{
 "cells": [
  {
   "metadata": {
    "id": "RxgD1bLv3QGc"
   },
   "cell_type": "markdown",
   "source": "## <b>Uplift Моделирования</b>\n\n### <b><span style='color:#686dec'>Обзор базовых подходов</span></b>\n\n- В практической части вебинара посмотрим на базовые подходы `Uplift` Моделирования\n\n> - Задача `Uplift` моделирования помогает нам выявить **чистый эффект от коммуникации**, или выявить убеждаемых клиетнов \n> - `Uplift` Это разница между результатами группы, которая подверглась воздействию (например, увидела рекламу), и контрольной группой, которая не подвергалась воздействию. \n\n\n- Для простоты, будем использовать библиотеку `sklift`\n\nВажно отметить \n\n- `КГ` (Контрольная группа) коммуникации нет и `ЦГ` (Целевая группа) коммуникация есть"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sy1kZGQWKkRX",
    "outputId": "c33a299a-7f56-4531-f66a-376ba72dc8a9",
    "trusted": false
   },
   "cell_type": "code",
   "source": "# import sys\n# !{sys.executable} -m pip install scikit-uplift==0.5.1 catboost pandas==1.3.5",
   "execution_count": 2,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Импортируем библиотеки</span></b>"
  },
  {
   "metadata": {
    "id": "yRRmiRm4KkRY",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklift.datasets import fetch_x5\nfrom sklift.metrics import uplift_at_k\nimport pandas as pd\nimport numpy as np\n\nrandom_state = 47\nk = 0.1",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Читаем данные</span></b>\n\nДанные у нас будут от X5, используем только часть данных, опираясть только на 5 фичей (для простоты)"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gL4L0c-MKkRZ",
    "outputId": "fe152020-e22e-4fe2-f624-976e7d91785a",
    "trusted": true
   },
   "cell_type": "code",
   "source": "df_clients = pd.read_csv('fetch_x5_clients.csv')\ndf_train = pd.read_csv('fetch_x5_train.csv')\ndf_clients = df_clients.dropna()\ndf_all = df_clients.merge(df_train,on='client_id')\ndf_clients = df_all[['client_id','first_issue_date','first_redeem_date','age','gender']]\ndf_train = df_all[['client_id','treatment_flg','target']]",
   "execution_count": 8,
   "outputs": []
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "id": "Sz9Hp0deImNc",
    "outputId": "3fa95f6a-5d1a-4a2c-d69b-624d98693f58",
    "trusted": true
   },
   "cell_type": "code",
   "source": "df_clients.head(2)",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 3,
     "data": {
      "text/plain": "    client_id     first_issue_date    first_redeem_date  age gender\n0  000012768d  2017-08-05 15:40:48  2018-01-04 19:30:07   45      U\n1  000036f903  2017-04-10 13:54:23  2017-04-23 12:37:56   72      F",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>client_id</th>\n      <th>first_issue_date</th>\n      <th>first_redeem_date</th>\n      <th>age</th>\n      <th>gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>000012768d</td>\n      <td>2017-08-05 15:40:48</td>\n      <td>2018-01-04 19:30:07</td>\n      <td>45</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000036f903</td>\n      <td>2017-04-10 13:54:23</td>\n      <td>2017-04-23 12:37:56</td>\n      <td>72</td>\n      <td>F</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "id": "9k5guUIYIn3L",
    "outputId": "6311c5c1-7d63-4ef0-8a08-74a15d26ec0e",
    "trusted": false
   },
   "cell_type": "code",
   "source": "df_train.head(2)",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>client_id</th>\n      <th>treatment_flg</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>000012768d</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000036f903</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "    client_id  treatment_flg  target\n0  000012768d              0       1\n1  000036f903              1       1"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UmFVN0WcQty8",
    "outputId": "bcfec041-540a-45cf-8473-8e77b8a38687",
    "trusted": true
   },
   "cell_type": "code",
