Shifts_evaluation_metrics.ipynb

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      "name": "Shifts_evaluation_metrics.ipynb",
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      "include_colab_link": true
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ptnv-s/Shifts_evaluation_metrics_regression/blob/main/Shifts_evaluation_metrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SAcKT71juUY"
      },
      "source": [
        "# Shifts_evaluation_metrics\n",
        "\n",
        "\n",
        "## Outline\n",
        "\n",
        "1. Repository download and Dataset Download\n",
        "2. Data loading\n",
        "3. Training & Saving\n",
        "4. Model loading\n",
        "5. Evaluation & Inference\n",
        "6. New Proposed Evaluation Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1cFKk05juUh"
      },
      "source": [
        "## 1. Repository Download and Dataset Download\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "The Github repository with useful scripts is at: [repo](https://github.com/yandex-research/shifts.git)\n",
        "\n",
        "Data is stored at: [data](https://storage.yandexcloud.net/yandex-research/shifts/weather/canonical-trn-dev-data.tar)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ari7dmBfn97T"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fNng-bojuUj",
        "outputId": "6c67cd81-55d7-408b-a18c-49b8d119049d"
      },
      "source": [
        "# Clone the repository\n",
        "! git clone https://github.com/yandex-research/shifts"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'shifts'...\n",
            "remote: Enumerating objects: 2409, done.\u001b[K\n",
            "remote: Counting objects: 100% (657/657), done.\u001b[K\n",
            "remote: Compressing objects: 100% (231/231), done.\u001b[K\n",
            "remote: Total 2409 (delta 439), reused 627 (delta 425), pack-reused 1752\u001b[K\n",
            "Receiving objects: 100% (2409/2409), 10.52 MiB | 23.07 MiB/s, done.\n",
            "Resolving deltas: 100% (1215/1215), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9iQONtAjuUn"
      },
      "source": [
        "# Add to path\n",
        "import sys\n",
        "sys.path.append('../shifts/weather/')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ml2wwzkGjuUp",
        "outputId": "39a67d42-97db-4666-c73b-ab70544915b4"
      },
      "source": [
        "# Download the data. Note, by downloading the data you agree to it's CC BY SA NC 4.0 license!\n",
        "! wget https://storage.yandexcloud.net/yandex-research/shifts/weather/canonical-trn-dev-data.tar"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-10-07 09:33:49--  https://storage.yandexcloud.net/yandex-research/shifts/weather/canonical-trn-dev-data.tar\n",
            "Resolving storage.yandexcloud.net (storage.yandexcloud.net)... 213.180.193.243, 2a02:6b8::1d9\n",
            "Connecting to storage.yandexcloud.net (storage.yandexcloud.net)|213.180.193.243|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5573713920 (5.2G) [application/x-tar]\n",
            "Saving to: ‘canonical-trn-dev-data.tar’\n",
            "\n",
            "canonical-trn-dev-d 100%[===================>]   5.19G  20.6MB/s    in 4m 31s  \n",
            "\n",
            "2021-10-07 09:38:21 (19.6 MB/s) - ‘canonical-trn-dev-data.tar’ saved [5573713920/5573713920]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5q7emfNejuUq"
      },
      "source": [
        "## 2. Data Loading\n",
        "\n",
        "All data is provided as csv files. You should have downloaded the following data files:\n",
        "\n",
        "- `train.csv`\n",
        "- `dev_in.csv`\n",
        "- `dev_out.csv`\n",
        "\n",
        "`dev_in` consists of data in-domain with `train` in terms of time and climate. `dev_out` consists of data shifted in time and climates with respect to `train`.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzPB3h3-mmCV"
      },
      "source": [
        "!mkdir Dataset"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-zTuqlzmWnl"
      },
      "source": [
        "import shutil\n",
        "shutil.unpack_archive(\"/content/canonical-trn-dev-data.tar\", \"/content/Dataset/\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "XweupaJijuUr",
        "outputId": "7df43da5-3cf1-40b4-ba3f-7ff22c5a7b48"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load each data file as a pandas data frame\n",
        "df_train = pd.read_csv('/content/Dataset/data/train.csv')\n",
        "df_train.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>gfs_total_clouds_cover_low</th>\n",
              "      <th>gfs_total_clouds_cover_middle</th>\n",
              "      <th>gfs_u_wind</th>\n",
              "      <th>gfs_v_wind</th>\n",
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              "      <th>wrf_wind_v</th>\n",
              "      <th>wrf_rain</th>\n",
              "      <th>wrf_snow</th>\n",
              "      <th>wrf_graupel</th>\n",
              "      <th>wrf_hail</th>\n",
              "      <th>wrf_t2_interpolated</th>\n",
              "      <th>wrf_t2_grad</th>\n",
              "      <th>cmc_0_1_65_0_grad</th>\n",
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              "      <th>0</th>\n",
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              "      <td>26.968800</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>dry</td>\n",
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              "      <td>-0.465033</td>\n",
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              "      <td>7.926080</td>\n",
              "      <td>8.841516</td>\n",
              "      <td>9.350854</td>\n",
              "      <td>9.077112</td>\n",
              "      <td>1.966156</td>\n",
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              "      <td>11.043268</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.328149</td>\n",
              "      <td>0.509014</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>100927.414062</td>\n",
              "      <td>0.623658</td>\n",
              "      <td>-2.533346</td>\n",
              "      <td>-3.091468</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>280.888000</td>\n",
              "      <td>-0.467407</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.600006</td>\n",
              "      <td>-2.750006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.538776e+09</td>\n",
              "      <td>29.374201</td>\n",
              "      <td>-100.927002</td>\n",
              "      <td>31.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>mild temperate</td>\n",
              "      <td>297.0</td>\n",
              "      <td>41.531032</td>\n",
              "      <td>26.992143</td>\n",
              "      <td>733.117168</td>\n",
              "      <td>305.860669</td>\n",
              "      <td>-1.361548</td>\n",
              "      <td>303.629541</td>\n",
              "      <td>302.267993</td>\n",
              "      <td>303.629541</td>\n",
              "      <td>267.178882</td>\n",
              "      <td>286.211719</td>\n",
              "      <td>291.642969</td>\n",
              "      <td>298.615479</td>\n",
              "      <td>293.701019</td>\n",
              "      <td>11.500</td>\n",
              "      <td>9.875</td>\n",
              "      <td>22.468424</td>\n",
              "      <td>26.500</td>\n",
              "      <td>1.375</td>\n",
              "      <td>6.250</td>\n",
              "      <td>0.0172</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.67340</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-3.568011</td>\n",
              "      <td>-3.632005</td>\n",
              "      <td>4.370947</td>\n",
              "      <td>0.653320</td>\n",
              "      <td>-3.227249</td>\n",
              "      <td>-4.499048</td>\n",
              "      <td>7.287793</td>\n",
              "      <td>...</td>\n",
              "      <td>24.699792</td>\n",
              "      <td>27.050012</td>\n",
              "      <td>29.526605</td>\n",
              "      <td>31.950006</td>\n",
              "      <td>32.831812</td>\n",
              "      <td>-2.257446</td>\n",
              "      <td>32.831812</td>\n",
              "      <td>30.574365</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3.851841</td>\n",
              "      <td>7.031523</td>\n",
              "      <td>8.017418</td>\n",
              "      <td>1.0</td>\n",
              "      <td>302.893005</td>\n",
              "      <td>303.356781</td>\n",
              "      <td>97645.507812</td>\n",
              "      <td>0.651412</td>\n",
