spam hypot
This commit is contained in:
188
spam_hypot/01_stat_analysis.ipynb
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188
spam_hypot/01_stat_analysis.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4d7d3347",
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"metadata": {},
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"source": [
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"# Спам-гипотеза: плотность показов vs CTR/CR\n",
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"\n",
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"Цель: проверить, что высокая плотность показов на контактный день снижает CTR и CR (спам-эффект)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7acbd1c8",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sqlite3\n",
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"from pathlib import Path\n",
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"import sys\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy import stats\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.metrics import roc_auc_score\n",
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"\n",
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"sns.set_theme(style=\"whitegrid\")\n",
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"plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
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"\n",
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"project_root = Path.cwd().resolve()\n",
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"while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
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" project_root = project_root.parent\n",
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"sys.path.append(str(project_root / \"preanalysis\"))\n",
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"import eda_utils as eda\n",
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"\n",
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"db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
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"conn = sqlite3.connect(db_path)\n",
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"df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
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"conn.close()\n",
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"\n",
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"for cols, name in [\n",
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" (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
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" (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
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" (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
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" (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
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" (eda.ORDER_COLS, \"orders_amt_total\"),\n",
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"]:\n",
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" df[name] = df[cols].sum(axis=1)\n",
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"\n",
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"df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
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"df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
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"\n",
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"contact_days = df.groupby(\"id\")[\"business_dt\"].nunique().rename(\"contact_days\")\n",
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"client = df.groupby(\"id\").agg(\n",
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" {\n",
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" \"imp_total\": \"sum\",\n",
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" \"click_total\": \"sum\",\n",
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" \"orders_amt_total\": \"sum\",\n",
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" \"age\": \"median\",\n",
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" \"gender_cd\": lambda s: s.mode().iat[0],\n",
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" \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
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" }\n",
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").merge(contact_days, on=\"id\", how=\"left\").reset_index()\n",
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"\n",
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"client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
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"client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
