{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b7e9fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from datetime import date, timedelta\n",
    "from typing import Optional\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from catboost import CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f78e415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30631006, 18)\n"
     ]
    }
   ],
   "source": [
    "# Much data wow\n",
    "data = pl.read_parquet('data/train.parquet')\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ce73fb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Date, Int64, Int64, Int64, Int64, Int64, Int64, Int64, Int64, Int64, Int64, Int64, Float64, Float64, Int64, Int64, Float64, Int64]\n",
      "shape: (1, 18)\n",
      "┌────────────┬─────────┬────────┬─────┬───┬─────────┬────────┬─────┬──────────┐\n",
      "│ event_date ┆ user_id ┆ search ┆ cat ┆ … ┆ to_cart ┆ to_ord ┆ gmv ┆ searches │\n",
      "│ ---        ┆ ---     ┆ ---    ┆ --- ┆   ┆ ---     ┆ ---    ┆ --- ┆ ---      │\n",
      "│ u32        ┆ u32     ┆ u32    ┆ u32 ┆   ┆ u32     ┆ u32    ┆ u32 ┆ u32      │\n",
      "╞════════════╪═════════╪════════╪═════╪═══╪═════════╪════════╪═════╪══════════╡\n",
      "│ 0          ┆ 0       ┆ 0      ┆ 0   ┆ … ┆ 0       ┆ 0      ┆ 0   ┆ 0        │\n",
      "└────────────┴─────────┴────────┴─────┴───┴─────────┴────────┴─────┴──────────┘\n"
     ]
    }
   ],
   "source": [
    "# Sanity checks\n",
    "print(data.dtypes)\n",
    "print(data.null_count()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd58fe5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-01 to 2026-02-13\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 18)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>event_date</th><th>user_id</th><th>search</th><th>cat</th><th>has_search_to_cart</th><th>has_search_to_ord</th><th>has_cat_to_cart</th><th>has_cat_to_ord</th><th>search_to_cart</th><th>search_to_ord</th><th>cat_to_cart</th><th>cat_to_ord</th><th>gmv_search</th><th>gmv_cat</th><th>to_cart</th><th>to_ord</th><th>gmv</th><th>searches</th></tr><tr><td>date</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td></tr></thead><tbody><tr><td>2025-06-16</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>2</td></tr><tr><td>2025-06-22</td><td>2</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>1</td><td>0</td><td>0.0</td><td>1</td></tr><tr><td>2025-08-08</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>1</td></tr><tr><td>2025-08-18</td><td>2</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>1</td><td>0</td><td>0.0</td><td>1</td></tr><tr><td>2025-08-23</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>1</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 18)\n",
       "┌────────────┬─────────┬────────┬─────┬───┬─────────┬────────┬─────┬──────────┐\n",
       "│ event_date ┆ user_id ┆ search ┆ cat ┆ … ┆ to_cart ┆ to_ord ┆ gmv ┆ searches │\n",
       "│ ---        ┆ ---     ┆ ---    ┆ --- ┆   ┆ ---     ┆ ---    ┆ --- ┆ ---      │\n",
       "│ date       ┆ i64     ┆ i64    ┆ i64 ┆   ┆ i64     ┆ i64    ┆ f64 ┆ i64      │\n",
       "╞════════════╪═════════╪════════╪═════╪═══╪═════════╪════════╪═════╪══════════╡\n",
       "│ 2025-06-16 ┆ 2       ┆ 1      ┆ 0   ┆ … ┆ 0       ┆ 0      ┆ 0.0 ┆ 2        │\n",
       "│ 2025-06-22 ┆ 2       ┆ 1      ┆ 0   ┆ … ┆ 1       ┆ 0      ┆ 0.0 ┆ 1        │\n",
       "│ 2025-08-08 ┆ 2       ┆ 1      ┆ 0   ┆ … ┆ 0       ┆ 0      ┆ 0.0 ┆ 1        │\n",
       "│ 2025-08-18 ┆ 2       ┆ 1      ┆ 0   ┆ … ┆ 1       ┆ 0      ┆ 0.0 ┆ 1        │\n",
       "│ 2025-08-23 ┆ 2       ┆ 1      ┆ 0   ┆ … ┆ 0       ┆ 0      ┆ 0.0 ┆ 1        │\n",
       "└────────────┴─────────┴────────┴─────┴───┴─────────┴────────┴─────┴──────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A sneak peek into the data itself\n",