              "      <td>-3.482976</td>\n",
              "      <td>5.238701</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>302.893005</td>\n",
              "      <td>0.463776</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.673400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.600006</td>\n",
              "      <td>17.950006</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.552115e+09</td>\n",
              "      <td>22.149599</td>\n",
              "      <td>113.592003</td>\n",
              "      <td>17.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>mild temperate</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>43.916531</td>\n",
              "      <td>18.842143</td>\n",
              "      <td>761.571076</td>\n",
              "      <td>294.469702</td>\n",
              "      <td>0.282837</td>\n",
              "      <td>295.153361</td>\n",
              "      <td>295.341919</td>\n",
              "      <td>295.059082</td>\n",
              "      <td>268.584375</td>\n",
              "      <td>280.319653</td>\n",
              "      <td>288.922583</td>\n",
              "      <td>292.259375</td>\n",
              "      <td>293.914505</td>\n",
              "      <td>0.750</td>\n",
              "      <td>1.000</td>\n",
              "      <td>25.403430</td>\n",
              "      <td>1.625</td>\n",
              "      <td>2.375</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82.66500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00003</td>\n",
              "      <td>0.674461</td>\n",
              "      <td>0.812148</td>\n",
              "      <td>28.046518</td>\n",
              "      <td>20.135425</td>\n",
              "      <td>11.603281</td>\n",
              "      <td>5.047708</td>\n",
              "      <td>1.483844</td>\n",
              "      <td>...</td>\n",
              "      <td>17.325555</td>\n",
              "      <td>18.692010</td>\n",
              "      <td>20.252496</td>\n",
              "      <td>21.868280</td>\n",
              "      <td>23.101434</td>\n",
              "      <td>0.287476</td>\n",
              "      <td>23.197260</td>\n",
              "      <td>23.388910</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.512065</td>\n",
              "      <td>4.074417</td>\n",
              "      <td>4.345942</td>\n",
              "      <td>1.0</td>\n",
              "      <td>295.782043</td>\n",
              "      <td>295.726929</td>\n",
              "      <td>100751.773438</td>\n",
              "      <td>0.989764</td>\n",
              "      <td>3.433286</td>\n",
              "      <td>5.181113</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>295.782043</td>\n",
              "      <td>-0.055115</td>\n",
              "      <td>0.771400</td>\n",
              "      <td>83.436400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.233978</td>\n",
              "      <td>21.450006</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.549566e+09</td>\n",
              "      <td>34.678699</td>\n",
              "      <td>-86.684799</td>\n",
              "      <td>24.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>mild temperate</td>\n",
              "      <td>193.0</td>\n",
              "      <td>40.240955</td>\n",
              "      <td>8.303571</td>\n",
              "      <td>747.524910</td>\n",
              "      <td>295.118774</td>\n",
              "      <td>1.091162</td>\n",
              "      <td>296.376611</td>\n",
              "      <td>297.467773</td>\n",
              "      <td>296.376611</td>\n",
              "      <td>262.566675</td>\n",
              "      <td>278.622852</td>\n",
              "      <td>285.923389</td>\n",
              "      <td>290.274927</td>\n",
              "      <td>288.752838</td>\n",
              "      <td>6.125</td>\n",
              "      <td>7.500</td>\n",
              "      <td>5.767445</td>\n",
              "      <td>14.000</td>\n",
              "      <td>4.875</td>\n",
              "      <td>4.125</td>\n",
              "      <td>0.0120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.08775</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2.791129</td>\n",
              "      <td>2.760229</td>\n",
              "      <td>29.527142</td>\n",
              "      <td>25.678796</td>\n",
              "      <td>14.091809</td>\n",
              "      <td>6.503445</td>\n",
              "      <td>6.488293</td>\n",
              "      <td>...</td>\n",
              "      <td>12.791437</td>\n",
              "      <td>13.875513</td>\n",
              "      <td>15.751978</td>\n",
              "      <td>17.743524</td>\n",
              "      <td>19.321313</td>\n",
              "      <td>2.133026</td>\n",
              "      <td>19.321313</td>\n",
              "      <td>21.454340</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.277510</td>\n",
              "      <td>7.151445</td>\n",
              "      <td>7.505346</td>\n",
              "      <td>1.0</td>\n",
              "      <td>295.235687</td>\n",
              "      <td>296.000824</td>\n",
              "      <td>99450.273438</td>\n",
              "      <td>0.749565</td>\n",
              "      <td>-0.158933</td>\n",
              "      <td>8.494842</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>295.235687</td>\n",
              "      <td>0.765137</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.087750</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059448</td>\n",
              "      <td>16.150018</td>\n",
              "      <td>-58.0</td>\n",
              "      <td>41.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.552910e+09</td>\n",
              "      <td>46.066667</td>\n",
              "      <td>41.966667</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>dry</td>\n",
              "      <td>90.0</td>\n",
              "      <td>30.394660</td>\n",
              "      <td>6.451429</td>\n",
              "      <td>753.168113</td>\n",
              "      <td>281.233203</td>\n",
              "      <td>-1.461133</td>\n",
              "      <td>282.810181</td>\n",
              "      <td>281.349048</td>\n",
              "      <td>282.810181</td>\n",
              "      <td>242.905981</td>\n",
              "      <td>258.822461</td>\n",
              "      <td>269.483154</td>\n",
              "      <td>274.965063</td>\n",
              "      <td>275.815735</td>\n",
              "      <td>7.000</td>\n",
              "      <td>6.875</td>\n",
              "      <td>19.727903</td>\n",
              "      <td>4.125</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.250</td>\n",
              "      <td>0.0057</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.04475</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2.635770</td>\n",
              "      <td>3.278284</td>\n",
              "      <td>8.752283</td>\n",
              "      <td>6.223914</td>\n",
              "      <td>3.826746</td>\n",
              "      <td>3.674579</td>\n",
              "      <td>-1.011131</td>\n",
              "      <td>...</td>\n",
              "      <td>0.783167</td>\n",
              "      <td>2.854852</td>\n",
              "      <td>5.040155</td>\n",
              "      <td>7.147913</td>\n",
              "      <td>10.949976</td>\n",
              "      <td>-1.080627</td>\n",
              "      <td>10.949976</td>\n",
              "      <td>9.869348</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.866836</td>\n",
              "      <td>-1.849202</td>\n",
              "      <td>3.411494</td>\n",
              "      <td>1.0</td>\n",
              "      <td>281.861969</td>\n",
              "      <td>282.492462</td>\n",
              "      <td>100790.546875</td>\n",
              "      <td>0.707056</td>\n",
              "      <td>1.406434</td>\n",
              "      <td>1.084141</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>281.861969</td>\n",
              "      <td>0.630493</td>\n",
              "      <td>0.142125</td>\n",
              "      <td>4.186875</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.400024</td>\n",
              "      <td>3.150018</td>\n",
              "      <td>18.0</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 129 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fact_time  ...  gfs_total_clouds_cover_low_next\n",
              "0  1.543321e+09  ...                              0.0\n",
              "1  1.538776e+09  ...                             11.0\n",
