{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d7d3347",
   "metadata": {},
   "source": [
    "# Спам-гипотеза: плотность показов vs CTR/CR\n",
    "\n",
    "Цель: проверить, что высокая плотность показов на контактный день снижает CTR и CR (спам-эффект)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7acbd1c8",
   "metadata": {},
   "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 scipy import stats\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.impute import SimpleImputer\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",
    "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",
    "\n",
    "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",
    "contact_days = df.groupby(\"id\")[\"business_dt\"].nunique().rename(\"contact_days\")\n",
    "client = df.groupby(\"id\").agg(\n",
    "    {\n",
    "        \"imp_total\": \"sum\",\n",
    "        \"click_total\": \"sum\",\n",
    "        \"orders_amt_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",
    ").merge(contact_days, on=\"id\", how=\"left\").reset_index()\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[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94eb2d26",
   "metadata": {},
   "source": [
    "## Базовые статистики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "287a09b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = client[[\"imp_total\", \"click_total\", \"orders_amt_total\", \"contact_days\", \"avg_imp_per_day\", \"ctr_all\", \"cr_click2order\"]].describe().T\n",
    "missing = client.isna().mean().sort_values(ascending=False)\n",
    "summary, missing.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10cd44b7",
   "metadata": {},
   "source": [
    "## Корреляции и тесты\n",
    "Спирмен между плотностью и CTR/CR, а также Mann–Whitney между Q1 и Q4 по плотности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88714a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_ctr = stats.spearmanr(client[\"avg_imp_per_day\"], client[\"ctr_all\"])\n",
    "corr_cr = stats.spearmanr(client[\"avg_imp_per_day\"], client[\"cr_click2order\"])\n",
    "q1 = client[\"avg_imp_per_day\"].quantile(0.25)\n",
    "q4 = client[\"avg_imp_per_day\"].quantile(0.75)\n",
    "low = client.loc[client[\"avg_imp_per_day\"] <= q1, \"ctr_all\"].dropna()\n",
    "high = client.loc[client[\"avg_imp_per_day\"] >= q4, \"ctr_all\"].dropna()\n",
    "wu = stats.mannwhitneyu(low, high, alternative=\"greater\")\n",
    "{ \"spearman_ctr\": corr_ctr, \"spearman_cr\": corr_cr, \"mw_low_gt_high\": wu }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d492fa",
   "metadata": {},
   "source": [
    "bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
    "stats_bin = client.groupby(bins, observed=False).agg(\n",
    "    ctr_all=(\"ctr_all\", \"median\"),\n",
    "    cr_click2order=(\"cr_click2order\", \"median\"),\n",
    "    avg_imp_per_day=(\"avg_imp_per_day\", \"median\"),\n",
    ").reset_index()\n",
    "stats_bin[\"bin_label\"] = stats_bin[\"avg_imp_per_day\"].round(2).astype(str)\n",
    "fig, ax1 = plt.subplots(figsize=(12, 5))\n",
    "ax2 = ax1.twinx()\n",
    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"ctr_all\", marker=\"o\", ax=ax1, color=\"#4c72b0\", label=\"CTR\")\n",
    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"cr_click2order\", marker=\"o\", ax=ax2, color=\"#c44e52\", label=\"CR\")\n",
    "ax1.set_ylabel(\"CTR\")\n",
    "ax2.set_ylabel(\"CR click→order\")\n",
    "plt.xticks(rotation=35)\n",
    "ax1.set_title(\"CTR и CR по децилям avg_imp_per_day\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "stats_bin[[\"bin_label\", \"ctr_all\", \"cr_click2order\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "943f0d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
    "stats_bin = client.groupby(bins).agg({\"ctr_all\": \"median\", \"cr_click2order\": \"median\", \"avg_imp_per_day\": \"median\"}).reset_index()\n",
    "stats_bin[\"bin_label\"] = stats_bin[\"avg_imp_per_day\"].round(2).astype(str)\n",
    "fig, ax1 = plt.subplots(figsize=(12, 5))\n",
    "ax2 = ax1.twinx()\n",
    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"ctr_all\", marker=\"o\", ax=ax1, color=\"#4c72b0\", label=\"CTR\")\n",
    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"cr_click2order\", marker=\"o\", ax=ax2, color=\"#c44e52\", label=\"CR\")\n",
    "ax1.set_ylabel(\"CTR\")\n",
    "ax2.set_ylabel(\"CR click→order\")\n",
    "plt.xticks(rotation=35)\n",
    "ax1.set_title(\"CTR и CR по децилям avg_imp_per_day\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "stats_bin[[\"bin_label\", \"ctr_all\", \"cr_click2order\"]]\n"
   ]
  }
 ],
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