfa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-62468f07-723f-4ee4-87da-205c05aa1bfa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6b30a6e8-8851-42af-b7c2-d88d0c1b256e\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b30a6e8-8851-42af-b7c2-d88d0c1b256e')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6b30a6e8-8851-42af-b7c2-d88d0c1b256e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "# Download validation data\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
+        "\n",
+        "# Load the validation data, filtering for data_type == \"validation\"\n",
+        "validation = pd.read_parquet(\n",
+        "    f\"{DATA_VERSION}/validation.parquet\",\n",
+        "    columns=[\"era\", \"data_type\"] + feature_cols + target_cols\n",
+        ")\n",
+        "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
+        "del validation[\"data_type\"]\n",
+        "\n",
+        "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n",
+        "# Comment out the line below to use all the data\n",
+        "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
+        "\n",
+        "# Embargo overlapping eras from training data\n",
+        "last_train_era = int(train[\"era\"].unique()[-1])\n",
+        "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
+        "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n",
+        "\n",
+        "# Generate validation predictions for each model\n",
+        "for target in TARGET_CANDIDATES:\n",
+        "    validation[f\"prediction_{target}\"] = models[target].predict(validation[feature_cols])\n",
+        "\n",
+        "pred_cols = [f\"prediction_{target}\" for target in TARGET_CANDIDATES]\n",
+        "validation[pred_cols]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ea2Z98CIxnNe"
+      },
+      "source": [
+        "### Evaluating the performance of each model\n",
+        "\n",
+        "Now we can evaluate the performance of our models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "NjuAERHhxnNe"
+      },
+      "outputs": [],
+      "source": [
+        "# install Numerai's open-source scoring tools\n",
+        "!pip install -q --no-deps numerai-tools\n",
+        "\n",
+        "# import the 2 scoring functions\n",
+        "from numerai_tools.scoring import numerai_corr, correlation_contribution"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q8aLpCC3xnNf"
+      },
+      "source": [
+        "As you can see in the performance chart below, models trained on the auxiliary target are able to predict the main target pretty well, but the model trained on the main target performs the best."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 614
+        },
+        "id": "WUvsFi-VxnNf",
+        "outputId": "39a65698-cf42-4a58-fe75-eaca8ec2d224"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-13-1867405524.py:5: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  correlations = validation.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "prediction_cols = [\n",
+        "    f\"prediction_{target}\"\n",
+        "    for target in TARGET_CANDIDATES\n",
+        "]\n",
+        "correlations = validation.groupby(\"era\").apply(\n",
+        "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
+        ")\n",
+        "cumsum_corrs = correlations.cumsum()\n",
+        "cumsum_corrs.plot(\n",
+        "  title=\"Cumulative Correlation of validation Predictions\",\n",
+        "  figsize=(10, 6),\n",
+        "  xticks=[]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EDUjJ3guxnNf"
+      },
+      "source": [
+        "Looking at the summary metrics below:\n",
+        "- the models trained on `victor` and `xerxes` have the highest means, but `victor` is less correlated with `cyrus` than `xerxes` is, which means `victor` could be better in ensembling\n",
+        "- the model trained on `teager` has the lowest mean, but `teager` is significantly less correlated with `cyrus` than any other target shown"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "id": "smz_GLLAxnNf",
+        "outputId": "75d86754-9e57-492a-c776-748dd04ca32f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
+            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                  mean      std   sharpe  max_drawdown  \\\n",
+              "prediction_target_cyrusd_20   0.017011 0.018632 0.912998      0.040911   \n",
+              "prediction_target_victor_20   0.016341 0.018440 0.886145      0.039038   \n",
+              "prediction_target_xerxes_20   0.017252 0.018529 0.931050      0.043307   \n",
+              "prediction_target_teager2b_20 0.014269 0.017068 0.835990      0.052751   \n",
+              "\n",
+              "                               mean_corr_with_cryus  \n",
+              "prediction_target_cyrusd_20                0.017468  \n",
+              "prediction_target_victor_20                0.016538  \n",
+              "prediction_target_xerxes_20                0.017690  \n",
+              "prediction_target_teager2b_20              0.015044  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-63115654-e64c-42b8-8e09-cf0a15f503d4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sharpe</th>\n",
+              "      <th>max_drawdown</th>\n",
+              "      <th>mean_corr_with_cryus</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_cyrusd_20</th>\n",
+              "      <td>0.017011</td>\n",
+              "      <td>0.018632</td>\n",
+              "      <td>0.912998</td>\n",
+              "      <td>0.040911</td>\n",
+              "      <td>0.017468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_victor_20</th>\n",
+              "      <td>0.016341</td>\n",
+              "      <td>0.018440</td>\n",
+              "      <td>0.886145</td>\n",
+              "      <td>0.039038</td>\n",
+              "      <td>0.016538</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_xerxes_20</th>\n",
+              "      <td>0.017252</td>\n",
+              "      <td>0.018529</td>\n",
+              "      <td>0.931050</td>\n",
+              "      <td>0.043307</td>\n",
+              "      <td>0.017690</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_teager2b_20</th>\n",
+              "      <td>0.014269</td>\n",
+              "      <td>0.017068</td>\n",
+              "      <td>0.835990</td>\n",
+              "      <td>0.052751</td>\n",
+              "      <td>0.015044</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63115654-e64c-42b8-8e09-cf0a15f503d4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-63115654-e64c-42b8-8e09-cf0a15f503d4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-63115654-e64c-42b8-8e09-cf0a15f503d4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-bcf11ce9-b717-46fd-9d8b-03bba5d67b47\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bcf11ce9-b717-46fd-9d8b-03bba5d67b47')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-bcf11ce9-b717-46fd-9d8b-03bba5d67b47 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_2ec38695-2eca-4439-8834-ec5b874ae511\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2ec38695-2eca-4439-8834-ec5b874ae511 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('summary');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "summary",
+              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0013552110339609415,\n        \"min\": 0.014269053347590915,\n        \"max\": 0.017251514021665453,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.01634063554282721,\n          0.014269053347590915,\n          0.017010595159348097\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0007367392693322167,\n        \"min\": 0.01706845463891172,\n        \"max\": 0.01863158016168851,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.018440140254275643,\n          0.01706845463891172,\n          0.01863158016168851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04137802965784783,\n        \"min\": 0.8359897629549377,\n        \"max\": 0.9310500321598153,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8861448621052853,\n          0.8359897629549377,\n          0.9129979857707619\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00608894516944781,\n        \"min\": 0.03903809673347092,\n        \"max\": 0.052751291342750584,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.03903809673347092,\n          0.052751291342750584,\n          0.04091138900274505\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_corr_with_cryus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0012024478703817265,\n        \"min\": 0.015044443122822021,\n        \"max\": 0.017690434011823932,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.016537659386694534,\n          0.015044443122822021,\n          0.017468332085298733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "def get_summary_metrics(scores, cumsum_scores):\n",
+        "    summary_metrics = {}\n",
+        "    # per era correlation between predictions of the model trained on this target and cyrus\n",
+        "    mean = scores.mean()\n",
+        "    std = scores.std()\n",
+        "    sharpe = mean / std\n",
+        "    rolling_max = cumsum_scores.expanding(min_periods=1).max()\n",
+        "    max_drawdown = (rolling_max - cumsum_scores).max()\n",
+        "    return {\n",
+        "        \"mean\": mean,\n",
+        "        \"std\": std,\n",
+        "        \"sharpe\": sharpe,\n",
+        "        \"max_drawdown\": max_drawdown,\n",
+        "    }\n",
+        "\n",
+        "target_summary_metrics = {}\n",
+        "for pred_col in prediction_cols:\n",
+        "  target_summary_metrics[pred_col] = get_summary_metrics(\n",
+        "      correlations[pred_col], cumsum_corrs[pred_col]\n",
+        "  )\n",
+        "  # per era correlation between this target and cyrus\n",
+        "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
+        "      lambda d: d[pred_col].corr(d[MAIN_TARGET])\n",
+        "  ).mean()\n",
+        "  target_summary_metrics[pred_col].update({\n",
+        "      \"mean_corr_with_cryus\": mean_corr_with_cryus\n",
+        "  })\n",
+        "\n",
+        "\n",
+        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
+        "summary = pd.DataFrame(target_summary_metrics).T\n",
+        "summary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yju_5cHJxnNf"
+      },
+      "source": [
+        "### Selecting our favorite target\n",
+        "Based on our observations above, it seems like target `victor` is the best candidate target for our ensemble since it has great performance and it is not too correlated with `cyrus`. However, it's interesting to look at how models that are very uncorrelated ensemble together we are going to also look at how `teager` ensembles with `cyrus`.\n",
+        "\n",
+        "What do you think?\n",
+        "\n",