    "print(data[\"event_date\"].min(), \"to\", data[\"event_date\"].max())\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a46067",
   "metadata": {},
   "source": [
    "### What is up with the overall data timeline?\n",
    "\n",
    "Let's aggregate a few daily global stats and plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65bebf62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>event_date</th><th>n_users</th><th>gmv_sum</th><th>to_cart_sum</th><th>to_ord_sum</th><th>searches_sum</th></tr><tr><td>date</td><td>u32</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>2025-01-01</td><td>43516</td><td>374066.469016</td><td>38548</td><td>8827</td><td>136443</td></tr><tr><td>2025-01-02</td><td>52556</td><td>489227.996022</td><td>51779</td><td>12381</td><td>174063</td></tr><tr><td>2025-01-03</td><td>53609</td><td>473332.477438</td><td>51388</td><td>12340</td><td>175456</td></tr><tr><td>2025-01-04</td><td>54882</td><td>450219.389198</td><td>54055</td><td>12785</td><td>182234</td></tr><tr><td>2025-01-05</td><td>55531</td><td>465926.986665</td><td>54809</td><td>12893</td><td>184256</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 6)\n",
       "┌────────────┬─────────┬───────────────┬─────────────┬────────────┬──────────────┐\n",
       "│ event_date ┆ n_users ┆ gmv_sum       ┆ to_cart_sum ┆ to_ord_sum ┆ searches_sum │\n",
       "│ ---        ┆ ---     ┆ ---           ┆ ---         ┆ ---        ┆ ---          │\n",
       "│ date       ┆ u32     ┆ f64           ┆ i64         ┆ i64        ┆ i64          │\n",
       "╞════════════╪═════════╪═══════════════╪═════════════╪════════════╪══════════════╡\n",
       "│ 2025-01-01 ┆ 43516   ┆ 374066.469016 ┆ 38548       ┆ 8827       ┆ 136443       │\n",
       "│ 2025-01-02 ┆ 52556   ┆ 489227.996022 ┆ 51779       ┆ 12381      ┆ 174063       │\n",
       "│ 2025-01-03 ┆ 53609   ┆ 473332.477438 ┆ 51388       ┆ 12340      ┆ 175456       │\n",
       "│ 2025-01-04 ┆ 54882   ┆ 450219.389198 ┆ 54055       ┆ 12785      ┆ 182234       │\n",
       "│ 2025-01-05 ┆ 55531   ┆ 465926.986665 ┆ 54809       ┆ 12893      ┆ 184256       │\n",
       "└────────────┴─────────┴───────────────┴─────────────┴────────────┴──────────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_agg = (\n",
    "    data.group_by(\"event_date\")\n",
    "    .agg(\n",
    "        pl.len().alias(\"n_users\"),\n",
    "        pl.sum(\"gmv\").alias(\"gmv_sum\"),\n",
    "        pl.sum(\"to_cart\").alias(\"to_cart_sum\"),\n",
    "        pl.sum(\"to_ord\").alias(\"to_ord_sum\"),\n",
    "        pl.sum(\"searches\").alias(\"searches_sum\"),\n",
    "    )\n",
    "    .sort(\"event_date\")\n",
    ")\n",
    "\n",
    "daily_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de9a951a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)\n",
    "\n",
    "# Active users\n",
    "axes[0].plot(daily_agg[\"event_date\"], daily_agg[\"n_users\"], marker=\".\", linestyle=\"-\", color=\"steelblue\")\n",
    "axes[0].set_ylabel(\"Number of Users\")\n",
    "axes[0].set_title(\"Daily Active Users\")\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# Total GMV\n",
    "axes[1].plot(daily_agg[\"event_date\"], daily_agg[\"gmv_sum\"], marker=\".\", linestyle=\"-\", color=\"darkorange\")\n",
    "axes[1].set_ylabel(\"Total GMV\")\n",
    "axes[1].set_title(\"Daily GMV\")\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.xlabel(\"Date\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e675e4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(daily_agg[\"event_date\"], daily_agg[\"searches_sum\"], label=\"Searches\", color=\"purple\")\n",
    "plt.plot(daily_agg[\"event_date\"], daily_agg[\"to_cart_sum\"], label=\"Add to cart\", color=\"green\")\n",
