notebooks/uplift_metrics_tutorial_advanced.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATqE_EOoymEd"
      },
      "source": [
        "# 🎯 Uplift modeling `metrics` advanced\n",
        "\n",
        "<br>\n",
        "<center>\n",
        "    <a href=\"https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial_advanced.ipynb\">\n",
        "        <img src=\"https://colab.research.google.com/assets/colab-badge.svg\">\n",
        "    </a>\n",
        "    <br>\n",
        "    <b><a href=\"https://github.com/maks-sh/scikit-uplift/\">SCIKIT-UPLIFT REPO</a> | </b>\n",
        "    <b><a href=\"https://scikit-uplift.readthedocs.io/en/latest/\">SCIKIT-UPLIFT DOCS</a> | </b>\n",
        "    <b><a href=\"https://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html\">USER GUIDE</a></b>\n",
        "    <br>\n",
        "</center>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmg3AprtymEg"
      },
      "source": [
        "import sys\n",
        "\n",
        "# install uplift library scikit-uplift and other libraries \n",
        "!{sys.executable} -m pip install scikit-uplift dill catboost\n",
        "from IPython.display import clear_output\n",
        "clear_output()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHFiqvnrymEh"
      },
      "source": [
        "# 📝 Load data\n",
        "\n",
        "We are going to use a `Lenta dataset` from the BigTarget Hackathon hosted in summer 2020 by Lenta and Microsoft.\n",
        "\n",
        "Lenta is a russian food retailer. \n",
        "\n",
        "### Data description\n",
        "\n",
        "✏️ Dataset can be loaded from `sklift.datasets` module using `fetch_lenta` function.\n",
        "\n",
        "Read more about dataset <a href=\"https://www.uplift-modeling.com/en/latest/api/datasets/fetch_lenta.html\">in the api docs</a>.  \n",
        "\n",
        "This is an uplift modeling dataset containing data about Lenta's customers grociery shopping, marketing campaigns communications as `treatment` and store visits as `target`.\n",
        "\n",
        "####  ✏️ Major columns:\n",
        "\n",
        "- `group` - treatment / control flag\n",
        "- `response_att` - binary target\n",
        "- `CardHolder` - customer id\n",
        "- `gender` - customer gender \n",
        "- `age` - customer age"
      ]
    },
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        "id": "5o0Hm-iqymEi",
        "scrolled": true,
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      "source": [
        "from sklift.datasets import fetch_lenta\n",
        "\n",
        "# returns sklearn Bunch object\n",
        "# with data, target, treatment keys\n",
        "# data features (pd.DataFrame), target (pd.Series), treatment (pd.Series) values \n",
        "dataset = fetch_lenta()"
      ],
      "execution_count": 2,
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqKZR1pzymEi",
        "outputId": "b78c2a1b-b1ce-4762-d5d6-24925a08dc6d"
      },
      "source": [
        "print(f\"Dataset type: {type(dataset)}\\n\")\n",
        "print(f\"Dataset features shape: {dataset.data.shape}\")\n",
        "print(f\"Dataset target shape: {dataset.target.shape}\")\n",
        "print(f\"Dataset treatment shape: {dataset.treatment.shape}\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset type: <class 'sklearn.utils.Bunch'>\n",
            "\n",
            "Dataset features shape: (687029, 193)\n",
            "Dataset target shape: (687029,)\n",
            "Dataset treatment shape: (687029,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4-jlzDbymEk"
      },
      "source": [
        "# 📝 EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "t7kA0MxxymEk",
        "outputId": "d5d5637e-ce85-4fcb-eb8c-695733919b99"
      },
      "source": [
        "dataset.data.head().append(dataset.data.tail())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>...</th>\n",
              "      <th>perdelta_days_between_visits_15_30d</th>\n",
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              "      <th>sale_count_3m_g33</th>\n",
              "      <th>sale_count_3m_g57</th>\n",
              "      <th>sale_count_6m_g24</th>\n",
              "      <th>sale_count_6m_g25</th>\n",
              "      <th>sale_count_6m_g32</th>\n",
              "      <th>sale_count_6m_g33</th>\n",
              "      <th>sale_count_6m_g44</th>\n",
              "      <th>sale_count_6m_g54</th>\n",
              "      <th>sale_count_6m_g57</th>\n",
              "      <th>sale_sum_12m_g24</th>\n",
              "      <th>sale_sum_12m_g25</th>\n",
              "      <th>sale_sum_12m_g26</th>\n",
              "      <th>sale_sum_12m_g27</th>\n",
              "      <th>sale_sum_12m_g32</th>\n",
              "      <th>sale_sum_12m_g44</th>\n",
              "      <th>sale_sum_12m_g54</th>\n",
              "      <th>sale_sum_3m_g24</th>\n",
              "      <th>sale_sum_3m_g26</th>\n",
              "      <th>sale_sum_3m_g32</th>\n",
              "      <th>sale_sum_3m_g33</th>\n",
              "      <th>sale_sum_6m_g24</th>\n",
              "      <th>sale_sum_6m_g25</th>\n",
              "      <th>sale_sum_6m_g26</th>\n",
              "      <th>sale_sum_6m_g32</th>\n",
              "      <th>sale_sum_6m_g33</th>\n",
              "      <th>sale_sum_6m_g44</th>\n",
              "      <th>sale_sum_6m_g54</th>\n",
              "      <th>stdev_days_between_visits_15d</th>\n",
              "      <th>stdev_discount_depth_15d</th>\n",
              "      <th>stdev_discount_depth_1m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>47.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.3393</td>\n",
              "      <td>0.5821</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>10.0</td>\n",
              "      <td>84.314</td>\n",
              "      <td>98.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>137.282</td>\n",
              "      <td>28.776</td>\n",
              "      <td>6.0</td>\n",
              "      <td>169.658</td>\n",
              "      <td>10.680</td>\n",
              "      <td>7.0</td>\n",
              "      <td>28.776</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4469.86</td>\n",
              "      <td>658.85</td>\n",
              "      <td>1286.32</td>\n",
              "      <td>7736.05</td>\n",
              "      <td>418.80</td>\n",
              "      <td>3233.31</td>\n",
              "      <td>811.73</td>\n",
              "      <td>2321.61</td>\n",
              "      <td>182.82</td>\n",
              "      <td>283.84</td>\n",
              "      <td>3648.23</td>\n",
              "      <td>3141.25</td>\n",
              "      <td>356.67</td>\n",
              "      <td>237.25</td>\n",
              "      <td>283.84</td>\n",
              "      <td>3648.23</td>\n",
              "      <td>1195.37</td>\n",