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"client[\"avg_imp_per_day\"] = eda.safe_divide(client[\"imp_total\"], client[\"contact_days\"])\n",
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"client[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
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"client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "94eb2d26",
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"metadata": {},
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"source": [
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"## Базовые статистики"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "287a09b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"summary = client[[\"imp_total\", \"click_total\", \"orders_amt_total\", \"contact_days\", \"avg_imp_per_day\", \"ctr_all\", \"cr_click2order\"]].describe().T\n",
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"missing = client.isna().mean().sort_values(ascending=False)\n",
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"summary, missing.head(10)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "10cd44b7",
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"metadata": {},
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"source": [
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"## Корреляции и тесты\n",
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"Спирмен между плотностью и CTR/CR, а также Mann–Whitney между Q1 и Q4 по плотности."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "88714a03",
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"metadata": {},
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"outputs": [],
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"source": [
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"corr_ctr = stats.spearmanr(client[\"avg_imp_per_day\"], client[\"ctr_all\"])\n",
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"corr_cr = stats.spearmanr(client[\"avg_imp_per_day\"], client[\"cr_click2order\"])\n",
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"q1 = client[\"avg_imp_per_day\"].quantile(0.25)\n",
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"q4 = client[\"avg_imp_per_day\"].quantile(0.75)\n",
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"low = client.loc[client[\"avg_imp_per_day\"] <= q1, \"ctr_all\"].dropna()\n",
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"high = client.loc[client[\"avg_imp_per_day\"] >= q4, \"ctr_all\"].dropna()\n",
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"wu = stats.mannwhitneyu(low, high, alternative=\"greater\")\n",
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"{ \"spearman_ctr\": corr_ctr, \"spearman_cr\": corr_cr, \"mw_low_gt_high\": wu }\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "20d492fa",
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"metadata": {},
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"source": [
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"bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
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"stats_bin = client.groupby(bins, observed=False).agg(\n",
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" ctr_all=(\"ctr_all\", \"median\"),\n",
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" cr_click2order=(\"cr_click2order\", \"median\"),\n",
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" avg_imp_per_day=(\"avg_imp_per_day\", \"median\"),\n",
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").reset_index()\n",
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"stats_bin[\"bin_label\"] = stats_bin[\"avg_imp_per_day\"].round(2).astype(str)\n",
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"fig, ax1 = plt.subplots(figsize=(12, 5))\n",
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"ax2 = ax1.twinx()\n",
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"sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"ctr_all\", marker=\"o\", ax=ax1, color=\"#4c72b0\", label=\"CTR\")\n",
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"sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"cr_click2order\", marker=\"o\", ax=ax2, color=\"#c44e52\", label=\"CR\")\n",
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"ax1.set_ylabel(\"CTR\")\n",
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"ax2.set_ylabel(\"CR click→order\")\n",
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"plt.xticks(rotation=35)\n",
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"ax1.set_title(\"CTR и CR по децилям avg_imp_per_day\")\n",
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"fig.tight_layout()\n",
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"plt.show()\n",
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"stats_bin[[\"bin_label\", \"ctr_all\", \"cr_click2order\"]]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "943f0d4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