    "plt.plot(daily_agg[\"event_date\"], daily_agg[\"to_ord_sum\"], label=\"Orders\", color=\"red\")\n",
    "plt.legend()\n",
    "plt.title(\"Daily Searches, Cart Additions, and Orders\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "985e9357",
   "metadata": {},
   "source": [
    "### Let's prepare some features \n",
    "\n",
    "- Not everyone has large RAM machines, especially in mid 2026\n",
    "- So let's first build time-based features and targets...\n",
    "- And build them batched by users\n",
    "- After we're done we can work with extracted data directly as if it was a much simpler regression task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b511a407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep extra features for homework\n",
    "DEFAULT_AGGS = [\"sum\", \"max\", \"std\", \"mean\"]\n",
    "DEFAULT_VALUE_COLS = [\"gmv\", \"searches\"]\n",
    "\n",
    "# and a place to save our stuff\n",
    "FEATURES_DIR = \"data/v2/features\"\n",
    "\n",
    "# and choose a simple path for now\n",
    "N_FOLDS = 4\n",
    "\n",
    "# mind your RAM capabilities\n",
    "# in case of really low-RAM machines mind that data is sorted by user (and by event_date later)\n",
    "BATCH_SIZE = 50_000\n",
    "\n",
    "# window = [anchor - start_off, anchor - end_off] (inclusive)\n",
    "DEFAULT_WINDOWS = [\n",
    "    (\"30d\", 29, 0),    # [t-29, t-0]  (includes anchor)\n",
    "    (\"60d\", 59, 30),   # [t-59, t-30]\n",
    "    (\"90d\", 89, 60),   # [t-89, t-60]\n",
    "]\n",
    "\n",
    "output_dir = Path(FEATURES_DIR)\n",
    "output_dir.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f6133d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build key anchor dates for time-CV windows\n",
    "def generate_cv_anchor_dates(\n",
    "    data: pl.DataFrame,\n",
    "    prediction_horizon_days: int = 30,\n",
    "    stride_days: int = 14,\n",
    "    min_history_days: int = 90,\n",
    "    n_folds: Optional[int] = None,\n",
    ") -> list[date]:\n",
    "   \n",
    "    min_date = data[\"event_date\"].min()\n",
    "    max_date = data[\"event_date\"].max()\n",
    "\n",
    "    latest_anchor = max_date - timedelta(days = prediction_horizon_days)\n",
    "    earliest_anchor = min_date + timedelta(days = min_history_days - 1)\n",
    "\n",
    "    # sanity check\n",
    "    if latest_anchor < earliest_anchor:\n",
    "        raise ValueError(\n",
    "            f\"Not enough data: need >={min_history_days + prediction_horizon_days - 1} days, \"\n",
    "            f\"got {(max_date - min_date).days}.\"\n",
    "        )\n",
    "\n",
    "    n_steps = (latest_anchor - earliest_anchor).days // stride_days\n",
    "    all_anchors = [latest_anchor - timedelta(days=i * stride_days) for i in range(n_steps + 1)]\n",
    "    all_anchors = sorted(all_anchors)\n",
    "\n",
    "    if n_folds is not None:\n",
    "        if n_folds <= 0:\n",
    "            raise ValueError(\"n_folds must be a positive integer.\")\n",
    "        return all_anchors[-n_folds:]\n",
    "    \n",
    "    return all_anchors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4031ea72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper feature recepies\n",
    "# BYOF yet consider a tokenizer path instead :)\n",
    "def _window_agg_exprs(\n",
    "    anchor_val: date,\n",
    "    windows: list[tuple[str, int, int]],\n",
    "    value_cols: list[str],\n",
    "    aggs: list[str],\n",
    ") -> list[pl.Expr]:\n",
    "    exprs = []\n",
    "    for w_name, start_off, end_off in windows:\n",
    "        w_start = anchor_val - timedelta(days = start_off)\n",
    "        w_end = anchor_val - timedelta(days = end_off)\n",