              "2  1.552115e+09  ...                              8.0\n",
              "3  1.549566e+09  ...                             41.0\n",
              "4  1.552910e+09  ...                             92.0\n",
              "\n",
              "[5 rows x 129 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fu2qm1HKjuUv"
      },
      "source": [
        "df_dev_in = pd.read_csv('/content/Dataset/data/dev_in.csv')\n",
        "df_dev_out = pd.read_csv('/content/Dataset/data/dev_out.csv')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1z-0LI9LkYYS"
      },
      "source": [
        "# Visualize the temperature range in the data\n",
        "def visualize_temperatures(df_list, df_names, plot_type='histogram'):\n",
        "    '''\n",
        "    Compare the distribution of temperature labels in datasets\n",
        "\n",
        "    Supported visualization formats:\n",
        "     - histogram\n",
        "     - violin\n",
        "    '''\n",
        "    temperatures = np.concatenate([df['fact_temperature'] for df in df_list])\n",
        "    names = np.concatenate([[name]*len(df) for name, df in zip(df_names, df_list)])\n",
        "    df_to_plot = pd.DataFrame(data={'Temperature': temperatures, 'Dataset Name':names})\n",
        "\n",
        "    if plot_type == 'histogram':\n",
        "        sns.histplot(\n",
        "            data=df_to_plot, x=\"Temperature\", hue=\"Dataset Name\",\n",
        "            element=\"step\", stat=\"density\", common_norm=False)\n",
        "    elif plot_type == 'violin':\n",
        "        sns.violinplot(x='Temperature', y='Dataset Name',\n",
        "                              data=df_to_plot, scale='width', palette='Set3')\n",
        "    else:\n",
        "        raise ValueError(\"Unsupported plot_type\")\n",
        "    \n",
        "    plt.show()\n",
        "    plt.clf()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pM8rWHLkjuUx"
      },
      "source": [
        "df_names = ['train', 'dev_in', 'dev_out']\n",
        "df_list = [df_train, df_dev_in, df_dev_out]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "JKtcunYMjuUz",
        "outputId": "ffa0b433-1db1-4044-8fb7-2bfbd66bce12"
      },
      "source": [
        "# Violin Plot\n",
        "visualize_temperatures(df_list, df_names, plot_type='violin')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "1B6E7MhcjuU0",
        "outputId": "6b2112b3-0673-4240-d405-60a40c1ab9e7"
      },
      "source": [
        "# Histogram\n",
        "visualize_temperatures(df_list, df_names, plot_type='histogram')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8Yv7gQojuU1"
      },
      "source": [
        "## 3. Training and Saving\n",
        "\n",
        "### Training\n",
        "\n",
        "In this analysis, the `CatBoostRegressor` is used as the model.\n",
        "- An ensemble of models are trained.\n",
        "- We use `RMSEWithUncertainty` as the loss function during training time in order to be able to calculate uncertainty measures during inference.\n",
        "- The models are trained using `df_train` and the hyperparameters should be finetuned using `df_dev_in`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Wa47lYWjuU2"
      },
      "source": [
        "# Extract features and targets, and remove redundant meta-data\n",
        "X_train = df_train.iloc[:,6:]\n",
        "X_dev_in = df_dev_in.iloc[:,6:]\n",
        "y_train = df_train['fact_temperature']\n",
        "y_dev_in = df_dev_in['fact_temperature']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fX0oAQCXjuU2"
      },
      "source": [
        "# Set training hyperparameters (note these are dummy hyperparameters - you will need to select your own)\n",
        "ensemble_size = 3\n",
        "depth = 2\n",
        "iterations = 200\n",
        "learning_rate = 0.03"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4K6elO2qjuU3",
        "outputId": "64682b10-81fe-48ea-87bd-597258b6abf6"
      },
      "source": [
        "# Train ensemble of models\n",
        "!pip install catboost\n",
        "import catboost\n",
        "\n",
        "trained_models = []\n",
        "for seed in range(ensemble_size):\n",
        "    \n",
        "    model = catboost.CatBoostRegressor(\n",
        "        learning_rate = learning_rate,\n",
        "        iterations = iterations,\n",
        "        depth = depth,\n",
        "        loss_function = 'RMSEWithUncertainty',\n",
        "        eval_metric = 'RMSE',\n",
        "        random_seed = seed)\n",
        "    \n",
        "    print(f'\\n Model index: {seed}\\n')\n",
        "    \n",
        "    model.fit(\n",
        "        X_train,\n",
        "        y_train,\n",
        "        verbose = 100,\n",
        "        eval_set = (X_dev_in, y_dev_in))\n",
        "    \n",
        "    trained_models.append(model)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.0.0-cp37-none-manylinux1_x86_64.whl (76.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 76.4 MB 57 kB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.2)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.0.0\n",
            "\n",
            " Model index: 0\n",
            "\n",
            "0:\tlearn: 10.0772485\ttest: 10.0762054\tbest: 10.0762054 (0)\ttotal: 2.36s\tremaining: 7m 49s\n",
            "100:\tlearn: 2.8933962\ttest: 2.8692723\tbest: 2.8692723 (100)\ttotal: 3m 17s\tremaining: 3m 13s\n",
            "199:\tlearn: 2.4636227\ttest: 2.4470545\tbest: 2.4470545 (199)\ttotal: 6m 9s\tremaining: 0us\n",
            "\n",
            "bestTest = 2.447054531\n",
            "bestIteration = 199\n",
            "\n",
            "\n",
            " Model index: 1\n",
            "\n",
            "0:\tlearn: 10.0797424\ttest: 10.0788222\tbest: 10.0788222 (0)\ttotal: 2.26s\tremaining: 7m 29s\n",
            "100:\tlearn: 2.8958509\ttest: 2.8717422\tbest: 2.8717422 (100)\ttotal: 3m 11s\tremaining: 3m 7s\n",
            "199:\tlearn: 2.4668849\ttest: 2.4504754\tbest: 2.4504754 (199)\ttotal: 6m\tremaining: 0us\n",
            "\n",
            "bestTest = 2.450475398\n",
            "bestIteration = 199\n",
            "\n",
            "\n",
            " Model index: 2\n",
            "\n",
            "0:\tlearn: 10.0766372\ttest: 10.0755385\tbest: 10.0755385 (0)\ttotal: 2.31s\tremaining: 7m 38s\n",
            "100:\tlearn: 2.8947113\ttest: 2.8704513\tbest: 2.8704513 (100)\ttotal: 3m 25s\tremaining: 3m 21s\n",
            "199:\tlearn: 2.4646485\ttest: 2.4479654\tbest: 2.4479654 (199)\ttotal: 6m 24s\tremaining: 0us\n",
            "\n",
            "bestTest = 2.447965358\n",
            "bestIteration = 199\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiFcK54kjuU4"
      },
      "source": [
        "### Saving\n",
        "\n",
        "The trained models are saved below into the current directory. The directory path can be changed to save the models in any preferred location."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxeD5rKFjuU6"
      },
      "source": [
        "dir_path = '.'\n",
        "for i, model in enumerate(trained_models):\n",
        "    model.save_model(f'{dir_path}/model{i+1}.cbm')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fm3xoOuYjuU6"
      },
      "source": [
        "## 4. Model Loading\n",
        "\n",
        "### Loading Trained Models\n",
        "\n",
        "This section assumes that the trained models have been saved in the current directory. The directory path can be altered to point to any location with the trained models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfP0wtWqjuU8"
      },
      "source": [
        "dir_path = '.'\n",
        "trained_models = []\n",
        "\n",
        "# 3 models trained above in this tutorial\n",
        "ensemble_size=3\n",
        "\n",
        "for ind in range(1, ensemble_size+1):\n",
        "    model = catboost.CatBoostRegressor()\n",
        "    model.load_model(f'{dir_path}/model{ind}.cbm')\n",
        "    trained_models.append(model)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GJ2xxe3juU8"
      },
      "source": [
        "### Loading Baseline Models\n",
        "\n",
        "The trained baseline models are provided at: [baseline models](https://storage.yandexcloud.net/yandex-research/shifts/weather/baseline-models.tar)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hB6xFlxM277a"
      },
      "source": [
        "!mkdir Baseline"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEf6HiGhjuU8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75b26ffe-6a97-4644-a74e-3cbc60ec7b1c"
      },
      "source": [
        "# Download baselines\n",