              "      <td>535.42</td>\n",
              "      <td>1.7078</td>\n",
              "      <td>0.2798</td>\n",
              "      <td>0.3008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.744</td>\n",
              "      <td>2.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>113.39</td>\n",
              "      <td>62.69</td>\n",
              "      <td>58.71</td>\n",
              "      <td>93.35</td>\n",
              "      <td>87.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>122.98</td>\n",
              "      <td>0.00</td>\n",
              "      <td>58.71</td>\n",
              "      <td>87.01</td>\n",
              "      <td>179.83</td>\n",
              "      <td>113.39</td>\n",
              "      <td>62.69</td>\n",
              "      <td>58.71</td>\n",
              "      <td>87.01</td>\n",
              "      <td>179.83</td>\n",
              "      <td>0.00</td>\n",
              "      <td>122.98</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7256</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>21.102</td>\n",
              "      <td>50.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.594</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.294</td>\n",
              "      <td>11.084</td>\n",
              "      <td>3.0</td>\n",
              "      <td>11.158</td>\n",
              "      <td>31.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1564.91</td>\n",
              "      <td>971.09</td>\n",
              "      <td>177.93</td>\n",
              "      <td>3257.49</td>\n",
              "      <td>975.21</td>\n",
              "      <td>2555.27</td>\n",
              "      <td>6351.29</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>783.87</td>\n",
              "      <td>1239.19</td>\n",
              "      <td>533.46</td>\n",
              "      <td>83.37</td>\n",
              "      <td>593.13</td>\n",
              "      <td>1217.43</td>\n",
              "      <td>1336.83</td>\n",
              "      <td>3709.82</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>65.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.544</td>\n",
              "      <td>49.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.778</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000</td>\n",
              "      <td>34.212</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.778</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>358.22</td>\n",
              "      <td>3798.18</td>\n",
              "      <td>680.93</td>\n",
              "      <td>1425.07</td>\n",
              "      <td>175.73</td>\n",
              "      <td>602.81</td>\n",
              "      <td>3544.76</td>\n",
              "      <td>0.00</td>\n",
              "      <td>119.99</td>\n",
              "      <td>73.24</td>\n",
              "      <td>346.74</td>\n",
              "      <td>139.68</td>\n",
              "      <td>1849.91</td>\n",
              "      <td>360.40</td>\n",
              "      <td>175.73</td>\n",
              "      <td>496.73</td>\n",
              "      <td>172.58</td>\n",
              "      <td>1246.21</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7865</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.454</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.454</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.036</td>\n",
              "      <td>12.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.454</td>\n",
              "      <td>8.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>226.98</td>\n",
              "      <td>168.05</td>\n",
              "      <td>960.37</td>\n",
              "      <td>1560.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>342.45</td>\n",
              "      <td>1039.85</td>\n",
              "      <td>0.00</td>\n",
              "      <td>66.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>87.94</td>\n",
              "      <td>226.98</td>\n",
              "      <td>168.05</td>\n",
              "      <td>461.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>237.93</td>\n",
              "      <td>225.51</td>\n",
              "      <td>995.27</td>\n",
              "      <td>1.4142</td>\n",
              "      <td>0.3495</td>\n",
              "      <td>0.3495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687024</th>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.3333</td>\n",
              "      <td>0.4002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.000</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.856</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.856</td>\n",
              "      <td>29.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.000</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>550.09</td>\n",
              "      <td>695.32</td>\n",
              "      <td>111.87</td>\n",
              "      <td>114.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1173.84</td>\n",
              "      <td>147.68</td>\n",
              "      <td>550.09</td>\n",
              "      <td>111.87</td>\n",
              "      <td>0.00</td>\n",
              "      <td>330.96</td>\n",
              "      <td>550.09</td>\n",
              "      <td>669.33</td>\n",
              "      <td>111.87</td>\n",
              "      <td>0.00</td>\n",
              "      <td>330.96</td>\n",
              "      <td>1173.84</td>\n",
              "      <td>119.99</td>\n",
              "      <td>2.6458</td>\n",
              "      <td>0.3646</td>\n",
              "      <td>0.3282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687025</th>\n",
              "      <td>33.0</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>0.0</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>2.0</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>28.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>28.01</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687026</th>\n",
              "      <td>36.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>0.0</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.9847</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>155.97</td>\n",
              "      <td>23.99</td>\n",
              "      <td>41.51</td>\n",
              "      <td>0.00</td>\n",
              "      <td>615.77</td>\n",
              "      <td>87.47</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>449.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687027</th>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.8318</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.476</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>81.90</td>\n",
              "      <td>29.82</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>46.72</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687028</th>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.452</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.660</td>\n",