    "        mask = pl.col(\"event_date\").is_between(w_start, w_end)\n",
    "\n",
    "        for col in value_cols:\n",
    "            for agg in aggs:\n",
    "                if agg == \"sum\":\n",
    "                    e = pl.when(mask).then(pl.col(col)).otherwise(0.0).sum()\n",
    "                elif agg == \"max\":\n",
    "                    e = pl.when(mask).then(pl.col(col)).otherwise(None).max()\n",
    "                elif agg == \"std\":\n",
    "                    e = pl.when(mask).then(pl.col(col)).otherwise(None).std()\n",
    "                elif agg == \"mean\":\n",
    "                    e = pl.when(mask).then(pl.col(col)).otherwise(None).mean()\n",
    "                elif agg == \"count\":\n",
    "                    e = pl.when(mask).then(1).otherwise(0).sum()\n",
    "                else:\n",
    "                    raise ValueError(f\"Unknown agg: {agg}\")\n",
    "                exprs.append(e.alias(f\"{col}_{agg}_{w_name}\"))\n",
    "    return exprs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef1581f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# batch-ready features\n",
    "# you can extract features right away, however it is likely to eat 40GB+ RAM\n",
    "def generate_features(\n",
    "    data: pl.DataFrame,\n",
    "    anchor_dates: list[date],\n",
    "    user_ids: Optional[list[int]] = None,\n",
    "    value_cols: list[str] = DEFAULT_VALUE_COLS,\n",
    "    windows: list[tuple[str, int, int]] = DEFAULT_WINDOWS,\n",
    "    aggs: list[str] = DEFAULT_AGGS,\n",
    ") -> pl.DataFrame:\n",
    "    if user_ids is None:\n",
    "        user_ids = data[\"user_id\"].unique().sort().to_list()\n",
    "\n",
    "    max_back = max(w[1] for w in windows)\n",
    "    min_anchor, max_anchor = min(anchor_dates), max(anchor_dates)\n",
    "\n",
    "    data_f = data.filter(\n",
    "        pl.col(\"user_id\").is_in(user_ids)\n",
    "        & pl.col(\"event_date\").is_between(\n",
    "            min_anchor - timedelta(days=max_back),\n",
    "            max_anchor,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    parts = []\n",
    "    for a in anchor_dates:\n",
    "        ad = data_f.filter(pl.col(\"event_date\") >= a - timedelta(days=max_back))\n",
    "        if len(ad) > 0:\n",
    "            features = (\n",
    "                ad.group_by(\"user_id\")\n",
    "                  .agg(_window_agg_exprs(a, windows, value_cols, aggs))\n",
    "                  .with_columns(anchor_date=pl.lit(a))\n",
    "            )\n",
    "            parts.append(features)\n",
    "\n",
    "    features_df = pl.concat(parts, how=\"diagonal_relaxed\") if parts else pl.DataFrame()\n",
    "    index_df = (\n",
    "        pl.DataFrame({\"anchor_date\": anchor_dates})\n",
    "        .join(pl.DataFrame({\"user_id\": user_ids}), how=\"cross\")\n",
    "    )\n",
    "    result = index_df.join(features_df, on=[\"anchor_date\", \"user_id\"], how=\"left\")\n",
    "\n",
    "    feature_cols = [c for c in result.columns if c not in [\"anchor_date\", \"user_id\"]]\n",
    "    fill_exprs = [pl.col(c).fill_null(0.0) for c in feature_cols]\n",
    "\n",
    "    \n",
    "    return result.with_columns(fill_exprs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "791889ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# batch-ready target construction\n",
    "def generate_targets(\n",
    "    data: pl.DataFrame,\n",
    "    anchor_dates: list[date],\n",
    "    user_ids: Optional[list[int]] = None,\n",
    "    horizon_days: int = 30,\n",
    "    target_col: str = \"gmv\",\n",
    ") -> pl.DataFrame:\n",
    "    if user_ids is None:\n",
    "        user_ids = data[\"user_id\"].unique().sort().to_list()\n",
    "\n",
    "    index_df = (\n",