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"stats_bin = client.groupby(bins).agg({\"ctr_all\": \"median\", \"cr_click2order\": \"median\", \"avg_imp_per_day\": \"median\"}).reset_index()\n",
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"stats_bin[\"bin_label\"] = stats_bin[\"avg_imp_per_day\"].round(2).astype(str)\n",
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"fig, ax1 = plt.subplots(figsize=(12, 5))\n",
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"ax2 = ax1.twinx()\n",
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"sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"ctr_all\", marker=\"o\", ax=ax1, color=\"#4c72b0\", label=\"CTR\")\n",
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"sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"cr_click2order\", marker=\"o\", ax=ax2, color=\"#c44e52\", label=\"CR\")\n",
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"ax1.set_ylabel(\"CTR\")\n",
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"ax2.set_ylabel(\"CR click→order\")\n",
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"plt.xticks(rotation=35)\n",
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"ax1.set_title(\"CTR и CR по децилям avg_imp_per_day\")\n",
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"fig.tight_layout()\n",
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"plt.show()\n",
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"stats_bin[[\"bin_label\", \"ctr_all\", \"cr_click2order\"]]\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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161
spam_hypot/02_models.ipynb
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161
spam_hypot/02_models.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7254b4c1",
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"metadata": {},
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"source": [
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"# Спам-гипотеза: сравнение моделей\n",
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"\n",
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"Target: `high_ctr` (верхний квартиль CTR)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c7f54168",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sqlite3\n",
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"from pathlib import Path\n",
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"import sys\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy import stats\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.metrics import roc_auc_score\n",
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"\n",
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"sns.set_theme(style=\"whitegrid\")\n",
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"plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
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"\n",
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"project_root = Path.cwd().resolve()\n",
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"while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
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" project_root = project_root.parent\n",
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"sys.path.append(str(project_root / \"preanalysis\"))\n",
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"import eda_utils as eda\n",
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"\n",
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"db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
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"conn = sqlite3.connect(db_path)\n",
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"df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
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"conn.close()\n",
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"\n",
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"for cols, name in [\n",
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" (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
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" (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
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" (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
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" (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
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" (eda.ORDER_COLS, \"orders_amt_total\"),\n",
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"]:\n",
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" df[name] = df[cols].sum(axis=1)\n",
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"\n",
