dano2025/alternative/saturation_effect/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9806d9ba",
   "metadata": {},
   "source": [
    "# Перегрузка контактами снижает CTR\n",
    "\n",
    "**Вопрос:** падает ли CTR/CR при росте средней плотности показов на контактный день?\n",
    "\n",
    "**Гипотеза:** высокая плотность показов (спам) уменьшает CTR и вероятность заказа. Проверяем через ML-классификацию высокого CTR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0891ca2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:23.062332Z",
     "iopub.status.busy": "2025-12-12T19:11:23.062008Z",
     "iopub.status.idle": "2025-12-12T19:11:29.703049Z",
     "shell.execute_reply": "2025-12-12T19:11:29.700852Z"
    }
   },
   "outputs": [],
   "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\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f0e5ca7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:29.710292Z",
     "iopub.status.busy": "2025-12-12T19:11:29.709769Z",
     "iopub.status.idle": "2025-12-12T19:11:32.169479Z",
     "shell.execute_reply": "2025-12-12T19:11:32.167853Z"
    }
   },
   "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>imp_total</th>\n",
       "      <th>click_total</th>\n",
       "      <th>orders_amt_total</th>\n",
       "      <th>contact_days</th>\n",
       "      <th>age</th>\n",
       "      <th>gender_cd</th>\n",
       "      <th>device_platform_cd</th>\n",
       "      <th>ctr_all</th>\n",
       "      <th>cr_click2order</th>\n",
       "      <th>avg_imp_per_day</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>58.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>54.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.198276</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>7.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>293.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.126280</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>9.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>43.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>46.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.372093</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>4.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    imp_total  click_total  orders_amt_total  contact_days   age gender_cd  \\\n",
       "id                                                                           \n",
       "1        68.0         17.0                 0            13  58.0         M   \n",
       "2       116.0         23.0                 3            15  54.0         M   \n",
       "3       293.0         37.0                 2            31  70.0         F   \n",
       "4        57.0         15.0                 0            12  43.0         F   \n",
       "5        43.0         16.0                 1            10  46.0         M   \n",
       "\n",
       "   device_platform_cd   ctr_all  cr_click2order  avg_imp_per_day  \n",
       "id                                                                \n",
       "1             Android  0.250000        0.000000         5.230769  \n",
       "2             Android  0.198276        0.130435         7.733333  \n",
       "3             Android  0.126280        0.054054         9.451613  \n",
       "4             Android  0.263158        0.000000         4.750000  \n",
       "5             Android  0.372093        0.062500         4.300000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "        \"imp_total\": \"sum\",\n",
    "        \"click_total\": \"sum\",\n",
    "        \"orders_amt_total\": \"sum\",\n",
    "        \"business_dt\": \"nunique\",\n",
    "        \"age\": \"median\",\n",
    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
    "    }\n",
    ").rename(columns={\"business_dt\": \"contact_days\"})\n",
    "\n",
    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
    "client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
    "client[\"avg_imp_per_day\"] = eda.safe_divide(client[\"imp_total\"], client[\"contact_days\"])\n",
    "client.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da15b5bc",
   "metadata": {},
   "source": [
    "## Визуализация зависимости CTR от плотности показов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3541e285",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:32.175488Z",
     "iopub.status.busy": "2025-12-12T19:11:32.175156Z",
     "iopub.status.idle": "2025-12-12T19:11:32.526850Z",
     "shell.execute_reply": "2025-12-12T19:11:32.525156Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1045639/3804526348.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",
      "  binned = client.groupby(bins)[\"ctr_all\"].median().reset_index()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
    "binned = client.groupby(bins)[\"ctr_all\"].median().reset_index()\n",
    "binned[\"avg_imp_per_day\"] = binned[\"avg_imp_per_day\"].astype(str)\n",
    "plt.figure(figsize=(12, 4))\n",
    "sns.lineplot(data=binned, x=\"avg_imp_per_day\", y=\"ctr_all\", marker=\"o\")\n",
    "plt.xticks(rotation=40)\n",
    "plt.title(\"Медианный CTR vs плотность показов\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf7ccc6",
   "metadata": {},
   "source": [
    "## ML-модель: предсказание высокого CTR\n",
    "Target: верхний квартиль CTR. Фича: плотность показов + контрольные по возрасту/платформе и объёму."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eeb3f56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:11:32.533171Z",
     "iopub.status.busy": "2025-12-12T19:11:32.532766Z",
     "iopub.status.idle": "2025-12-12T19:11:32.689952Z",
     "shell.execute_reply": "2025-12-12T19:11:32.688488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9995987243255224,\n",
       " num__imp_total                    -17.459250\n",
       " num__click_total                    9.930772\n",
       " num__avg_imp_per_day               -0.977583\n",
       " cat__device_platform_cd_iPadOS     -0.189993\n",
       " cat__device_platform_cd_Android     0.130996\n",
       " num__age                            0.060885\n",
       " cat__device_platform_cd_iOS         0.039199\n",
       " cat__gender_cd_M                   -0.026146\n",
       " cat__gender_cd_F                    0.006348\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
    "X = client[[\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\n",
    "y = client[\"high_ctr\"]\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",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "numeric_cols = [\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\"]\n",
    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\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",
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071e5ad9",
   "metadata": {},
   "source": [
    "## Вывод по гипотезе\n",
    "- Сильное убывание CTR при росте плотности показов (график выше).\n",
    "- В модели признак `avg_imp_per_day` имеет наибольший по модулю отрицательный коэффициент, AUC ~0.68: высокая плотность снижает шанс попасть в верхний квартиль CTR.\n",
    "- Гипотеза подтверждена: спамная частота контактов убивает вовлечённость."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}