    "        pl.DataFrame({\"anchor_date\": anchor_dates})\n",
    "        .join(pl.DataFrame({\"user_id\": user_ids}), how=\"cross\")\n",
    "    )\n",
    "\n",
    "    parts = []\n",
    "    for a in anchor_dates:\n",
    "        t_start = a + timedelta(days=1)\n",
    "        t_end   = a + timedelta(days=horizon_days)\n",
    "        tgt = (\n",
    "            data.filter(\n",
    "                pl.col(\"user_id\").is_in(user_ids)\n",
    "                & pl.col(\"event_date\").is_between(t_start, t_end)\n",
    "            )\n",
    "            .group_by(\"user_id\")\n",
    "            .agg(pl.col(target_col).sum().alias(\"target\"))\n",
    "            .with_columns(anchor_date=pl.lit(a))\n",
    "        )\n",
    "        parts.append(tgt)\n",
    "\n",
    "    tgt_df = pl.concat(parts, how=\"diagonal_relaxed\") if parts else pl.DataFrame()\n",
    "    targets = index_df.join(tgt_df, on=[\"anchor_date\", \"user_id\"], how=\"left\")\n",
    "    return targets.with_columns(pl.col(\"target\").fill_null(0.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "176f9368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time-CV folds   : 4 | 2025-12-03 → 2026-01-14 | stride = 14 days\n",
      "Prediction fold : 2026-02-13 → predict [2026-02-14, 2026-03-15]\n"
     ]
    }
   ],
   "source": [
    "# prepare our time anchor structure\n",
    "anchors_time_folds = generate_cv_anchor_dates(data, n_folds = N_FOLDS)\n",
    "anchor_end_of_time = data[\"event_date\"].max()\n",
    "\n",
    "print(f\"Time-CV folds   : {len(anchors_time_folds)} | {anchors_time_folds[0]} → \"\n",
    "    f\"{anchors_time_folds[-1]} | stride = 14 days\")\n",
    "print(f\"Prediction fold : {anchor_end_of_time} → predict [{anchor_end_of_time + timedelta(days = 1)}, \"\n",
    "    f\"{anchor_end_of_time + timedelta(days = 30)}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cab0e62c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "250000 users → 5 batches per fold (batch_size = 50000)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# prepare for batch extraction\n",
    "user_ids = data[\"user_id\"].unique().sort().to_list()\n",
    "\n",
    "n_users   = len(user_ids)\n",
    "n_batches = (n_users + BATCH_SIZE - 1) // BATCH_SIZE\n",
    "print(f\"\\n{n_users} users → {n_batches} batches per fold (batch_size = {BATCH_SIZE})\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2cad0d8",
   "metadata": {},
   "source": [
    "### Let's roll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7471c4f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Processed fold_00 (anchor: 2025-12-03)\n",
      "  Processed fold_01 (anchor: 2025-12-17)\n",
      "  Processed fold_02 (anchor: 2025-12-31)\n",
      "  Processed fold_03 (anchor: 2026-01-14)\n"
     ]
    }
   ],
   "source": [
    "# 1. Process CV Folds \n",
    "\n",
    "for fold_idx, anchor in enumerate(anchors_time_folds):\n",
    "\n",
    "    fold_name = f\"fold_{fold_idx:02d}\"\n",
    "    fold_dir = output_dir / fold_name\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    for batch in range(n_batches):\n",
    "        current_batch = user_ids[batch * BATCH_SIZE : (batch + 1) * BATCH_SIZE]\n",
    "        features = generate_features(data, [anchor], user_ids = current_batch)\n",
    "        targets = generate_targets(data, [anchor], user_ids = current_batch,\n",
    "                                horizon_days = 30)\n",
    "            \n",
    "        out_df = features.join(targets, on=[\"anchor_date\", \"user_id\"], how=\"left\")\n",
    "        out_df = out_df.with_columns(pl.col(\"target\").fill_null(0.0))\n",
    "        out_df.write_parquet(fold_dir / f\"batch_{batch:04d}.parquet\")\n",
    "            \n",