   "source": "print(f\"Dataset features shape: {df_clients.shape}\")\nprint(f\"Dataset train shape: {df_train.shape}\")\nprint(f\"Dataset target mean: {df_train.target.mean()}\")\nprint(f\"Dataset treatment mean: {df_train.treatment_flg.mean()}\")",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Dataset features shape: (182493, 5)\nDataset train shape: (182493, 3)\nDataset target mean: 0.6444904736072069\nDataset treatment mean: 0.5016247198522683\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "id": "ylVJV4iWLqw6",
    "trusted": true
   },
   "cell_type": "code",
   "source": "df_clients = df_clients.set_index('client_id')\ndf_train = df_train.set_index('client_id')",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Предобработка</span></b>\n\nУдостоверимся что категориальные фичи имеют формат `category`"
  },
  {
   "metadata": {
    "id": "eAD05clgN52X",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklearn.preprocessing import LabelEncoder\n\nencoder = LabelEncoder()\ndf_clients['gender'] = encoder.fit_transform(df_clients['gender'])",
   "execution_count": 11,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Превращаем `datetime` в относительное время (в секундах) от 1970 года"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "x56jWhYsKkRa",
    "outputId": "d43a5650-0483-4d5e-fec4-803b6dcdb7c6",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# Извлечение признаков\ndf_features = df_clients.copy()\ndf_features['first_issue_time'] = (pd.to_datetime(df_features['first_issue_date'])- pd.Timestamp('1970-01-01')) // pd.Timedelta('1s')\ndf_features['first_redeem_time'] = (pd.to_datetime(df_features['first_redeem_date']) - pd.Timestamp('1970-01-01')) // pd.Timedelta('1s')\ndf_features['issue_redeem_delay'] = df_features['first_redeem_time'] - df_features['first_issue_time']\ndf_features = df_features.drop(['first_issue_date', 'first_redeem_date'], axis=1)\n\ndf_features.head(2)",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "text/plain": "            age  gender  ...  first_redeem_time  issue_redeem_delay\nclient_id                ...                                       \n000012768d   45       2  ...         1515094207            13146559\n000036f903   72       0  ...         1492951076             1118613\n\n[2 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>gender</th>\n      <th>first_issue_time</th>\n      <th>first_redeem_time</th>\n      <th>issue_redeem_delay</th>\n    </tr>\n    <tr>\n      <th>client_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>000012768d</th>\n      <td>45</td>\n      <td>2</td>\n      <td>1501947648</td>\n      <td>1515094207</td>\n      <td>13146559</td>\n    </tr>\n    <tr>\n      <th>000036f903</th>\n      <td>72</td>\n      <td>0</td>\n      <td>1491832463</td>\n      <td>1492951076</td>\n      <td>1118613</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Разбиваем выборки на две подвыборки"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "# отделяем 30% - пятую часть всего - на тестовую выборку\ntrain, test = train_test_split(df_features, test_size=0.3, random_state=random_state)\n\nindices_train = train.index.tolist()\nindices_test = test.index.tolist()\n\nX_train = df_features.loc[indices_train, :]\ny_train = df_train.loc[indices_train, 'target']\ntreat_train = df_train.loc[indices_train, 'treatment_flg']\n\nX_test = df_features.loc[indices_test, :]\ny_test = df_train.loc[indices_test, 'target']\ntreat_test =  df_train.loc[indices_test, 'treatment_flg']\n\ntargets = [y_train, y_test]\ntreatmnets = [treat_train, treat_test]\nnames = ['train:',  'test:']",
   "execution_count": 13,
   "outputs": []
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T22:36:15.655683Z",
     "start_time": "2020-05-30T22:36:12.560096Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VT5IYIZU3QGx",
    "outputId": "e5fde118-78f8-4d14-fc25-e78adb496061",
    "trusted": true
   },
   "cell_type": "code",