              "      <td>1.344</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.660</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.344</td>\n",
              "      <td>18.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>531.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>916.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2407.56</td>\n",
              "      <td>1304.03</td>\n",
              "      <td>290.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>228.47</td>\n",
              "      <td>290.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>228.47</td>\n",
              "      <td>752.32</td>\n",
              "      <td>596.86</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 193 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         age  ...  stdev_discount_depth_1m\n",
              "0       47.0  ...                   0.3008\n",
              "1       57.0  ...                   0.0000\n",
              "2       38.0  ...                   0.0803\n",
              "3       65.0  ...                   0.0000\n",
              "4       61.0  ...                   0.3495\n",
              "687024  35.0  ...                   0.3282\n",
              "687025  33.0  ...                   0.0000\n",
              "687026  36.0  ...                      NaN\n",
              "687027  37.0  ...                      NaN\n",
              "687028  40.0  ...                   0.0000\n",
              "\n",
              "[10 rows x 193 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNSQsJcqymEk"
      },
      "source": [
        "### 🤔 target share for `treatment / control` "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "d0BPrhjnymEl",
        "outputId": "a57f8423-1dd4-42ed-f2d4-63b939077770"
      },
      "source": [
        "import pandas as pd \n",
        "\n",
        "pd.crosstab(dataset.treatment, dataset.target, normalize='index')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>response_att</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>control</th>\n",
              "      <td>0.897421</td>\n",
              "      <td>0.102579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test</th>\n",
              "      <td>0.889874</td>\n",
              "      <td>0.110126</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "response_att         0         1\n",
              "group                           \n",
              "control       0.897421  0.102579\n",
              "test          0.889874  0.110126"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q48zr_exymEl"
      },
      "source": [
        "# make treatment binary\n",
        "treat_dict = {\n",
        "    'test': 1,\n",
        "    'control': 0\n",
        "}\n",
        "\n",
        "dataset.treatment = dataset.treatment.map(treat_dict)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oGypq_JymEm",
        "outputId": "90909780-1bb9-4166-90a2-1f343803959e"
      },
      "source": [
        "# fill NaNs in the categorical feature `gender` \n",
        "# for CatBoostClassifier\n",
        "dataset.data['gender'] = dataset.data['gender'].fillna(value='Не определен')\n",
        "\n",
        "print(dataset.data['gender'].value_counts(dropna=False))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ж               433448\n",
            "М               243910\n",
            "Не определен      9671\n",
            "Name: gender, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKHT1JzsymEm"
      },
      "source": [
        "### ✂️ train test split\n",
        "\n",
        "- stratify by two columns: treatment and target. \n",
        "\n",
        "`Intuition:` In a binary classification problem definition we stratify train set by splitting target `0/1` column. In uplift modeling we have two columns instead of one. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4lM58UMymEm",
        "outputId": "84cc2abf-b854-41a4-8bb8-bbddeb97baab"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "stratify_cols = pd.concat([dataset.treatment, dataset.target], axis=1)\n",
        "\n",
        "X_train, X_val, trmnt_train, trmnt_val, y_train, y_val = train_test_split(\n",
        "    dataset.data,\n",
        "    dataset.treatment,\n",
        "    dataset.target,\n",
        "    stratify=stratify_cols,\n",
        "    test_size=0.3,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "print(f\"Train shape: {X_train.shape}\")\n",
        "print(f\"Validation shape: {X_val.shape}\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape: (480920, 193)\n",
            "Validation shape: (206109, 193)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYzpcKwuymEn"
      },
      "source": [
        "# 👾 Class Transformation uplift model and Two Models\n",
        "\n",
        "### For example, let's take the models [ Class Transformation ](https://github.com/maks-sh/scikit-uplift/blob/c9dd56aa0277e81ef7c4be62bf2fd33432e46f36/sklift/models/models.py#L181) and [Two Models](https://github.com/maks-sh/scikit-uplift/blob/c9dd56aa0277e81ef7c4be62bf2fd33432e46f36/sklift/models/models.py#L271). Let's display their uplift scores on one graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBwZVdIEymEn"
      },
      "source": [
        "from catboost import CatBoostClassifier\n",
        "from sklearn.base import clone\n",
        "\n",
        "from sklift.models import TwoModels\n",
        "from sklift.models import ClassTransformation\n",
        "\n",
        "first_estimator = CatBoostClassifier(verbose=100,\n",
        "                               task_type=\"GPU\",\n",
        "                               devices='0:1',\n",
        "                               cat_features=['gender'],\n",
        "                               random_state=42,\n",
        "                               thread_count=1)\n",
        "second_estimator = clone(first_estimator)\n",
        "\n",
        "transform_model = ClassTransformation(estimator=first_estimator)\n",
        "two_model = TwoModels(estimator_trmnt=first_estimator, estimator_ctrl=second_estimator)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IcMWundymEn",
        "outputId": "5f59657c-2a7d-47aa-cb21-89a900e47e23"
      },
      "source": [
        "transform_model = transform_model.fit(\n",
        "    X=X_train, \n",
        "    y=y_train, \n",
        "    treatment=trmnt_train\n",
        ")\n",
        "\n",
        "two_model = two_model.fit(\n",
        "    X=X_train, \n",
        "    y=y_train, \n",
        "    treatment=trmnt_train\n",
        ")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.024003\n",
            "0:\tlearn: 0.6893849\ttotal: 59ms\tremaining: 58.9s\n",
            "100:\tlearn: 0.6100331\ttotal: 5.39s\tremaining: 48s\n",