    "    print(f\"  Processed {fold_name} (anchor: {anchor})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "96386fdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Processed fold_last (anchor: 2026-02-13)\n"
     ]
    }
   ],
   "source": [
    "# 2. Process Final Prediction Fold\n",
    "\n",
    "fold_dir = output_dir / \"fold_end\"\n",
    "fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "for batch in range(n_batches):\n",
    "    current_batch = user_ids[batch * BATCH_SIZE : (batch + 1) * BATCH_SIZE]\n",
    "    feats = generate_features(data, [anchor_end_of_time], user_ids = current_batch)\n",
    "    out_df = feats.with_columns(pl.lit(None).cast(pl.Float64).alias(\"target\"))\n",
    "    out_df.write_parquet(fold_dir / f\"batch_{batch:04d}.parquet\")\n",
    "        \n",
    "print(f\"  Processed fold_last (anchor: {anchor_end_of_time})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0b0428",
   "metadata": {},
   "source": [
    "### Now on to ML\n",
    "\n",
    "- Let's check what we are up for\n",
    "- After that let's decide our next best plan of action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0563a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# safe clipped log1p RMSLE\n",
    "def rmsle(y_true, y_pred):\n",
    "    log_true = np.log1p(np.clip(y_true, 0, None))\n",
    "    log_pred = np.log1p(np.clip(y_pred, 0, None))\n",
    "    return np.sqrt(mean_squared_error(log_true, log_pred))\n",
    "\n",
    "# small helper to read our processed feature folds\n",
    "def read_fold(output_dir: str | Path, fold_name: str):\n",
    "    pattern = str(Path(output_dir) / fold_name / \"batch_*.parquet\")\n",
    "    return pl.read_parquet(pattern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f3e294a",
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = []\n",
    "for fold_idx, anchor in enumerate(anchors_time_folds):\n",
    "    fold_name = f\"fold_{fold_idx:02d}\"\n",
    "    fold_df = read_fold(FEATURES_DIR, fold_name)\n",
    "    folds.append(fold_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d7c62c4",
   "metadata": {},
   "source": [
    "### Some catboosting maybe?\n",
    "\n",
    "- Maybe with Tweedie due to heavy zero-inflation?\n",
    "- Maybe some extra features as well, as one could have guessed gmv_sum_30/60/90 are ther most important?\n",
    "- ...\n",
    "- Nah, let's roll with simple autyoregression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "202dfe61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 27)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>anchor_date</th><th>user_id</th><th>gmv_sum_30d</th><th>gmv_max_30d</th><th>gmv_std_30d</th><th>gmv_mean_30d</th><th>searches_sum_30d</th><th>searches_max_30d</th><th>searches_std_30d</th><th>searches_mean_30d</th><th>gmv_sum_60d</th><th>gmv_max_60d</th><th>gmv_std_60d</th><th>gmv_mean_60d</th><th>searches_sum_60d</th><th>searches_max_60d</th><th>searches_std_60d</th><th>searches_mean_60d</th><th>gmv_sum_90d</th><th>gmv_max_90d</th><th>gmv_std_90d</th><th>gmv_mean_90d</th><th>searches_sum_90d</th><th>searches_max_90d</th><th>searches_std_90d</th><th>searches_mean_90d</th><th>target</th></tr><tr><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2026-01-14</td><td>2</td><td>16.315287</td><td>16.315287</td><td>8.157644</td><td>4.078822</td><td>13.0</td><td>5.0</td><td>1.5</td><td>3.25</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>9.0</td><td>9.0</td><td>0.0</td><td>9.