+        "Note that this target selection heuristic is extremely basic. In your own research, you will most likely want to consider all targets instead of just our favorites, and may want to experiment with different ways of selecting your ensemble targets."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eeqgd58hxnNg"
+      },
+      "source": [
+        "## 3. Ensembling\n",
+        "\n",
+        "Now that we have reviewed and selected our favorite targets, let's ensemble our predictions and re-evaluate performance."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Rap7tHjGxnNg"
+      },
+      "source": [
+        "### Creating the ensemble\n",
+        "\n",
+        "For simplicity, we will equal weight the predictions from target `victor` and `cyrus`. Note that this is an extremely basic and arbitrary way of selecting ensemble weights. In your research, you may want to experiment with different ways of setting ensemble weights.\n",
+        "\n",
+        "Tip: remember to always normalize (percentile rank) your predictions before averaging so that they are comparable!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "y27jxEUwxnNg",
+        "outputId": "17962176-35ec-4d5a-e891-1b51779b961a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
+              "id                                                                           \n",
+              "n000c290e4364875                     0.495167                     0.491972   \n",
+              "n002a15bc5575bbb                     0.516067                     0.512950   \n",
+              "n00309caaa0f955e                     0.513778                     0.512101   \n",
+              "n0039cbdcf835708                     0.507834                     0.505156   \n",
+              "n004143458984f89                     0.484917                     0.485125   \n",
+              "...                                       ...                          ...   \n",
+              "nffc5b7319b4b998                     0.497589                     0.491416   \n",
+              "nffd7ad35b86d121                     0.509668                     0.504195   \n",
+              "nffdb1a3a768a420                     0.498573                     0.502095   \n",
+              "nffdc129924fae18                     0.493419                     0.489640   \n",
+              "nfff193e9bccc4f1                     0.494057                     0.494121   \n",
+              "\n",
+              "                  prediction_target_teager2b_20  ensemble_cyrus_victor  \\\n",
+              "id                                                                       \n",
+              "n000c290e4364875                       0.496844               0.183879   \n",
+              "n002a15bc5575bbb                       0.508923               0.965491   \n",
+              "n00309caaa0f955e                       0.505769               0.952369   \n",
+              "n0039cbdcf835708                       0.504405               0.807941   \n",
+              "n004143458984f89                       0.490126               0.012526   \n",
+              "...                                         ...                    ...   \n",
+              "nffc5b7319b4b998                       0.498360               0.247458   \n",
+              "nffd7ad35b86d121                       0.501186               0.814744   \n",
+              "nffdb1a3a768a420                       0.502693               0.533047   \n",
+              "nffdc129924fae18                       0.498717               0.120829   \n",
+              "nfff193e9bccc4f1                       0.495440               0.207340   \n",
+              "\n",
+              "                  ensemble_cyrus_teager  \n",
+              "id                                       \n",
+              "n000c290e4364875               0.253067  \n",
+              "n002a15bc5575bbb               0.968643  \n",
+              "n00309caaa0f955e               0.917604  \n",
+              "n0039cbdcf835708               0.831118  \n",
+              "n004143458984f89               0.020024  \n",
+              "...                                 ...  \n",
+              "nffc5b7319b4b998               0.380044  \n",
+              "nffd7ad35b86d121               0.750000  \n",
+              "nffdb1a3a768a420               0.571656  \n",
+              "nffdc129924fae18               0.291945  \n",
+              "nfff193e9bccc4f1               0.195742  \n",
+              "\n",
+              "[916263 rows x 5 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8a0b7416-00c1-4ced-9eed-5cde61884c7a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>prediction_target_cyrusd_20</th>\n",
+              "      <th>prediction_target_victor_20</th>\n",
+              "      <th>prediction_target_teager2b_20</th>\n",
+              "      <th>ensemble_cyrus_victor</th>\n",
+              "      <th>ensemble_cyrus_teager</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n000c290e4364875</th>\n",
+              "      <td>0.495167</td>\n",
+              "      <td>0.491972</td>\n",
+              "      <td>0.496844</td>\n",
+              "      <td>0.183879</td>\n",
+              "      <td>0.253067</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002a15bc5575bbb</th>\n",
+              "      <td>0.516067</td>\n",
+              "      <td>0.512950</td>\n",
+              "      <td>0.508923</td>\n",
+              "      <td>0.965491</td>\n",
+              "      <td>0.968643</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n00309caaa0f955e</th>\n",
+              "      <td>0.513778</td>\n",
+              "      <td>0.512101</td>\n",
+              "      <td>0.505769</td>\n",
+              "      <td>0.952369</td>\n",
+              "      <td>0.917604</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0039cbdcf835708</th>\n",
+              "      <td>0.507834</td>\n",
+              "      <td>0.505156</td>\n",
+              "      <td>0.504405</td>\n",
+              "      <td>0.807941</td>\n",
+              "      <td>0.831118</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n004143458984f89</th>\n",
+              "      <td>0.484917</td>\n",
+              "      <td>0.485125</td>\n",
+              "      <td>0.490126</td>\n",
+              "      <td>0.012526</td>\n",
+              "      <td>0.020024</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffc5b7319b4b998</th>\n",
+              "      <td>0.497589</td>\n",
+              "      <td>0.491416</td>\n",
+              "      <td>0.498360</td>\n",
+              "      <td>0.247458</td>\n",
+              "      <td>0.380044</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd7ad35b86d121</th>\n",
+              "      <td>0.509668</td>\n",
+              "      <td>0.504195</td>\n",
+              "      <td>0.501186</td>\n",
+              "      <td>0.814744</td>\n",
+              "      <td>0.750000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdb1a3a768a420</th>\n",
+              "      <td>0.498573</td>\n",
+              "      <td>0.502095</td>\n",
+              "      <td>0.502693</td>\n",
+              "      <td>0.533047</td>\n",
+              "      <td>0.571656</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffdc129924fae18</th>\n",
+              "      <td>0.493419</td>\n",
+              "      <td>0.489640</td>\n",
+              "      <td>0.498717</td>\n",
+              "      <td>0.120829</td>\n",
+              "      <td>0.291945</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff193e9bccc4f1</th>\n",
+              "      <td>0.494057</td>\n",
+              "      <td>0.494121</td>\n",
+              "      <td>0.495440</td>\n",
+              "      <td>0.207340</td>\n",
+              "      <td>0.195742</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>916263 rows × 5 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a0b7416-00c1-4ced-9eed-5cde61884c7a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8a0b7416-00c1-4ced-9eed-5cde61884c7a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8a0b7416-00c1-4ced-9eed-5cde61884c7a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-8f751442-7038-499a-9695-1cb8757475e6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f751442-7038-499a-9695-1cb8757475e6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8f751442-7038-499a-9695-1cb8757475e6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "# Ensemble predictions together with a simple average\n",
+        "validation[\"ensemble_cyrus_victor\"] = (\n",
+        "    validation\n",
+        "    .groupby(\"era\")[[\n",
+        "      f\"prediction_{MAIN_TARGET}\",\n",
+        "      \"prediction_target_victor_20\",\n",
+        "    ]]\n",
+        "    .rank(pct=True)\n",
+        "    .mean(axis=1)\n",
+        ")\n",
+        "validation[\"ensemble_cyrus_teager\"] = (\n",
+        "    validation\n",
+        "    .groupby(\"era\")[[\n",
+        "      f\"prediction_{MAIN_TARGET}\",\n",
+        "      \"prediction_target_teager2b_20\",\n",
+        "    ]]\n",
+        "    .rank(pct=True)\n",
+        "    .mean(axis=1)\n",
+        ")\n",
+        "\n",
+        "# Print the ensemble predictions\n",
+        "prediction_cols = [\n",
+        "  \"prediction_target_cyrusd_20\",\n",
+        "  \"prediction_target_victor_20\",\n",
+        "  \"prediction_target_teager2b_20\",\n",
+        "  \"ensemble_cyrus_victor\",\n",
+        "  \"ensemble_cyrus_teager\"\n",
+        "]\n",
+        "validation[prediction_cols]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sMLgtnXTxnNg"
+      },
+      "source": [
+        "### Evaluating performance of the ensemble\n",
+        "Looking at the performance chart below, we can see that the peformance of our ensembles are better than that of the models trained on individual targets. Is this a result you would have expected or does it surprise you?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 614
+        },
+        "id": "jCRbKxZmxnNg",
+        "outputId": "313c0821-f81d-46fe-f54c-e5ce95a583f7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-16-2516148216.py:1: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  correlations = validation.groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "correlations = validation.groupby(\"era\").apply(\n",
+        "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
+        ")\n",
+        "cumsum_corrs = correlations.cumsum()\n",
+        "\n",
+        "cumsum_corrs = pd.DataFrame(cumsum_corrs)\n",
+        "cumsum_corrs.plot(\n",
+        "    title=\"Cumulative Correlation of validation Predictions\",\n",
+        "    figsize=(10, 6),\n",
+        "    xticks=[]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4FjFZq-hxnNh"
+      },
+      "source": [