            "200:\tlearn: 0.6019326\ttotal: 12s\tremaining: 47.8s\n",
            "300:\tlearn: 0.6000429\ttotal: 18.7s\tremaining: 43.4s\n",
            "400:\tlearn: 0.5992161\ttotal: 25.4s\tremaining: 37.9s\n",
            "500:\tlearn: 0.5986674\ttotal: 32s\tremaining: 31.8s\n",
            "600:\tlearn: 0.5982996\ttotal: 38.5s\tremaining: 25.5s\n",
            "700:\tlearn: 0.5980941\ttotal: 44.8s\tremaining: 19.1s\n",
            "800:\tlearn: 0.5979237\ttotal: 51.2s\tremaining: 12.7s\n",
            "900:\tlearn: 0.5976503\ttotal: 57.5s\tremaining: 6.32s\n",
            "999:\tlearn: 0.5975015\ttotal: 1m 3s\tremaining: 0us\n",
            "Learning rate set to 0.02591\n",
            "0:\tlearn: 0.6711650\ttotal: 23.1ms\tremaining: 23.1s\n",
            "100:\tlearn: 0.2887976\ttotal: 3.03s\tremaining: 27s\n",
            "200:\tlearn: 0.2763838\ttotal: 6.53s\tremaining: 26s\n",
            "300:\tlearn: 0.2729584\ttotal: 10.2s\tremaining: 23.7s\n",
            "400:\tlearn: 0.2713649\ttotal: 13.9s\tremaining: 20.8s\n",
            "500:\tlearn: 0.2703728\ttotal: 17.6s\tremaining: 17.6s\n",
            "600:\tlearn: 0.2696703\ttotal: 21.3s\tremaining: 14.1s\n",
            "700:\tlearn: 0.2691328\ttotal: 24.9s\tremaining: 10.6s\n",
            "800:\tlearn: 0.2686616\ttotal: 28.6s\tremaining: 7.11s\n",
            "900:\tlearn: 0.2682632\ttotal: 32.3s\tremaining: 3.55s\n",
            "999:\tlearn: 0.2678762\ttotal: 36s\tremaining: 0us\n",
            "Learning rate set to 0.024384\n",
            "0:\tlearn: 0.6735712\ttotal: 44.9ms\tremaining: 44.9s\n",
            "100:\tlearn: 0.3063022\ttotal: 4.82s\tremaining: 42.9s\n",
            "200:\tlearn: 0.2925770\ttotal: 10.2s\tremaining: 40.4s\n",
            "300:\tlearn: 0.2895685\ttotal: 15.6s\tremaining: 36.3s\n",
            "400:\tlearn: 0.2880540\ttotal: 21.3s\tremaining: 31.9s\n",
            "500:\tlearn: 0.2872389\ttotal: 26.9s\tremaining: 26.8s\n",
            "600:\tlearn: 0.2866951\ttotal: 32.6s\tremaining: 21.6s\n",
            "700:\tlearn: 0.2863474\ttotal: 38.1s\tremaining: 16.3s\n",
            "800:\tlearn: 0.2860138\ttotal: 43.6s\tremaining: 10.8s\n",
            "900:\tlearn: 0.2857359\ttotal: 49.2s\tremaining: 5.41s\n",
            "999:\tlearn: 0.2854954\ttotal: 54.8s\tremaining: 0us\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POrn2kgwymEo"
      },
      "source": [
        "### Uplift prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xx_hHajjymEo"
      },
      "source": [
        "uplift_transform_model_val = transform_model.predict(X_val)\n",
        "uplift_transform_model_train = transform_model.predict(X_train)\n",
        "\n",
        "uplift_two_model = two_model.predict(X_val)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5PofV6aymEp"
      },
      "source": [
        "# 🚀🚀🚀 Uplift metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmvFxIALymEp"
      },
      "source": [
        "###  🚀 `uplift@k`\n",
        "\n",
        "- uplift at first k%\n",
        "- usually falls between [0; 1] depending on k, model quality and data\n",
        "\n",
        "\n",
        "### `uplift@k` = `target mean at k% in the treatment group` - `target mean at k% in the control group`\n",
        "\n",
        "___\n",
        "\n",
        "How to count `uplift@k`:\n",
        "\n",
        "1. sort by predicted uplift\n",
        "2. select first k%\n",
        "3. count target mean in the treatment group\n",
        "4. count target mean in the control group\n",
        "5. substract the mean in the control group from the mean in the treatment group\n",
        "\n",
        "---\n",
        "\n",
        "Code parameter options:\n",
        "\n",
        "- `strategy='overall'` - sort by uplift treatment and control together\n",
        "- `strategy='by_group'` - sort by uplift treatment and control separately"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWBOEv0Z6daH"
      },
      "source": [
        "## `🚀uplift@k with a small step ot the k parameter`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWjC06aQymEp"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from sklift.metrics import uplift_at_k\n",
        "\n",
        "values_uplift_k_transform = []\n",
        "values_uplift_k_two = []\n",
        "values_k = []\n",
        "for k in np.arange(0.01,1,0.01):\n",
        "  values_uplift_k_transform.append(uplift_at_k(y_val, uplift_transform_model_val, trmnt_val, strategy='overall', k=k))\n",
        "  values_uplift_k_two.append(uplift_at_k(y_val, uplift_two_model, trmnt_val, strategy='overall', k=k))\n",
        "  values_k.append(k)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oshHc_VWlKmw"
      },
      "source": [
        "### `For ClassTransformation model`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "O6tfXwaLlHJV",
        "outputId": "03c001f3-b386-4838-a6dd-e7a215b235f2"
      },
      "source": [
        "plt.plot(values_k, values_uplift_k_transform)\n",
        "plt.title('Dependence of uplift@k on k')\n",
        "plt.xlabel('The value of k')\n",
        "plt.ylabel('The value of uplift@k')\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "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": "7eRHptiLlXpb"
      },
      "source": [
        "### `For TwoModels`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "lQGkD3dTlEQn",
        "outputId": "ff2debcb-030e-4158-ed64-24216a341985"
      },
      "source": [
        "plt.plot(values_k, values_uplift_k_two)\n",
        "plt.title('Dependence of uplift@k on k')\n",
        "plt.xlabel('The value of k')\n",
        "plt.ylabel('The value of uplift@k')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "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": "KD5ZUzlEJN1b"
      },
      "source": [
        "# 🚀 `ASD metric`\n",
        "### `The average squared deviation (ASD) is a model stability metric that shows how much the model overfits the training data. Larger values of ASD mean greater overfit.`\n",
        "\n",
        "## Code parameter options:\n",
        "\n",
        "- `strategy='overall'` - The first step is taking the first k observations of all test data ordered by uplift prediction (overall both groups - control and treatment) and conversions in treatment and control groups calculated only on them. Then the difference between these conversions is calculated.\n",
        "- `strategy='by_group'` - Separately calculates conversions in top k observations in each group (control and treatment) sorted by uplift predictions. Then the difference between these conversions is calculated\n",
        "- `bins=10` - Determines the number of bins (and the relative percentile) in the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnqffihzymEv",
        "outputId": "30a329cc-387b-4500-8c3e-f8f6ab043f37"
      },
      "source": [
        "from sklift.metrics import average_squared_deviation\n",