        "! wget https://storage.yandexcloud.net/yandex-research/shifts/weather/baseline-models.tar"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-10-07 10:03:03--  https://storage.yandexcloud.net/yandex-research/shifts/weather/baseline-models.tar\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1243392000 (1.2G) [application/x-tar]\n",
            "Saving to: ‘baseline-models.tar’\n",
            "\n",
            "baseline-models.tar 100%[===================>]   1.16G  18.3MB/s    in 60s     \n",
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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9KNHW1I2lff"
      },
      "source": [
        "shutil.unpack_archive(\"/content/baseline-models.tar\", \"/content/Baseline/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7Xft5MqjuU9"
      },
      "source": [
        "# Loading Trained Baseline Models\n",
        "# Assume models saved as dir_path/seedi.cbm\n",
        "\n",
        "dir_path = '/content/Baseline/baseline-models'\n",
        "baseline_models = []\n",
        "\n",
        "# 10 models provided\n",
        "ensemble_size=10\n",
        "\n",
        "for ind in range(1, ensemble_size+1):\n",
        "    model = catboost.CatBoostRegressor()\n",
        "    model.load_model(f'{dir_path}/seed{ind}.cbm')\n",
        "    baseline_models.append(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5-PxPJvjuU9"
      },
      "source": [
        "## 5. Evaluation and Inference\n",
        "\n",
        "Evaluation and Inference here is shown for the `baseline_models`, but it can be performed in exactly the same manner for the `trained_models`.\n",
        "\n",
        "All evaluation and inference in this section is carried out on a combined dataset of `dev_in` + `dev_out` = `dev`.\n",
        "The objective here is two fold:\n",
        "\n",
        "1. Evaluate the ensemble of trained models to get predictions for each data point\n",
        "2. Use the predictions to determine an uncertainty score for each data point using any chosen uncertainty measure\n",
        "\n",
        "It is hoped that the uncertainty measure chosen ensures that data points with greater errors yield greater uncertainties. The uncertainty scores are used to generate retention curves (lowest x% of uncertainties are kept). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfkwXdMAjuU-"
      },
      "source": [
        "# Create a combined evaluation dataset and keep only the features\n",
        "df_dev = pd.concat([df_dev_in, df_dev_out])\n",
        "X_dev = df_dev.iloc[:,6:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQyFi4chjuU-"
      },
      "source": [
        "# Get ensemble of predictions for each data point\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "def get_predictions(features_df, model):\n",
        "    '''\n",
        "    Calculates predictions on df features for specified model\n",
        "    \n",
        "    Return: array [num_samples x 2],\n",
        "        where\n",
        "            num_samples = number of rows in features_df\n",
        "            2 = [mean, variance]\n",
        "    \n",
        "    '''\n",
        "    return model.predict(features_df)\n",
        "\n",
        "\n",
        "def get_all_predictions(features_df, models_list):\n",
        "    '''\n",
        "    Return: array [ensemble_size x num_samples x 2],\n",
        "        where\n",
        "            ensemble_size = number of models in models_list\n",
        "            num_samples = number of rows in features_df\n",
        "            2 = [mean, variance]\n",
        "    '''\n",
        "    all_preds = []\n",
        "    for model in models_list:\n",
        "        preds = np.asarray(get_predictions(features_df, model))\n",
        "        all_preds.append(preds)\n",
        "    return np.stack(all_preds, axis=0)\n",
        "\n",
        "\n",
        "all_preds = get_all_predictions(X_dev, baseline_models)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbLsZAD4kl6_"
      },
      "source": [
        "def entropy_of_expected_class(probs, epsilon=1e-10):\n",
        "    \"\"\"\n",
        "    :param probs: array [num_models, num_examples, num_classes]\n",
        "    :return: array [num_examples}\n",
        "    \"\"\"\n",
        "    mean_probs = np.mean(probs, axis=0)\n",
        "    log_probs = -np.log(mean_probs + epsilon)\n",
        "    return np.sum(mean_probs * log_probs, axis=1)\n",
        "\n",
        "\n",
        "def expected_entropy_class(probs, epsilon=1e-10):\n",
        "    \"\"\"\n",
        "    :param probs: array [num_models, num_examples, num_classes]\n",
        "    :return: array [num_examples}\n",
        "    \"\"\"\n",
        "    log_probs = -np.log(probs + epsilon)\n",
        "\n",
        "    return np.mean(np.sum(probs * log_probs, axis=2), axis=0)\n",
        "\n",
        "\n",
        "def ensemble_uncertainties_classification(probs, epsilon=1e-10):\n",
        "    \"\"\"\n",
        "    :param probs: array [num_models, num_examples, num_classes]\n",
        "    :return: Dictionary of uncertaintties\n",
        "    \"\"\"\n",
        "    mean_probs = np.mean(probs, axis=0)\n",
        "    mean_lprobs = np.mean(np.log(probs + epsilon), axis=0)\n",
        "    conf = np.max(mean_probs, axis=1)\n",
        "\n",
        "    eoe = entropy_of_expected_class(probs, epsilon)\n",
        "    exe = expected_entropy_class(probs, epsilon)\n",
        "\n",
        "    mutual_info = eoe - exe\n",
        "\n",
        "    epkl = -np.sum(mean_probs * mean_lprobs, axis=1) - exe\n",
        "\n",
        "    uncertainty = {'confidence': conf,\n",
        "                   'entropy_of_expected': eoe,\n",
        "                   'expected_entropy': exe,\n",
        "                   'mutual_information': mutual_info,\n",
        "                   'epkl': epkl,\n",
        "                   'reverse_mutual_information': epkl - mutual_info\n",
        "                   }\n",
        "\n",
        "    return uncertainty\n",
        "\n",
        "\n",
        "def epkl_reg(preds, epsilon=1e-10):\n",
        "    \"\"\"\n",
        "    preds: array [n_models, n_samples, 2] - mean and var along last axis.\n",
        "    \"\"\"\n",
        "    means = preds[:, :, 0]\n",
        "    vars = preds[:, :, 1] + epsilon\n",
        "    logvars = np.log(vars)\n",
        "\n",
        "    avg_means = np.mean(means, axis=0)\n",
        "    avg_second_moments = np.mean(means * means + vars, axis=0)\n",
        "\n",
        "    inv_vars = 1.0 / vars\n",
        "    avg_inv_vars = np.mean(inv_vars, axis=0)\n",
        "    mean_inv_var = inv_vars * means\n",
        "    avg_mean_inv_var = np.mean(mean_inv_var, axis=0)\n",
        "    avg_mean2_inv_var = np.mean(means * mean_inv_var + logvars, axis=0) + np.log(2 * np.pi)\n",
        "\n",
        "    epkl = 0.5 * (avg_second_moments * avg_inv_vars - 2 * avg_means * avg_mean_inv_var + avg_mean2_inv_var)\n",
        "\n",
        "    return epkl\n",
        "\n",
        "\n",
        "def ensemble_uncertainties_regression(preds):\n",
        "    \"\"\"\n",
        "    preds: array [n_models, n_samples, 2] - last dim is mean, var\n",
        "    \"\"\"\n",
        "    epkl = epkl_reg(preds)\n",
        "\n",
        "    var_mean = np.var(preds[:, :, 0], axis=0)\n",
        "    mean_var = np.mean(preds[:, :, 1], axis=0)\n",
        "\n",
        "    uncertainty = {'tvar': var_mean + mean_var,\n",
        "                   'mvar': mean_var,\n",
        "                   'varm': var_mean,\n",
        "                   'epkl': epkl}\n",
        "\n",
        "    return uncertainty"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oqe8HdTyjuU_"
      },
      "source": [
        "# Choose any uncertainty measure to calculate uncertainty scores\n",
        "# This tutorial uses total variance as the uncertainty measure\n",
        "all_uncertainty = ensemble_uncertainties_regression(all_preds)\n",
        "uncertainties = all_uncertainty['tvar']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6zJsF9ZjuU_"
      },
      "source": [
        "### Retention Curves\n",
        "\n",
        "We plot the retention curves for two different metrics:\n",
        "1. Mean Squared Error (MSE)\n",
        "2. F1-score\n",
        "\n",
        "In each case, the x-axis represents the fraction of data points retained, where the data points are ordered by the chosen uncertainty measure."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OeM_j3VjuVA"
      },
      "source": [
        "# Get ensemble predictions\n",
        "all_preds_mean = all_preds[:,:,0]\n",