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"df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
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"df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
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"\n",
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"contact_days = df.groupby(\"id\")[\"business_dt\"].nunique().rename(\"contact_days\")\n",
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"client = df.groupby(\"id\").agg(\n",
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" {\n",
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" \"imp_total\": \"sum\",\n",
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" \"click_total\": \"sum\",\n",
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" \"orders_amt_total\": \"sum\",\n",
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" \"age\": \"median\",\n",
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" \"gender_cd\": lambda s: s.mode().iat[0],\n",
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" \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
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" }\n",
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").merge(contact_days, on=\"id\", how=\"left\").reset_index()\n",
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"\n",
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"client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
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"client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
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"client[\"avg_imp_per_day\"] = eda.safe_divide(client[\"imp_total\"], client[\"contact_days\"])\n",
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"client[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
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"client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "21786c63",
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"metadata": {},
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"source": [
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"## Модели: Logistic Regression vs GradientBoosting"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc8dbc94",
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"metadata": {},
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"outputs": [],
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"source": [
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"X = client[[\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\n",
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"X = X.copy()\n",
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"X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
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"X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
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"y = client[\"high_ctr\"]\n",
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"\n",
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"num_cols = [\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\"]\n",
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"cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
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"pre = ColumnTransformer([\n",
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" (\"num\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]), num_cols),\n",
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" (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
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"])\n",
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"\n",
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"log_reg = Pipeline([(\"pre\", pre), (\"clf\", LogisticRegression(max_iter=1000))])\n",
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"gb = Pipeline([(\"pre\", pre), (\"clf\", GradientBoostingClassifier(random_state=42))])\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
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"res = {}\n",
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"for name, model in [(\"log_reg\", log_reg), (\"gb\", gb)]:\n",
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" model.fit(X_train, y_train)\n",
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" proba = model.predict_proba(X_test)[:, 1]\n",
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" res[name] = roc_auc_score(y_test, proba)\n",
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||||
"res\n"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "203acf70",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"## Важности признаков (GradientBoosting)"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "3eac9e17",
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||||
"metadata": {},