        "\n",
        "asd_overall = average_squared_deviation(y_train, uplift_transform_model_train, trmnt_train, y_val,\n",
        "                                          uplift_transform_model_val, trmnt_val, strategy='overall')\n",
        "asd_by_group = average_squared_deviation(y_train, uplift_transform_model_train, trmnt_train, y_val, \n",
        "                                          uplift_transform_model_val, trmnt_val, strategy='by_group')\n",
        "\n",
        "print(f\"average squared deviation by overall strategy for the ClassTransformation model: {asd_overall:.6f}\")\n",
        "print(f\"average squared deviation by group strategy for the ClassTransformation model: {asd_by_group:.6f}\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "average squared deviation by overall strategy for the ClassTransformation model: 0.000007\n",
            "average squared deviation by group strategy for the ClassTransformation model: 0.000011\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSg7zXHGG_76"
      },
      "source": [
        "# `↗️Display 2 different model uplift scores on one qini plot`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrRjY_zlYThJ"
      },
      "source": [
        "### `Only qiwi curves`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-WFTIgynAI28",
        "outputId": "b37fb698-7522-44ac-c786-21ea6f8e21a7"
      },
      "source": [
        "from sklift.viz import plot_qini_curve\n",
        "\n",
        "fig, ax_roc = plt.subplots(1, 1)\n",
        "plot_qini_curve(y_val, uplift_transform_model_val, trmnt_val, name='Transform model', random=False, perfect=False, ax=ax_roc)\n",
        "plot_qini_curve(y_val, uplift_two_model, trmnt_val, name='Two models', random=False, perfect=False, ax=ax_roc)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklift.viz.base.UpliftCurveDisplay at 0x7fe6cbfe8710>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IUZmMW7I7L1lamxtZaL25OX3I9TcPN+g3ifl/H2LkfAfrNQlRAg888MC5sLCwYsf8VCUHDhzwnjx5coknMLhyltw9DopnFVF/AjDBQfkKYEUZhiaEEG5zIi2TV37czYz7O/P7X1s59NcqMI8iWZB7XV69jaN6GyvQmY3NeYS3zmQSM1ST1uo2Ro03Jh1/4Gv//OwGnfF28XpMomoZOXLkZdWC2KtXL6eWQMhP9pITQohyMvDjDexOMiYwNRuzggTfe7nOZt7RH6Z2xP63H77VCkwiAiADX3736cL+rQWG3MBrp8DDS5IlIVxEEiYhxOVpyQhjQcjXTpT6Eeezcog9lkrnprVZt/cEfyecIeF0BuvjTpKWaQw+3vNmP654faXdfQoTG3yeK/C8nZMG2xeMSYLUoxDcgoeX7+WLPw7y4Bdb8i7X8LH5L9zzknahEEIUQxImIYR7XTgLMUuh0wOl39oj+kfYvQiS/4XMVBidWHT95J2w3TwL7cI58KtV4le2efVnsnKKX8Ylf7IEcND3Pude4lMDQloD8NrAK/jij4N5l8bf0pZh3ZtB3AI4WSHWAhSiSpPNd4UQ7vVOGPz0jLG1x/hA2Ptzye7XGr4fBnuWwrlDkHkO4sybCowPND6mXPu1jj7raT1e8ADkZFvr7l9T7Ct/jzvpVLLkSOybN5bqPqUUsf+17jY1rHsz46D1TdD9mVI9UwjhPEmYhBDus+XzgmXf/QdWvOz8MxY9WrBs7iCY0dt6/mZtIyH75bWCdQ/+DlOvtJ5/fYfxNX+SBWitCRu93K5bDCCAdLy5yNR7ruSf127g20euJmGS/b5uW17py9ZXr8c345jzv7Z8fKt5kjBpQIFnV2XHjh3ztOw3VqdOnci6det2sJxnZmZWiAFby5Ytq9m7d++Wl1rHVnp6urrqqqva2K4r9eabb9b18fHpdPr06bxBblOnTg1+4IEH7La36NKlS5v169f7A6SkpHjce++9TZs0adIuIiLiii5durRZs2ZNdad/cQ6YTCaGDRvWJDQ0tF3r1q3bbty40eGGzBs2bPBv3bp129DQ0HbDhg1rYjIZP2Q8/vjjjZs1axbRunXrtjfccEOLU6dOeQJs2bLF76677gq7lNhcSRImIYT7rHjRcfmWz6wtPoU5ude4vut747zVjdDvHev1o/8UvGfT1IKb3AKk5htEPT7QmmSZxR5LpdkY+wm7CZMG8O0jV7PT9zHiqj/KrZENqe2j6b71WRgfyIGrFrP+pd4kTBpA3Zq+1KnhA+cLGTN126eF/1ovY/Xr18+1rAZt2RzXcm7ZTqQq+vjjj+vceuutefvlASxcuLB2u3btzn/zzTdO9yEPHTo0LCgoKCchIWF3dHT0njlz5hw8ceLEJQ3H+f777wMPHDjgm5CQsHvatGmJTz31lMP9yJ566qmm06ZNS0xISNh94MAB34ULFwYA3HTTTalxcXHRcXFxMS1btsx87bXX6gN06dLlQnJysve+fftKtMdbeZExTEKI8pOVBj7mTcltk6HRh8DDCyY62Kd0fKB1kUYLreGTLvZlQ82J08pRRcfwRsnHKwGMnTKL0V7bmJRjrJjSobERf/em5h/Wc7Nhaic4sz/vHo9dCwhN/hdG/G19UJI5kbvtE7jyPuvvQ6SjlVgqmMVPN+FEjMPWhFKr2zaD2z9xek0ck8lERETEFdHR0Xs2b97sd80117SNi4vb1apVq+wmTZq0i4mJiTl69KjXgw8+GHbmzBmv4ODgnDlz5iS0atXKbq2hkSNHNkxISPBOTEz0SU5O9n777bcPb968ucaaNWsC6tWrd/G3336L9/Hx0UuWLKk5evToJrm5uURGRmbMmTMn0c/PTy9cuDDgpZdeauLn52fq0qVLuuW5qampHsOHDw+NjY31y8nJUa+88srR++67z26rjuXLl9d44YUXQsHoat20aVNsUFCQXR/vggULgufNm3fAch4dHe2TkZHhOWXKlMSJEyc2ePbZZ08X93sVHR3ts3379uqLFy8+4OlpNEqFh4dnh4eHX9K6S0uWLKk1dOjQ0x4eHvTt2/d8amqqV2JiYrWmTZvmreydmJhYLT093cOySfHQoUNPL168OGjw4MGpd955Z95ed926dTu/cOHCvH3o+vfvf2727NlBb7311vFLidEVpIVJCOF66SeNxODtxgVbjno8D76B4F0dnt0BTXsU/7zf37E/f2R14XXHpxRMuAACndyk+39XcTbtAot8xvOE1098XG0qCb73svSUuVvskM2+4TbJUp5TcXDMvLFu2nFr7KHdjK9B5rFIHvLfsTM8PDzIysryOHPmjMfatWtrREREZPz222814uLivIODg3Nq1qxpevLJJ0OHDh16Oi4uLmbIkCGnn3zyySaOnpWYmOizadOmuB9++CH+iSeeaNanT5/UuLi4GF9fX9OCBQsCMzIy1OOPP95s/vz5++Pi4mJycnJ47733QjIyMtSIESPCli5dGr979+49J06cyJuiOHbs2Aa9e/dO3bVr154NGzbsffXVVxvn389t8uTJ9adOnZoYGxsb8+eff8babiQMkJmZqQ4fPuzTpk2bvMRmzpw5QXfccceZfv36pR88eND38OHDxTZ4/Pvvv75t27bNsG2lKsyAAQOaW7o6bT//+9//gvPXTU5Orma72GWDBg2yExMT7aZpJiYmVmvQoEFeAtW0adPs5OTkAlM5v/rqqzr9+vXL+wd69dVXn9+0aVPNYgN2g2J/F5VS/sALQKjW+lGlVCugjdb68tgSWwhRepYZaO8XMXSj12jrcVAYPLQcLmbCvHthvzkROrEHPu1a8N7wgfCffHt4v3oC3qprHLe9DYC0zIsU+B+4yVXw1CYjibM8q3EU/Dbevt6pOIIm1887vcXTJkFK3Axf3174r81iendo0cd+QHlQmPH1sbXG71NlUIKWIFeKiopK/+2332ps3Lix5ssvv5y8cuXKQK01Xbt2TQfYvn179Z9//nk/wJNPPnnmjTfeaOzoOddff32Kj4+P7tKly4Xc3Fx19913pwJERERcOHjwoPeOHTt8GzdunNWhQ4csgGHDhp3+5JNP6l5//fVpjRs3zmrfvn0WGK0nM2fODAFYt25dwKpVq2pNnTq1PkBWVpaKj4+362Lq2rVr+osvvthk8ODBZ+65556zLVq0sEuYjh075lWzZk27TfEWLVoUvGjRonhPT09uvvnms19//XXQ2LFjTxa2BU5Jt8ZZvnz5geJrla1Ro0bV9/T01E888cQZS1mDBg1yjh8/XiHXyHCmS+5LYBtg/nGIJOB7QBImIUojJxtMF40Wlcrm7SaQlQqvHIdqvo7rbPm88LFJ+T26xvFzqvnC/YusLVGOkiWAW6YULPPygeejwbsG6R41yMnIpuObvxLt40N1lWWtN/BDlu1NY6DlfMg3xrIGjaKg8VXGjLtPrio6/i2fFSxrfh3cvxhyMmGCNdEqMPvOwzxu1y/I+Ain9ezZM239+vU1jxw54j106NBzkydPrg/ogQMHlmgzVR8fHw3g6emJl5eX9jC38nl4eJCTk1OqAeVaaxYuXBgfGRmZZVt+9OjRvCRg4sSJx26//faUJUuWBPbs2TN8+fLl+6688spMy/Xq1aubsrOz81qltmzZ4peYmOjTr1+/1gAXL15UjRs3zh47duzJOnXq5Jw7d85updNz58551qtXL6d27dq5e/bs8c/JyaG4VqYBAwY0379/f4F/jCNGjDg+YsQIu+6/Bg0aXExISMhLApOTk71tu+MAmjZtetG2RSkxMdHbtsVp6tSpwatWraq1YcOGOA+b1tULFy54+Pr6lm4Kqos50wbcQmv9LnARQGudgbEprhCipEwmeCvE8VidkkjaZiQTKUcg9yKcOQiZKcaaRqWltf16PiZTwQHSWeahBxPqwWH7mWJ5CkuWAhrD01vgvh9g3Dmjm6xR59LHOz4FqtdxfC2wMR9sPEG7cavo+OavAERkfUlY5ly+zLkJgPd+T2bE3O30yXqfw0PXW9eAatbTSNicWZsp+seCZUMXGs+q5gcvlfsP7ZeF66+/Pv2HH36o3axZsyxPT09q1aqVs3bt2sAbbrghHeDKK688P3PmzCCAzz77rHZUVFR60U90LDIyMjMpKcl79+7dPgBz5swJ7tmzZ1rHjh0zk5KSvKOjo30A5s2bV9tyT+/evVMnT55czzIj7I8//vDL/9zo6GifLl26XJgwYcKxDh06nN+9e7ddohISEpKbm5urMjIylPm9tV944YWjSUlJu5KSknadOHFi5/Hjx6vFxcV59+jR4/y2bdtqHDp0yAtg/fr1/tnZ2R4tWrTIjoiIyOrQocP5kSNHNrTEs3fvXu958+YVmE2xfPnyA5bB9Laf/MkSwK233nru22+/DTaZTKxevbp6zZo1cx0lTDVq1DCtXr26uslk4ttvvw2+7bbbzgEsXLgwYMqUKfVXrFgRX7NmTbvkKCYmxqdNmzYXnPnzKW/OtDBlK6X8AA2glGoBZBV9ixCigHlDIdamYXb7N8agX1uWFpWxR+HMAajXruBijrbjfz6MKPgeR+N1nDG1I5xNKPx5+WeszboBwnrCMJtf06ybHD+7Rj0Yad43O6SN8zGNO2d0mXn5wHO74c9PocMQCGhU7K1TV+9zWP5GzoO8kfMgrDXGGx3QDVmY4MfzrSDmaCqpmRe5ulltmr31N7WZzhlqkuA71Li53yRYObrgQ5/6y/iz1dp+xe3qBYZ/iDLQpk2bbK216tmzZxpAt27d0pOTk71DQkJyAaZPn37ogQceCJsyZUp9y6Dv0rzH399fT58+PWHQoEEtLIO+X3zxxZN+fn76448/Thw4cGBLPz8/09VXX52enp7uCTBp0qSjjz32WGh4eHhbk8mkmjRpkrV27dp42+e+++67dTdt2hSglNJt2rS5cPfddxf4R3vttdem/PLLLzVuv/32tMWLF9f+6aef7P5C9+/f/+zs2