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",
   "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"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:48.305598Z",
     "start_time": "2025-12-12T19:27:47.155254Z"
    }
   },
   "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"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "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"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:48.938590Z",
     "start_time": "2025-12-12T19:27:48.314667Z"
    }
   },
   "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"
   ],
   "outputs": [
    {
     "data": {
      "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  "
      ],
      "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>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "da15b5bc",
   "metadata": {},
   "source": [
    "## Визуализация зависимости CTR от плотности показов"
   ]
  },
  {
   "cell_type": "code",
   "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"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:49.183790Z",
     "start_time": "2025-12-12T19:27:49.074446Z"
    }
   },
   "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"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_85425/2642699463.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": {
      "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": "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",
   "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"
    },
    "ExecuteTime": {
     "end_time": "2025-12-12T19:27:49.254084Z",
     "start_time": "2025-12-12T19:27:49.213434Z"
    }
   },
   "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"
   ],
   "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"
    }
   ],
   "execution_count": 4
  },
  {
   "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."
   ]
  }
 ],
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