{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34468500",
   "metadata": {},
   "source": [
    "# Доля пассивных показов и заказы\n",
    "\n",
    "**Вопрос:** повышает ли высокая доля пассивных показов вероятность заказа при контроле объёма коммуникаций?\n",
    "\n",
    "**Гипотеза:** большая доля пассивных показов связана с большей вероятностью заказа (проверяем ML)."
   ]
  },
  {
   "cell_type": "code",
   "id": "46fb7ac5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:43.639846Z",
     "iopub.status.busy": "2025-12-12T19:11:43.638998Z",
     "iopub.status.idle": "2025-12-12T19:11:50.215868Z",
     "shell.execute_reply": "2025-12-12T19:11:50.213723Z"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:46.168843Z",
     "start_time": "2025-12-12T19:27:44.987935Z"
    }
   },
   "source": [
    "import sqlite3\n",
    "from pathlib import Path\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "\n",
    "project_root = Path.cwd().resolve()\n",
    "while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
    "    project_root = project_root.parent\n",
    "    project_root = project_root.parent\n",
    "sys.path.append(str(project_root / \"preanalysis\"))\n",
    "import eda_utils as eda\n",
    "\n",
    "db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
    "conn = sqlite3.connect(db_path)\n",
    "df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
    "conn.close()\n"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "73842cf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:50.222842Z",
     "iopub.status.busy": "2025-12-12T19:11:50.222356Z",
     "iopub.status.idle": "2025-12-12T19:11:52.672337Z",
     "shell.execute_reply": "2025-12-12T19:11:52.670490Z"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:46.794213Z",
     "start_time": "2025-12-12T19:27:46.179705Z"
    }
   },
   "source": [
    "for cols, name in [\n",
    "    (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
    "    (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
    "    (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
    "    (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
    "    (eda.ORDER_COLS, \"orders_amt_total\"),\n",
    "]:\n",
    "    df[name] = df[cols].sum(axis=1)\n",
    "\n",
    "df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
    "df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
    "\n",
    "client = df.groupby(\"id\").agg(\n",
    "    {\n",
    "        \"active_imp_total\": \"sum\",\n",
    "        \"passive_imp_total\": \"sum\",\n",
    "        \"active_click_total\": \"sum\",\n",
    "        \"passive_click_total\": \"sum\",\n",
    "        \"orders_amt_total\": \"sum\",\n",
    "        \"imp_total\": \"sum\",\n",
    "        \"click_total\": \"sum\",\n",
    "        \"age\": \"median\",\n",
    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
    "    }\n",
    ")\n",
    "\n",
    "client[\"passive_share\"] = eda.safe_divide(client[\"passive_imp_total\"], client[\"imp_total\"])\n",
    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n",
    "client.head()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    active_imp_total  passive_imp_total  active_click_total  \\\n",
       "id                                                            \n",
       "1               33.0               35.0                14.0   \n",
       "2               27.0               89.0                19.0   \n",
       "3               57.0              236.0                37.0   \n",
       "4               20.0               37.0                14.0   \n",
       "5               23.0               20.0                13.0   \n",
       "\n",
       "    passive_click_total  orders_amt_total  imp_total  click_total   age  \\\n",
       "id                                                                        \n",
       "1                   3.0                 0       68.0         17.0  58.0   \n",
       "2                   4.0                 3      116.0         23.0  54.0   \n",
       "3                   0.0                 2      293.0         37.0  70.0   \n",
       "4                   1.0                 0       57.0         15.0  43.0   \n",
       "5                   3.0                 1       43.0         16.0  46.0   \n",
       "\n",
       "   gender_cd device_platform_cd  passive_share   ctr_all  has_order  \n",
       "id                                                                   \n",
       "1          M            Android       0.514706  0.250000          0  \n",
       "2          M            Android       0.767241  0.198276          1  \n",
       "3          F            Android       0.805461  0.126280          1  \n",
       "4          F            Android       0.649123  0.263158          0  \n",
       "5          M            Android       0.465116  0.372093          1  "
      ],
      "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>active_imp_total</th>\n",
       "      <th>passive_imp_total</th>\n",
       "      <th>active_click_total</th>\n",
       "      <th>passive_click_total</th>\n",
       "      <th>orders_amt_total</th>\n",
       "      <th>imp_total</th>\n",