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"outputs": [],
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"source": [
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"gb_model = gb\n",
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"feat_names = gb_model.named_steps[\"pre\"].get_feature_names_out()\n",
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"importances = gb_model.named_steps[\"clf\"].feature_importances_\n",
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"imp_df = pd.DataFrame({\"feature\": feat_names, \"importance\": importances}).sort_values(\"importance\", ascending=False)\n",
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"plt.figure(figsize=(8, 5))\n",
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"sns.barplot(data=imp_df.head(15), x=\"importance\", y=\"feature\", palette=\"viridis\")\n",
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"plt.title(\"Top-15 feature importances (GB)\")\n",
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||||
"plt.tight_layout()\n",
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||||
"plt.show()\n",
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||||
"imp_df.head(15)\n"
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||||
]
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||||
}
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||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.13"
|
||||
}
|
||||
},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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206
spam_hypot/03_best_model.ipynb
Normal file
206
spam_hypot/03_best_model.ipynb
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@@ -0,0 +1,206 @@
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{
|
||||
"cells": [
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||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d88bf2d8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Спам-гипотеза: лучшая модель и визуализации\n",
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||||
"\n",
|
||||
"Используем GradientBoostingClassifier (лучше логрега по AUC) для подтверждения гипотезы."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "87f3f728",
|
||||
"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": "17da010c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Обучение лучшей модели"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "81433d7e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X = client[[\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\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[\"high_ctr\"]\n",
|
||||
"\n",
|
||||
"num_cols = [\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\"]\n",
|
||||
"cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
|
||||
"pre = ColumnTransformer([\n",
|
||||
" (\"num\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]), num_cols),\n",
|
||||
" (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"best = Pipeline([(\"pre\", pre), (\"clf\", GradientBoostingClassifier(random_state=42))])\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
|
||||
"best.fit(X_train, y_train)\n",
|
||||
"proba = best.predict_proba(X_test)[:, 1]\n",
|
||||
"auc = roc_auc_score(y_test, proba)\n",
|
||||
"auc\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "63f4db9b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Прогноз vs плотность показов"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f48584b5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"grid = pd.DataFrame({\"avg_imp_per_day\": np.linspace(client[\"avg_imp_per_day\"].min(), client[\"avg_imp_per_day\"].max(), 50)})\n",
|
||||
"base = client.median(numeric_only=True)\n",
|
||||
"base_gender = client[\"gender_cd\"].mode().iat[0]\n",
|
||||
"base_device = client[\"device_platform_cd\"].mode().iat[0]\n",
|
||||
"grid[\"imp_total\"] = base[\"imp_total\"]\n",
|
||||
"grid[\"click_total\"] = base[\"click_total\"]\n",
|
||||
"grid[\"age\"] = base[\"age\"]\n",
|
||||
"grid[\"gender_cd\"] = base_gender\n",
|
||||
"grid[\"device_platform_cd\"] = base_device\n",
|
||||
"proba_grid = best.predict_proba(grid)[:, 1]\n",
|
||||
"plt.figure(figsize=(10, 4))\n",
|
||||
"plt.plot(grid[\"avg_imp_per_day\"], proba_grid, marker=\"o\")\n",
|
||||
"plt.xlabel(\"avg_imp_per_day\")\n",
|
||||
"plt.ylabel(\"P(high CTR)\")\n",
|
||||
"plt.title(\"Предсказанная вероятность высокого CTR vs плотность показов\")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "32f73b44",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## График CTR и CR по тонким бинам (две оси)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bb4d0190",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"bins = pd.qcut(client[\"avg_imp_per_day\"], 15, 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",
|
||||