   "source": "print('Баланс таргета на разбиениях:\\n')\ni = 0\nfor target in targets:\n    pc = target.mean()\n    print(names[i], pc)\n    i += 1\n\nprint('\\nБаланс воздействия на разбиениях:\\n')\ni = 0\nfor treatmnet in treatmnets:\n    pc = treatmnet.mean()\n    print(names[i], pc)\n    i += 1",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Баланс таргета на разбиениях:\n\ntrain: 0.644831500254413\ntest: 0.6436947468400672\n\nБаланс воздействия на разбиениях:\n\ntrain: 0.5018983130455204\ntest: 0.500986337400453\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Создалим словарь куда будем сохранять результаты метрики `uplift`"
  },
  {
   "metadata": {
    "id": "H9bnZxb7NLoy",
    "trusted": true
   },
   "cell_type": "code",
   "source": "models_results = {\n    'approach': [],\n    f'train_uplift@{k*100}%': [],\n    f'test_uplift@{k*100}%': []\n}",
   "execution_count": 15,
   "outputs": []
  },
  {
   "metadata": {
    "id": "CWjvp9vbW9ft"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Подходы с Одной Модели</span></b>\n\n#### (1) Трансформация Класса\n"
  },
  {
   "metadata": {
    "id": "kOzp0hLGXQZW",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklift.models import ClassTransformation\nfrom sklearn.ensemble import GradientBoostingClassifier",
   "execution_count": 17,
   "outputs": []
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ck6M2rZOXUq4",
    "outputId": "14c20744-1ee5-484d-cfda-743856cb942b",
    "trusted": true
   },
   "cell_type": "code",
   "source": "ct = ClassTransformation(\n    GradientBoostingClassifier()\n    )\n\nct = ct.fit(X_train, y_train, treat_train)\n\nuplift_ct_train = ct.predict(X_train)\nuplift_ct = ct.predict(X_test)\nprint(uplift_ct[:5])",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[ 0.03508148  0.03092437  0.04037575 -0.05105889  0.01682562]\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "2 стратегии (`strategy`) в `uplift_at_k`\n- **by_group** (делаем сортировку по uplift внутри каждой персентильки)\n- **overall** (делаем сортировку глобально)"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T22:36:19.851314Z",
     "start_time": "2020-05-30T22:36:18.757023Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "V6yPXbJs3QIu",
    "outputId": "eb5cc68a-9f1f-4692-915d-a827155a28f0",
    "trusted": true
   },
   "cell_type": "code",
   "source": "ct_score_train = uplift_at_k(y_true=y_train, \n                             uplift=uplift_ct_train, \n                             treatment=treat_train, \n                             strategy='by_group', k=k)\n\nct_score = uplift_at_k(y_true=y_test, \n                       uplift=uplift_ct, \n                       treatment=treat_test, \n                       strategy='by_group', k=k)\n\nmodels_results['approach'].append('ClassTransformation')\nmodels_results[f'train_uplift@{k*100}%'].append(ct_score_train)\nmodels_results[f'test_uplift@{k*100}%'].append(ct_score)\n\n# явное переобучение\npd.DataFrame(models_results) ",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 19,
     "data": {
      "text/plain": "              approach  train_uplift@10.0%  test_uplift@10.0%\n0  ClassTransformation            0.201149           0.118589",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>approach</th>\n      <th>train_uplift@10.0%</th>\n      <th>test_uplift@10.0%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ClassTransformation</td>\n      <td>0.201149</td>\n      <td>0.118589</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Как и другие метрики, мы можем сравнить метрику на train и test подвыборках, сдесь у нас явное переобучение"
  },
  {
   "metadata": {
    "id": "ZUBv8tHI3QHu"
   },
   "cell_type": "markdown",
   "source": "#### (2) Одна модель с признаком коммуникации\n\nСамое простое и интуитивное решение: \n- модель обучается одновременно на двух группах, при этом **бинарный флаг коммуникации** выступает в качестве дополнительного признака (k+1) фичу\n- Каждый объект из тестовой выборки скорим дважды: с флагом коммуникации равным 1 и равным 0. \n- Вычитая вероятности по каждому наблюдению, получим искомы uplift.\n- В `sklift` реализация в методе `SoloModel`"
  },
  {
   "metadata": {
    "id": "0oU0ND4aOb2P",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklift.metrics import uplift_at_k\nfrom sklift.viz import plot_uplift_preds\nfrom sklift.models import SoloModel",