       "      <th>click_total</th>\n",
       "      <th>age</th>\n",
       "      <th>gender_cd</th>\n",
       "      <th>device_platform_cd</th>\n",
       "      <th>passive_share</th>\n",
       "      <th>ctr_all</th>\n",
       "      <th>has_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.514706</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>116.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.767241</td>\n",
       "      <td>0.198276</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>293.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.805461</td>\n",
       "      <td>0.126280</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.649123</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.465116</td>\n",
       "      <td>0.372093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "98ac09e6",
   "metadata": {},
   "source": [
    "## Визуализация: заказы vs доля пассивных показов"
   ]
  },
  {
   "cell_type": "code",
   "id": "35bfe71d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:52.678022Z",
     "iopub.status.busy": "2025-12-12T19:11:52.677564Z",
     "iopub.status.idle": "2025-12-12T19:11:52.998699Z",
     "shell.execute_reply": "2025-12-12T19:11:52.997056Z"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:46.985756Z",
     "start_time": "2025-12-12T19:27:46.877380Z"
    }
   },
   "source": [
    "bins = pd.qcut(client[\"passive_share\"], 8, duplicates=\"drop\")\n",
    "order_rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\n",
    "order_rate[\"passive_share\"] = order_rate[\"passive_share\"].astype(str)\n",
    "plt.figure(figsize=(12, 4))\n",
    "sns.lineplot(data=order_rate, x=\"passive_share\", y=\"has_order\", marker=\"o\")\n",
    "plt.xticks(rotation=40)\n",
    "plt.title(\"Доля клиентов с заказом vs доля пассивных показов\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_85284/3960648772.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  order_rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "6def67b9",
   "metadata": {},
   "source": [
    "## ML-модель: влияние доли пассивных показов на заказ\n",
    "Target: `has_order`. Фичи: объёмы актив/пассив, клики, возраст, пол, платформа, пассивная доля."
   ]
  },
  {
   "cell_type": "code",
   "id": "ae61b923",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:53.004801Z",
     "iopub.status.busy": "2025-12-12T19:11:53.004396Z",
     "iopub.status.idle": "2025-12-12T19:11:53.143675Z",
     "shell.execute_reply": "2025-12-12T19:11:53.141866Z"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:47.045615Z",
     "start_time": "2025-12-12T19:27:47.013172Z"
    }
   },
   "source": [
    "X = client[[\n",
    "    \"active_imp_total\",\n",
    "    \"passive_imp_total\",\n",
    "    \"active_click_total\",\n",
    "    \"passive_click_total\",\n",
    "    \"passive_share\",\n",
    "    \"age\",\n",
    "    \"gender_cd\",\n",
    "    \"device_platform_cd\",\n",
    "]]\n",
    "X = X.copy()\n",
    "X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
    "X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
    "y = client[\"has_order\"]\n",
    "\n",
    "numeric_cols = [\"active_imp_total\", \"passive_imp_total\", \"active_click_total\", \"passive_click_total\", \"passive_share\", \"age\"]\n",
    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
    "\n",
    "preprocess = ColumnTransformer(\n",
    "    [\n",
    "        (\"num\", Pipeline([(\"scaler\", StandardScaler())]), numeric_cols),\n",
    "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model = Pipeline([(\"pre\", preprocess), (\"clf\", LogisticRegression(max_iter=1000))])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "model.fit(X_train, y_train)\n",
    "proba = model.predict_proba(X_test)[:, 1]\n",
    "auc = roc_auc_score(y_test, proba)\n",
    "coef = model.named_steps[\"clf\"].coef_[0]\n",
    "features = model.named_steps[\"pre\"].get_feature_names_out()\n",
    "coef_series = pd.Series(coef, index=features).sort_values(key=abs, ascending=False)\n",
    "auc, coef_series.head(10)\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6804173758429694,\n",
       " num__passive_click_total           0.638861\n",
       " num__passive_share                 0.303223\n",
       " num__active_imp_total              0.216964\n",
       " cat__device_platform_cd_Android    0.186635\n",
       " num__active_click_total           -0.150704\n",
       " cat__gender_cd_M                   0.130234\n",
       " cat__device_platform_cd_iPadOS    -0.105558\n",
       " num__passive_imp_total            -0.087140\n",
       " num__age                          -0.072639\n",
       " cat__device_platform_cd_iOS        0.038500\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "7df5ccb7",
   "metadata": {},
   "source": [
    "## Вывод по гипотезе\n",
    "- Линейный рост доли клиентов с заказом при увеличении `passive_share`.\n",
    "- В модели коэффициент при `passive_share` положительный и по модулю в топ‑фичах; AUC ~0.68. Гипотеза подтверждается: высокая доля пассивных показов ассоциирована с большей вероятностью заказа при контроле объёма и кликов."
   ]
  }
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