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>7.0</td><td>5.0</td><td>2.309401</td><td>2.333333</td><td>0.0</td></tr><tr><td>2026-01-14</td><td>7</td><td>288.283114</td><td>257.468616</td><td>68.67056</td><td>20.591651</td><td>42.0</td><td>17.0</td><td>4.402796</td><td>3.0</td><td>181.124603</td><td>70.065728</td><td>27.937945</td><td>15.093717</td><td>32.0</td><td>5.0</td><td>1.61433</td><td>2.666667</td><td>28.615134</td><td>16.246318</td><td>7.955584</td><td>5.723027</td><td>7.0</td><td>2.0</td><td>0.547723</td><td>1.4</td><td>486.96535</td></tr><tr><td>2026-01-14</td><td>15</td><td>2264.174894</td><td>2237.575574</td><td>620.005196</td><td>174.1673</td><td>26.0</td><td>14.0</td><td>3.719319</td><td>2.0</td><td>133.692441</td><td>133.692441</td><td>42.277262</td><td>13.369244</td><td>8.0</td><td>2.0</td><td>0.788811</td><td>0.8</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>4.0</td><td>4.0</td><td>0.0</td><td>4.0</td><td>0.0</td></tr><tr><td>2026-01-14</td><td>18</td><td>584.802181</td><td>250.101994</td><td>64.463223</td><td>30.779062</td><td>55.0</td><td>10.0</td><td>3.413938</td><td>2.894737</td><td>25.739683</td><td>25.739683</td><td>6.645958</td><td>1.715979</td><td>38.0</td><td>23.0</td><td>5.717975</td><td>2.533333</td><td>227.428092</td><td>168.281835</td><td>51.870829</td><td>22.742809</td><td>17.0</td><td>7.0</td><td>1.946507</td><td>1.7</td><td>276.0091</td></tr><tr><td>2026-01-14</td><td>23</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>10.0</td><td>5.0</td><td>1.527525</td><td>3.333333</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5.0</td><td>4.0</td><td>2.12132</td><td>2.5</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>18.0</td><td>9.0</td><td>3.224903</td><td>3.0</td><td>0.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 27)\n",
       "┌────────────┬─────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ anchor_dat ┆ user_id ┆ gmv_sum_3 ┆ gmv_max_3 ┆ … ┆ searches_ ┆ searches_ ┆ searches_ ┆ target    │\n",
       "│ e          ┆ ---     ┆ 0d        ┆ 0d        ┆   ┆ max_90d   ┆ std_90d   ┆ mean_90d  ┆ ---       │\n",
       "│ ---        ┆ i64     ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ f64       │\n",
       "│ date       ┆         ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆           │\n",
       "╞════════════╪═════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2026-01-14 ┆ 2       ┆ 16.315287 ┆ 16.315287 ┆ … ┆ 5.0       ┆ 2.309401  ┆ 2.333333  ┆ 0.0       │\n",
       "│ 2026-01-14 ┆ 7       ┆ 288.28311 ┆ 257.46861 ┆ … ┆ 2.0       ┆ 0.547723  ┆ 1.4       ┆ 486.96535 │\n",
       "│            ┆         ┆ 4         ┆ 6         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2026-01-14 ┆ 15      ┆ 2264.1748 ┆ 2237.5755 ┆ … ┆ 4.0       ┆ 0.0       ┆ 4.0       ┆ 0.0       │\n",
       "│            ┆         ┆ 94        ┆ 74        ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2026-01-14 ┆ 18      ┆ 584.80218 ┆ 250.10199 ┆ … ┆ 7.0       ┆ 1.946507  ┆ 1.7       ┆ 276.0091  │\n",
       "│            ┆         ┆ 1         ┆ 4         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2026-01-14 ┆ 23      ┆ 0.0       ┆ 0.0       ┆ … ┆ 9.0       ┆ 3.224903  ┆ 3.0       ┆ 0.0       │\n",
       "└────────────┴─────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is our last train data fold, with fair target \n",
    "folds[3].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70ee6a49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(250000, 2)\n"
     ]
    }
   ],
   "source": [
    "# let's use this target as the basis fort our submit\n",
    "submit_naive = folds[3].select([\"user_id\", \"target\"])\n",
    "submit_naive.columns = [\"user_id\", \"predict\"]\n",
    "print(submit_naive.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee66cdec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now quickly save it before any ML happens and find ourselves on leaderboard!\n",
    "submit_naive.write_csv(\"data/sample_naive_submit.csv\")"
   ]
  }
 ],
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