   "execution_count": 21,
   "outputs": []
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g4yy5RaYOeTg",
    "outputId": "bcfcd671-b5e3-4e9d-cd21-b02f5e141789",
    "trusted": true
   },
   "cell_type": "code",
   "source": "sm = SoloModel(\n    GradientBoostingClassifier()\n    )\n\n# Обучаем модель\nsm = sm.fit(X_train, y_train, treat_train)\n\nuplift_sm_train = sm.predict(X_train)\nuplift_sm = sm.predict(X_test)\nprint(uplift_sm[:5])",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[ 0.01861624  0.03623787  0.02475317 -0.01291694  0.02082576]\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "id": "jjMXT6neWRsc",
    "outputId": "673ff768-ee3a-4865-fee8-8beeaede2485",
    "trusted": true
   },
   "cell_type": "code",
   "source": "sm_score_train = uplift_at_k(y_true=y_train, uplift=uplift_sm_train, treatment=treat_train, strategy='by_group', k=k)\nsm_score = uplift_at_k(y_true=y_test, uplift=uplift_sm, treatment=treat_test, strategy='by_group', k=k)\n\nmodels_results['approach'].append('SoloModel')\nmodels_results[f'train_uplift@{k*100}%'].append(sm_score_train)\nmodels_results[f'test_uplift@{k*100}%'].append(sm_score)\n\npd.DataFrame(models_results)",
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 24,
     "data": {
      "text/plain": "              approach  train_uplift@10.0%  test_uplift@10.0%\n0  ClassTransformation            0.201149           0.118589\n1            SoloModel            0.088193           0.083730",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>approach</th>\n      <th>train_uplift@10.0%</th>\n      <th>test_uplift@10.0%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ClassTransformation</td>\n      <td>0.201149</td>\n      <td>0.118589</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SoloModel</td>\n      <td>0.088193</td>\n      <td>0.083730</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T22:36:18.730254Z",
     "start_time": "2020-05-30T22:36:16.771531Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "Pqquz4nU3QHx",
    "outputId": "a8652a96-9b25-4c88-bddf-d29c1bd79850",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# Получим условные вероятности выполнения целевого действия при взаимодействии для каждого объекта\nsm_trmnt_preds = sm.trmnt_preds_\n# И условные вероятности выполнения целевого действия без взаимодействия для каждого объекта\nsm_ctrl_preds = sm.ctrl_preds_\n\n# Отрисуем распределения вероятностей и их разность (uplift)\nplot_uplift_preds(trmnt_preds=sm_trmnt_preds, ctrl_preds=sm_ctrl_preds);",
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 2000x700 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 1634,
       "height": 604
      }
     }
    }
   ]
  },
  {
   "metadata": {
    "id": "sAACdNv13QI_"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Подходы с двумя моделями</span></b>\n\n#### (1) Две независимые модели (t-Learner)\n\n- Реализация `sklift` в `TwoModels` + **vanilla**\n- Обучить модель на данных целевой группы (t) (modelt)\n- Обучить Вторую модель на данных контрольной группы (c) (modelc)\n- Чтобы получить **uplift** для тестовых клиентов:\n\n    - (а) проскорить их первой моделью \n    - (б) проскорить их второй моделью \n    - Найти разницу двух векторов"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T22:36:21.644377Z",
     "start_time": "2020-05-30T22:36:19.854107Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S9mOrhPg3QJC",
    "outputId": "3550bc5f-9d5b-4ba2-c613-2e77b26c09d3",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklift.models import TwoModels\n\ntm = TwoModels(\n    estimator_trmnt = GradientBoostingClassifier(),\n    estimator_ctrl = GradientBoostingClassifier(),\n    method='vanilla'\n)\n\ntm = tm.fit(X_train, y_train, treat_train)\n\nuplift_tm_train = tm.predict(X_train)\nuplift_tm = tm.predict(X_test)\nprint(uplift_tm[:5])",
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[ 0.03633365  0.03156543  0.03352265 -0.05856611  0.02573542]\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "6KFAIGVAZHFs",