"ax1.plot(stats_bin[\"bin_label\"], stats_bin[\"ctr_all\"], marker=\"o\", color=\"#4c72b0\", label=\"CTR\")\n",
|
||||
"ax2.plot(stats_bin[\"bin_label\"], stats_bin[\"cr_click2order\"], marker=\"s\", color=\"#c44e52\", label=\"CR\")\n",
|
||||
"ax1.set_ylabel(\"CTR\")\n",
|
||||
"ax2.set_ylabel(\"CR click→order\")\n",
|
||||
"ax1.set_xlabel(\"avg_imp_per_day bins\")\n",
|
||||
"plt.xticks(rotation=35)\n",
|
||||
"ax1.set_title(\"CTR и CR по 15 бинам avg_imp_per_day\")\n",
|
||||
"fig.tight_layout()\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ebb2ca5e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Вывод\n",
|
||||
"- AUC модели GradientBoosting > логрега; `avg_imp_per_day` ключевой драйвер: рост плотности снижает шанс попасть в верхний квартиль CTR.\n",
|
||||
"- Биновые графики показывают монотонное падение CTR и CR при росте avg_imp_per_day.\n",
|
||||
"- Гипотеза о спам-эффекте подтверждается как статистически, так и по ML-модели."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
BIN
spam_hypot/best_bins.png
Normal file
BIN
spam_hypot/best_bins.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 109 KiB |
114
spam_hypot/best_model_and_plots.py
Normal file
114
spam_hypot/best_model_and_plots.py
Normal file
@@ -0,0 +1,114 @@
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
import sys
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler, OneHotEncoder
|
||||
from sklearn.compose import ColumnTransformer
|
||||
from sklearn.pipeline import Pipeline
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.ensemble import GradientBoostingClassifier
|
||||
from sklearn.metrics import roc_auc_score
|
||||
|
||||
sns.set_theme(style="whitegrid")
|
||||
plt.rcParams["figure.figsize"] = (10, 5)
|
||||
|
||||
project_root = Path(__file__).resolve().parent.parent
|
||||
sys.path.append(str(project_root / "preanalysis"))
|
||||
import eda_utils as eda # noqa: E402
|
||||
|
||||
db_path = project_root / "dataset" / "ds.sqlite"
|
||||
conn = sqlite3.connect(db_path)
|
||||
df = pd.read_sql_query("select * from communications", conn, parse_dates=["business_dt"])
|
||||
conn.close()
|
||||
|
||||
for cols, name in [
|
||||
(eda.ACTIVE_IMP_COLS, "active_imp_total"),
|
||||
(eda.PASSIVE_IMP_COLS, "passive_imp_total"),
|
||||
(eda.ACTIVE_CLICK_COLS, "active_click_total"),
|
||||
(eda.PASSIVE_CLICK_COLS, "passive_click_total"),
|
||||
(eda.ORDER_COLS, "orders_amt_total"),
|
||||
]:
|
||||
df[name] = df[cols].sum(axis=1)
|
||||
|
||||
df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
|
||||
df["click_total"] = df["active_click_total"] + df["passive_click_total"]
|
||||
contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
|
||||
client = (
|
||||
df.groupby("id")
|
||||
.agg(
|
||||
imp_total=("imp_total", "sum"),
|
||||
click_total=("click_total", "sum"),
|
||||
orders_amt_total=("orders_amt_total", "sum"),
|
||||
age=("age", "median"),
|
||||
gender_cd=("gender_cd", lambda s: s.mode().iat[0]),
|
||||
device_platform_cd=("device_platform_cd", lambda s: s.mode().iat[0]),
|
||||
)
|
||||
.merge(contact_days, on="id", how="left")
|
||||
.reset_index()
|
||||
)
|
||||
client["ctr_all"] = eda.safe_divide(client["click_total"], client["imp_total"])
|
||||
client["cr_click2order"] = eda.safe_divide(client["orders_amt_total"], client["click_total"])
|
||||
client["avg_imp_per_day"] = eda.safe_divide(client["imp_total"], client["contact_days"])
|
||||
client["high_ctr"] = (client["ctr_all"] >= client["ctr_all"].quantile(0.75)).astype(int)
|
||||
|
||||
X = client[["avg_imp_per_day", "imp_total", "click_total", "age", "gender_cd", "device_platform_cd"]]
|
||||
X = X.copy()
|
||||
X["gender_cd"] = eda.normalize_gender(X["gender_cd"])
|
||||
X["device_platform_cd"] = eda.normalize_device(X["device_platform_cd"])
|
||||
y = client["high_ctr"]
|
||||
|
||||
num_cols = ["avg_imp_per_day", "imp_total", "click_total", "age"]
|
||||
cat_cols = ["gender_cd", "device_platform_cd"]
|
||||
pre = ColumnTransformer([
|
||||
("num", Pipeline([("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]), num_cols),
|
||||
("cat", OneHotEncoder(handle_unknown="ignore"), cat_cols),
|
||||
])
|
||||
|
||||
model = Pipeline([("pre", pre), ("clf", GradientBoostingClassifier(random_state=42))])
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
|
||||
model.fit(X_train, y_train)
|
||||
proba = model.predict_proba(X_test)[:, 1]
|
||||
auc = roc_auc_score(y_test, proba)
|
||||
print("Best model AUC:", auc)
|
||||
|
||||
# Probability vs avg_imp_per_day
|
||||
grid = pd.DataFrame({"avg_imp_per_day": np.linspace(client["avg_imp_per_day"].min(), client["avg_imp_per_day"].max(), 50)})
|
||||
base = client.median(numeric_only=True)
|
||||
base_gender = client["gender_cd"].mode().iat[0]
|
||||
base_device = client["device_platform_cd"].mode().iat[0]
|
||||
grid["imp_total"] = base["imp_total"]
|
||||
grid["click_total"] = base["click_total"]
|
||||