        "avg_preds = np.squeeze(np.mean(all_preds_mean, axis=0))\n",
        "\n",
        "# Get target values\n",
        "targets = np.asarray(df_dev['fact_temperature'])\n",
        "\n",
        "# squared error\n",
        "errors = (avg_preds - targets) ** 2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SK6lfEXkt0-"
      },
      "source": [
        "from sklearn.metrics import *\n",
        "from sklearn.utils.multiclass import type_of_target\n",
        "from sklearn.utils import check_consistent_length, column_or_1d, assert_all_finite\n",
        "from sklearn.utils.extmath import stable_cumsum\n",
        "\n",
        "\n",
        "def calc_uncertainty_regection_curve(errors, uncertainty, group_by_uncertainty=True):\n",
        "    n_objects = errors.shape[0]\n",
        "    if group_by_uncertainty:\n",
        "        data = pd.DataFrame(dict(\n",
        "            errors=errors,\n",
        "            uncertainty=uncertainty\n",
        "        ))\n",
        "        mean_errors = data.groupby(\"uncertainty\").mean()\n",
        "        mean_errors.rename(columns={\"errors\": \"mean_errors\"}, inplace=True)\n",
        "        data = data.join(mean_errors, \"uncertainty\")\n",
        "        data.drop(\"errors\", axis=1, inplace=True)\n",
        "\n",
        "        uncertainty_order = data[\"uncertainty\"].argsort()\n",
        "        errors = data[\"mean_errors\"][uncertainty_order]\n",
        "    else:\n",
        "        uncertainty_order = uncertainty.argsort()\n",
        "        errors = errors[uncertainty_order]\n",
        "\n",
        "    error_rates = np.zeros(n_objects + 1)\n",
        "    error_rates[:-1] = np.cumsum(errors)[::-1] / n_objects\n",
        "    return error_rates\n",
        "\n",
        "\n",
        "assert np.allclose(\n",
        "    calc_uncertainty_regection_curve(np.array([2, 1]), np.array([1, 0])).mean(),\n",
        "    2 / 3\n",
        ")\n",
        "assert np.allclose(\n",
        "    calc_uncertainty_regection_curve(np.arange(5), np.array([0, 0, 2, 1, 1])).mean(),\n",
        "    0.8\n",
        ")\n",
        "debug_errors = np.random.rand(10)\n",
        "assert np.allclose(\n",
        "    calc_uncertainty_regection_curve(debug_errors, np.zeros_like(debug_errors)).mean(),\n",
        "    debug_errors.mean() / 2\n",
        ")\n",
        "\n",
        "\n",
        "def calc_aucs(errors, uncertainty):\n",
        "    uncertainty_rejection_curve = calc_uncertainty_regection_curve(errors, uncertainty)\n",
        "    uncertainty_rejection_auc = uncertainty_rejection_curve.mean()\n",
        "    random_rejection_auc = uncertainty_rejection_curve[0] / 2\n",
        "    ideal_rejection_auc = calc_uncertainty_regection_curve(errors, errors).mean()\n",
        "\n",
        "    rejection_ratio = (uncertainty_rejection_auc - random_rejection_auc) / (\n",
        "            ideal_rejection_auc - random_rejection_auc) * 100.0\n",
        "    return rejection_ratio, uncertainty_rejection_auc\n",
        "\n",
        "\n",
        "def prr_classification(labels, probs, measure, rev: bool):\n",
        "    if rev:\n",
        "        measure = -measure\n",
        "    preds = np.argmax(probs, axis=1)\n",
        "    errors = (labels != preds).astype(\"float32\")\n",
        "    return calc_aucs(errors, measure)\n",
        "\n",
        "\n",
        "def prr_regression(targets, preds, measure):\n",
        "    preds = np.squeeze(preds)\n",
        "    # Compute MSE errors\n",
        "    errors = (preds - targets) ** 2\n",
        "    return calc_aucs(errors, measure)\n",
        "\n",
        "\n",
        "def ood_detect(domain_labels, in_measure, out_measure, mode='ROC', pos_label=1):\n",
        "    scores = np.concatenate((in_measure, out_measure), axis=0)\n",
        "    scores = np.asarray(scores)\n",
        "    if pos_label != 1:\n",
        "        scores *= -1.0\n",
        "\n",
        "    if mode == 'PR':\n",
        "        precision, recall, thresholds = precision_recall_curve(domain_labels, scores)\n",
        "        aupr = auc(recall, precision)\n",
        "        return aupr\n",
        "\n",
        "    elif mode == 'ROC':\n",
        "        roc_auc = roc_auc_score(domain_labels, scores)\n",
        "        return roc_auc\n",
        "\n",
        "\n",
        "def nll_regression(target, mu, var, epsilon=1e-8, raw=False):\n",
        "    nll = (target - mu) ** 2 / (2.0 * var + epsilon) + np.log(var + epsilon) / 2.0 + np.log(2 * np.pi) / 2.0\n",
        "    if raw:\n",
        "        return nll\n",
        "    return np.mean(nll)\n",
        "\n",
        "\n",
        "def nll_class(target, probs, epsilon=1e-10):\n",
        "    log_p = -np.log(probs + epsilon)\n",
        "    return target * log_p[:, 1] + (1 - target) * log_p[:, 0]\n",
        "\n",
        "\n",
        "def ens_nll_regression(target, preds, epsilon=1e-8, raw=False):\n",
        "    mu = preds[:, :, 0]\n",
        "    var = preds[:, :, 1]\n",
        "    nll = (target - mu) ** 2 / (2.0 * var + epsilon) + np.log(var + epsilon) / 2.0 + np.log(2 * np.pi) / 2.0\n",
        "    proba = np.exp(-1 * nll)\n",
        "    if raw:\n",
        "        return -1 * np.log(np.mean(proba, axis=0))\n",
        "    return np.mean(-1 * np.log(np.mean(proba, axis=0)))\n",
        "\n",
        "\n",
        "def calc_rmse(preds, target, raw=False):\n",
        "    if raw:\n",
        "        return (preds - target) ** 2\n",
        "    return np.sqrt(np.mean((preds - target) ** 2))\n",
        "\n",
        "\n",
        "def ens_rmse(target, preds, epsilon=1e-8, raw=False):\n",
        "    means = preds[:, :, 0]  # mean values predicted by all models\n",
        "    avg_mean = np.mean(means, axis=0)  # average predicted mean value\n",
        "    if raw:\n",
        "        return calc_rmse(avg_mean, target, raw=True)\n",
        "    return calc_rmse(avg_mean, target)\n",
        "\n",
        "\n",
        "def _check_pos_label_consistency(pos_label, y_true):\n",
        "    # ensure binary classification if pos_label is not specified\n",
        "    # classes.dtype.kind in ('O', 'U', 'S') is required to avoid\n",
        "    # triggering a FutureWarning by calling np.array_equal(a, b)\n",
        "    # when elements in the two arrays are not comparable.\n",
        "    classes = np.unique(y_true)\n",
        "    if (pos_label is None and (\n",
        "            classes.dtype.kind in 'OUS' or\n",
        "            not (np.array_equal(classes, [0, 1]) or\n",
        "                 np.array_equal(classes, [-1, 1]) or\n",
        "                 np.array_equal(classes, [0]) or\n",
        "                 np.array_equal(classes, [-1]) or\n",
        "                 np.array_equal(classes, [1])))):\n",
        "        classes_repr = \", \".join(repr(c) for c in classes)\n",
        "        raise ValueError(\n",
        "            f\"y_true takes value in {{{classes_repr}}} and pos_label is not \"\n",
        "            f\"specified: either make y_true take value in {{0, 1}} or \"\n",
        "            f\"{{-1, 1}} or pass pos_label explicitly.\"\n",
        "        )\n",
        "    elif pos_label is None:\n",
        "        pos_label = 1.0\n",
        "\n",
        "    return pos_label\n",
        "\n",
        "\n",
        "def _binary_clf_curve_ret(y_true, y_score, pos_label=None, sample_weight=None):\n",
        "    # Check to make sure y_true is valid\n",
        "    y_type = type_of_target(y_true)\n",
        "    if not (y_type == \"binary\" or\n",
        "            (y_type == \"multiclass\" and pos_label is not None)):\n",
        "        raise ValueError(\"{0} format is not supported\".format(y_type))\n",
        "\n",
        "    check_consistent_length(y_true, y_score, sample_weight)\n",
        "    y_true = column_or_1d(y_true)\n",
        "    y_score = column_or_1d(y_score)\n",
        "    assert_all_finite(y_true)\n",
        "    assert_all_finite(y_score)\n",
        "\n",
        "    if sample_weight is not None:\n",
        "        sample_weight = column_or_1d(sample_weight)\n",
        "\n",
        "    pos_label = _check_pos_label_consistency(pos_label, y_true)\n",
        "\n",
        "    # make y_true a boolean vector\n",
        "    y_true = (y_true == pos_label)\n",
        "\n",
        "    # sort scores and corresponding truth values\n",
        "    desc_score_indices = np.argsort(y_score, kind=\"mergesort\")[::-1]\n",
        "    y_score = y_score[desc_score_indices]\n",
        "    y_true = y_true[desc_score_indices]\n",
        "    if sample_weight is not None:\n",