bNrT5gw4dg777xzuF+/fq1MJpOqXr167jfffJM3yPubb75JeOqpp5o0bdq0na+vrw4KCsp57733LqlrdfDgwSnLly8PbNq0aTs/Pz/TzJkzEyzXwsPD28bGxsYAfPLJJ4nDhw9vlpmZqXr37p06aNCgFICRI0eGZmdne/Tp06c1QKdOndLnzp17CGDNmjUBJW0pLC9KO5pia1tBqRuAV4G2wC9Ad2CY1nqdy6MrpaioKL1161Z3hyGE1S+vGVPa8xubDN/cCYc2O74vKMwYCG0RvRi+f7Dod3UYAnfOKFl8J+OK7n6q2QDSkgu/3rQHJG60L3tyM9S9wjgu5/3Noo+mMGDqxuIrmt3esSEf/edKwkYvByCiYQDRR/Mm8rDcewwRHonW5PH7YfatS0UlqbaJ5v/9A8EtnI6rvCmltmmto2zLduzYkRAZGXlZbc5aEWzcuNH//fffr7d48eKDxdeuGi5cuKC6du3aZuvWrbHVqrlvGNOOHTvqREZGhuUvL7aFSWv9q1LqH6ArRlfcs1pr+ccjhK3cHJg9EO5bBN75Zl1fzHScLAFMbFD0c88mFL0WkSM758Mdn5UsSSlurI5tsjQ+pWBM+ZOl0rZylYH4E+l2yZLtIo8pGRf5aedRVkUfY8M+639ji/89yuJ/j+ad2yZLXcJqU+++v6GGj/Uld33huDvOkRfjjUHvD/1coZMlUbH06NEjY+vWranOjD+qKuLj470nTJiQ5M5kqSjFtjABKKU6AGHYJFha60WuC+vSSAuTcBlTLlzMMMYOfdoV7v4SUg7Dr69b69gmC2cTYUqHgs+p3wGO7Sx9HKMPwySbmdL3/QB12sBH7QrWHZ8Ch/+GZc/DkzaJTdI/8HlvUJ6gc42ypt2Nwchdn4ZNHxedCDlK5IYuhFY3lP7XdQmOpWTS9W375QU2je5Dw1oFhpCgtabZmBWsffE6er+/rtBnFrmi9u/vQoOO0LKvdQB3JVdIC9OB9u3bn/Xw8Kiyi0QKYWEymdSuXbuCIiMjm+e/5syyAl8AHYBowDI4SwMVNmESwiXeCSs4qHrhQwXrjQ80xrTUamKfLN38PnQxb+OxZoLjhKnXqIJrDDniG+B8K45tYjM+0BgXdDLWSN4EFOsAACAASURBVJbAmiwBPGSzknWbfpCSBB+2dfzc/ON5Iu5wW7KUlnmxQLI07Jowh8kSGFOuLcnQ5EGRvPD9jgJ1PhgcWfRLe5Vg+5bKbffJkyfbhoSEpEjSJKoyk8mkTp48GQjsdnTdmTFMMVrrQv7HrJikhUmUudVvwobJl/YM2wRHa2PFaU9vY4r+r69B/Gp42maNn2k94Pgu43hsMhyPNhKU4b8U3qKxYx78+HjRcTy9peAq2QB3zoQOgxzfczHTGHidv5tv22zoOBQ83dtlcN17a0k4nZF3Pv2+TvRrV0x3p41jKZls2n+K2zs2wsPj8p0E7KiFadu2bXW9vLxmAu2QxY5F1WYCdufk5DzSuXPnAnsYOZMwzQIma61jXBRgmZOESZSp49Ew7Zqi64ReAydiILOQBQgf+x0adizZe/csg/nm2VklGROU9I8xtT9pW8ne58ZxR5fCMlAb4LaODZl4R3uq+1weYz7KmqOESQhhcOZ/lTnAZqXUMYzlBBSgtdYOBmYIUQUVlywBPPyz8TUz1X5sERgtNyVNlgCuGGhMpQ8o4ZpNjToZC0Kaco0NZC06D4NtX9nXHZ8C5w4bXXyVUIzN4GyAj4Z0LPEKx0II4QxnEqZZwP3ALqxjmIS4PGRbu3lo0BEe/91YAfp4NBxYB39Nt6/vGwCvnoSEDcZyAfXbF97N5YxaDrfAco6HZ8FWI9uEyXLtUt7hRuezcnhpoXXs0VVhQZIsCSFcxpkuuc1a625FVqpgpEtOlBm7AdP5kg/LOKT7foCW15dvXJfi5F6o07rc10YqS4M/28yWg3nbT7HnzX74eVeNmWruJF1yQhTOmQF825VSc5VS9yil7rR8irtJKfWFUuqEUmq3TVltpdSvSql95q9B5nKllJqqlIpXSu1USnWyuedBc/19SqliVuwTogwdWFf0daWMJKoyJUtgrLRdCZMlk8n44S5s9HK7ZAmQZEkI4XLOJEx+GGOXbgRuMX8GFnmH4SugX76y0cBqrXUrYLX5HKA/0Mr8eQyYBkaCBYwDrga6AOMsSZYQLjf3P9bj1wpspyTKQObFXKKPpqC15nyW3ebsjFm0k8e/3kquSbPonyM0H7vCboC3xfuDipn+L4QQZcCZlb4dLDRTPK31eqVUWL7i24DrzMezgXXAKHP5HG30D/6plKqllGpgrvur1voMgFLqV4wk7LvSxCREieSY9398YKnbp81XNafSs9i0/zTPfLfdrnzDy71pHORHszHW9aBajF2R/3YAtozty5aEMwzscIkbGQshhBOcWbiyMfAxxh5yABswtkc5Uor31dNaW/ZYOAbUMx83Amw3AzxiLius3FGcj2G0ThEaGlqK0ISwYTu2L6yn++KooqLe+s1h+Znz2ayJLbD8SQE7Xr+RQP9qkiwJIcqNM11yXwJLgYbmz0/msktibk0qs1VjtdYztNZRWuuokJCQsnqsuFzZ7p3mIWv1laWXHKyqbfHVpgTGLY0u8v7bOjYk0L9i7jUlhKi6nOlnCNFa2yZIXymlnivl+44rpRporZPNXW6WHyWTANu5zY3NZUlYu/As5etK+W4hnFPSzW5FoSx7tjnrx+1Jecf7JvSn1Ss/550vebo7kU1qlWl8QgjhLGcSptNKqfuwjhu6ByjtCNilwIPAJPPXJTblI5RS8zAGeKeYk6pVwESbgd43AmNK+W4hipd23P584EfuiaMKOHAynQVbC++5X/Z/PQiq7k2jWn7kmnSBsUrVPD2IefMmEk9n0DykOj5eMhNOCOE+ziRMD2OMYfoQowttE1DsQHCl1HcYrUN1lFJHMGa7TQIWKKWGA4nAYHP1FcDNQDyQYXm+1vqMUuq/wN/mem9aBoALUeZMJpjc2r4s3JkJoSK/+X8fYtQPuwq9vnFUbxoH+eede9rs3+bt6cG/44xNfP29vbiiQeVchVwIUbUUu3BlZSQLV4oS2f4NLHna8bVKur+au+Wf/l+nhg/rX76OyDd+Ydf4m/CtVrC1aE9yKhnZOXRuWrvANVE+ZOFKIQrnzCy52Riz4s6Zz4MwNuN92NXBCVEuHCVLkiiV2ub9BXvst75qLO65b8LNhd4nLUlCiIrMmek/HSzJEoDW+ixwpetCEkJUZvd8/qfd+YpnZFkGIUTl58wYJg+lVJA5UbKsvi2r+InKz7IXnK2uT0O/ie6Jp4r59/UbqOXv7e4whBCiTDiT+EwGNiulvjefDwLkO4qo/D7tan9+5X1w/Tj3xFJF2C5IKcmSEKIqcWZrlDlKqa1AH3PRnVrrGNeGJUQ5OBlrPR53rlJuSFvRnErPAuS3UghR9RQ7hkkp9bXWOkZr/T/zJ0Yp9XV5BCeEy+RfnLKKfIePOZrKvC2HXP6e32KO8+eB04SNXk66edPc5JQLedcPvj3A5TEIIUR5cqZLLsL2RCnlCXR2TThClIP8ydIw51eirgjW7T3BnwfOcGtkQ9o2NGaWrYo+RuMgPwZM3QjALZENqe5T+qGGJpMmx6Tx9ir4M1XmxVwemWNdtqPduFUcmHgzvd9fB0DLujVK/V4hhKioCv0fVSk1BhgL+CmlUgHLj+DZwIxyiE0I13tsHTSs+JM+D53OAODa99bmlU3/fT+/Pn8tC7Ye5vMNB+3qR4xbRcIk51t5Ek+fp9d76xjYoQHLdlr30UuYNIBck+bH7UnccWWjAqtxWzS3KV/8dHeHdYQQojIrduFKpdTbWutKtR2JLFwpCshMgaR/IKwH/LeOUTZyDwRU/N3uS7ofm0VJEqb8C01eipK8V1QssnClEIVzps3+Z6XUtfkLtdbrXRCPEK4xKbRgWSVIlgBu+d/GUt2ntUY5MTbr5YU7SvX8+An98fL0sEu2pHVJCFFVObNw5Us2n9eAn4DxLoxJCNfr8ri7I3BKWuZFdiel2pWte/E6p1pxmo1ZQVZOLvP/LnwQeMzR1CI3yC3Mhpd74+Vp/PexxJwkbR7Th45NahV1mxBCVFol3ktOKdUE+EhrfZdrQrp00iUn2P4N5GTBVcON8/wDvV8/Cx7O/LzgPjm5Jlq+8nPeebM61VnxTE/8vI192P5OOMOg6Zvt7gkL9qdJbX827DtlV961eW3mPdatwDtsW4ee6NWC0f3DC1y/sW09fok5nlcmXW5Vl3TJCVG40kyjOQJcUdaBCFFm1r0D68xrq0Y9DNnnrdfGHAGfmu6JqxgpGRcJ8PMqdLzS2hevszu/Ksy6SW3+JCb/mKQ/D5yx1j11nuvMM9ps5U+WbJ+blZPLD9uSuLtz4yJ/DUIIUVU5s/nux4ClGcoD6Aj848qghLgk62wWos+/9UkFTZYe/GILv8edLPF9Xw/vQnaOyam6h05nEBrs7zBZiv1vvyLv9fHy5N6rHYwDE0KIy4QzLUy2fVs5wHda6z9cFI8Ql+b8qcKvPber/OIooaKSpZkPRNH3iroOr/VsFeKwfMe4G4l84xe7smvfW0uf8ILPGXZNGL7VPEsQrRBCXH6KHcShtZ4NfAdsA3YAW1wdlLjMJe+E6T2M40mh9uOP0o7D13fAkULGqL3XovDn1qp8LSRzH7ma69vWc2q2m61Av2p5xw93b5Z3vCb2RIG642+NKFAmhBDCnjNbo1wH7AM+AT4F4hwtMyBEmfmsJxzbZSRKmSlG2fhAOLAOJreG/WtgZl8wFdEVdcvUcgm1LMSfSCtQ5qFg2tBOXNOyTqmfmzBpAAmTBjC0a8FE8beRvQAI8C39auBCCHE5ceZ/y8nAjVrrvQBKqdYYLU6yPYooeyteLvzanNvszw+shZZ9Hdft/CD8+jpkniu72Fzk+g+sS5q5YgZa8zrVC5S1rFtDZrsJIUQJODOvupolWQLQWscB1YqoL0TpbfnM+brf3Gm0PFm67PIvHfDiPuvxuIqZONku6/HKza6ZfKqUshvU/YZ0wQkhRIk5kzBtVUrNVEpdZ/58jv1AcCFK72yCkeisftP5e279uGDZj08WLPPyhjFJ8NIBKOEYoPKy5aB1uv+j1zZ32Xt8q3myc/yNjLyhNQ90a+qy9wghRFXlTML0JBADPGP+xJjLhLh0s240vm6YbB2vBPDsTuuxf75xPBF3FnzOjrnW46setR771IDqwZcepwucSMtkyIw/AWgQ6Ovy9wX4VuOZvq1KPIBcCCGEc7PksrTWH2it7zR/PtRaZ13KS5VSzyulopVSu5VS3ymlfJVSzZRSfyml4pVS85VS3ua6PubzePP1sEt5t3Cz7PNw/rT1vK3NuCTb/d6CmhrdaONToJ1NgnTHZ0YSVJQB75dNrC70R/wpukxYnXf+y/Myj0IIISqyct8bQinVCKOlKkpr3Q7wBP4DvAN8qLVuCZwFzHtaMBw4ay7/0FxPVCZJ/1hbjyY2hPeaw8VM43zLjIL12w8yvlpaQm5+D+6YAc2vg8j/GGWFjUm69qWyitqlhs78y+68pq8MCxRCiIrMXXOKvQA/pdRFwB9IBvoA95qvz8bY4HcacBvWzX4XAv9TSild0k3whHtkn4fPexvHza+zlq8aAwM/dHzPnZ8XLIscYnwslILHN0CNerDjO6gXAc17g2fFnya/bOdRu/PiVtkWQgjhfuX+3UVrnaSUeh84BFwAfsFYFPOc1jrHXO0I0Mh83Ag4bL43RymVAgQDdks6K6UeAx4DCA2tfAsUVjnnTxVcRPLAOuvx1i/sxxpZdH7I+QHaDToYX3s8V6oQXeF0ehY7jpyjT3i9QuuMmLvd7lxW2RZCiIqv0IRJKfUT1j3kCtBa31qaFyqlgjBajZoB54DvgUv+EVtrPQOYARAVFSWtT+5W1IrbFrE2G8SOPgQXzkJQmMtCcrXN+09zz+fGIO6tr15PnRo+bo5ICCFEWSmqhclVI2evBw5qrU8CKKUWAd2BWkopL3MrU2MgyVw/CWgCHFFKeQGBwOmCjxWVztq3jK+9RoFvoPGpxCzJEhhbkAyOalJk/fUv9aZugCRVQghRGRQ66Ftr/XtRn0t45yGgq1LKXxnzm/tiLFWwFrjbXOdBYIn5eKn5HPP1NTJ+qRKqbt70ta6DRRO7V5wutbLy8sKdhI1eTnZO4du3hAb7S3ecEEJUEs7sJddKKbVQKRWjlDpg+ZT2hVrrvzAGb/8D7DLHMAMYBYxUSsVjjFGaZb5lFhBsLh8JjC7tu0U5yb/i9nVj4aYJxnFXB0t4efu7PiYXKyyHb/3qz4xfGs3+k+kcS8kkbPRyh/WEEEJUbM4M+v4SGIcxpb838BCXuByB1nqc+Zm2DgBdHNTNBAZdyvtEOZo31P58vHk5Aa2heh1jJptvACx4oPxjc6E/4gvvJf5qUwJfbUoov2CEEEKUOWcSHz+t9WpAaa0TtdbjAdm1UzgWu8x67GGztpBS0KKP8bXtbQXvq+Tum2Wsq9Sqbg2i37ip2Pr7JvR3dUhCCCHKkDMtTFlKKQ9gn1JqBMYg7GKWWhaXldjlUM0fu0mVzXvDfYsKv2dMEqwcBQOnuDw8VzuZZl34ftJd7anu48XuN27i7Plser67tkD9f167gWqe5b5mrBBCiEugihs/rZS6CtgD1AL+CwQA75rHIlVIUVFReutW2R+43OQfswTWrrgqzGTSNB+7wq4sYZJ946vWmmZjjDrxE/pzPiuXQH9Z1VtUTEqpbVrrKHfHIURF5EwLU5jW+m8gHWP8EkqpQUCFTZhEOYpbVbDs2R3lH4cbXPf+OrvzYdeEFaijlLJLogL9pWVJCCEqI2cSpjEYi0sWVyYuR3MHFyyrxItPlsShMxl5xyuf60l4/QA3RiOEEMKVilrpuz9wM9BIKTXV5