    "outputId": "36be9b6f-4398-4b04-f96b-7a64fa7a70cc",
    "trusted": true
   },
   "cell_type": "code",
   "source": "tm_score_train = uplift_at_k(y_true=y_train, uplift=uplift_tm_train, treatment=treat_train, strategy='by_group', k=k)\ntm_score = uplift_at_k(y_true=y_test, uplift=uplift_tm, treatment=treat_test, strategy='by_group', k=k)\n\nmodels_results['approach'].append('TwoModels')\nmodels_results[f'train_uplift@{k*100}%'].append(tm_score_train)\nmodels_results[f'test_uplift@{k*100}%'].append(tm_score)\n\npd.DataFrame(models_results)",
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 28,
     "data": {
      "text/plain": "              approach  train_uplift@10.0%  test_uplift@10.0%\n0  ClassTransformation            0.201149           0.118589\n1            SoloModel            0.088193           0.083730\n2            TwoModels            0.159321           0.070591",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>approach</th>\n      <th>train_uplift@10.0%</th>\n      <th>test_uplift@10.0%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ClassTransformation</td>\n      <td>0.201149</td>\n      <td>0.118589</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SoloModel</td>\n      <td>0.088193</td>\n      <td>0.083730</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TwoModels</td>\n      <td>0.159321</td>\n      <td>0.070591</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "1UncElP6Y2ja",
    "outputId": "cf6d6c01-7f35-46b7-ea8b-dfbf0fbe2949",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# Получим условные вероятности выполнения целевого действия при взаимодействии для каждого объекта\ntm_trmnt_preds = tm.trmnt_preds_\n# И условные вероятности выполнения целевого действия без взаимодействия для каждого объекта\ntm_ctrl_preds = tm.ctrl_preds_\n\n# Отрисуем распределения вероятностей и их разность (uplift)\nplot_uplift_preds(trmnt_preds=tm_trmnt_preds, ctrl_preds=tm_ctrl_preds);",
   "execution_count": 29,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 2000x700 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 1634,
       "height": 604
      }
     }
    }
   ]
  },
  {
   "metadata": {
    "id": "BVdXAMsO3QLM"
   },
   "cell_type": "markdown",
   "source": "#### (2) Две зависимые модели\n\n- Реализация `sklift` в `TwoModels` + **ddr_control**\n- Обучаем модель на данных контрольной группы (c) (modelc)\n- С помощью этой модели получить предикт на клиетнах целевой группы (modelc).pred_proba(t)\n- Обучаем вторую модель на (признаках целевой группы (t) + предикт с прошлого шага как доп. фича) (modelt)*\n- Чтобы получить **uplift** для тестовых клиентах\n\n    - (а) Проскорьте клиентов первой моделью \n    - (б) Добавьте полученные предикты как фичу и проскорьте второй моделью\n    - Найдите разность пунктов (а) и (б)\n\n*Можно и обратным образом "
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T22:36:24.308273Z",
     "start_time": "2020-05-30T22:36:21.647973Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C654kkas3QLR",
    "outputId": "5ea874c2-876c-4806-fb8f-ac25b82e5518",
    "trusted": true
   },
   "cell_type": "code",
   "source": "tm_ctrl = TwoModels(\n    estimator_trmnt=GradientBoostingClassifier(),\n    estimator_ctrl=GradientBoostingClassifier(),\n    method='ddr_control'\n)\n\ntm_ctrl = tm_ctrl.fit(X_train, y_train, treat_train)\n\nuplift_tm_ctrl_train = tm_ctrl.predict(X_train)\nuplift_tm_ctrl = tm_ctrl.predict(X_test)\nprint(uplift_tm_ctrl[:5])",
   "execution_count": 35,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[ 0.03621028  0.03075091  0.03341193 -0.04307654  0.0292164 ]\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "SAOXtjeUfCcE",
    "outputId": "e9a5973a-ef73-498b-a4be-fceaab9b489d",
    "trusted": true
   },
   "cell_type": "code",