grid["age"] = base["age"]
|
||||
grid["gender_cd"] = base_gender
|
||||
grid["device_platform_cd"] = base_device
|
||||
proba_grid = model.predict_proba(grid)[:, 1]
|
||||
plt.figure(figsize=(10, 4))
|
||||
plt.plot(grid["avg_imp_per_day"], proba_grid, marker="o")
|
||||
plt.xlabel("avg_imp_per_day")
|
||||
plt.ylabel("P(high CTR)")
|
||||
plt.title("Предсказанная вероятность высокого CTR vs плотность показов")
|
||||
plt.tight_layout()
|
||||
plt.savefig(project_root / "spam_hypot" / "best_model_prob.png", dpi=150)
|
||||
print("Saved best_model_prob.png")
|
||||
|
||||
# Dual axis CTR/CR vs fine bins
|
||||
bins = pd.qcut(client["avg_imp_per_day"], 15, duplicates="drop")
|
||||
stats_bin = client.groupby(bins, observed=False)[["ctr_all", "cr_click2order"]].median().reset_index().rename(columns={"index": "bin"})
|
||||
stats_bin["avg_imp_per_day"] = client.groupby(bins, observed=False)["avg_imp_per_day"].median().values
|
||||
stats_bin["bin_label"] = stats_bin["avg_imp_per_day"].round(2).astype(str)
|
||||
fig, ax1 = plt.subplots(figsize=(12, 5))
|
||||
ax2 = ax1.twinx()
|
||||
ax1.plot(stats_bin["bin_label"], stats_bin["ctr_all"], marker="o", color="#4c72b0", label="CTR")
|
||||
ax2.plot(stats_bin["bin_label"], stats_bin["cr_click2order"], marker="s", color="#c44e52", label="CR")
|
||||
ax1.set_ylabel("CTR")
|
||||
ax2.set_ylabel("CR click→order")
|
||||
ax1.set_xlabel("avg_imp_per_day bins")
|
||||
plt.xticks(rotation=35)
|
||||
ax1.set_title("CTR и CR по 15 бинам avg_imp_per_day")
|
||||
fig.tight_layout()
|
||||
plt.savefig(project_root / "spam_hypot" / "best_bins.png", dpi=150)
|
||||
print("Saved best_bins.png")
|
||||
BIN
spam_hypot/best_model_prob.png
Normal file
BIN
spam_hypot/best_model_prob.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 45 KiB |
80
spam_hypot/model_compare.py
Normal file
80
spam_hypot/model_compare.py
Normal file
@@ -0,0 +1,80 @@
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
import sys
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler, OneHotEncoder
|
||||
from sklearn.compose import ColumnTransformer
|
||||
from sklearn.pipeline import Pipeline
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.ensemble import GradientBoostingClassifier
|
||||
from sklearn.metrics import roc_auc_score
|
||||
|
||||
project_root = Path(__file__).resolve().parent.parent
|
||||
sys.path.append(str(project_root / "preanalysis"))
|
||||
import eda_utils as eda # noqa: E402
|
||||
|
||||
db_path = project_root / "dataset" / "ds.sqlite"
|
||||
conn = sqlite3.connect(db_path)
|
||||
df = pd.read_sql_query("select * from communications", conn, parse_dates=["business_dt"])
|
||||
conn.close()
|
||||
|
||||
for cols, name in [
|
||||
(eda.ACTIVE_IMP_COLS, "active_imp_total"),
|
||||
(eda.PASSIVE_IMP_COLS, "passive_imp_total"),
|
||||
(eda.ACTIVE_CLICK_COLS, "active_click_total"),
|
||||
(eda.PASSIVE_CLICK_COLS, "passive_click_total"),
|
||||
(eda.ORDER_COLS, "orders_amt_total"),
|
||||
]:
|
||||
df[name] = df[cols].sum(axis=1)
|
||||
|
||||
df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
|
||||
df["click_total"] = df["active_click_total"] + df["passive_click_total"]
|
||||
contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
|
||||
client = (
|
||||
df.groupby("id")
|
||||
.agg(
|
||||
imp_total=("imp_total", "sum"),
|
||||
click_total=("click_total", "sum"),
|
||||
orders_amt_total=("orders_amt_total", "sum"),
|
||||
age=("age", "median"),
|
||||
gender_cd=("gender_cd", lambda s: s.mode().iat[0]),
|
||||
device_platform_cd=("device_platform_cd", lambda s: s.mode().iat[0]),
|
||||
)
|
||||
.merge(contact_days, on="id", how="left")
|
||||
.reset_index()
|
||||
)
|
||||
client["ctr_all"] = eda.safe_divide(client["click_total"], client["imp_total"])
|
||||
client["avg_imp_per_day"] = eda.safe_divide(client["imp_total"], client["contact_days"])
|
||||
client["high_ctr"] = (client["ctr_all"] >= client["ctr_all"].quantile(0.75)).astype(int)
|
||||
|
||||
X = client[["avg_imp_per_day", "imp_total", "click_total", "age", "gender_cd", "device_platform_cd"]]
|
||||
X = X.copy()
|
||||
X["gender_cd"] = eda.normalize_gender(X["gender_cd"])
|
||||
X["device_platform_cd"] = eda.normalize_device(X["device_platform_cd"])
|
||||
y = client["high_ctr"]
|
||||
|
||||
num_cols = ["avg_imp_per_day", "imp_total", "click_total", "age"]
|
||||
cat_cols = ["gender_cd", "device_platform_cd"]
|
||||
pre = ColumnTransformer([
|
||||
("num", Pipeline([("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]), num_cols),
|
||||
("cat", OneHotEncoder(handle_unknown="ignore"), cat_cols),
|
||||
])
|
||||
|
||||
log_reg = Pipeline([("pre", pre), ("clf", LogisticRegression(max_iter=1000))])
|
||||
gb = Pipeline([("pre", pre), ("clf", GradientBoostingClassifier(random_state=42))])
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
|
||||
results = {}
|
||||