        "        weight = sample_weight[desc_score_indices]\n",
        "    else:\n",
        "        weight = 1.\n",
        "\n",
        "    # y_score typically has many tied values. Here we extract\n",
        "    # the indices associated with the distinct values. We also\n",
        "    # concatenate a value for the end of the curve.\n",
        "    # distinct_value_indices = np.where(np.diff(y_score))[0]\n",
        "    # threshold_idxs = np.r_[distinct_value_indices, y_true.size - 1]\n",
        "\n",
        "    # accumulate the true positives with decreasing threshold\n",
        "    tps = stable_cumsum(y_true * weight)  # [threshold_idxs]\n",
        "    if sample_weight is not None:\n",
        "        # express fps as a cumsum to ensure fps is increasing even in\n",
        "        # the presence of floating point errors\n",
        "        fps = stable_cumsum((1 - y_true) * weight)  # [threshold_idxs]\n",
        "    else:\n",
        "        fps = stable_cumsum((1 - y_true))  # [threshold_idxs]\n",
        "    return fps, tps, y_score  # [threshold_idxs]\n",
        "\n",
        "\n",
        "def _precision_recall_curve_retention(y_true, probas_pred, *, pos_label=None,\n",
        "                                      sample_weight=None):\n",
        "    fps, tps, thresholds = _binary_clf_curve_ret(y_true, probas_pred,\n",
        "                                                 pos_label=pos_label,\n",
        "                                                 sample_weight=sample_weight)\n",
        "\n",
        "    precision = tps / (tps + fps)\n",
        "    precision[np.isnan(precision)] = 0\n",
        "    recall = tps / tps[-1]\n",
        "\n",
        "    # stop when full recall attained\n",
        "    # and reverse the outputs so recall is decreasing\n",
        "    last_ind = tps.searchsorted(tps[-1])\n",
        "    sl = slice(-1, None, -1)\n",
        "    return np.r_[precision[sl], 1], np.r_[recall[sl], 0], thresholds[sl]\n",
        "\n",
        "\n",
        "def _acceptable_error(errors, threshold):\n",
        "    return np.asarray(errors <= threshold, dtype=np.float32)\n",
        "\n",
        "\n",
        "def _calc_fbeta_regection_curve(errors, uncertainty, threshold, beta=1.0, group_by_uncertainty=True, eps=1e-10):\n",
        "    ae = _acceptable_error(errors, threshold)\n",
        "    pr, rec, _ = _precision_recall_curve_retention(ae, -uncertainty)\n",
        "    pr = np.asarray(pr)\n",
        "    rec = np.asarray(rec)\n",
        "    f_scores = (1 + beta ** 2) * pr * rec / (pr * beta ** 2 + rec + eps)\n",
        "\n",
        "    return f_scores, pr, rec\n",
        "\n",
        "\n",
        "def f_beta_metrics(errors, uncertainty, threshold, beta=1.0):\n",
        "    \"\"\"\n",
        "\n",
        "    :param errors: Per sample errors - array [n_samples]\n",
        "    :param uncertainty: Uncertainties associated with each prediction. rray [n_samples]\n",
        "    :param threshold: The error threshold below which we consider the prediction acceptable\n",
        "    :param beta: The beta value for the F_beta metric. Defaults to 1\n",
        "    :return: fbeta_auc, fbeta_95, retention\n",
        "    \"\"\"\n",
        "    f_scores, pr, rec = _calc_fbeta_regection_curve(errors, uncertainty, threshold, beta)\n",
        "    ret = np.arange(pr.shape[0]) / pr.shape[0]\n",
        "\n",
        "    f_auc = auc(ret[::-1], f_scores)\n",
        "    f95 = f_scores[::-1][np.int(0.95 * pr.shape[0])]\n",
        "\n",
        "    return f_auc, f95, f_scores[::-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6MF7Y5ljuVA"
      },
      "source": [
        "# MSE retention curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# MSE retention values\n",
        "rejection_mse = calc_uncertainty_regection_curve(errors, uncertainties)\n",
        "retention_mse = rejection_mse[::-1]\n",
        "\n",
        "retention_fractions = np.linspace(0,1,len(retention_mse))\n",
        "\n",
        "# plot\n",
        "plt.plot(retention_fractions, retention_mse)\n",
        "plt.ylabel('MSE')\n",
        "plt.xlabel(\"Retention Fraction\")\n",
        "plt.show()\n",
        "plt.clf()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5KBLPrljuVB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "84ff02f7-fb64-4bb0-ef6c-a7824673fd8d"
      },
      "source": [
        "# F1-score retention curve\n",
        "\n",
        "# Use an acceptable error threshold of 1 degree\n",
        "thresh = 1.0\n",
        "\n",
        "# Get all metrics\n",
        "f_auc, f95, retention_f1 = f_beta_metrics(errors, uncertainties, thresh, beta=1.0)\n",
        "print(\"F1 score at 95% retention: \", f95)\n",
        "\n",
        "retention_fractions = np.linspace(0,1,len(retention_f1))\n",
        "\n",
        "# plot\n",
        "plt.plot(retention_fractions, retention_f1)\n",
        "plt.ylabel('F1')\n",
        "plt.xlabel(\"Retention Fraction\")\n",
        "plt.show()\n",
        "plt.clf()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 score at 95% retention:  0.6583114832320486\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9p_NfA--48w"
      },
      "source": [
        "## 6. New Proposed Evaluation Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mQmPucZ_6DT"
      },
      "source": [
        "### Log-Loss Fisher Uncertainity\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3AAeXKCrAOnC"
      },
      "source": [
        "####Calculating LL-FU for ensmble model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hb-ESAgYlBW2"
      },
      "source": [
        "def LL_FU_Ensmble(preds, errors):\n",
        "  means = np.mean(preds[:,:,0],axis=0)\n",
        "  vars  = np.mean(preds[:,:,1],axis=0)\n",
        "  print(vars.shape)\n",
        "  fi = 0\n",
        "  fi_list = []\n",
        "  for i in range(vars.shape[0]):\n",
        "    fi = max((0.5*np.log10(2*np.pi*vars[i])),0.0) + (errors[i]/(2*vars[i]))\n",
        "    fi_list.append(fi)\n",
        "  #fisher_info = -0.5*np.log(2*np.pi*vars) - ((errors)/2*vars)\n",
        "  return fi_list"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQkdEBO4lOWN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efe28407-463a-4513-c481-fcb0a39cdbd1"
      },
      "source": [
        "uncertainties_ens = np.array(LL_FU_Ensmble(all_preds, errors))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iv6OblFlZNs"
      },
      "source": [
        "errors_sin = (all_preds[0,:,0] - targets) ** 2"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-633IF9TApRi"
      },
      "source": [
        "####Calculating LL-FU for single model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_bsTojWlfpf"
      },
      "source": [
        "def LL_FU_Single(preds, errors):\n",
        "  means = preds[0,:,0]\n",
        "  vars  = preds[0,:,1]\n",
        "  fi = 0\n",
        "  fi_list = []\n",
        "  for i in range(vars.shape[0]):\n",
        "    fi = max((0.5*np.log10(2*np.pi*vars[i])),0.0) + (errors[i]/(2*vars[i]))\n",
        "    fi_list.append(fi)\n",
        "  #fisher_info = -0.5*np.log(2*np.pi*vars) - ((errors)/2*vars)\n",
        "  return fi_list"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8T-XmYYll8T"
      },
      "source": [
        "uncertainties_sin = np.array(LL_FU_Single(all_preds, errors_sin))\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Cn8b_TZAtyz"
      },
      "source": [
        "####Plotting F1-retention curve for ensmble and single models for an acceptable prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyqinVva40oS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "94d89c7b-ee11-436f-cdd8-80619bc2dd49"
      },
      "source": [
        "# F1-score retention curve\n",
        "\n",
        "# Use an acceptable error threshold of 1 degree\n",
        "thresh = 1.0\n",
        "\n",
        "# Get all metrics\n",
        "f_auce, f95, retention_f1_ens = f_beta_metrics(errors, uncertainties_ens, thresh, beta=1.0)\n",
        "retention_fractions = np.linspace(0,1,len(retention_f1_ens))\n",
        "f_aucs, f95, retention_f1_single = f_beta_metrics(errors_sin, uncertainties_sin, thresh, beta=1.0)\n",
        "plt.plot(retention_fractions, retention_f1_single)\n",
        "plt.plot(retention_fractions, retention_f1_ens)\n",
        "plt.ylabel('F1 Score')\n",
        "plt.xlabel(\"Retention fraction\")\n",
        "s='Single'\n",
        "e='Ensemble'\n",
        "plt.legend([s,e])\n",