   "source": "tm_ctrl_score_train = uplift_at_k(y_true=y_train, \n                                  uplift=uplift_tm_ctrl_train, \n                                  treatment=treat_train, \n                                  strategy='by_group', k=k)\n\ntm_ctrl_score = uplift_at_k(y_true=y_test, \n                            uplift=uplift_tm_ctrl, \n                            treatment=treat_test, \n                            strategy='by_group', k=k)\n\nmodels_results['approach'].append('TwoModels_ddr_control')\nmodels_results[f'train_uplift@{k*100}%'].append(tm_ctrl_score_train)\nmodels_results[f'test_uplift@{k*100}%'].append(tm_ctrl_score)\n\n\npd.DataFrame(models_results)",
   "execution_count": 36,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 36,
     "data": {
      "text/plain": "                approach  train_uplift@10.0%  test_uplift@10.0%\n0    ClassTransformation            0.201149           0.118589\n1              SoloModel            0.088193           0.083730\n2              TwoModels            0.159321           0.070591\n3  TwoModels_ddr_control            0.172037           0.123702",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>approach</th>\n      <th>train_uplift@10.0%</th>\n      <th>test_uplift@10.0%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ClassTransformation</td>\n      <td>0.201149</td>\n      <td>0.118589</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SoloModel</td>\n      <td>0.088193</td>\n      <td>0.083730</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TwoModels</td>\n      <td>0.159321</td>\n      <td>0.070591</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TwoModels_ddr_control</td>\n      <td>0.172037</td>\n      <td>0.123702</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "id": "TuhWTP8gfj5x",
    "trusted": true
   },
   "cell_type": "code",
   "source": "plot_uplift_preds(trmnt_preds=tm_ctrl.trmnt_preds_, ctrl_preds=tm_ctrl.ctrl_preds_);",
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 2000x700 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 1634,
       "height": 604
      }
     }
    }
   ]
  },
  {
   "metadata": {
    "id": "Kcy2kN3T3QLg"
   },
   "cell_type": "markdown",
   "source": "\n\n*Аналогичным образом можно сначала обучить классификатор $P^T$, а затем использовать его предсказания в качестве признака для классификатора $P^C$."
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T22:36:26.302525Z",
     "start_time": "2020-05-30T22:36:24.311349Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z9Db6zMB3QLn",
    "outputId": "d2e34ddc-2c12-4bdd-ff8e-689176770531",
    "trusted": true
   },
   "cell_type": "code",
   "source": "tm_trmnt = TwoModels(\n    estimator_trmnt=GradientBoostingClassifier(),\n    estimator_ctrl=GradientBoostingClassifier(),\n    method='ddr_treatment'\n)\n\ntm_trmnt = tm_trmnt.fit(\n    X_train, y_train, treat_train\n)\n\nuplift_tm_trmnt_train = tm_trmnt.predict(X_train)\nuplift_tm_trmnt = tm_trmnt.predict(X_test)\nprint(uplift_tm_trmnt[:5])",
   "execution_count": 39,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[ 0.03124574  0.01680991  0.03615345 -0.09655285  0.02634465]\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "6-K3tFnDhSjk",
    "outputId": "cc6bfcd4-e87e-4f70-f4a2-ec82ebfb56e2",
    "trusted": true
   },
   "cell_type": "code",
   "source": "tm_trmnt_score_train = uplift_at_k(y_true=y_train, uplift=uplift_tm_trmnt_train, treatment=treat_train, strategy='by_group', k=k)\ntm_trmnt_score = uplift_at_k(y_true=y_test, uplift=uplift_tm_trmnt, treatment=treat_test, strategy='by_group', k=k)\n\nmodels_results['approach'].append('TwoModels_ddr_treatment')\nmodels_results[f'train_uplift@{k*100}%'].append(tm_trmnt_score_train)\nmodels_results[f'test_uplift@{k*100}%'].append(tm_trmnt_score)\n\npd.DataFrame(models_results)",
   "execution_count": 40,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 40,
     "data": {