+        "Looking at the summary metrics below, we can see that our ensemble seems to have better `mean`, `sharpe`, and `max_drawdown` than our original model. Much more interestingly, however, is that our ensemble with `teager` has even higher sharpe than the ensemble with `victor`!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "_79JgZULxnNh",
+        "outputId": "b843f522-90c8-48a2-af55-a5b11cc19555"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                  mean      std   sharpe  max_drawdown\n",
+              "prediction_target_cyrusd_20   0.017011 0.018632 0.912998      0.040911\n",
+              "prediction_target_victor_20   0.016341 0.018440 0.886145      0.039038\n",
+              "prediction_target_teager2b_20 0.014269 0.017068 0.835990      0.052751\n",
+              "ensemble_cyrus_victor         0.016925 0.018702 0.904973      0.040448\n",
+              "ensemble_cyrus_teager         0.016209 0.017988 0.901077      0.046404"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-28906ba3-4fa4-461b-a9b7-69aac3d713e6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sharpe</th>\n",
+              "      <th>max_drawdown</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_cyrusd_20</th>\n",
+              "      <td>0.017011</td>\n",
+              "      <td>0.018632</td>\n",
+              "      <td>0.912998</td>\n",
+              "      <td>0.040911</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_victor_20</th>\n",
+              "      <td>0.016341</td>\n",
+              "      <td>0.018440</td>\n",
+              "      <td>0.886145</td>\n",
+              "      <td>0.039038</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_teager2b_20</th>\n",
+              "      <td>0.014269</td>\n",
+              "      <td>0.017068</td>\n",
+              "      <td>0.835990</td>\n",
+              "      <td>0.052751</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_victor</th>\n",
+              "      <td>0.016925</td>\n",
+              "      <td>0.018702</td>\n",
+              "      <td>0.904973</td>\n",
+              "      <td>0.040448</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_teager</th>\n",
+              "      <td>0.016209</td>\n",
+              "      <td>0.017988</td>\n",
+              "      <td>0.901077</td>\n",
+              "      <td>0.046404</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28906ba3-4fa4-461b-a9b7-69aac3d713e6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-28906ba3-4fa4-461b-a9b7-69aac3d713e6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-28906ba3-4fa4-461b-a9b7-69aac3d713e6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ed1bb218-19d8-4c12-8277-0d00f59ed43d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed1bb218-19d8-4c12-8277-0d00f59ed43d')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ed1bb218-19d8-4c12-8277-0d00f59ed43d button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_7202e2c2-6457-4122-a03f-7cace40b4378\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7202e2c2-6457-4122-a03f-7cace40b4378 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('summary');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "summary",
+              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0011089376634754338,\n        \"min\": 0.014269053347590913,\n        \"max\": 0.017010595159348104,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01634063554282721,\n          0.016208824024693678,\n          0.014269053347590913\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0006737207554049439,\n        \"min\": 0.01706845463891172,\n        \"max\": 0.018702182308678757,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.018440140254275643,\n          0.017988282096915168,\n          0.01706845463891172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.030790512224679364,\n        \"min\": 0.8359897629549377,\n        \"max\": 0.9129979857707623,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8861448621052853,\n          0.9010768197521957,\n          0.8359897629549377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005681096501031452,\n        \"min\": 0.03903809673347092,\n        \"max\": 0.052751291342750584,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.03903809673347092,\n          0.04640399457982047,\n          0.052751291342750584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "summary_metrics = get_summary_metrics(correlations, cumsum_corrs)\n",
+        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
+        "summary = pd.DataFrame(summary_metrics)\n",
+        "summary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kns8BQ1kL2mE"
+      },
+      "source": [
+        "You can see below that ensembling also improves MMC performance significantly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 803
+        },
+        "id": "k9jKnsnuL1se",
+        "outputId": "85142579-bb14-4847-e6ba-108aa6caf235"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v4.3/meta_model.parquet: 29.0MB [00:00, 36.3MB/s]                            \n",
+            "/tmp/ipython-input-18-2344724651.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                  mean      std   sharpe  max_drawdown\n",
+              "prediction_target_cyrusd_20   0.006842 0.016903 0.404755      0.063878\n",
+              "prediction_target_victor_20   0.006539 0.017877 0.365776      0.076714\n",
+              "prediction_target_teager2b_20 0.007756 0.016656 0.465640      0.082137\n",
+              "ensemble_cyrus_victor         0.006798 0.017585 0.386574      0.072389\n",
+              "ensemble_cyrus_teager         0.007615 0.016783 0.453734      0.074955"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e45df66a-389f-4c5f-9cd6-ac6813df5d02\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sharpe</th>\n",
+              "      <th>max_drawdown</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_cyrusd_20</th>\n",
+              "      <td>0.006842</td>\n",
+              "      <td>0.016903</td>\n",
+              "      <td>0.404755</td>\n",
+              "      <td>0.063878</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_victor_20</th>\n",
+              "      <td>0.006539</td>\n",
+              "      <td>0.017877</td>\n",
+              "      <td>0.365776</td>\n",
+              "      <td>0.076714</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_teager2b_20</th>\n",
+              "      <td>0.007756</td>\n",
+              "      <td>0.016656</td>\n",
+              "      <td>0.465640</td>\n",
+              "      <td>0.082137</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_victor</th>\n",
+              "      <td>0.006798</td>\n",
+              "      <td>0.017585</td>\n",
+              "      <td>0.386574</td>\n",
+              "      <td>0.072389</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_teager</th>\n",
+              "      <td>0.007615</td>\n",
+              "      <td>0.016783</td>\n",
+              "      <td>0.453734</td>\n",
+              "      <td>0.074955</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e45df66a-389f-4c5f-9cd6-ac6813df5d02')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e45df66a-389f-4c5f-9cd6-ac6813df5d02 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e45df66a-389f-4c5f-9cd6-ac6813df5d02');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c5067848-c347-40d5-a4a7-b82e0e8a0ddb\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5067848-c347-40d5-a4a7-b82e0e8a0ddb')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c5067848-c347-40d5-a4a7-b82e0e8a0ddb button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_e2453f9e-2c4a-47aa-a6bb-2370be6fa934\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_e2453f9e-2c4a-47aa-a6bb-2370be6fa934 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('summary');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "summary",
+              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005403236654931237,\n        \"min\": 0.006538985500454161,\n        \"max\": 0.007755686441149832,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.006538985500454161,\n          0.007615234289732852,\n          0.007755686441149832\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005376619402493899,\n        \"min\": 0.01665597857523989,\n        \"max\": 0.017877044515714754,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.017877044515714754,\n          0.016783463842404655,\n          0.01665597857523989\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04301257695323672,\n        \"min\": 0.36577553379732813,\n        \"max\": 0.46563979451073056,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.36577553379732813,\n          0.453734363849994,\n          0.46563979451073056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006699420402922103,\n        \"min\": 0.06387804840326167,\n        \"max\": 0.08213650174076936,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07671380497621222,\n          0.07495501510345892,\n          0.08213650174076936\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from numerai_tools.scoring import correlation_contribution\n",
+        "\n",
+        "# Download and join in the meta_model for the validation eras\n",
+        "napi.download_dataset(f\"v4.3/meta_model.parquet\", round_num=842)\n",
+        "validation[\"meta_model\"] = pd.read_parquet(\n",
+        "    f\"v4.3/meta_model.parquet\"\n",
+        ")[\"numerai_meta_model\"]\n",
+        "\n",
+        "def get_mmc(validation, meta_model_col):\n",
+        "    # Compute the per-era mmc between our predictions, the meta model, and the target values\n",
+        "    per_era_mmc = validation.dropna().groupby(\"era\").apply(\n",
+        "        lambda x: correlation_contribution(\n",
+        "            x[prediction_cols], x[meta_model_col], x[\"target\"]\n",
+        "        )\n",
+        "    )\n",
+        "\n",
+        "    cumsum_mmc = per_era_mmc.cumsum()\n",
+        "\n",
+        "    # compute summary metrics\n",
+        "    summary_metrics = get_summary_metrics(per_era_mmc, cumsum_mmc)\n",
+        "    summary = pd.DataFrame(summary_metrics)\n",
+        "\n",
+        "    return per_era_mmc, cumsum_mmc, summary\n",
+        "\n",
+        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"meta_model\")\n",
+        "# plot the cumsum mmc performance\n",
+        "cumsum_mmc.plot(\n",
+        "  title=\"Cumulative MMC of Neutralized Predictions\",\n",
+        "  figsize=(10, 6),\n",
+        "  xticks=[]\n",
+        ")\n",
+        "\n",
+        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
+        "summary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bijsOnqqOqpf"
+      },
+      "source": [
+        "#### Benchmark Models\n",
+        "\n",