        "plt.savefig(\"ll-fu\", dpi=80)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GyJY8P7nHhSSQewI+uMEqnXDrB9YiLW4kKMCXm/u25ua+rSksdrDpwAmWx6awam8aK/em8aSHMCwsiIk9WzK6ewgN/Vw89LNphwu3jfYssOYkfPe4NXO55y3WENTmPVwbQzW7tI9g3Lhx/O1vV26dvPLKK6xZswYPDw+6d+/O+PHj8fX1Ze/evQwaNAiwVj378MMP8fQse8uuZ8+ejBgxgoyMDP785z/TsmXLi24duaqctc4jUNXim53H+N387Yzo0oy37+yLt6ezMZqTCR9MthZiufVD6DzG3kBrEGMMu49msXRPCl9vP8qxrDy8PISo0CZc27kZo8ND6Bhcv3r6FY5th81zYM8XUJQHbQZA37uttRS8/St16powj6CuqVETykRkHPAfwBOYY4z52yWvtwXmAY2dxzxhjFlS2jk1EdQ+a+PTmDkvmj5tm/D+jP4XZuGePQnzJkLGfrjtI+g0yt5AazBjDNuPnGJ5TCrr9qWz97i1wHu7wHqM7BrC6PAQ+oU2wcvTxXd7c09YBe+i34UTieDX2Jrt3W+G1ZKoAE0EVa/GJAIR8QT2AaOBZGALMNUYE1vimFnAdmPMmyISDiwxxoSWdl5NBLXL1kMnmDZnEx2CAph/38ALtzXyTlu3g1J2W2PbwzQJlMfRU2dZHZfGithUNh3IJL/IQdP6Pozt3pzxPZozqGPghVaXKxgDST/AlrnWXA9HsTVjuf+91gxmj7J/tiaCqleTZhb3BxKMMQecQcwHJgOxJY4xwLmKWI2AYy6MR1WzvcdPc/e7W2jRyJ95v+5/IQmcmyx2fCfc8oEmgQpo1difOwe2486B7cjJL2LdvnS+25PCwu1H+WTzYZrU82Zs9+aM69GcoZ2Cqr6lIALtr7Eep4/D1ves9aI/uhmatLdaCJHTyrxutDGm9q0RUUNV5Mu9K1sENwPjjDEzndt3AgOMMQ+WOKYFsBxoAtQHRhljtl7mXPcC9wK0bdu276FDh1wSs6o6R07kcuObP+Epwhf3D6J1k3rWC4VnrSSQtN5aWrL7L+0NtI7JKyzm+33pLN51nNVxaWTnFxHS0JfrIlowoWdL+rRt7LpfuEUFEPcNbJ4NhzeAl581D6TfTGjV54pvO3jwIA0aNCAwMFCTQSUZY8jMzOTMmTM/m1tg162hsiSCR5wx/EtEBgFzgR7GGMeVzqu3hmq+U7kF3PjmT2ScyeeL+wfTOaSB9UJRPsyfZg1L/OXb0OtWewOt4/KLilkTl8aCbUdZty+dgiIH7QLrMblXSyb2aknYuX8XV0jZY81c3vkpFOZYM5f7zYTuN1pzF0ooLCwkOTmZvLw818XjRvz8/GjdujXe3hePLrMrEQwCnjXGjHVuPwlgjPlriWNisJLFEef2AWCgMeaKg281EdRseYXF3Dl3EzuPZPHBjP4M6BBovVBcaNW1if8WJr4Kfe+yN1A3cyavkKXOW0cbD2TiMNY6C6PDQxjRpZnrOprzTsOuT2HLHEiPA79G0Ot2axJbDS2LXVfZlQi8sDqLRwJHsTqLbzfGxJQ4ZinwqTHmPRHpBqwCWplSgtJEUHM5HIYHP9nGkt0p/Pf23kzo2fLcC/DlPdbQw+tegv732Buom0s7ncd3MSmsiE1l44FMCosNTep5M65HC8b1aM6gDoH4eFVxUjAGDv1ozUmIXWSVxW43FKLutlZVu8oiQ6ry7Bw+eh3wCtbQ0HeMMS+IyHNAtDFmkXOk0GwgAKvj+DFjzPLSzqmJoOZ6fnEsc9cf5KnruzFzmHMooTGw5FGrdMGov8DQ39sbpLpIdn4RP+xLZ8meFFbtTSW3oJim9X0Y36M5Y7o3Z7ArRh9lp8OOD60O5pNJUC/QGoLa925oWrmaOerKdGEa5XJz1x/k+cWxTB8cenHpiDV/hXV/g8G/gzHP2xukKlVeYTE/7M9g4Y6jrIlLO58UxnZvzoSeLRjQvmnV3j5yOODgWmsIavxSMA7oNNKqb9R5LHjUkFXf6ghNBMqllu4+zm8/3sbY8Oa8Pq0PnufWFNj0Nix9zPq2N+m/NW49AXVl50YfLdp5jNXOpBAU4MN1ES0YE96cgR2qOCmcPmYtnrNtHpw5Dg1aQu9p1s9Ok9Cq+xw3polAucyOI6e49e0N9GjViI9mDrgwa3jX5/DlTOg6wSoi5+I1hpXr5BVao4++2WUlhbxCB4H1fZjYqyU3921N95ZVWD67uNBqHWx73xpdhoEOw6HPXdbPkp1rJdRymgiUSxzPOsuk//6In7cHC387hMAAZ4ff/hXwyW3QZiDcsQC8/ewNVFWZswXFrI23ksLK2DQKih10DK7PpF6tmNCrBR2DA6ruw7KSYftHsP1DyDoM9YIg8nYrKQR1qrrPcROaCFSVyy0oYspbGziUmcuXvy0xV+DIZpg3CYLCYPpia7igqpNO5RaweNdxvtl5jM1JJzAGwpoFMDo8hHE9mhPRqlHVtBQcxXBgjdW5HLcETDGEDoPed0L4pEoXvXMXmghUlXI4DPd/tJUVsanMnd6PEV2cC6Bn7Ie5o8G/Cfx6mS6M7kZSsvJYuue4Vfvo4AmKHYZWjf25sY+1QlunZlXUUjiTYhW92zoPTh0C30bQc4qVFFpGXv39bkwTgapS//gujjfWJvL0hHB+PdQ53C87HeaOgvxsmLmiwpUoVe13MqeAVXFpLNp5jPX703EY6BzibCl0b1E1S3I6HFbRu+0fWPMSivOttYl73wkRU8pc48idaCJQVebLbck88tlOpvZvy4u/dC4uU5AL8yZAaqx1O6j1ZX/WlBtKPZ3H4l3HWRmbyuYkq6XQtmk9RoeHcF1Ei6qpfXT2JOz+wkoKx3eCpw90vR4i74COI3QYqpMmAlUlth46wdRZm4gKbcK8X/e3Jho5iuHTOyF+ibWmQNfr7Q5T1VAncwpYEZvKt7uPsyExk4JiB22a+nN9REsm9GxRNaOPUnZbHcy7PoWzJ6BhK+g11epkDuxYNRdSS2kiUJWWejqPCa+tp76PJwsfGELjej7WrOGlj8HmWTD+HzDgvqufSCms2kfLYlL5ZucxfkzIoMhhCA20WgrjI1rQu00lWwpF+dYw1B0fWcNQjcMaxdbrNqvirX/jqruYWkITgaqU/KJips7aSFzKGRY+MOTCCKGf/gvL/wSDHoSxL9gbpKq1TuQUsCwmhaV7UtiQmEFhsaF1E3/Gdm/O2O7N6duuyYVJihVx+hjsnA87P4GMfeDpC12vs4rfdfyF28xx0USgKuV/v9rNx5sO88a0PlwX0cLaGfu1VU2020Rrwlg5VqRS6kpO5xWyPCaVJbuPs35/BgXFDoICfK2WQo/mDKxMQTxjrLWXd86H3Z9bt47qN4MeN0LELdaaCXV49rsmAlVhn2w+zJNf7ub+4R15fFxXa+ex7fDOeGuUxl2LdBy3cokzeYWsjbdWXlsTb5W5aODrxchuzRgf0YJrwoLx96lgR3BRASSssJLCvmXWqKOmHaHnLdaoozrYn6CJQFXItsMnue3tjQzo0JT37u5vNc/PpMAs50iMe9ZAQLDdYSo3kFdYzPr9GSyLSWHF3lRO5Rbi6+XBsLAgxvVowejwEBr5e1/9RJdz9hTsXQS7PrNWzsNAq75WK6H7L6FBSJVei100EahySzuTx8TX1uPj5cE3Dw61OocLz8K710F6PMxYDs172B2mckOFxQ42HTjByr2pLI9J4VhWHj6eHlzTOZjxPZozqlsIjepVMClkHYU9C2D3Z9YIJPGwZjFH3GzdBvVvUrUXU400EahyKSx2cPvsjew+msWX9w8hvGVD6/7qghmw50sdJqpqDGMMO46cYvGu4yzZfZzjWXl4eQiDOgYyJjyEMd2bE9KwgrWu0uOt+Ql7voATB6z5CZ1GQ8RN0Hn8z5bcrOk0EahyeeHbWGb/cJD/3BbJ5MhW1s51/4A1L8DIZ2DYI/YGqNRlOByGXUezWLrnOMtjUjmYkYMI9G3bhFHhIYwOD6FDUP3yD0s1Bo5tg90LIOZLq0y2d33oMs5ag7nTqFpRWFETgSqz5TEp3PvBVu4c2I7nb3De+on9Gj77FfS81Vp0vg6PrFB1gzGGxPRsluy2hqXuPX4agA7B9Rnfoznje1RwApujGA79ZN0+iv3aGnnk29BqIXe/0SqZXUNLZWsiUGVy5EQu17/6A20D67Hg/sH4enlaU/bnjrX6A+5aXCu++Sh1qaOnzrIyNpVlMSlsPJCJw3B+rsKY8BCiQpuWf65CcSEcXAd7voK4byAvy+pD6DbRSgqhw2rUHAVNBOqq8ouKueWtDRzIyOHbh4bRNrAe5J6AWddCcRHcu7bOjJ5Q7i0zO5+Ve1NZFpN6fq5C0/o+jOzajJHdQhgWFkR933L+Ai/Kh8TVVh9a/BIoyIb6wdBtklUqu91Q25OCJgJ1Vc8uiuG9n5J4646+jOvR3GoCf3gTHPoR7v4OWve1O0Slqlx2fhHr4tNZFpPC2vg0TucV4ePlweCOgYzqFsKY8BCalbezufCstTjTngWwfzkU5oJ/U2s2c7fJ0OFa8PJ1zQWVQhOBKtWS3cf57Ufb+PWQ9jw9MdzaufIvsP5lmPgq9L3L3gCVqgaFxQ62JJ1g1d40VsSmcvhELiLQp20TxnYPYXR4c9oH1S/fSQtyIXGV1Z8Q/x0UnLH6FDqPs1oKnUZV24RMTQTqipIycpjw2no6NQvgs/sGWdP3YxfBZ3daSwJOetXuEJWqdsYY9qdls2xPCt/FpBBzzOpsPrcC25juzenZqhEe5elXKMqHA2ut/1/x31rls73rQdho6xZS2GiXruiniUBdVkGRg5ve/InDJ3L59ndDad2knjV2evYvILgL3L3UliasUjXNkRO5zglsF9ZVaN7Qz5kUQhjQvpw1kIoLrVnMexfB3sWQkwYe3hA61BqB1OU6aNSqSq9BE4G6rL8u3cvb6w5c6BfIO20lgbxTcO+6Kv9BVKouOJVbwOq4NJbFpLBuXzp5hQ4a+HpxTZdgxoSHMLxzs/LNbHYUQ/IWiPvW6mjOTLD2t4i8kBRCuld62LYmAvUzPyZkcMfcTc6VxiKsSTOf3mHVcL9rkfXNRClVqrMFxfyYkMGquFRWxKaRkZ2Pp4fQL7QJo8OtoaltmpZzBnL6PuvWUdwSK0FgoHFbq2R2r6nQdmCFYtVEoC5yIqeA8f/5ngBfLxY/NMyq4PjTa7D8KRj7Igx6wO4Qlap1HA7DzuRT5zub41PPANC1eQPGOOcrlHsS25lU2Ped1VI4vNH6/9l7WoXi00SgzjPGcO8HW1kXn86Xvx1Mj1aN4MhmeHe8NZLh1g915rBSVSApI4cVsaksj00h+tBJjIEWjfwY1S2EUeEhDOzQ1Jq0WR7GVPj/pyYCdd5Hmw7xp6/28NT13Zg5rIM1aeytYVZZ6fu+d8sl/JRytYzsfNbEpbFybyrf78vgbGEx9X08ubZLMCO7hjC8SzCBAa4dmFFaIqg585+Vy+1PPcPzi2O5pnMwvx7SHhwO+Oo3kJ1qlZXWJKCUSwQF+DIlqg1TotqQV1jMhsRMVuxNZWVsKkt2pyACvds0ZmS3EEZ2a0aXkAaVW7O5nLRF4CYKihxMfv1H0k7nsfT3w2jWwA9+/A+seFoXnlfKJg6HIebYaVbFpbI6Lo1dyVkAtGrsz4iuwVwTFszgTkEElLfkxWVoi0Dx6qr97D1+mjm/irKSwOFN1uzhbpOg/712h6eUW/LwECJaNyKidSN+P6ozaafzWBOfxsq9aXy57SgfbjyMl4fQt10T+rZrwtjuzenVpupb7poI3MD2wyd5Y20CU/q2ZlR4CORkwhd3Q+M2MPm/2jmsVA3RrKEft/Zry6392lJQ5CD60Am+35fBun3pvLUukbZN62kiUOV3tqCY//lsJy0a+Vt1hIyBrx+AnHSrX8CFU9qVUhVnFb8LYnDHIJ4Y3xWHw+Bw0a18TQR13D+WxXEgI4ePZw6ggZ83bJ4N+5bC2L9Cy952h6eUKiMPD8ED17Tey1Eco/xEZJyIxItIgog8cYVjbhGRWBGJEZGPXRmPu/kpMYN3f0xi+uBQBncKgrS91qSxjiNhwG/sDk8pVUO4rEUgIp7A68BoIBnYIiKLjDGxJY4JA54Ehllxf8EAABqwSURBVBhjTopIM1fF427O5BXy6Oe7aB9Un8fHdYXCPFgwE3wC4IY3wcOl3wGUUrWIK38b9AcSjDEHjDEFwHxg8iXH3AO8bow5CWCMSXNhPG7l/xbv5XjWWf51Sy+rhMSqv0DqHrjhDV1pTCl1EVcmglbAkRLbyc59JXUGOovIjyKyUUTGXe5EInKviESLSHR6erqLwq071u1L59PoI9x3bUf6tG0C+1fCxjesYaKdx9odnlKqhrH7/oAXEAYMB6YCs0XkZ2OjjDGzjDFRxpio4ODgag6xdsnOL+J/v9xNx+D6/H5UGGSnw8L7oVk4jH7O7vCUUjWQKxPBUaBNie3Wzn0lJQOLjDGFxpiDwD6sxKAq6KVl8RzLOss/bu6Jr6cHLHoQ8rLgpjnVtiSeUqp2cWUi2AKEiUh7EfEBbgMWXXLMQqzWACIShHWr6IALY6rTopNOMG9DEncNCqVvu6aw/QOrhO2oZ62FLZRS6jJclgiMMUXAg8AyYC/wmTEmRkSeE5FJzsOWAZkiEgusAR41xmS6Kqa6LK+wmMcX7KJlI38eHdsFTh6C756E0GE6VFQpVSqXTigzxiwBllyy7+kSzw3wiPOhKuH1NQkkpucw79f9qe/tYc0eBpj8ug4VVUqVSmcW1wGxx07z5tpEbuzTims7B8OmWZD0A0x8FZq0szs8pVQNp18Va7miYgePL9hF43re/Pn6cMhMtEpLdxoNfX5ld3hKqVpAE0EtN2/DIXYfzeLZSd1p4u9pLTTj5QuTXtOqokqpMilTIhCRoSJyt/N5sIi0d21YqixSsvJ4eXk8w7sEc31EC2sB+uTNcN1L0LCF3eEppWqJqyYCEXkGeByrJhCAN/ChK4NSZfP84liKHIbnJvVA0uNhzQvWQjMRN9sdmlKqFilLi+CXwCQgB8AYcwxo4Mqg1NWtjU/j293HeXBEJ9o28bUmjvkEwPUv6y0hpVS5lGXUUIExxoiIARCR+i6OSV1FXmExT38dQ4fg+tx7bQdrjYHkLXDjbAjQEhxKqfIpS4vgMxF5G2gsIvcAK4HZrg1LleaNNQkcPpHL/03uge+ZZFj1nDVKKGKK3aEppWqhUlsEIiLAp0BX4DTQBXjaGLOiGmJTl5GYns1b6w5wQ2RLBncMhA/vsW4FTfi33hJSSlVIqYnAeUtoiTEmAtBf/jYzxvDnhXvw9fbgT9eHw875kLjaGiXUuM3VT6CUUpdRlltD20Skn8sjUVf17e7j/JSYyWPjuhIsWbDsSWgzEKJm2B2aUqoWK0tn8QBgmogcwho5JFiNhZ4ujUxd5GxBMS9+u5fuLRtye/+2sOBuKMixJo5pLSGlVCWUJRHoklY1wJvrEjmWlccrt/XGc/93EPMVjHgKgjvbHZpSqpa76ldJY8whoDEw0flo7NynqknyyVzeXpfIxF4t6d/KF5Y8CsHdYMjDdoemlKoDyjKz+GHgI6CZ8/GhiDzk6sDUBS8u2YsIPDm+K6z7B2QdsUYJefnYHZpSqg4oy62hGcAAY0wOgIj8HdgAvObKwJTlp8QMluxO4ZHRnWlZkAQb/guRd0C7QXaHppSqI8qSCAQoLrFd7NynXKyo2MFfFsXSuok/9w5rDx9NAt8Gugi9UqpKlSURvAtsEpGvnNs3AHNdF5I65+PNh4lPPcNbd/TBL/ZzOPSjtdhM/UC7Q1NK1SFXTQTGmJdFZC0w1LnrbmPMdpdGpTiVW8C/lu9jcMdAxnbwhf8+Ba37Q+877Q5NKVXHXDURiMhAIMYYs8253VBEBhhjNrk8Ojf22uoEzuQV8vTEcGTV03D2pNVBrHMGlFJVrCy/Vd4EsktsZzv3KRc5nJnL+xuSmNK3DV2L9sHW92DAb6B5D7tDU0rVQWXqLDbGmHMbxhiHiOii9y7092VxeHl48MjoTvDpddCgOYx48upvVEqpCihLi+CAiPxORLydj4eBA64OzF1tO3ySb3cd555rOhCSuACObbdGCfnqWkBKKdcoSyL4DTAYOOp8DADudWVQ7soYw4vf7iUowJf7+gfBqr9AmwG6zoBSyqXKMmooDbitGmJxe8tiUok+dJIXfxlB/Y3/gpwMmPa5rjOglHKpK7YIROQeEQlzPhcReUdEskRkl4j0qb4Q3UNhsYO/fxdHWLMAbgk9C5vegt53QMvedoemlKrjSrs19DCQ5Hw+FegFdAAeAf7j2rDcz8ebDnMwI4cnr+uK14o/gXc9GPmM3WEppdxAaYmgyBhT6Hw+AXjfGJNpjFkJ6AL2VSg7v4j/rNrPoA6BjJDtkLACrn1cF6JXSlWL0hKBQ0RaiIgfMBJr0fpz/F0blnuZ+8NBTuQU8OSY9siy/4XAMOiv/fFKqepRWmfx00A04AksMsbEAIjItejw0SpzMqeA2T8cYFz35vQ8+imcSIRpC7TEtFKq2lwxERhjFotIO6CBMeZkiZeigVtdHpmbeHNdIjkFRTx6TTB8/BJ0Gg1ho+wOSynlRkodPmqMKQJOXrIvx6URuZGUrDzm/ZTEL3u3ouPeN6HgjJaYVkpVO61gZqPXVu/HYQx/jPKBzbOt4aIh4XaHpZRyM5oIbHIoM4dPtxxhav+2tIz+O3h6w4g/2R2WUsoNVSgRiEjXMh43TkTiRSRBRJ4o5bibRMSISFRF4qmN/r1iH16ewu+7nITYhTD4d1ZxOaWUqmYVbREsv9oBIuIJvA