      "text/plain": "                  approach  train_uplift@10.0%  test_uplift@10.0%\n0      ClassTransformation            0.201149           0.118589\n1                SoloModel            0.088193           0.083730\n2                TwoModels            0.159321           0.070591\n3    TwoModels_ddr_control            0.172037           0.123702\n4  TwoModels_ddr_treatment            0.141788           0.051622",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>approach</th>\n      <th>train_uplift@10.0%</th>\n      <th>test_uplift@10.0%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ClassTransformation</td>\n      <td>0.201149</td>\n      <td>0.118589</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SoloModel</td>\n      <td>0.088193</td>\n      <td>0.083730</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TwoModels</td>\n      <td>0.159321</td>\n      <td>0.070591</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TwoModels_ddr_control</td>\n      <td>0.172037</td>\n      <td>0.123702</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>TwoModels_ddr_treatment</td>\n      <td>0.141788</td>\n      <td>0.051622</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "id": "xkRgaPeTkTdU",
    "trusted": true
   },
   "cell_type": "code",
   "source": "plot_uplift_preds(trmnt_preds=tm_trmnt.trmnt_preds_, ctrl_preds=tm_trmnt.ctrl_preds_);",
   "execution_count": 41,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 2000x700 with 3 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 1634,
       "height": 604
      }
     }
    }
   ]
  },
  {
   "metadata": {
    "id": "ohv6eLRJ3QMh"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Сравним Метрики</span></b>\n\n#### (1) uplift_at_k\n\n- метрика `uplift_at_k` полезна для понимания реального воздействия на целевую аудиторию и для оптимизации стратегий взаимодействия с пользователями."
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-30T22:36:26.323056Z",
     "start_time": "2020-05-30T22:36:26.305696Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "X_u-k5i93QMp",
    "outputId": "862db76b-c454-4a03-d4b0-2987d5ebfe97",
    "trusted": true
   },
   "cell_type": "code",
   "source": "pd.DataFrame(data=models_results).sort_values(f'test_uplift@{k*100}%', ascending=False)",
   "execution_count": 42,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 42,
     "data": {
      "text/plain": "                  approach  train_uplift@10.0%  test_uplift@10.0%\n3    TwoModels_ddr_control            0.172037           0.123702\n0      ClassTransformation            0.201149           0.118589\n1                SoloModel            0.088193           0.083730\n2                TwoModels            0.159321           0.070591\n4  TwoModels_ddr_treatment            0.141788           0.051622",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>approach</th>\n      <th>train_uplift@10.0%</th>\n      <th>test_uplift@10.0%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>TwoModels_ddr_control</td>\n      <td>0.172037</td>\n      <td>0.123702</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>ClassTransformation</td>\n      <td>0.201149</td>\n      <td>0.118589</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SoloModel</td>\n      <td>0.088193</td>\n      <td>0.083730</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TwoModels</td>\n      <td>0.159321</td>\n      <td>0.070591</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>TwoModels_ddr_treatment</td>\n      <td>0.141788</td>\n      <td>0.051622</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F5iIZWW3Cyrq",
    "outputId": "824e2a75-119a-444d-cc86-14516272631d",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# Проверка структуры данных. Необходимо, чтобы запустился расчет метрик на особых версиях библиотек\nprint(f'y_test структура: {y_test.shape}, тип: {y_test.dtype}')\nprint(f'uplift_ct структура: {uplift_ct.shape}, тип: {uplift_ct.dtype}')\nprint(f'treat_test структура: {treat_test.shape}, тип: {treat_test.dtype}')\n\n# Если обнаружены вложенные структуры, преобразовать их в одномерные массивы\nif y_test.ndim > 1:\n    y_test = y_test.flatten()\n\nif uplift_ct.ndim > 1:\n    uplift_ct = uplift_ct.flatten()\n\nif treat_test.ndim > 1:\n    treat_test = treat_test.flatten()",
   "execution_count": 53,
   "outputs": [
    {
     "output_type": "stream",