+        "It's no accident that a model trained on `teager` nicely ensembles with `cyrus`. We have seen in our research that models trained or ensembled using `teager` perform well. We even released a benchmark for a [teager ensemble](https://numer.ai/v42_teager_ensemble). We submit predictions for all internally known models [here](https://numer.ai/~benchmark_models) and release files with their predictions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 473
+        },
+        "id": "QNSLiLDWOx6D",
+        "outputId": "18224a74-f6de-4bc8-cab1-84d7552b1873"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/validation_benchmark_models.parquet: 144MB [00:02, 58.2MB/s]                           \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   era  v5_lgbm_cyrusd20  v5_lgbm_teager2b20  v5_lgbm_ct_blend\n",
+              "id                                                                            \n",
+              "n000101811a8a843  0575          0.323319            0.506974          0.404506\n",
+              "n001e1318d5072ac  0575          0.860873            0.823677          0.851931\n",
+              "n002a9c5ab785cbb  0575          0.749285            0.950823          0.883941\n",
+              "n002ccf6d0e8c5ad  0575          0.981402            0.977289          0.981760\n",
+              "n0041544c345c91d  0575          0.862482            0.831903          0.857117\n",
+              "...                ...               ...                 ...               ...\n",
+              "nffaa77add7e2a53  1175          0.085748            0.071036          0.070590\n",
+              "nffd21984a44c53f  1175          0.350572            0.440184          0.392183\n",
+              "nffd33ef0b6cd58e  1175          0.913211            0.987517          0.969089\n",
+              "nffe015b219dd580  1175          0.413583            0.331253          0.368405\n",
+              "nfff7094a2835336  1175          0.308813            0.147867          0.210284\n",
+              "\n",
+              "[3720004 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5b565ee8-cfd2-4024-aa8e-474663c69c93\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>era</th>\n",
+              "      <th>v5_lgbm_cyrusd20</th>\n",
+              "      <th>v5_lgbm_teager2b20</th>\n",
+              "      <th>v5_lgbm_ct_blend</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n000101811a8a843</th>\n",
+              "      <td>0575</td>\n",
+              "      <td>0.323319</td>\n",
+              "      <td>0.506974</td>\n",
+              "      <td>0.404506</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n001e1318d5072ac</th>\n",
+              "      <td>0575</td>\n",
+              "      <td>0.860873</td>\n",
+              "      <td>0.823677</td>\n",
+              "      <td>0.851931</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002a9c5ab785cbb</th>\n",
+              "      <td>0575</td>\n",
+              "      <td>0.749285</td>\n",
+              "      <td>0.950823</td>\n",
+              "      <td>0.883941</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002ccf6d0e8c5ad</th>\n",
+              "      <td>0575</td>\n",
+              "      <td>0.981402</td>\n",
+              "      <td>0.977289</td>\n",
+              "      <td>0.981760</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0041544c345c91d</th>\n",
+              "      <td>0575</td>\n",
+              "      <td>0.862482</td>\n",
+              "      <td>0.831903</td>\n",
+              "      <td>0.857117</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffaa77add7e2a53</th>\n",
+              "      <td>1175</td>\n",
+              "      <td>0.085748</td>\n",
+              "      <td>0.071036</td>\n",
+              "      <td>0.070590</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd21984a44c53f</th>\n",
+              "      <td>1175</td>\n",
+              "      <td>0.350572</td>\n",
+              "      <td>0.440184</td>\n",
+              "      <td>0.392183</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd33ef0b6cd58e</th>\n",
+              "      <td>1175</td>\n",
+              "      <td>0.913211</td>\n",
+              "      <td>0.987517</td>\n",
+              "      <td>0.969089</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffe015b219dd580</th>\n",
+              "      <td>1175</td>\n",
+              "      <td>0.413583</td>\n",
+              "      <td>0.331253</td>\n",
+              "      <td>0.368405</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff7094a2835336</th>\n",
+              "      <td>1175</td>\n",
+              "      <td>0.308813</td>\n",
+              "      <td>0.147867</td>\n",
+              "      <td>0.210284</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3720004 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b565ee8-cfd2-4024-aa8e-474663c69c93')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5b565ee8-cfd2-4024-aa8e-474663c69c93 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5b565ee8-cfd2-4024-aa8e-474663c69c93');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-0efdd40e-2dd4-45d5-97e9-0271f2b9d40c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0efdd40e-2dd4-45d5-97e9-0271f2b9d40c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-0efdd40e-2dd4-45d5-97e9-0271f2b9d40c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_bacd99dd-9f1a-47c0-bc9d-ddb3d454f94f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('benchmark_models')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_bacd99dd-9f1a-47c0-bc9d-ddb3d454f94f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('benchmark_models');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "benchmark_models"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "# download Numerai's benchmark models\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/validation_benchmark_models.parquet\")\n",
+        "benchmark_models = pd.read_parquet(\n",
+        "    f\"{DATA_VERSION}/validation_benchmark_models.parquet\"\n",
+        ")\n",
+        "benchmark_models"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dZNr_xbQWJsf"
+      },
+      "source": [
+        "Because models trained on newer targets perform so well and we release their predictions, it's likely many users will begin to shift their models to include newer data and targets. By extension, the Meta Model will begin to include information from from these new targets.\n",
+        "\n",
+        "This means that MMC over the validation period may not be truly indicative of out-of-sample performance. The Meta Model over the early validation period did not have access to newer data/targets and MMC over the validation period may be misleading.\n",
+        "\n",
+        "So if the Meta Model was much closer to our teager ensemble, what would your MMC look like?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 785
+        },
+        "id": "OcUNnnkUWnwg",
+        "outputId": "65de24b0-9515-4205-ede5-8d5649fadc23"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-18-2344724651.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                  mean      std   sharpe  max_drawdown\n",
+              "prediction_target_cyrusd_20   0.002249 0.017214 0.130649      0.136309\n",
+              "prediction_target_victor_20   0.000769 0.017628 0.043622      0.182239\n",
+              "prediction_target_teager2b_20 0.001023 0.015379 0.066516      0.142705\n",
+              "ensemble_cyrus_victor         0.001499 0.017574 0.085287      0.163304\n",
+              "ensemble_cyrus_teager         0.001728 0.016234 0.106472      0.141152"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sharpe</th>\n",
+              "      <th>max_drawdown</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_cyrusd_20</th>\n",
+              "      <td>0.002249</td>\n",
+              "      <td>0.017214</td>\n",
+              "      <td>0.130649</td>\n",
+              "      <td>0.136309</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_victor_20</th>\n",
+              "      <td>0.000769</td>\n",
+              "      <td>0.017628</td>\n",
+              "      <td>0.043622</td>\n",
+              "      <td>0.182239</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_teager2b_20</th>\n",
+              "      <td>0.001023</td>\n",
+              "      <td>0.015379</td>\n",
+              "      <td>0.066516</td>\n",
+              "      <td>0.142705</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_victor</th>\n",
+              "      <td>0.001499</td>\n",
+              "      <td>0.017574</td>\n",
+              "      <td>0.085287</td>\n",
+              "      <td>0.163304</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_teager</th>\n",
+              "      <td>0.001728</td>\n",
+              "      <td>0.016234</td>\n",
+              "      <td>0.106472</td>\n",
+              "      <td>0.141152</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-bc3e8048-9a50-4cd7-97d8-04cfeeae0a14\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc3e8048-9a50-4cd7-97d8-04cfeeae0a14')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-bc3e8048-9a50-4cd7-97d8-04cfeeae0a14 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_b3fc122e-9528-4542-afd3-4094c0927687\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_b3fc122e-9528-4542-afd3-4094c0927687 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('summary');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "summary",
+              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005840424856666204,\n        \"min\": 0.0007689598505097739,\n        \"max\": 0.002248971292499629,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0007689598505097739,\n          0.001728447785690628,\n          0.001022914220732764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0009741581656185877,\n        \"min\": 0.015378548779248912,\n        \"max\": 0.01762771845091716,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01762771845091716,\n          0.01623383171438237,\n          0.015378548779248912\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03386493463644543,\n        \"min\": 0.04362219947243175,\n        \"max\": 0.13064866701232172,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.04362219947243175,\n          0.10647195413263456,\n          0.06651565342192992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019273897831462303,\n        \"min\": 0.1363086457209609,\n        \"max\": 0.18223874478023522,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.18223874478023522,\n          0.1411517070138636,\n          0.14270543918631223\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "validation[FAVORITE_MODEL] = benchmark_models[FAVORITE_MODEL]\n",
+        "\n",
+        "\n",
+        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, FAVORITE_MODEL)\n",
+        "# plot the cumsum mmc performance\n",
+        "cumsum_mmc.plot(\n",
+        "  title=\"Contribution of Neutralized Predictions to Numerai's Teager Ensemble\",\n",