lIAkOP4LnHZu0xal175cZfduSRLQghRtRXVwnQU2ArcirHXm0Ua8LwrgxKVRMwS6/HrZ8Dj8lmE8du/DuUd7xx/oxsjEUIIUR4KTZi01juAHUqpuVrri+UYk6gsbNdSuoySpdRM6z+H2tW9CfCVQdxCCFHVOTOGqYtSajzQ1FxfAVpr3dyVgYkKbvOn1uPndrsvDjfoMP6XvOOtr1zvxkiEEEKUF2cSplkYXXDbgFzXhiMqjVVjrMe1it5ktirJybXfG87DQ7kpEiGEEOXJmYQpRWv9s8sjEaISGP9TdN5x3FuyWrcQQlwunEmY1iql3gMWAXlLGmut/3FZVKJiy7UZ0nbbp4XXq4K++dMY7O3t5YG3l6ypJIQQlwtnEqarzV9tV3/VQJ+yD0dUeClJ8GFb6/mVQwuvW4VtGi1//YUQ4nJSbMKkte5dHoGISmKZzYoSvV9xXxxuEHM0Ne+4Tg0fN0YihBCivBXbp6CUqqeUmqWU+tl83lYpNdz1oYkKya+W9TjxD/fFUc6yc0zcPHWDu8MQQgjhJs4MwvgKWAU0NJ/HAc+5KiBRQaUeNTbZ3TnfWtbt/9wXTzlr/ap13sOI3i3dGIkQQgh3cCZhqqO1XgCYALTWOcjyApePi5nGmksfXFHwWovLcxzPize1cXcIQgghypkzg77PK6WCMQZ6o5TqCqS4NCpRcUyo57j8vh/A4/KYJXYsJTPvePtrN7gxEiGEEO7iTMI0ElgKtFBK/QGEAHe7NCpR8bW8fFa4/mpTQt5xUHVv9wUihBDCbZyZJfePUqoX0AZjW5S9srfcZe71s+6OoFxN/30/AK3q1nBzJEIIIdyl2IRJKeUJ3AyEmevfqJRCa/2Bi2MT7pZ2zP78tk/gyvvcE0s5mbXxIP9dFgNA7H/7cTYjO+/atPs6uSssIYQQbuZMl9xPQCawC/PA70ullKoFzATaYYyNehjYC8zHSMwSgMFa67NKKQVMwUjaMoBhssq4i+VeBFMuHP7LOK/XDu6aBXXD3RuXi5lMOi9ZAgh/baXd9ZZ1a5Z3SEIIISoIZxKmxlrrDmX83inASq313Uopb8AfGAus1lpPUkqNBkYDo4D+QCvz52pgGtbVx4Ur/LeO/XnHe6t8sgTQfOyKQq/VqSFjl4QQ4nLmzDSnn5VSN5bVC5VSgcC1wCwArXW21voccBsw21xtNnC7+fg2YI42/AnUUko1KKt4RD7Z5wuWRVX9dUov5hbdeLr1VZkdJ4QQlzNnEqY/gR+VUheUUqlKqTSlVGqxdxWuGXAS+FIptV0pNVMpVR2op7VONtc5BljmszcCDtvcf8RcZkcp9ZhSaqtSauvJkycvIbzLXKaDP9pqvuUfRzlo9coKwkYv5+i5C9w38y93hyOEEKICcyZh+gDoBvhrrQO01jW11gGX8E4voBMwTWt9JXAeo/stj9ZaY173yVla6xla6yitdVRISMglhHeZO7TJ3RGUi5W7k7mYa/wVu2bSGv5OOANA/QBffht5bV69B7o15cDEm90SoxBCiIrDmTFMh4Hd5iSmLBwBjmitLT/SL8RImI4rpRporZPNXW4nzNeTgCY29zc2lwlXWPiw/fltn7onDhfKvJjLE9/Yzxswmf92Lx3RnboBvrx7VwcGRjbA39uZfyJCCCGqOme+GxwA1pk3382yFJZ2WQGt9TGl1GGlVBut9V6gLxBj/jwITDJ/XWK+ZSkwQik1D2Owd4pN151wtSuHujuCMvf15sRCr9Wp4QPA4KuaFFpHCCHE5ceZhOmg+eNt/pSF/wO+Nc+QOwA8hNE9uEApNRxIBAab667AWFIgHmNZgYfKKAaR36l4d0dQLias2FPoNQ8PVY6RCCGEqCycWen7DQCllL/WOqMsXqq1/heIcnCpr4O6Gni6LN4ripC4Cb7sbz1/5VjhdSupMYt28d2WQ3Zl8RP60/KVn90UkRBCiMqi2EHfSqluSqkYINZ8HqmUqnoDWy53tslS/Q5Qzc/4VFK7k1IIG72cT9ZaW83yJ0ux/+2Hl6f1n8BtHRuWW3xCCCEqF2e65D4CbsIYS4TWeodS6tqibxGVQlYa+DhYvfrOGeUfSxkb+PFGAN5btZf3Vu11WMe3midgJE6HzmTQup6s5C2EEMIxp6YAaa0PGzuU5Ml1TTii3OxZBvPNA7o75xsWFlJ5V/UOG7282Dr9Iurz7iDr4vW+1TwlWRJCCFEkp5YVUEpdA2ilVDXgWaDwUbOicphvM/tt25fW49fPgKqcA5//iD9VbJ35j3Xl6ubB5RCNEEKIqsSZhSufwBh03Qhj/aOOyCDsyq2oJbU8PMsvDielZ+UQNno5G/cVnRA9Omdrsc+SZEkIIURpFNnCpJTyBKZoraveYjxV3YVzsP0b8Kxm7AXn6QXjA4u+p+tT5RNbCbUbtwqA+2b9RcKkAYXWy8i27ykOru7N6fPZeec1fGQRSiGEEKVT5HcQrXWuUqqpUspba51dVF1RwbzT1HoctxLu/7Ho+s16Qb+3XRtTCWzcd4ojZzP4T5dQu/JtiWdoVMufegE+DJnxJ52bBjGqn+MxV/4+npy22Ut4zYu9XBmyEEKIKszZlb7/UEotxdj3DSj9St/CDfavgZiljq/VbgFNroY7ppVvTMW4b5axc86p9Cy78rumbQbgx6euYcvBM2w5eIZp6/bjabPg5JCoJtQN8OH2KxvRd/LvAPw1ti91a1bNTYSFEEK4njMJ037zxwOQqUSVgcnBJMYF9zuu+8w/jsvdKP5EWt7x+7/EOaxzx6f2mwTnmqzjst652zoDbvbDXWjfKJDa1ctqkXohhBCXI6dX+haVSPxq5+r9Z65r4yilwZ/9Wep7n7yuhd15r9YhlxqOEEIIUXzCpJT6FRiktT5nPg8C5mmtb3J1cKKU5t1b+LWxyXAxA+J/g/DCB1C705nzpR8uN6hz4zKMRAghhDA4s6xAiCVZAtBanwXqui4kcclMF42vPV+E52Os5ZH3grc/VK8Dkf9xT2yldPDtm9nwcm/euat9XtmT17Vg9QvWgdyLn+5O85Aa7ghPCCFEFefMGKZcpVSo1voQgFKqKVDEQj6iwmjWEwIbWc9vfMt9sZTCgse7MfizzQyOaoxSiia1/RlSO5Ra/t5c3aw2tfyNcUn7J95M4unzkiwJIYRwGWcSpleAjUqp3wEF9AQec2lUovSObLMeN8s3jd43oHxjKaGkcxfs1krq0qw2sx/uwtXNatvVuymivt25p4eSZEkIIYRLOTPoe6VSqhPQ1Vz0nNa6+D0ohHvM7GM9tmxxMu6cMXPOs2Iv3Nh90poCZTJoWwghREXg7HdQH+CMuX5bpRRa6/WuC0uUyql4x+VKVfhkSQghhKjInJkl9w4wBIgGTOZiDUjCVNFsn2M9fux398VRAiaTJldrMrLs1456oleLQu4QQgghyp8zzQ63A2201lnF1hTu9ccU63HDju6LowSaj13hsHx0f8fbnQghhBDu4MyyAgeAaq4ORFyiJJsVu1864L44SiBs9HJ3hyCEEEI4xZkWpgzgX6XUaiCvlUlr/YzLohIl93lv63H1YPfF4SStHa9MUd3bk99f7u3wmhBCCOEuziRMS80fUVHlXnR3BE6ZueEAe5LTmDw4ksGfbXZYJ/rNfuUclRBCCFE8Z5YVmO2KFyulPIGtQJLWeqBSqhkwDwgGtgH3a62zlVI+wBygM3AaGKK1TnBFTJXWzgXujqBY+0+m89byPQBMuqs9fyecdXNEQgghhPMKHcOklFpg/rpLKbUz/6cM3v0ssMfm/B3gQ611S+AsMNxcPhw4ay7/0FxPWORkwblD1vMX4twXSyHWxp6g72TrrL1Wr/ycdzz9vs7UrekDwE8jepR7bEIIIYQzimphetb8dWBZv1Qp1RgYAEwARiqlFNAHsOwaOxsYD0wDbjMfAywE/qeUUrqwQTCXm7dstvV7YAnUrOe+WAqxKyml0Gv92tWnX7v6hV4XQgghKoJCEyatdbL5a6IL3vsR8DJQ03weDJzTWueYz48Alk3QGgGHzbHkKKVSzPXtVhtXSj2GecuW0NBQF4RcCQS3dHcEDm05eMbdIQghhBCXxJllBcqUUmogcEJrva3YyiWgtZ6htY7SWkeFhFTh7TTGBxofgNP77a/VbFj+8ThhY7zjnXQOvn1zOUcihBBClE65J0xAd+BWpVQCxiDvPsAUoJZSytLi1RhIMh8nAU0AzNcDMQZ/X95Sj8LHnezLPNzxx+m8NS/0Yv9EI0m6q1NjlGWvOyGEEKKCK7RLTim1WmvdVyn1jtZ6VFm9UGs9Bhhjfsd1wIta66FKqe+BuzGSqAeBJeZblprPN5uvr7lsxy+dsVmQcu0E+2vPx5RvLMU4eOo8S/89Sg1f61+x5iE1AEiYNMBdYQkhhBClUtSg7wZKqWswWoPmAXbNAVrrfxzfVmqjgHlKqbeA7cAsc/ks4GulVDzGBsD/KeP3Vh57V1qPm3aH7d8Yx02uhoCK1R3X+/117g5BCCGEKDNFJUyvA69hdI99kO+axuhKuyRa63XAOvPxAaCLgzqZwKBLfVeVsGqM9Xjxk9bj4b+Ufywl5OUh3W9CCCEqr6JmyS0EFiqlXtNa/7ccYxKVTObFXM5n5RBcw6fQOtteu6EcIxJCCCHKljMrff9XKXUrcK25aJ3WeplrwxJOU57ujoDe768jOSWT7i2D+SP+NDvH31igToCvM7vwCCGEEBVTsdOqlFJvYyxiGWP+PKuUmujqwEQ+GYWsZdSwY/nG4UBySiYAf8Qbkxc7jLfvInz3rg4yI04IIUSl5syP/QOAjlprE4BSajbGoOyxrgxM5PNuM+txs2vh4HrjuOcL7onH7HR6VrF16gX6lkMkQgghhOs4209SC2OGGhjrIInykpMF520WflQecP8SOLTJWNm7pnu3Fen81m+FXvv8gSj8vT3p3rJOOUYkhBBClD1nEqa3ge1KqbUYSwtcC4x2aVSXmx8eAQ8vuGN6wWu2e8UBDP3eWKAyrOJvVHtt6zr4eLl/jJUQQghxqZwZ9P2dUmodcJW5aJTW+phLo6qqstLhbfMWeS/EGRvlbvwIdn1vlOVPmEwm+/MOQ6Dl9a6PswzI4pRCCCGqEqf20tBaJ2utc97V/AAAFw9JREFUl5o/kiyVliVZApjcGrLPw2/jrGXJO+zrr3nT/rzt7a6LrRTmbE7IO373rg55x3d3blz+wQghhBAuJHO93WlivtW5P7sWxqdATjb8+Sls/ND+ukfF+uN6fUl03vHgq5ow+KompGfl4F9NuuGEEEJULRXrO3BV16gzJG0rus6f02FlIVv31Yso+5hKKTvH5LC8ho/8lRJCCFH1FNklp5TyVErFllcwVV5xyRIUniwBBDYq/JoLpGflsHn/aYfXLlzMzTuOefOm8gpJCCGEcIsiEyatdS6wVykVWk7xVF0xS6zHvfJNMhyfUvh9PV+EQV/BQysLr+Mi7cat4p7P/yRs9HK01gC8uzKWsNHLScu8CMD4W9ri7y2tSkIIIao2Z77TBQHRSqktwHlLodb6VpdFVdWYTLDgAev56fiCdR5ZDTP7Fizv+5rr4ipCqjkhsmgxdgUmbT3v8c5aAHYfTS3PsIQQQgi3cCZhcs937Krkwln789SjBes0joJ+79h3ybXo49q4ijB4+ma7c9tkydbx1MxyiEYIIYRwr2KXFdBa/w4kANXMx38D/7g4rqpl88f25wPetx6/etJ63PUJ6/G4c3D/j66NqxBHz10g9liaU3VvaFvPxdEIIYQQ7ldsC5NS6lHgMaA20AJoBEwHHPQfCYdslwfo8pgx262ocUsAbtisVmtNdq6JayatcfqeB7qFuS4gIYQQooJwZuHKp4HuQCqA1nofULfIOy5nJpN1he7ci/Djk/bXb36v/GNyUvhrK2nzqv3g8q2v2q8sHuBrzbH3TehfLnEJIYQQ7ubMGKYsrXW2Mrd4KKW8gEJGtAjeDDK+jk8xZsbtmGu99ooTi6Q36gy1W7gmtmJkOVhbqbq3F8/2bcWGfSdpUz+At+9sz4K/D7P98DmqeTq1ULwQQghR6TmTMP2ulBoL+CmlbgCeAn5ybViV1NYvrMcXzsIPw+2vV/Mr/hmPOt8dVpaenbe9QNno/uH4eXvy/A2tef6G1nnlllW9hRBCiMuFM00Eo4GTwC7gcWAF8Korg6q0lj1vPX4nzG1hlMaSf+1n7g3v0YwnermnpUsIIYSoaIptYdJam5RSs4G/MLri9mrLKoaiSkqYNMDdIQghhBAVSrEtTEqpAcB+YCrwPyBeKVXq0b5KqSZKqbVKqRilVLRS6llzeW2l1K9KqX3mr0HmcqWUmqqUildK7VRKdSrtu90q9Bp3R1CohFPni68khBBCXMac6ZKbDPTWWl+nte4F9AY+LOaeouQAL2it2wJdgaeVUm0xuv5Wa61bAavN5wD9gVbmz2PAtEt4t3u8dAAeWuHuKHh+/r+8vWIPAK8v2c1Bc6L05rIYd4YlhBBCVHjODPpO01rb7uVxAHBuVUMHtNbJQLL5OE0ptQdjbafbgOvM1WYD64BR5vI55m7AP5VStZRSDczPqTgyzliPh/8Ks26wnlcPLv94HPhxexIAn60/AMCczYmsf6k3a2JP5NV549YIt8QmhBBCVGSFJkxKqTvNh1uVUiuABRhjmAZhrPZ9yZRSYcCVGOOj6tkkQccAyxLSjYDDNrcdMZdVrITp3WbW4yZdIKwnJGxwXzz5pOXbG87i2vfW2p23bxxYHuEIIYQQlUpRLUy32BwfB3qZj08CTsyPL5pSqgbwA/Cc1jpV2axsrbXWSqkSDSxXSj2G0WVHaGjopYZ36YYtg+QdUK26uyMBoP34X4qtM++xrnQKDSqHaIQQQojKpdCESWv9kKteqpSqhpEsfau1XmQuPm7palNKNQAs/URJgO2iP