for name, model in [("log_reg", log_reg), ("gb", gb)]:
|
||||
model.fit(X_train, y_train)
|
||||
proba = model.predict_proba(X_test)[:, 1]
|
||||
results[name] = roc_auc_score(y_test, proba)
|
||||
print("AUC results:", results)
|
||||
|
||||
imp = gb.named_steps["clf"].feature_importances_
|
||||
feat = gb.named_steps["pre"].get_feature_names_out()
|
||||
imp_df = pd.DataFrame({"feature": feat, "importance": imp}).sort_values("importance", ascending=False)
|
||||
print(imp_df.head(15))
|
||||
87
spam_hypot/stat_analysis.py
Normal file
87
spam_hypot/stat_analysis.py
Normal file
@@ -0,0 +1,87 @@
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
import sys
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy import stats
|
||||
|
||||
sns.set_theme(style="whitegrid")
|
||||
plt.rcParams["figure.figsize"] = (10, 5)
|
||||
|
||||
project_root = Path(__file__).resolve().parent.parent
|
||||
sys.path.append(str(project_root / "preanalysis"))
|
||||
import eda_utils as eda # noqa: E402
|
||||
|
||||
db_path = project_root / "dataset" / "ds.sqlite"
|
||||
conn = sqlite3.connect(db_path)
|
||||
df = pd.read_sql_query("select * from communications", conn, parse_dates=["business_dt"])
|
||||
conn.close()
|
||||
|
||||
for cols, name in [
|
||||
(eda.ACTIVE_IMP_COLS, "active_imp_total"),
|
||||
(eda.PASSIVE_IMP_COLS, "passive_imp_total"),
|
||||
(eda.ACTIVE_CLICK_COLS, "active_click_total"),
|
||||
(eda.PASSIVE_CLICK_COLS, "passive_click_total"),
|
||||
(eda.ORDER_COLS, "orders_amt_total"),
|
||||
]:
|
||||
df[name] = df[cols].sum(axis=1)
|
||||
|
||||
df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
|
||||
df["click_total"] = df["active_click_total"] + df["passive_click_total"]
|
||||
|
||||
contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
|
||||
client = (
|
||||
df.groupby("id")
|
||||
.agg(
|
||||
imp_total=("imp_total", "sum"),
|
||||
click_total=("click_total", "sum"),
|
||||
orders_amt_total=("orders_amt_total", "sum"),
|
||||
age=("age", "median"),
|
||||
gender_cd=("gender_cd", lambda s: s.mode().iat[0]),
|
||||
device_platform_cd=("device_platform_cd", lambda s: s.mode().iat[0]),
|
||||
)
|
||||
.merge(contact_days, on="id", how="left")
|
||||
.reset_index()
|
||||
)
|
||||
|
||||
client["ctr_all"] = eda.safe_divide(client["click_total"], client["imp_total"])
|
||||
client["cr_click2order"] = eda.safe_divide(client["orders_amt_total"], client["click_total"])
|
||||
client["avg_imp_per_day"] = eda.safe_divide(client["imp_total"], client["contact_days"])
|
||||
client["high_ctr"] = (client["ctr_all"] >= client["ctr_all"].quantile(0.75)).astype(int)
|
||||
client["has_order"] = (client["orders_amt_total"] > 0).astype(int)
|
||||
|
||||
# Summary
|
||||
summary = client[["imp_total", "click_total", "orders_amt_total", "contact_days", "avg_imp_per_day", "ctr_all", "cr_click2order"]].describe().T
|
||||
print("Summary\n", summary)
|
||||
missing = client.isna().mean().sort_values(ascending=False)
|
||||
print("Missing\n", missing.head(10))
|
||||
|
||||
# Correlations and Mann-Whitney
|
||||
corr_ctr = stats.spearmanr(client["avg_imp_per_day"], client["ctr_all"])
|
||||
corr_cr = stats.spearmanr(client["avg_imp_per_day"], client["cr_click2order"])
|
||||
q1 = client["avg_imp_per_day"].quantile(0.25)
|
||||
q4 = client["avg_imp_per_day"].quantile(0.75)
|
||||
low = client.loc[client["avg_imp_per_day"] <= q1, "ctr_all"].dropna()
|
||||
high = client.loc[client["avg_imp_per_day"] >= q4, "ctr_all"].dropna()
|
||||
wu = stats.mannwhitneyu(low, high, alternative="greater")
|
||||
print({"spearman_ctr": corr_ctr, "spearman_cr": corr_cr, "mw_low_gt_high": wu})
|
||||
|
||||
# Bin stats and dual-axis plot
|
||||
bins = pd.qcut(client["avg_imp_per_day"], 10, duplicates="drop")
|
||||
stats_bin = client.groupby(bins, observed=False)[["ctr_all", "cr_click2order"]].median().reset_index().rename(columns={"index": "bin"})
|
||||
stats_bin["avg_imp_per_day"] = client.groupby(bins, observed=False)["avg_imp_per_day"].median().values
|
||||
stats_bin["bin_label"] = stats_bin["avg_imp_per_day"].round(2).astype(str)
|
||||
fig, ax1 = plt.subplots(figsize=(12, 5))
|
||||
ax2 = ax1.twinx()
|
||||
ax1.plot(stats_bin["bin_label"], stats_bin["ctr_all"], marker="o", color="#4c72b0", label="CTR")
|
||||
ax2.plot(stats_bin["bin_label"], stats_bin["cr_click2order"], marker="s", color="#c44e52", label="CR")
|
||||
ax1.set_ylabel("CTR")
|
||||
ax2.set_ylabel("CR click→order")
|
||||
ax1.set_xlabel("avg_imp_per_day bins")
|
||||
plt.xticks(rotation=35)
|
||||
ax1.set_title("CTR и CR по децилям avg_imp_per_day")
|
||||
fig.tight_layout()
|
||||
plt.savefig(project_root / "spam_hypot" / "stat_bins.png", dpi=150)
|
||||
print("Saved plot stat_bins.png")
|
||||
BIN
spam_hypot/stat_bins.png
Normal file
BIN
spam_hypot/stat_bins.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 97 KiB |
Reference in New Issue
Block a user