+        "  figsize=(10, 6),\n",
+        "  xticks=[]\n",
+        ")\n",
+        "\n",
+        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
+        "summary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1KSqVvJBxnNh"
+      },
+      "source": [
+        "Ouch. Our teager models actually perform the worst. This means we aren't adding very useful signal to a model that Numerai already created, but this should not be surprising since we are training basically the same model. The model trained with `xerxes`, however, still does well against Numerai's model. What do you think this means?\n",
+        "\n",
+        "It's also helpful to if we measured the contribution of your models to all of Numerai's benchmark models. We call this Benchmark Model Contribution or `BMC`.  On the website, `BMC` measures your model's contribution to a weighted ensemble of all of our Benchmark Models.\n",
+        "\n",
+        "This is an important metric to track because it tells you how additive your model is to Numerai's known models and, by extension, how additive you might be to the Meta Model in the future.\n",
+        "\n",
+        "To keep things simple, we will use an unweighted ensemble of Numerai's Benchmarks to measure your models' BMC, let's take a look:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 785
+        },
+        "id": "39UfnEmifTMh",
+        "outputId": "827dd9fb-4682-418c-eecd-f3d1e5c90114"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-18-2344724651.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                  mean      std   sharpe  max_drawdown\n",
+              "prediction_target_cyrusd_20   0.002276 0.017224 0.132137      0.135835\n",
+              "prediction_target_victor_20   0.000803 0.017623 0.045593      0.181569\n",
+              "prediction_target_teager2b_20 0.001069 0.015360 0.069606      0.142287\n",
+              "ensemble_cyrus_victor         0.001530 0.017576 0.087045      0.162722\n",
+              "ensemble_cyrus_teager         0.001762 0.016228 0.108594      0.140513"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-29f7286c-8964-4335-b3cd-7eab7651951a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sharpe</th>\n",
+              "      <th>max_drawdown</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_cyrusd_20</th>\n",
+              "      <td>0.002276</td>\n",
+              "      <td>0.017224</td>\n",
+              "      <td>0.132137</td>\n",
+              "      <td>0.135835</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_victor_20</th>\n",
+              "      <td>0.000803</td>\n",
+              "      <td>0.017623</td>\n",
+              "      <td>0.045593</td>\n",
+              "      <td>0.181569</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction_target_teager2b_20</th>\n",
+              "      <td>0.001069</td>\n",
+              "      <td>0.015360</td>\n",
+              "      <td>0.069606</td>\n",
+              "      <td>0.142287</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_victor</th>\n",
+              "      <td>0.001530</td>\n",
+              "      <td>0.017576</td>\n",
+              "      <td>0.087045</td>\n",
+              "      <td>0.162722</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ensemble_cyrus_teager</th>\n",
+              "      <td>0.001762</td>\n",
+              "      <td>0.016228</td>\n",
+              "      <td>0.108594</td>\n",
+              "      <td>0.140513</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29f7286c-8964-4335-b3cd-7eab7651951a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-29f7286c-8964-4335-b3cd-7eab7651951a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-29f7286c-8964-4335-b3cd-7eab7651951a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6e8ea0d7-eea4-41bb-8951-c4c96167ac55\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e8ea0d7-eea4-41bb-8951-c4c96167ac55')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6e8ea0d7-eea4-41bb-8951-c4c96167ac55 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_e45f80ae-0cf6-4105-ae67-98b002b52c2e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_e45f80ae-0cf6-4105-ae67-98b002b52c2e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('summary');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "summary",
+              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005791811664620577,\n        \"min\": 0.0008034731248098968,\n        \"max\": 0.0022759138927917312,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0008034731248098968,\n          0.0017622507691957077,\n          0.0010691338428035462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0009823886659102547,\n        \"min\": 0.015359786624393811,\n        \"max\": 0.01762276674321235,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01762276674321235,\n          0.016227870265374658,\n          0.015359786624393811\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03357080235163665,\n        \"min\": 0.045592904707733556,\n        \"max\": 0.13213656698801804,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.045592904707733556,\n          0.10859408784871882,\n          0.06960603483290514\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019204231203145364,\n        \"min\": 0.13583481055610255,\n        \"max\": 0.18156932774894002,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.18156932774894002,\n          0.1405128471968606,\n          0.1422866260231198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 21
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "validation[\"numerai_benchmark\"] = (\n",
+        "    benchmark_models\n",
+        "    .groupby(\"era\")\n",
+        "    .apply(lambda x: x.mean(axis=1))\n",
+        "    .reset_index()\n",
+        "    .set_index(\"id\")[0]\n",
+        ")\n",
+        "\n",
+        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"numerai_benchmark\")\n",
+        "# plot the cumsum mmc performance\n",
+        "cumsum_mmc.plot(\n",
+        "  title=\"Cumulative BMC of Neutralized Predictions\",\n",
+        "  figsize=(10, 6),\n",
+        "  xticks=[]\n",
+        ")\n",
+        "\n",
+        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
+        "summary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q-FSdGrhqWkD"
+      },
+      "source": [
+        "Looking at the results above, none of these models seem very additive to models that Numerai can already create. It will take some research and experimentation to find something additive to Numerai's benchmarks.\n",
+        "\n",
+        "Ensembling models trained on different targets can be a very fruitful avenue of research. However, it is completely up to you whether or not to create an ensemble - there are many great performing models that don't make use of the auxilliary targets at all.\n",
+        "\n",
+        "If you are interested in learning more about targets, we highly encourage you to read up on these forum posts\n",
+        "- https://forum.numer.ai/t/how-to-ensemble-models/4034\n",
+        "- https://forum.numer.ai/t/target-jerome-is-dominating-and-thats-weird/6513"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ihVoBcnhxnNq"
+      },
+      "source": [
+        "## 4. Model Upload\n",
+        "To wrap up this notebook, let's pickle and upload our ensemble.\n",
+        "\n",
+        "As usual, we will be wrapping our submission pipeline into a function. Since we already have our favorite targets and trained models in memory, we can simply reference them in our function.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "WWdOTGy4xnNq"
+      },
+      "outputs": [],
+      "source": [
+        "# we now give you access to the live_benchmark_models if you want to use them in your ensemble\n",
+        "def predict_ensemble(\n",
+        "    live_features: pd.DataFrame,\n",
+        "    live_benchmark_models: pd.DataFrame\n",
+        ") -> pd.DataFrame:\n",
+        "    favorite_targets = [\n",
+        "        'target_cyrusd_20',\n",
+        "        'target_teager2b_20'\n",
+        "    ]\n",
+        "    # generate predictions from each model\n",
+        "    predictions = pd.DataFrame(index=live_features.index)\n",
+        "    for target in favorite_targets:\n",
+        "        predictions[target] = models[target].predict(live_features[feature_cols])\n",
+        "    # ensemble predictions\n",
+        "    ensemble = predictions.rank(pct=True).mean(axis=1)\n",
+        "    # format submission\n",
+        "    submission = ensemble.rank(pct=True, method=\"first\")\n",
+        "    return submission.to_frame(\"prediction\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 473
+        },
+        "id": "kPq_ATf0xnNr",
+        "outputId": "53bef369-0a53-4c1e-fd62-d9d4d6ec2c53"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "v5.0/live.parquet: 8.27MB [00:00, 21.9MB/s]                            \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  prediction\n",
+              "id                          \n",
+              "n001ba451c4cf24f    0.851406\n",
+              "n00208b1df989b47    0.450840\n",
+              "n002115a1e41ac5c    0.088353\n",
+              "n0021e1e026d7e47    0.433289\n",
+              "n002ddf4912dda8d    0.853191\n",
+              "...                      ...\n",
+              "nffcf4d74ac07190    0.626804\n",
+              "nffd30a4ec0c8662    0.227874\n",
+              "nffe131b7e72bc81    0.091031\n",
+              "nfff66b587bd248f    0.634836\n",
+              "nfff8eb83b5e7585    0.705191\n",
+              "\n",
+              "[6723 rows x 1 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0af64474-90bd-46e0-8abd-2ec67b404b54\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>prediction</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>n001ba451c4cf24f</th>\n",
+              "      <td>0.851406</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n00208b1df989b47</th>\n",
+              "      <td>0.450840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002115a1e41ac5c</th>\n",
+              "      <td>0.088353</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n0021e1e026d7e47</th>\n",
+              "      <td>0.433289</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>n002ddf4912dda8d</th>\n",
+              "      <td>0.853191</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffcf4d74ac07190</th>\n",
+              "      <td>0.626804</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffd30a4ec0c8662</th>\n",
+              "      <td>0.227874</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nffe131b7e72bc81</th>\n",
+              "      <td>0.091031</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff66b587bd248f</th>\n",
+              "      <td>0.634836</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nfff8eb83b5e7585</th>\n",