43NZfnjnQHMAIiKiirfWXyFTRpsEOmyV4aNXg7A3rf64ePlmVcefyKNsODqeNksKplaSOtSfl2bV4yuQyGEEKKicWaWXDOl1AdKqUVKqaWWT2lfqIympFnAHq31BzaXlgIPmo8fBJbYlD9gni3XFUipcOOXTsZaj699uVxffSo9O+940/5TXP/Belq+8rNdnQ5OtC7JUgJCCCFE4ZyZJbcYSAA+xpgxZ/mUVnfgfqCPUupf8+dmYBJwg1JqH3C9+RyMhTIPAPHA5xgrjVccWz6HT7taz/u8Uq6vf2HBv3nH3/55yOn7Xu7XxhXhCCGEEFWSM7PkMrXWU8vqhVrrjYAq5HJfB/U1/9/enYdJVZ15HP++DTQgO4oGQWURRowQRYLgghgQFTS4zsPouCDzGI0r6kxwGSGaKCMy0USeKG5xmxjiPhLFDTBohCYMuyK7goiIiEAQBN75457uvt1d1VU0XdVV3b/P89yn7z333HtOHaseXs85955oAeDc9Jebs17k6o2l7036cEXp03mTF5R2vG3+x/e02K8Bu/eUDhe+c9PJdG7TFIgmgd/7xpIs1FZERCT/pdPD9ICZjTazvmbWs3jLeM3y0ehvslLMyeOmJUw/r2f7kv1B909nzx7n/Ic+KEkrDpYAmjVqkLH6iYiI1Dbp9DB1JwyhAcXL2Xs4rtt2l5tMbck6zjJr9cZtHNyyMS/MWVOStv7bHTw7czX/92nyIG5Ij7ZMnr+Ov/7HKdmopoiISN5KJ2C6AOjk7jtT5qxLNq2GB3pkrTh354F3liZcEDdRj1OfTq35z1cWVXrPB//lGG4d3I12Lff5LREiIiK1WjoB00KgJaWP+cvObRWDpTGbM1rk2Nc/5uH3VnD/20vTyj/vs7L1uez4DhXymJmCJRERkTSkEzC1BD42syJgR3Giu/80Y7XKdXcfXPb4jHEZL7J4OZN0bf9+d5nj0WcdWZ3VERERqVPSCZhGZ7wW+WRrgo62H4/Ifj32wgPDjsZqaH6ViIhIbZAyYHL36dmoSN64r0vFtIJ6FdOy4OkRvbn4sVll0nq0b8EBTRuWWVB36NHtsl01ERGRWiVlwGRmW4ieigMoBBoA29y9eSYrlhcO7Qs/PDd1vn309N9WJUw/qUsbnhlxHN/v3sPxh+/P6FcWccsZ3fjRnanf7C0iIiLpS6eHqVnxfljWZCjQJ/kVdcjlb2T09tt37ub1hesSPu12+IHRO5VO7HJASdrY87L31J6IiEhdks6LK0t45GXgtAzVJ/cVhBc+XvxSxovqdscb3DhpXpm0qTf3p3mj+jx3RXox6zs3nZyJqomIiNQp6QzJxcecCoBewHcZq1Gua9UBfnAUdM7sezsXrq34moLiBXLnj6k8Xv3oztPpdkfU+xV/u7eIiIhUTTpPyZ0V299FtBDv0IzUJh9sXBptF/who8Wc+bsZVb62cWHNTEIXERGprdKZwzQ8GxXJC+WXQsmQz7/Zvs/3KO6NEhERkX2XNGAyszsquc7d/a4M1Ce3fReGyXpfkdFi/jjr0wppIwd2zWiZIiIiklxlk763JdgARgC/yHC9ctO4sI7brIkZLeZ37y6rkHbZCR0yWqaIiIgkl7SHyd3HF++bWTPgemA48BwwPtl1tdY3n5Xutz06K0Ue1a455/Vsz7QlG2jRuEFWyhQREZGKKp3DZGatgRuBi4AngZ7uvikbFcsVj81YyaAjD+KQ3x5VmpjBpVA2by+dJ/XiVSdQWL+A4Sd0zFh5IiIiklplc5jGAecCE4Hu7r41a7XKEV9s/o67XlvMXa8tZlWj2Imel1R7WTdOmsuMpV9xy+AjStIK6+/Va7JEREQkQyrrYboJ2AHcDtwWW7zViCZ91/qlUVZvjKZt1WdXxst6cc5aAB6eviLjZYmIiMjeSdqF4e4F7t7Y3Zu5e/PY1qwuBEusm89xT3WiDZu4tv7LJckP76rex/U/Wb+FD5Z9VXL88RdbAHj1mhOqtRwRERGpunReXFnn/Oq1xdw++yQAihpdXebcPbsu4mfVVM7S9VsY9Jv3Ep7r3q5FNZUiIiIi+0qTZMq57IlZPDpjZcJz/7rzFgCmLvmyWsqa82ny+fOxIVARERGpYXkTMJnZ6Wa2xMyWmdmoTJUzbckGCkn8Ru8utgaA4U8UVUtZHyzfWC33ERERkczKiyE5M6sHTABOBdYARWb2qrsvru6yTixYQCN2Jjz35O7SRW/37HEKCqJeoA6jJlNgsOKe9Oc3bdy6g1fmfr5vlRUREZGsyJcept7AMndf4e47iV6eWe0LAH/+2XKeKbyHRwsTv5fzl2f3KNl/Y9EXANw3ZQkAexy27Uj/abpjf/V2hbTrB3QB4PYh3dK+j4iIiGRevgRM7YDYq7ZZE9JKmNkVZjbbzGZv2LChSoU0aVrx4b+tZz0S7Rx2Ihf3Oawk/efPzuHvqzfx4NTSZUzi+zt37WHlV9tIpMOoyWWOX7jqeJbfPZiRp3Zl1dgh/NtJnapUfxEREcmMfAmYUnL3ie7ey917tWnTpkr3aNGq7HVDdvyapsf+M4zZDMMnV8j/8PTlZY5/P630eOiE9znlvmms/WZ7ynJ7HtqSegWa5C0iIpKr8iVgWgscEjtuH9IyqkmL1hXSnr+yb8n+m4vXJ732o3XfAnDzpHklQ3XLvtxSoXcJ9ESciIhIrsuLSd9AEdDFzDoSBUrDgAszXeikqwdUSDu4ZeOU18VfRPm3FRv54egp1VovERERya68CJjcfZeZXQNMAeoBj7v7oowXvKfi6wXatmiUIGOpoRPeZ95n32SqRiIiIlID8mVIDnf/i7t3dffO7v7rjBU0MhaHNWtb4bSZcVX/zmXSbhjYpWQ/3WDprZH9uOnUrqy4e3DV6ikiIiJZkzcBU9a0aB9N8h6zGQrqJczyi9OPKHM8/PiOe1XE2HO70+WgZlw7oEvJu5xEREQkdylgqgYt9muwV4vlDut9aAZrIyIiItVNAVM16dG+ZZnju8/pXuZ45T3R0FuTwsS9ViIiIpK7FDBV0dWnRPOYJlzYM+H5C48r7UW6fUg3zIxZtw6g6PaBWamfiIiIVJ+8eEouF10/oCtdDmzG4O4/KEn799P+iXFhqRSAotsGMnPlRs7scTAABzav/Ak7ERERyU3qYaqiwvoFnH1MuzIvnbyiXyda7teAKTf0A6BNs4YlwZKIiIjkL/UwVaMG9QqYe8egmq6GiIiIVDP1MImIiIikoIBJREREJAUFTCIiIiIpKGASERERSUEBk4iIiEgKCphEREREUlDAJCIiIpKCAiYRERGRFMzda7oO1c7MNgCr9+EWBwBfVVN1ajO1U2pqo/SondKT6XY6zN3bZPD+InmrVgZM+8rMZrt7r5quR65TO6WmNkqP2ik9aieRmqMhOREREZEUFDCJiIiIpKCAKbGJNV2BPKF2Sk1tlB61U3rUTiI1RHOYRERERFJQD5OIiIhICgqYRERERFJQwBRjZqeb2RIzW2Zmo2q6PtliZqvMbIGZzTWz2SGttZm9ZWZLw99WId3M7LehjeabWc/YfS4N+Zea2aWx9GPD/ZeFay37n3LvmdnjZvalmS2MpWW8XZKVkYuStNEYM1sbvk9zzWxw7Nwt4fMuMbPTYukJf3tm1tHMZob0P5lZYUhvGI6XhfMdsvOJq8bMDjGzqWa22MwWmdn1IV3fJ5F84e7aonlc9YDlQCegEJgHHFnT9crSZ18FHFAu7V5gVNgfBfxX2B8MvA4Y0AeYGdJbAyvC31Zhv1U4NyvktXDtGTX9mdNsl35AT2BhNtslWRm5uCVpozHAzQnyHhl+Vw2BjuH3Vq+y3x4wCRgW9h8Crgr7PwceCvvDgD/VdFukaKe2QM+w3wz4JLSHvk/atOXJph6mUr2BZe6+wt13As8BQ2u4TjVpKPBk2H8SODuW/pRHPgRamllb4DTgLXf/2t03AW8Bp4dzzd39Q3d34KnYvXKau78HfF0uORvtkqyMnJOkjZIZCjzn7jvcfSWwjOh3l/C3F3pIfgI8H64v397FbfQ8MCCXey7dfZ27zwn7W4CPgHbo+ySSNxQwlWoHfBY7XhPS6gIH3jSzv5vZFSHtIHdfF/a/AA4K+8naqbL0NQnS81U22iVZGfnkmjCU9HhsCGhv22h/4Bt331Uuvcy9wvnNIX/OC8OHxwAz0fdJJG8oYBKAE929J3AGcLWZ9YufDP/HqvdPlJONdsnTtv890Bk4GlgHjK/Z6uQOM2sKvADc4O7fxs/p+ySS2xQwlVoLHBI7bh/Saj13Xxv+fgm8RDREsj508xP+fhmyJ2unytLbJ0jPV9lol2Rl5AV3X+/uu919D/AI0fcJ9r6NNhINRdUvl17mXuF8i5A/Z5lZA6Jg6Vl3fzEk6/skkicUMJUqArqEp3IKiSaSvlrDdco4M2tiZs2K94FBwEKiz178BM6lwCth/1XgkvAUTx9gc+junwIMMrNWYQhmEDAlnPvWzPqEOSaXxO6Vj7LRLsnKyAvF/zgH5xB9nyD6XMPCE24dgS5EE5UT/vZCb8hU4Pxwffn2Lm6j84F3Q/6cFP4bPwZ85O7/HTul75NIvqjpWee5tBE9mfIJ0RM7t9V0fbL0mTsRPZU0D1hU/LmJ5oO8AywF3gZah3QDJoQ2WgD0it3rcqKJvMuA4bH0XkT/aC4HHiS8YT7XN+CPRENK3xPNCRmRjXZJVkYubkna6OnQBvOJ/rFuG8t/W/i8S4g9LZnstxe+n7NC2/0ZaBjSG4XjZeF8p5puixTtdCLRUNh8YG7YBuv7pE1b/mxaGkVEREQkBQ3JiYiIiKSggElEREQkBQVMIiIiIikoYBIRERFJQQGTiIiISAoKmKTWMDM3s/Gx45vNbEw13fsPZnZ+6pxp3+/W6rpXinL6m9nxVbhulZkdkIk6iYjkIwVMUpvsAM7NtX/oY2+qjtvrgMnM6lWh+P7AXgdMIiJSlgImqU12AROBkeVPlO8hMrOt4W9/M5tuZq+Y2QozG2tmF5nZLDNbYGadY7cZaGazzewTMzszXF/PzMaZWVFYbPZnsfv+1cxeBRaXq8tYoLGZzTWzZ0Pay2Hx40WxBZAxs61mNt7M5gF9zWxEKH+WmT1iZg+GfG3M7IVQjyIzOyEs8nolMDKUdVKifOH6/c3szVD+o0QvThQRkSDR//mK5LMJwHwzu3cvrvkR0A34GlgBPOruvc3seuBa4IaQrwPRumidgalmdjjREhSb3f3HZtYQeN/M3gz5ewJHufvKeGHuPsrMrnH3o2PJl7v712bWGCgysxfcfSPQBJjp7jeZ2cHAM+G+W4B3id7QDvAA8Bt3n2FmhxItl9HNzB4Ctrr7fQBm9j/l84XPPhqY4e53mtkQojd2i4hIoIBJahV3/9bMngKuA7aneVmRR2txYWbLgeKAZwFwSizfJI8WlF1qZiuAI4jW8uoR671qQbRG2k5gVvlgqRLXmdk5Yf+QcI+NwG6iBVshCtamu/vXoa5/BrqGcwOBI6NlxABobmZNE5STLF8/4FwAd59sZpvSrLeISJ2ggElqo/uBOcATsbRdhCFoMysACmPndsT298SO91D2N1J+HSEnGrq61t2nxE+YWX9gWzqVDXkHAn3d/R9mNo1orTSA79x9dxq3KQD6uPt35e5d1XwiIhKjOUxS64QemEmUHVZaBRwb9n8KNKjCrS8ws4Iwr6kT0QKyU4CrzKwBgJl1NbMmadzr++JriHqlNoVg6QigT5JrioCTw0r19YHzYufeJBo+JNSjeLhvC9AsjXzvAReGtDOAVml8BhGROkMBk9RW44H403KPEAUb84C+pNn7U86nwCzgdeDK0EvzKNGk7jlmthB4mPR6bicSzbV6FngDqG9mHwFjgQ8TXeDua4G7Qx3eJwoCN4fT1wG9wsTzxUSTvQH+FzineNJ3Jfl+CfQzs0VEQ3OfptMgIiJ1hbmXH2UQkVxlZk3dfWvoYXoJeNzdX6rpeomI1HbqYRLJL2PMbC6wEFgJvFzD9RERqRPUwyQiIiKSgnqYRERERFJQwCQiIiKSggImERERkRQUMImIiIikoIBJREREJIX/B+E8WErAdOJfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QI7Rn8viY2eF"
      },
      "source": [
        "### `Qini curves with a random curve and with a perfect curve`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "plgwP2Srq7ed",
        "outputId": "3cc745f4-b95d-4238-89b7-69127b234339"
      },
      "source": [
        "fig, ax_roc = plt.subplots(1, 1)\n",
        "plot_qini_curve(y_val, uplift_transform_model_val, trmnt_val, name='Transform model', random=True, perfect=True, ax=ax_roc)\n",
        "plot_qini_curve(y_val, uplift_two_model, trmnt_val, name='Two models', random=True, perfect=True, ax=ax_roc)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklift.viz.base.UpliftCurveDisplay at 0x7fe6cb0e9710>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": "7vCf0C89Oito"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}