     "text": "y_test структура: (54748,), тип: int64\nuplift_ct структура: (54748,), тип: float64\ntreat_test структура: (54748,), тип: int64\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### (2) uplift_by_percentile"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "id": "OMg71r_kEtg9",
    "outputId": "fede01a8-3331-4f2a-80de-c63ca076828b",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# from sklift.metrics import uplift_by_percentile\n# import warnings; warnings.filterwarnings('ignore')\n\n# # по бакетам данные о response_rate_treatment / response_rate_control\n# uplift_by_percentile(y_test, uplift_ct, treat_test,\n#                      strategy='overall',\n#                      total=True, std=True, bins=10)",
   "execution_count": 55,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### (3) weighted_average_uplift\n\nПо всей выборке, усредненная метрика `uplift`"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "40zWvlZbxulm",
    "outputId": "f423651f-581a-4d3a-eeb8-e13c866a9f31",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklift.metrics import weighted_average_uplift\n\nuplift_full_data_ct = weighted_average_uplift(y_test, uplift_ct, treat_test, bins=10)\nuplift_full_data_sm = weighted_average_uplift(y_test, uplift_sm, treat_test, bins=10)\nprint(f\"average uplift on test data CT: {uplift_full_data_ct:.4f}\")\nprint(f\"average uplift on test data SM: {uplift_full_data_sm:.4f}\")",
   "execution_count": 56,
   "outputs": [
    {
     "output_type": "stream",
     "text": "average uplift on test data CT: 0.0346\naverage uplift on test data SM: 0.0356\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### (5) plot_uplift_by_percentile\n\n- График показываем какой `uplift` на конкретном percentile\n- Дает возможность понять к кем коммуницировать \n    - Берем топ 30% и на них запускаем компанию \n- График должен быть монотонный "
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "id": "iwiTltFSz4hq",
    "outputId": "c04d89a4-d3e0-4a6b-ddce-b31d2e2fc394",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# from sklift.viz import plot_uplift_by_percentile\n\n# # line plot\n# plot_uplift_by_percentile(y_test, uplift_ct, treat_test,\n#                           strategy='overall', kind='line');",
   "execution_count": 62,
   "outputs": []
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 615
    },
    "id": "3OvtaF-CE-vJ",
    "outputId": "3d45fc2e-da50-4173-b01e-2b889be50900",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# # bar plot\n# plot_uplift_by_percentile(y_test, uplift_ct, treat_test, strategy='overall', kind='bar');",
   "execution_count": 64,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### (6) plot_uplift_curve, plot_qini_curve\n- `Uplift Curve` показывает, как изменяется прирост (uplift) по мере увеличения доли целевой аудитории, на которую воздействует кампания. Она помогает оценить, насколько эффективно воздействие на определённую группу пользователей.\n- `Qini Curve` является более продвинутой версией `Uplift Curve` и позволяет оценить эффективность модели с точки зрения её способности увеличивать доход или другие ключевые показатели.\n\n"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "tEl22awA4K0I",
    "outputId": "52dbf00f-4c29-4998-b3ce-102e3065dc1f",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklift.viz import plot_uplift_curve\n\n# with ideal curve\n# perfect=True\nplot_uplift_curve(y_test, uplift_ct, treat_test, perfect=False);",
   "execution_count": 65,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 480x360 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 594,
       "height": 340
      }
     }
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "ibgPdwtJ0kHU",
    "outputId": "9d2eee89-7f03-48a3-f53c-75c0aeacabad",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklift.viz import plot_qini_curve\n\n# with ideal Qini curve (red line)\n# perfect=True\nplot_qini_curve(y_test, uplift_ct, treat_test, perfect=False);",
   "execution_count": 66,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 480x360 with 1 Axes>",
      "image/png": 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\n"
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