+              "      <td>0.705191</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>6723 rows × 1 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0af64474-90bd-46e0-8abd-2ec67b404b54')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0af64474-90bd-46e0-8abd-2ec67b404b54 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0af64474-90bd-46e0-8abd-2ec67b404b54');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-eb82d4d8-fea3-46da-bcfa-beb5fbc871f4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eb82d4d8-fea3-46da-bcfa-beb5fbc871f4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-eb82d4d8-fea3-46da-bcfa-beb5fbc871f4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"predict_ensemble(live_features, benchmark_models)\",\n  \"rows\": 6723,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          \"nd9351550a167617\",\n          \"n83f3a823c3d3ce0\",\n          \"n998459b1b948928\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28869660301670663,\n        \"min\": 0.00014874312063067083,\n        \"max\": 1.0,\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          0.9815558530417968,\n          0.6483712628290942,\n          0.22772571768555705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "# Quick test\n",
+        "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
+        "live_features = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\", columns=feature_cols)\n",
+        "predict_ensemble(live_features, benchmark_models)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "5VTrZ1Q6xnNr"
+      },
+      "outputs": [],
+      "source": [
+        "# Use the cloudpickle library to serialize your function and its dependencies\n",
+        "import cloudpickle\n",
+        "p = cloudpickle.dumps(predict_ensemble)\n",
+        "with open(\"target_ensemble.pkl\", \"wb\") as f:\n",
+        "    f.write(p)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "RiJjBD-zxnNr",
+        "outputId": "dd372d69-e8f7-4fec-8211-2078a3e18c6d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_294d3996-c53b-463c-93e4-3171696b7157\", \"target_ensemble.pkl\", 13246614)"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Download file if running in Google Colab\n",
+        "try:\n",
+        "    from google.colab import files\n",
+        "    files.download('target_ensemble.pkl')\n",
+        "except:\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jxO2YzmIxnNr"
+      },
+      "source": [
+        "That's it! Now head back to [numer.ai](numer.ai) to upload your model!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "V28"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.14"
+    },
+    "accelerator": "TPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/signals/example_model.ipynb b/signals/example_model.ipynb
new file mode 100644
index 0000000..41ec947
--- /dev/null
+++ b/signals/example_model.ipynb
@@ -0,0 +1,2256 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "490a3ab6-52e3-46f9-85ae-70d637a3892a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:27.229399Z",
+          "iopub.status.busy": "2025-09-26T23:17:27.229211Z",
+          "iopub.status.idle": "2025-09-26T23:17:28.148987Z",
+          "shell.execute_reply": "2025-09-26T23:17:28.148598Z"
+        },
+        "id": "490a3ab6-52e3-46f9-85ae-70d637a3892a"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q numerapi lightgbm pyarrow scikit-learn scipy matplotlib\n",
+        "# make sure you restart your kernel session before continuing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "552f701c-1594-4ed7-940d-98470159e961",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:28.150414Z",
+          "iopub.status.busy": "2025-09-26T23:17:28.150307Z",
+          "iopub.status.idle": "2025-09-26T23:17:28.729060Z",
+          "shell.execute_reply": "2025-09-26T23:17:28.728797Z"
+        },
+        "id": "552f701c-1594-4ed7-940d-98470159e961"
+      },
+      "outputs": [],
+      "source": [
+        "from numerapi import NumerAPI\n",
+        "import pandas as pd\n",
+        "\n",
+        "DATASET_VERSION = 'v2.1'\n",
+        "\n",
+        "napi = NumerAPI()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "99e74658",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 70
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:28.730407Z",
+          "iopub.status.busy": "2025-09-26T23:17:28.730282Z",
+          "iopub.status.idle": "2025-09-26T23:17:30.457627Z",
+          "shell.execute_reply": "2025-09-26T23:17:30.456690Z"
+        },
+        "id": "99e74658",
+        "outputId": "cb6a0013-771d-420d-cab9-a6f1119d3388"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "signals/v2.1/train.parquet: 273MB [00:08, 33.3MB/s]                           \n",
+            "signals/v2.1/validation.parquet: 456MB [00:06, 66.7MB/s]                           \n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'signals/v2.1/validation.parquet'"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "napi.download_dataset(f'signals/{DATASET_VERSION}/train.parquet')\n",
+        "napi.download_dataset(f'signals/{DATASET_VERSION}/validation.parquet')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "4ec3babc-9d2d-4388-8fdc-5065805bb1a1",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:30.459614Z",
+          "iopub.status.busy": "2025-09-26T23:17:30.459396Z",
+          "iopub.status.idle": "2025-09-26T23:17:31.177151Z",
+          "shell.execute_reply": "2025-09-26T23:17:31.176844Z"
+        },
+        "id": "4ec3babc-9d2d-4388-8fdc-5065805bb1a1"
+      },
+      "outputs": [],
+      "source": [
+        "train = pd.read_parquet(f'signals/{DATASET_VERSION}/train.parquet')\n",
+        "validation = pd.read_parquet(f'signals/{DATASET_VERSION}/validation.parquet')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f4d97db1-cd89-4f0d-9533-c978950b629f",
+      "metadata": {
+        "id": "f4d97db1-cd89-4f0d-9533-c978950b629f"
+      },
+      "source": [
+        "# Tickers\n",
+        "\n",
+        "The Signals dataset contains two tickers: `numerai_ticker` and `composite_figi`:\n",
+        "- `numerai_ticker` is given for the entire history\n",
+        "- `composite_figi` only goes back to September 2022."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "eb85e867-9635-44d0-a89c-b928cd405413",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:31.178355Z",
+          "iopub.status.busy": "2025-09-26T23:17:31.178280Z",
+          "iopub.status.idle": "2025-09-26T23:17:32.101411Z",
+          "shell.execute_reply": "2025-09-26T23:17:32.101165Z"
+        },
+        "id": "eb85e867-9635-44d0-a89c-b928cd405413",
+        "outputId": "c9417dbd-cf04-46c6-ee9e-4a3166ae6b20"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_ticker_counts_per_date(df, title):\n",
+        "    df['date'] = pd.to_datetime(df['date'])\n",
+        "\n",
+        "    # Count unique 'numerai_ticker' and 'composite_figi' per 'date'\n",
+        "    nticker_count_per_date = df.groupby('date')['numerai_ticker'].nunique().reset_index(name='numerai_ticker_count')\n",
+        "    figi_count_per_date = df.groupby('date')['composite_figi'].nunique().reset_index(name='figi_count')\n",
+        "\n",
+        "    # Merge the counts into a single DataFrame for plotting\n",
+        "    merged_counts = pd.merge(nticker_count_per_date, figi_count_per_date, on='date')\n",
+        "\n",
+        "    # Plotting\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    plt.plot(merged_counts['date'], merged_counts['numerai_ticker_count'], label='Unique Numerai Tickers', marker='o')\n",
+        "    plt.plot(merged_counts['date'], merged_counts['figi_count'], label='Unique Composite FIGIs', marker='x')\n",
+        "\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Date')\n",
+        "    plt.ylabel('Count')\n",
+        "    plt.legend()\n",
+        "    plt.xticks(rotation=45)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_ticker_counts_per_date(validation, 'Validation Dataset numerai_ticker and composite_figi Counts per Date')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c60a1d87-0cbd-4a4b-be6b-66a882fc7895",
+      "metadata": {
+        "id": "c60a1d87-0cbd-4a4b-be6b-66a882fc7895"
+      },
+      "source": [
+        "If you have Bloomberg tickers, you can map to `numerai_ticker` by replacing the exchange code with the ISO country code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "8b0f3b02-577c-4ead-a11c-01fbbef72144",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:32.102516Z",
+          "iopub.status.busy": "2025-09-26T23:17:32.102380Z",
+          "iopub.status.idle": "2025-09-26T23:17:32.107915Z",
+          "shell.execute_reply": "2025-09-26T23:17:32.107679Z"
+        },
+        "id": "8b0f3b02-577c-4ead-a11c-01fbbef72144"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "\n",
+        "# Computed using https://stockmarketmba.com/globalstockexchanges.php\n",
+        "# and https://www.isin.net/country-codes/\n",
+        "# Converting Bloomberg exchange code -> Country -> ISO 3166\n",
+        "TICKER_CTRY_MAP = {\n",
+        "    \"AU\": \"AU\", \"AV\": \"AT\", \"BB\": \"BE\", \"BZ\": \"BR\", \"CA\": \"CA\",\n",
+        "    \"CB\": \"CO\", \"CH\": \"CN\", \"CI\": \"CL\", \"CN\": \"CA\", \"CP\": \"CZ\",\n",
+        "    \"DC\": \"DK\", \"EY\": \"EG\", \"FH\": \"FI\", \"FP\": \"FR\", \"GA\": \"GR\",\n",
+        "    \"GR\": \"DE\", \"GY\": \"DE\", \"HB\": \"HU\", \"HK\": \"HK\", \"ID\": \"IE\",\n",
+        "    \"IJ\": \"ID\", \"IM\": \"IT\", \"IN\": \"IN\", \"IT\": \"IL\", \"JP\": \"JP\",\n",
+        "    \"KS\": \"KR\", \"LN\": \"GB\", \"MF\": \"MX\", \"MK\": \"MY\", \"NA\": \"NL\",\n",
+        "    \"NO\": \"NO\", \"NZ\": \"NZ\", \"PE\": \"PE\", \"PL\": \"PT\", \"PM\": \"PH\",\n",
+        "    \"PW\": \"PL\", \"QD\": \"QA\", \"RM\": \"RU\", \"SJ\": \"ZA\", \"SM\": \"ES\",\n",
+        "    \"SP\": \"SG\", \"SS\": \"SE\", \"SW\": \"CH\", \"TB\": \"TH\", \"TI\": \"TR\",\n",
+        "    \"TT\": \"TW\", \"UH\": \"AE\", \"US\": \"US\", \"UQ\": \"US\",\n",
+        "}\n",
+        "\n",
+        "def map_country_code(row):\n",
+        "    if row[\"bloomberg_ticker\"] is None:\n",
+        "        return None\n",
+        "    split_ticker = row[\"bloomberg_ticker\"].split()\n",
+        "    if len(split_ticker) < 2:\n",
+        "        print(f'No country code for {row[\"bloomberg_ticker\"]}')\n",
+        "        return None\n",
+        "\n",
+        "    ticker = split_ticker[0]\n",
+        "    country_code = split_ticker[-1]\n",
+        "    iso_country_code = TICKER_CTRY_MAP.get(country_code)\n",
+        "    return f\"{ticker} {iso_country_code}\"\n",
+        "\n",
+        "# create test dataframe with Bloomberg tickers\n",
+        "df = pd.DataFrame([\n",
+        "  {'bloomberg_ticker': '000640 KS', 'signal': random.random()},\n",
+        "  {'bloomberg_ticker': '1103 TT', 'signal': random.random()},\n",