6MB8KBqSLys/seItIAa/Ka26xvEJdymq93HmP6oFCa/vg8BITA4IfsDksp5aau2FksIq9e6SWgcRnO3R9IMMYccJ5vPjAZiL3kuOeBvwOPluGcdcK/V+wjwMeLh1ruhc2bYOJ/wDfA7rCUUm6qtBbB3cAeYOslj2igoAznbgUcKbGd7Nx3nrNmURtjzLelnUhE7hWRaBGJTk9PL8NH11yxx06zLCaVmYNbU//75yG4G0TeYXdYSik3Vtrw0S3AHmPMT5e+ICLPVvaDRcQDeBmYfrVjjTGzgFkAUVFR5iqH12ivrtpPA18v7q3/A5w4ALd/Dp66zo9Syj6l/Qa6Gci73AvGmPZlOPdRoE2J7dbOfec0AHoAa8Uqs9wcWCQik4wx0WU4f62z9/hpvotJ4ZFrW+G/4UFoNxTCRtsdllLKzZV2ayjAGJNbiXNvAcJEpL2I+GCtabDo3IvGmCxjTJAxJtQYEwpsBOpsEgBr3kCArxf3+K6AnDQY+Wdda0ApZbvSEsHCc09EZEF5T+yclfwgsAzYC3xmjIkRkedEZFK5I63l4lPOsGR3Cr8ZEIj/5tcgbCy0HWh3WEopVeqtoZJfVTtU5OTGmCXAkkv2PX2FY4dX5DNqi1edrYGZnoshL8tqDSilVA1QWovAXOG5Kqf9qWdYsvs490cF4Bc9C3rcBM0j7A5LKaWA0lsEvUTkNFbLwN/5HOe2McY0dHl0dcSrqxOo5+3JDPMVFOXB8P+1OySllDqvtDLUntUZSF2VkJbN4l3HeGxgPfx2zoPe0yCok91hKaXUeVp0zsXeWpeIr5cHdxd+Boi1BKVSStUgmghc6OipsyzcfpQHIsAvZj70mwmNWtsdllJKXUQTgQvN+cFa0XOG4wvw9IWhf7A5IqWU+jlNBC5yIqeA+ZuPMKObg3rxX0K/GRAQbHdYSin1M5oIXOS9n5I4W1jMA15fWa2BIQ/bHZJSSl2WJgIXyM4vYt5PSdwRVkjDfedaA83sDksppS5LE4ELzN98mKyzhfzBdxF4+lirjymlVA2liaCK5RcVM/uHA0xum09g4kKI+jU0CLE7LKWUuiJNBFVs4fajpJ7O58mAb60F6bVvQClVw2kiqEIOh+Ht7w8wqnkOIQe/gr5364L0SqkaTxNBFVoTn8aB9Bz+3HAp4uGlrQGlVK2giaAKzfnhIJENz9A2eRH0vQsatrA7JKWUuipdLLeK7DmaxYYDmXzTaS1yDB0ppJSqNbRFUEXeWX+Q1j459EhZCBG3QOM2V3+TUkrVAJoIqkBKVh6Ldh7jxZY/IkVnYejv7Q5JKaXKTBNBFXh/QxL+JpehJ7+CrhMguIvdISmlVJlpIqik3IIiPtp0mGdbbsYjPwuGPmJ3SEopVS6aCCppwdZkzp7NZWLuV9D+Gmjd1+6QlFKqXHTUUCU4HIa56w/ycNBWfLLTYOgsu0NSSqly0xZBJayOS+NwZjZ3mYXQIhI6DLc7JKWUKjdNBJUwb0MSU+tvJyDnMAx7BETsDkkppcpNbw1VUGJ6Nj/sT2dT0FLw6WiNFlJKqVpIWwQV9MGGQwzy2kdIdiwMegA8PO0OSSmlKkRbBBWQnV/Egq3JfNR4FRQ3gV5T7Q5JKaUqTFsEFfDV9qMEFhwhIvtHiJoBPvXsDkkppSpME0E5GWP4YEMSf2y4xlp4pv+9doeklFKVoomgnDYeOEFqagrjilYhEVN0GUqlVK2niaCc3t+QxEz/tXgVn7U6iZVSqpbTRFAOx06dZU3sUaZ7LYcOIyCku90hKaVUpWkiKIePNx3mOvmJBoUZMOhBu8NRSqkq4dJEICLjRCReRBJE5InLvP6IiMSKyC4RWSUi7VwZT2UUFjv4dMthfl9/OQR3hU4j7Q5JKaWqhMsSgYh4Aq8D44FwYKqIhF9y2HYgyhjTE/gC+Ier4qmsVXvTaJ+zk7YFiTDwt1pOQilVZ7iyRdAfSDDGHDDGFADzgcklDzDGrDHG5Do3NwKtXRhPpXyy+TC/8V+J8W8CPW+xOxyllKoyrkwErYAjJbaTnfuuZAaw9HIviMi9IhItItHp6elVGGLZHDmRy779cQx3bEZ63wne/tUeg1JKuUqN6CwWkTuAKOCfl3vdGDPLGBNljIkKDg6u3uCAT7cc4XbPVQgO6Dej2j9fKaVcyZW1ho4CbUpst3buu4iIjAL+BFxrjMl3YTwVUljs4KstB1jisxYJGw9NQu0OSSmlqpQrWwRbgDARaS8iPsBtwKKSB4hIb+BtYJIxJs2FsVTY6rg0+uV+TyPHKeh/j93hKKVUlXNZIjDGFAEPAsuAvcBnxpgYEXlORCY5D/snEAB8LiI7RGTRFU5nm082H2am7wpMYBi0H253OEopVeVcWobaGLMEWHLJvqdLPB/lys+vrOSTuZzcv5EePvuh/z/Ao0Z0qSilVJXS32yl+GzLEX7luRyHd31dc0ApVWdpIriComIHy7bsYZLnRjwibwe/hnaHpJRSLqGJ4Ap+SMhgZO4yvCnUTmKlVJ2mieAKFkQf5nbvNTjaDYXgLnaHo5RSLqOJ4DJO5RaQu3clrUnDI+puu8NRSimX0kRwGd/sPMYUWUmRX1PoNtHucJRSyqU0EVzGyi27GOO5Fa8+d4CXr93hKKWUS2kiuER8yhm6p36DJw7oO93ucJRSyuU0EVzii+hDTPVaQ2HbYRDY0e5wlFLK5TQRlFBY7OD4tqW0kXS8+2snsVLKPWgiKGFdfDoTCpdR4NsUumonsVLKPWgiKGH5pp2M8tyKZ587wMvH7nCUUqpaaCJwOpFTQPMDn+OFA8+o6XaHo5RS1UYTgdO3O5OZImvIaTlEO4mVUm5FE4FTwpbvaOORTr2B2kmslHIvmgiAw5m5RGYsJt8zAOk2we5wlFKqWmkiAJZGxzPeYzOF4TeCt7/d4SilVLVy6QpltYExhpxtn+EnhTBgut3hKKVUtXP7FkHMsdMMP7ucUwEdoVUfu8NRSqlq5/aJ4IeffqSPRwK+UXeCiN3hKKVUtXPrRFDsMNTf+ynFeODf93a7w1FKKVu4dSLYmJDKuOK1pDe/FhqE2B2OUkrZwq0TQdz6hTSTUzQdonMHlFLuy20TQV5hMW0OfUm2ZyN8uo23OxyllLKN2yaC9TvjGU40WWG/1AJzSim35raJIHPTJ/hIMc2vmWF3KEopZSu3TAS5BUV0TltKql8HPFv2tDscpZSylVsmgo3R0fSW/eSH32x3KEopZTu3TAQ50Z8A0GrYr2yORCml7Od2iSAnr5Aemcs4GNAbzyZt7A5HKaVs53aJYOvG1bSX45iet9odilJK1QhulwgKt39CAV6EDr3N7lCUUqpGcKtEkH02j16nVpPQeCge9ZrYHY5SStUIbpUIdn+/kCDJwqf3VLtDUUqpGsOliUBExolIvIgkiMgTl3ndV0Q+db6+SURCXRrP7s/IIoAOg25w5ccopVSt4rJEICKewOvAeCAcmCoi4ZccNgM4aYzpBPwb+Lur4jlz+iQ9z6wnIWgkHj5+rvoYpZSqdVzZIugPJBhjDhhjCoD5wORLjpkMzHM+/wIYKeKa1WHi1n5KPcknoN80V5xeKaVqLVcmglbAkRLbyc59lz3GGFMEZAGBl55IRO4VkWgRiU5PT69QMN71GrG93mDCokZV6P1KKVVX1YrF640xs4BZAFFRUaYi54gcNRVGaSexUkpdypUtgqNAyam7rZ37LnuMiHgBjYBMF8aklFLqEq5MBFuAMBFpLyI+wG3AokuOWQTc5Xx+M7DaGFOhb/xKKaUqxmW3howxRSLyILAM8ATeMcbEiMhzQLQxZhEwF/hARBKAE1jJQimlVDVyaR+BMWYJsOSSfU+XeJ4HTHFlDEoppUrnVjOLlVJK/ZwmAqWUcnOaCJRSys1pIlBKKTcntW20poikA4cq+PYgIKMKw6kN9Jrdg16ze6jMNbczxgRf7oValwgqQ0SijTFRdsdRnfSa3YNes3tw1TXrrSGllHJzmgiUUsrNuVsimGV3ADbQa3YPes3uwSXX7FZ9BEoppX7O3VoESimlLqGJQCml3FydTAQiMk5E4kUkQUSeuMzrviLyqfP1TSISWv1RVq0yXPMjIhIrIrtEZJWItLMjzqp0tWsucdxNImJEpNYPNSzLNYvILc5/6xgR+bi6Y6xqZfjZbisia0Rku/Pn+zo74qwqIvKOiKSJyJ4rvC4i8qrz72OXiPSp9IcaY+rUA6vkdSLQAfABdgLhlxzzW+At5/PbgE/tjrsarnkEUM/5/H53uGbncQ2A74GNQJTdcVfDv3MYsB1o4txuZnfc1XDNs4D7nc/DgSS7467kNV8D9AH2XOH164ClgAADgU2V/cy62CLoDyQYYw4YYwqA+cDkS46ZDMxzPv8CGCkiUo0xVrWrXrMxZo0xJte5uRFrxbjarCz/zgDPA38H8qozOBcpyzXfA7xujDkJYIxJq+YYq1pZrtkADZ3PGwHHqjG+KmeM+R5rfZYrmQy8bywbgcYi0qIyn1kXE0Er4EiJ7WTnvsseY4wpArKAwGqJzjXKcs0lzcD6RlGbXfWanU3mNsaYb6szMBcqy79zZ6CziPwoIhtFZFy1RecaZbnmZ4E7RCQZa/2Th6onNNuU9//7VdWKxetV1RGRO4Ao4Fq7Y3ElEfEAXgam2xxKdfPCuj00HKvV972IRBhjTtkalWtNBd4zxvxLRAZhrXrYwxjjsDuw2qIutgiOAm1KbLd27rvsMSLihdWczKyW6FyjLNeMiIwC/gRMMsbkV1NsrnK1a24A9ADWikgS1r3URbW8w7gs/87JwCJjTKEx5iCwDysx1FZlueYZwGcAxpgNgB9Wcba6qkz/38ujLiaCLUCYiLQXER+szuBFlxyzCLjL+fxmYLVx9sLUUle9ZhHpDbyNlQRq+31juMo1G2OyjDFBxphQY0woVr/IJGNMtD3hVomy/GwvxGoNICJBWLeKDlRnkFWsLNd8GBgJICLdsBJBerVGWb0WAb9yjh4aCGQZY45X5oR17taQMaZIRB4ElmGNOHjHGBMjIs8B0caYRcBcrOZjAlanzG32RVx5ZbzmfwIBwOfOfvHDxphJtgVdSWW85jqljNe8DBgjIrFAMfCoMabWtnbLeM3/A8wWkT9gdRxPr81f7ETkE6xkHuTs93gG8AYwxryF1Q9yHZAA5AJ3V/oza/Hfl1JKqSpQF28NKaWUKgdNBEop5eY0ESillJvTRKCUUm5OE4FSSrk5TQSqRhORYhHZISJ7ROQbEWl8leNvEJHwSnxeqIjcXmI7SkRerej5Ljn3FBHZKyJrquBcF12niDznnDCoVLlpIlA13VljTKQxpgfWnI8HrnL8DVgVKCsqFDifCIwx0caY31XifCXNAO4xxowoudM5u728LrpOY8zTxpiVlYxPuSlNBKo22YCzuJaIdBSR70Rkq4j8ICJdRWQwMAn4p7MV0fFyxznf/56zpvtPInJARG52fsbfgGHO9/9BRIaLyGLne5qKyEJnDfiNItLTuf9ZZw35tc5z/SxxiMjTwFBgroj8U0Smi8giEVkNrBKRALHWidgmIrtFZHKJ9/7K+Zk7ReSDK1zne+euQURGilWbf7czLl/n/iQR+UuJz+jqin8kVQvZXXtbH/oo7QFkO//0BD4Hxjm3VwFhzucDsMqEALwH3Fzi/aUd9znWl6FwrFLHYM3oXFzi/ee3gdeAZ5zPfwHscD5/FvgJ8MWqcZMJeF/mWtbiXBMBqxheMtDUue0FNHQ+D8KaNSpAd6x6QUHO15pe4TrfwyqX4odVmbKzc//7wO+dz5OAh5zPfwvMsfvfVx8141HnSkyoOsdfRHZgtQT2AitEJAAYzIVyGWD9Er5IGY5baKwKlbEiElKGWIYCNwEYY1aLSKCInKuD/62xCvnli0gaEIL1i740K4wx5+rOC/CiiFwDOJzXG4KVcD43xmQ4P7e0OvUAXYCDxph9zu15WLfTXnFuf+n8cytw41XOpdyEJgJV0501xkSKSD2sejMPYH37PWWMibzKez2uclzJCqyVXZio5LmKKdv/rZwSz6cBwUBfY0yhWBVT/SoZ0+Wci7OsMSo3oH0EqlYw1upqv8MqMJYLHBSRKXB+DddezkPPYJWgxhhzupTjruT8+y/jB6xf2IjIcCDD+RlVoRGQ5kwCI4Bza0qvBqaISKDzc5teJc54IFREOjm37wTWVVGMqo7SRKBqDWPMdmAX1kIk04AZIrITiOHC8oXzgUednaUdSznuSnYBxc6O2T9c8tqzQF8R2YXVqXzXpW+uhI+AKBHZDfwKiAMwxsQALwDrnNfwsvP4S68T5/F5WNUoP3eeywG8VYVxqjpIq48qpZSb0xaBUkq5OU0ESinl5jQRKKWUm9NEoJRSbk4TgVJKuTlNBEop5eY0ESillJv7f6zQWqvN8IQUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwkhfJLU5RqO"
      },
      "source": [
        "uncertaintiesmv = all_uncertainty['mvar']\n",
        "# Predictions are the mean predictions across the ensemble of models\n",
        "preds = np.mean(all_preds[:,:,0], axis=0)\n",
        "df_submission = pd.DataFrame(data={\n",
        "        'PRED' : preds,\n",
        "        'UNCERTAINTY' : uncertainties,\n",
        "        'Errors' : errors,\n",
        "        'Errors_S' : errors_sin,\n",
        "        'LLFU_ENS' : uncertainties_ens,\n",
        "        'LLFU_S' : uncertainties_sin,\n",
        "        'target' : targets,\n",
        "        'mvar' : uncertaintiesmv,\n",
        "        })"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8fXxd9ABCt5"
      },
      "source": [
        "####Plotting LLFU-error scatter plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TeobFwwS7tXU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "0640e684-2ce1-4846-9050-a37712fb3ef7"
      },
      "source": [
        "df2 = df_submission[df_submission['Errors']<=1.0]\n",
        "df20 = df2[df2['LLFU_ENS']<5]\n",
        "plt.scatter(df20['Errors'],df20['LLFU_ENS'],alpha=0.5)\n",
        "plt.ylabel('Estimated Uncertainity -(LL-FU)')\n",
        "plt.xlabel('Error')\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fo5z_9FgBNC0"
      },
      "source": [
        "####Plotting Uncertainty-error scatter plot for LL-FU and mvar uncertainity metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuHqjq9m5ABB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "069c3f08-fdd4-4e44-d55a-2cfcfcbd2642"
      },
      "source": [
        "df2 = df_submission[df_submission['Errors']<=1.0]\n",
        "df21 = df2[df2['mvar']<5]\n",
        "df20 = df2[df2['LLFU_ENS']<5]\n",
        "plt.scatter(df21['Errors'],df21['mvar'],alpha=0.8)\n",
        "plt.scatter(df20['Errors'],df20['LLFU_ENS'],alpha=0.5)\n",
        "plt.ylabel('Estimated Uncertainty')\n",
        "plt.xlabel('Error')\n",
        "s='mvar'\n",
        "e='LL-FU'\n",
        "plt.legend([s,e], loc='upper left',framealpha=0.95)\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSZrST-aHIO2"
      },
      "source": [
        ""
      ],
      "execution_count": 38,
      "outputs": []
    }
  ]
}