+        "  {'bloomberg_ticker': 'A2A IM', 'signal': random.random()},\n",
+        "  {'bloomberg_ticker': 'ABBN SW', 'signal': random.random()}\n",
+        "])\n",
+        "\n",
+        "# convert to numerai_ticker\n",
+        "df['numerai_ticker'] = df.apply(\n",
+        "    map_country_code, axis=1\n",
+        ")\n",
+        "\n",
+        "assert df.iloc[0]['numerai_ticker'] == '000640 KR'\n",
+        "assert df.iloc[1]['numerai_ticker'] == '1103 TW'\n",
+        "assert df.iloc[2]['numerai_ticker'] == 'A2A IT'\n",
+        "assert df.iloc[3]['numerai_ticker'] == 'ABBN CH'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b09e5700-1208-4430-b623-c1b4265dc0f0",
+      "metadata": {
+        "id": "b09e5700-1208-4430-b623-c1b4265dc0f0"
+      },
+      "source": [
+        "# Features\n",
+        "\n",
+        "Features with `{n}(d|w)` in the name (for example, `feature_adv_20d_factor`) are time-series features that are computed over `n` days or `n` weeks.\n",
+        "\n",
+        "Features with `country_ranknorm` in the name are grouped by country, then ranked, then gaussianized.\n",
+        "\n",
+        "Features with `factor` in the name refer to risk factors that most of the targets are neutral to.\n",
+        "\n",
+        "PPO, RSI and TRIX are examples of technical indicators.\n",
+        "\n",
+        "PPO is a percentage price oscillator that compares shorter and longer moving averages in a ratio\n",
+        "RSI is the relative strength index usually used as an overbought/oversold indicator\n",
+        "TRIX is a triple exponential moving average indicator usually used as momentum or reversal feature\n",
+        "\n",
+        "`momentum_52w_less_4w` refers to one year return of a stock excluding the last 4 weeks.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "b655eb42",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:32.108945Z",
+          "iopub.status.busy": "2025-09-26T23:17:32.108877Z",
+          "iopub.status.idle": "2025-09-26T23:17:32.162024Z",
+          "shell.execute_reply": "2025-09-26T23:17:32.161802Z"
+        },
+        "id": "b655eb42",
+        "outputId": "e9bee097-5c54-4406-8328-d9f714b447e8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['feature_country',\n",
+              " 'feature_adv_20d_factor',\n",
+              " 'feature_beta_factor',\n",
+              " 'feature_book_to_price_factor',\n",
+              " 'feature_dividend_yield_factor',\n",
+              " 'feature_earnings_yield_factor',\n",
+              " 'feature_growth_factor',\n",
+              " 'feature_impact_cost_factor',\n",
+              " 'feature_market_cap_factor',\n",
+              " 'feature_momentum_12w_factor',\n",
+              " 'feature_momentum_26w_factor',\n",
+              " 'feature_momentum_52w_factor',\n",
+              " 'feature_momentum_52w_less_4w_factor',\n",
+              " 'feature_ppo_60d_130d_country_ranknorm',\n",
+              " 'feature_ppo_60d_90d_country_ranknorm',\n",
+              " 'feature_price_factor',\n",
+              " 'feature_rsi_130d_country_ranknorm',\n",
+              " 'feature_rsi_60d_country_ranknorm',\n",
+              " 'feature_rsi_90d_country_ranknorm',\n",
+              " 'feature_trix_130d_country_ranknorm',\n",
+              " 'feature_trix_60d_country_ranknorm',\n",
+              " 'feature_value_factor',\n",
+              " 'feature_volatility_factor']"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "train.filter(like=\"feature_\").columns.tolist()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "21782d68-7d7f-4df7-864d-0cdd92a00adb",
+      "metadata": {
+        "id": "21782d68-7d7f-4df7-864d-0cdd92a00adb"
+      },
+      "source": [
+        "# Modeling\n",
+        "\n",
+        "The dataset includes a small set of features that can be used on its own or in addition to your existing dataset. In this example, we will show how to use the V1 features to train and submit predictions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "7da5fa8a-d8a1-4bc7-8fe9-e2664aaaf55f",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:32.163127Z",
+          "iopub.status.busy": "2025-09-26T23:17:32.163046Z",
+          "iopub.status.idle": "2025-09-26T23:17:57.579571Z",
+          "shell.execute_reply": "2025-09-26T23:17:57.579301Z"
+        },
+        "id": "7da5fa8a-d8a1-4bc7-8fe9-e2664aaaf55f",
+        "outputId": "2e9df5d8-4fec-46e1-9bb6-441c21400c82"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.372282 seconds.\n",
+            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Total Bins 5610\n",
+            "[LightGBM] [Info] Number of data points in the train set: 2536318, number of used features: 22\n",
+            "[LightGBM] [Info] Start training from score 0.426373\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
+              "              n_estimators=2000)"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import lightgbm as lgb\n",
+        "\n",
+        "feature_cols = [col for col in train.columns if col.startswith('feature_')]\n",
+        "\n",
+        "# there are two non-numerical feature cols\n",
+        "feature_cols = [s for s in feature_cols if s not in (\"feature_country\", \"feature_exchange_code\")]\n",
+        "\n",
+        "# https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html\n",
+        "model = lgb.LGBMRegressor(\n",
+        "  n_estimators=2000,\n",
+        "  learning_rate=0.01,\n",
+        "  max_depth=5,\n",
+        "  num_leaves=2**5-1,\n",
+        "  colsample_bytree=0.1\n",
+        ")\n",
+        "\n",
+        "# This will take a few minutes 🍵\n",
+        "model.fit(\n",
+        "  train[feature_cols],\n",
+        "  train[\"target_chili_60\"]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8288cfd2",
+      "metadata": {
+        "id": "8288cfd2"
+      },
+      "source": [
+        "# Scores\n",
+        "\n",
+        "Signals uses `alpha` and `meta portfolio contribution` to determine the performance of a signal. These can be calculated with functions from our open-source `numerai-tools` package.\n",
+        "\n",
+        "First, let's download the required data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "11236339",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 70
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:57.580711Z",
+          "iopub.status.busy": "2025-09-26T23:17:57.580514Z",
+          "iopub.status.idle": "2025-09-26T23:17:58.651621Z",
+          "shell.execute_reply": "2025-09-26T23:17:58.651018Z"
+        },
+        "id": "11236339",
+        "outputId": "22df2342-48da-46ee-c365-e8cf70d97ea8"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "signals/v2.1/validation_neutralizer.parquet: 3.99GB [01:18, 50.8MB/s]                            \n",
+            "signals/v2.1/validation_sample_weights.parquet: 24.4MB [00:00, 71.8MB/s]                            \n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'signals/v2.1/validation_sample_weights.parquet'"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "napi.download_dataset(f'signals/{DATASET_VERSION}/validation_neutralizer.parquet')\n",
+        "napi.download_dataset(f'signals/{DATASET_VERSION}/validation_sample_weights.parquet')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2647e712",
+      "metadata": {
+        "id": "2647e712"
+      },
+      "source": [
+        "Then, we can use this data to calculate alpha over the validation period."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "d1da208e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:58.654087Z",
+          "iopub.status.busy": "2025-09-26T23:17:58.653878Z",
+          "iopub.status.idle": "2025-09-26T23:17:59.206668Z",
+          "shell.execute_reply": "2025-09-26T23:17:59.206187Z"
+        },
+        "id": "d1da208e"
+      },
+      "outputs": [],
+      "source": [
+        "# filter out NaN 60D targets at the end of the validation set\n",
+        "#  the rest should be filled\n",
+        "validation = validation.dropna(subset=[\"target_chili_60\"]).set_index(\"numerai_ticker\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "4967a8a7",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:59.208490Z",
+          "iopub.status.busy": "2025-09-26T23:17:59.208389Z",
+          "iopub.status.idle": "2025-09-26T23:17:59.854628Z",
+          "shell.execute_reply": "2025-09-26T23:17:59.854148Z"
+        },
+        "id": "4967a8a7"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q --no-deps numerai-tools"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "djwOHcmlw0kH",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:17:59.856531Z",
+          "iopub.status.busy": "2025-09-26T23:17:59.856386Z",
+          "iopub.status.idle": "2025-09-26T23:22:32.704646Z",
+          "shell.execute_reply": "2025-09-26T23:22:32.703495Z"
+        },
+        "id": "djwOHcmlw0kH",
+        "outputId": "e81c3d57-5a36-45b4-dc9b-9371157ed48b"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 651/651 [29:48<00:00,  2.75s/it]\n"
+          ]
+        }
+      ],
+      "source": [
+        "from tqdm import tqdm\n",
+        "from numerai_tools.scoring import alpha, center, filter_sort_index_many\n",
+        "\n",
+        "# read neutralizers and sample weights and format their date columns\n",
+        "sample_weights = (\n",
+        "    pd.read_parquet(f'signals/{DATASET_VERSION}/validation_sample_weights.parquet')\n",
+        "    .set_index(\"numerai_ticker\")\n",
+        ")\n",
+        "sample_weights[\"date\"] = pd.to_datetime(sample_weights[\"date\"])\n",
+        "\n",
+        "alpha_scores = {}\n",
+        "\n",
+        "for date, group in tqdm(\n",
+        "    validation.groupby(\"date\"),\n",
+        "    total=validation[\"date\"].nunique()\n",
+        "):\n",
+        "    # first, predict on the validation set\n",
+        "    predictions = pd.DataFrame(\n",
+        "        model.predict(group[feature_cols]),\n",
+        "        index=group.index,\n",
+        "        columns=[\"prediction\"],\n",
+        "    )\n",
+        "\n",
+        "    # then gather neutralizers and sample weights\n",
+        "    # the neutralizers are very big, so to reduce memory usage, we\n",
+        "    #  use parquet predicate filters to only load the relevant date\n",
+        "    neutralizers = (\n",
+        "        pd.read_parquet(\n",
+        "            f'signals/{DATASET_VERSION}/validation_neutralizer.parquet',\n",
+        "            filters=[(\"date\", \"=\", date.strftime(\"%Y-%m-%d\"))],\n",
+        "        ) # then set the index, filter to only the neutralizers, and drop NaNs\n",
+        "        .set_index(\"numerai_ticker\")\n",
+        "        .filter(like=\"neutralizer_\")\n",
+        "        .dropna(axis=0, how=\"all\")\n",
+        "    )\n",
+        "\n",
+        "    # get sample weights for this date, drop NaNs\n",
+        "    weights = (\n",
+        "        sample_weights.loc[sample_weights.date == date, \"sample_weights\"]\n",
+        "        .dropna()\n",
+        "    )\n",
+        "\n",
+        "    # align and sort all datasets by common ticker index\n",
+        "    predictions, neutralizers, weights, targets = filter_sort_index_many([\n",
+        "        predictions,\n",
+        "        neutralizers,\n",
+        "        weights,\n",
+        "        group[\"target_chili_60\"],\n",
+        "    ])\n",
+        "\n",
+        "    # finally, calculate alpha\n",
+        "    alpha_score = alpha(\n",
+        "        predictions=predictions,\n",
+        "        neutralizers=neutralizers,\n",
+        "        sample_weights=weights,\n",
+        "        targets=targets,\n",
+        "    )\n",
+        "\n",
+        "    alpha_scores[date] = alpha_score\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "87c08b98",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:22:32.709267Z",
+          "iopub.status.busy": "2025-09-26T23:22:32.708091Z",
+          "iopub.status.idle": "2025-09-26T23:22:32.869683Z",
+          "shell.execute_reply": "2025-09-26T23:22:32.868902Z"
+        },
+        "id": "87c08b98",
+        "outputId": "896851ea-d26c-4fc8-8d5e-9f2780531d8c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "alpha_scores = pd.DataFrame(alpha_scores).rename(columns={\"prediction\": \"alpha\"}).T\n",
+        "alpha_scores.cumsum().plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6bcd6927",
+      "metadata": {
+        "id": "6bcd6927"
+      },
+      "source": [
+        "We can see that a basic model trained on our dataset has okay performance, but it starts to level out toward the end. If we calculate the sharpe of these scores we see it's pretty low (the 10-year sharpe of the S&P500 is about 0.6):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "3fe7a3cd",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 115
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:22:32.871760Z",
+          "iopub.status.busy": "2025-09-26T23:22:32.871428Z",
+          "iopub.status.idle": "2025-09-26T23:22:32.877899Z",
+          "shell.execute_reply": "2025-09-26T23:22:32.876502Z"
+        },
+        "id": "3fe7a3cd",
+        "outputId": "fbbfc1b4-469e-4fdb-add2-ded56a7b1229"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>prediction</th>\n",
+              "      <td>0.802974</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "prediction    0.802974\n",
+              "dtype: float64"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "alpha_scores.mean() / alpha_scores.std()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7ba5b26e",
+      "metadata": {
+        "id": "7ba5b26e"
+      },
+      "source": [
+        "This is because our basic features and factors are really only useful to **remove** exposures to them, not rely on them for prediction - this is why you must bring your own data sources to Numerai Signals. We leave it as an exercise to the reader to gather data and train a model to be aware of the neutralization and the sample-weighting data. The goal should be to produce a model with good alpha.\n",
+        "\n",
+        "For now, let's continue with how to structure your live submission."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "84168430-581c-4d71-bc26-55ad94fe9c3c",
+      "metadata": {
+        "id": "84168430-581c-4d71-bc26-55ad94fe9c3c"
+      },
+      "source": [
+        "# Live Submission\n",
+        "\n",
+        "To make a live submission, you only need to submit a ticker column with its signal.\n",
+        "\n",
+        "We accept the following tickers for live submissions:\n",
+        "\n",
+        "* cusip\n",
+        "* sedol\n",
+        "* bloomberg_ticker\n",
+        "* composite_figi\n",
+        "* numerai_ticker"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cbf940f3",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "680c8c24-0e66-48ed-915c-055d81f50fe2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:22:32.879949Z",
+          "iopub.status.busy": "2025-09-26T23:22:32.879777Z",
+          "iopub.status.idle": "2025-09-26T23:22:33.511057Z",
+          "shell.execute_reply": "2025-09-26T23:22:33.510772Z"
+        },
+        "id": "680c8c24-0e66-48ed-915c-055d81f50fe2",
+        "outputId": "2070c6eb-b7cf-4b1b-f835-889b02ec5580"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"submission\",\n  \"rows\": 7168,\n  \"fields\": [\n    {\n      \"column\": \"numerai_ticker\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7168,\n        \"samples\": [\n          \"VMD US\",\n          \"185750 KR\",\n          \"VLGEA US\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"signal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28869527027192704,\n        \"min\": 6.975446428571428e-05,\n        \"max\": 0.9999302455357143,\n        \"num_unique_values\": 7168,\n        \"samples\": [\n          0.9831891741071429,\n          0.9806780133928571,\n          0.9608677455357143\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "submission"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f9e66909-0d97-4829-aaae-3331fc9e3785\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>numerai_ticker</th>\n",
+              "      <th>signal</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>000080 KR</td>\n",
+              "      <td>0.990723</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>000100 KR</td>\n",
+              "      <td>0.161063</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>000120 KR</td>\n",
+              "      <td>0.896694</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>000150 KR</td>\n",
+              "      <td>0.556431</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>000210 KR</td>\n",
+              "      <td>0.933245</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7163</th>\n",
+              "      <td>ZURN CH</td>\n",
+              "      <td>0.601493</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7164</th>\n",
+              "      <td>ZVRA US</td>\n",
+              "      <td>0.138184</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7165</th>\n",
+              "      <td>ZWS US</td>\n",
+              "      <td>0.366839</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7166</th>\n",
+              "      <td>ZYME US</td>\n",
+              "      <td>0.111258</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7167</th>\n",
+              "      <td>ZZB SE</td>\n",
+              "      <td>0.393345</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>7168 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9e66909-0d97-4829-aaae-3331fc9e3785')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f9e66909-0d97-4829-aaae-3331fc9e3785 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f9e66909-0d97-4829-aaae-3331fc9e3785');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-e8a86a76-325a-45a1-9ae2-3742a2c7781c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8a86a76-325a-45a1-9ae2-3742a2c7781c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-e8a86a76-325a-45a1-9ae2-3742a2c7781c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_effc4e3f-6179-4101-9e49-01daa80a39ea\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('submission')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_effc4e3f-6179-4101-9e49-01daa80a39ea button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('submission');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "     numerai_ticker    signal\n",
+              "0         000080 KR  0.990723\n",
+              "1         000100 KR  0.161063\n",
+              "2         000120 KR  0.896694\n",
+              "3         000150 KR  0.556431\n",
+              "4         000210 KR  0.933245\n",
+              "...             ...       ...\n",
+              "7163        ZURN CH  0.601493\n",
+              "7164        ZVRA US  0.138184\n",
+              "7165         ZWS US  0.366839\n",
+              "7166        ZYME US  0.111258\n",
+              "7167         ZZB SE  0.393345\n",
+              "\n",
+              "[7168 rows x 2 columns]"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from numerai_tools.scoring import tie_kept_rank\n",
+        "\n",
+        "napi.download_dataset(f'signals/{DATASET_VERSION}/live.parquet')\n",
+        "live = pd.read_parquet(f'signals/{DATASET_VERSION}/live.parquet')\n",
+        "\n",
+        "live['signal'] = model.predict(live[feature_cols])\n",
+        "# make sure we rank it to ensure output is between 0 and 1\n",
+        "live['signal'] = tie_kept_rank(live[['signal']])\n",
+        "\n",
+        "submission = live[['numerai_ticker', 'signal']]\n",
+        "submission"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "3f38bb35-5c5b-4a88-a7ed-c0174c2d3a94",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 121
+        },
+        "execution": {
+          "iopub.execute_input": "2025-09-26T23:22:33.512209Z",
+          "iopub.status.busy": "2025-09-26T23:22:33.512127Z",
+          "iopub.status.idle": "2025-09-26T23:22:33.589196Z",
+          "shell.execute_reply": "2025-09-26T23:22:33.588941Z"
+        },
+        "id": "3f38bb35-5c5b-4a88-a7ed-c0174c2d3a94",
+        "outputId": "824e26d9-f443-45e4-a00f-01835af6e091"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/tmp/ipython-input-1415450786.py:4: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  submission[['signal']] = tie_kept_rank(submission[['signal']])\n"
+          ]
+        },
+        {
+          "data": {
+            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "download(\"download_0d670336-388c-4442-89ba-2a8b26953591\", \"signals_example_preds.csv\", 189814)",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Save and download your predictions\n",
+        "filename = f'signals_example_preds.csv'\n",
+        "from numerai_tools.scoring import tie_kept_rank\n",
+        "submission[['signal']] = tie_kept_rank(submission[['signal']])\n",
+        "submission.to_csv(filename, index=False)\n",
+        "\n",
+        "# Download file if running in Google Colab\n",
+        "try:\n",
+        "    from google.colab import files\n",
+        "    files.download(filename)\n",
+        "except:\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "SjZUhwDoeOhZ",
+      "metadata": {
+        "id": "SjZUhwDoeOhZ"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "Now you can visit the [Submissions page](https://signals.numer.ai/submissions) to submit these predictions to your model. Once you submit, your predictions will begin scoring approximately 1 week later. Your final scores will be computed depending on the score - for example, Alpha is a 60D (or 60 business day) score, so it resolves approximately 13 weeks after you submit.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}