{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "M5 Sales Forecasting - Accuracy Point Prediction\n", "================================================\n", "\n", "* [0 Global Definitions](#global-def)\n", "* [1 Data Preparation](#data-preparation)\n", " * [1.1 Data Inspection and Visualization](#data-inspection)\n", " * [1.1.1 Inspect raw data tables](#inspect-raw-data)\n", " * [1.1.2 Plot sales time series](#plot-sales)\n", " * [1.2 Preprocess Sales Data](#preprocess-sales-data)\n", " * [1.2.1 Flatten Sales data](#flatten-sales-data)\n", " * [1.2.2 Add placeholding rows for test prediction](#add-placeholding-rows)\n", " * [1.2.3 Remove the leading 0-Sales entries before products went in-stock](#remove-leading-0-sales)\n", " * [1.2.3.1 Extract in-stock week from Prices data](#extract-in-stock-week)\n", " * [1.2.3.2 Extract current week from Calendar data](#extract-current-week)\n", " * [1.2.3.3 Remove the leading 0-Sales entries](#remove-leading-0-sale-entries)\n", " * [1.2.4 Convert IDs to Pandas Category for later use in lightgbm](#convert-ids-to-cat)\n", " * [1.2.5 Save flattened Sales data](#save-flattened-sales-data)\n", " * [1.3 Preprocess Weekly Price Data](#preprocess-weekly-price-data)\n", " * [1.3.1 Add summary statistical features to price series per store / item](#add-summary-stats-features)\n", " * [1.3.2 Add momentum features](#add-summary-stats-features)\n", " * [1.3.3 Re-index weekly Prices to flattend Sales index (id and d)](#reindex-weekly-prices-to-flat-sales)\n", " * [1.3.4 Save preprocessed Prices data](#save-preprocessed-prices-data)\n", " * [1.4 Preprocess Calendar data](#preprocess-calendar-data)\n", " * [1.4.1 Re-Index Calendar to the flattened Sales index (id and d)](#reindex-calendar-to-flat-sales)\n", " * [1.4.2 Convert event column types to category to save memory](#convert-event-column-types-to-cat)\n", " * [1.4.3 Convert date column and split to more features](#convert-date-column-split-features)\n", " * [1.4.4 Save preprocessed Calendar data](#save-preprocessed-calendar-data)\n", " * [1.5 Additional Cleaning](#additional-cleaning)\n", " * [1.6 Create lags and rolling mean / standard deviation](#create-lags-rolling-stats)\n", " * [1.7 Create mean / std for aggregation level 2-12 (Guide Table 1)](#create-stats-agg-level-2-12)\n", " * [1.8 Combine Sales, Prices and Calendar](#combine-sales-prices-calendar)\n", "* [2 Modeling](#modeling)\n", " * [2.1 Hyperparameters](#hyperparameters)\n", " * [2.2 Train Models per Agg Level and IDs](#train-models-per-agg-level-ids)\n", " * [2.3 Combine Predictions to Submission](#combine-predictions-to-submission)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Global definitions " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Python libs\n", "import warnings\n", "import joblib\n", "from pathlib import Path\n", "from itertools import product\n", "from math import ceil\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# Visualization\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import lightgbm as lgb\n", "\n", "# Module settings\n", "mpl.rc(\"figure\", facecolor=\"white\", dpi=196)\n", "pd.set_option('display.max_columns', None) # show all columns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "DATA_INPUT_DIR = 'data_input/'\n", "DATA_PROCESSED_DIR = 'data_processed/'\n", "RESULTS_DIR = 'results/'\n", "\n", "SALES_CSV = DATA_INPUT_DIR + 'sales_train_evaluation.csv'\n", "PRICES_CSV = DATA_INPUT_DIR + 'sell_prices.csv'\n", "CALENDAR_CSV = DATA_INPUT_DIR + 'calendar.csv'\n", "SAMPLE_SUBMISSION_CSV = DATA_INPUT_DIR + 'sample_submission.csv'\n", "\n", "SALES_DATA_FILE = DATA_PROCESSED_DIR + 'sales.pkl'\n", "PRICES_DATA_FILE = DATA_PROCESSED_DIR + 'prices.pkl'\n", "CALENDAR_DATA_FILE = DATA_PROCESSED_DIR + 'calendar.pkl'\n", "SALES_PRICES_CALENDAR_DATA_FILE = DATA_PROCESSED_DIR + 'sales_prices_calendar.pkl'\n", "SALES_LAG_ROLL_STATS_FILE = DATA_PROCESSED_DIR + 'sales_lag_roll_stats.pkl'\n", "SALES_AGG_STATS_FILE = DATA_PROCESSED_DIR + 'sales_agg_stats.pkl'\n", "\n", "SUBMISSION_FILE = RESULTS_DIR + 'submission.csv'\n", "\n", "# Globals / Class attributes\n", "FLAT_INDEX_COLS = ['id', 'd']\n", "TARGET_COL = 'sales'\n", "ID_D_TARGET_COLS = ['id', 'd', TARGET_COL]\n", "END_TRAIN = 1941\n", "SHIFT_DAY = 28" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def reduce_mem_usage(df, verbose=True):\n", " numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n", " start_mem = df.memory_usage().sum() / 1024**2 \n", " for col in df.columns:\n", " col_type = df[col].dtypes\n", " if col_type in numerics:\n", " c_min = df[col].min()\n", " c_max = df[col].max()\n", " if str(col_type)[:3] == 'int':\n", " if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n", " df[col] = df[col].astype(np.int8)\n", " elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n", " df[col] = df[col].astype(np.int16)\n", " elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n", " df[col] = df[col].astype(np.int32)\n", " elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n", " df[col] = df[col].astype(np.int64) \n", " else:\n", " if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n", " df[col] =-inspection df[col].astype(np.float16)\n", " elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n", " df[col] = df[col].astype(np.float32)\n", " else:\n", " df[col] = df[col].astype(np.float64) \n", " end_mem = df.memory_usage().sum() / 1024**2\n", " if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Preparation " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Inspection and Visualization \n", "\n", "Three data files given:\n", "- sales_train_evaluation.csv\n", "- sell_prices.csv\n", "- calendar.csv" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "sales = pd.read_csv(SALES_CSV)\n", "prices = pd.read_csv(PRICES_CSV)\n", "calendar = pd.read_csv(CALENDAR_CSV)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspect raw data tables " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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30490 rows × 1947 columns

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"30485 1 0 1 0 2 0 1 1 0 0 \n", "30486 0 1 2 1 0 0 0 0 2 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_626 d_627 d_628 d_629 d_630 d_631 d_632 d_633 d_634 d_635 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 2 2 1 1 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 2 2 0 4 0 6 3 0 0 0 \n", "4 1 1 1 0 2 2 0 0 2 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 0 3 1 0 0 1 1 1 \n", "30486 0 1 0 0 1 0 0 1 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_636 d_637 d_638 d_639 d_640 d_641 d_642 d_643 d_644 d_645 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 1 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 0 0 1 \n", "4 0 2 1 2 0 0 3 1 1 3 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 3 1 1 2 1 0 0 0 0 0 \n", "30486 0 0 1 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_646 d_647 d_648 d_649 d_650 d_651 d_652 d_653 d_654 d_655 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 1 1 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 1 4 0 0 0 0 0 0 0 \n", "4 2 2 0 0 4 1 1 7 2 3 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 1 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_656 d_657 d_658 d_659 d_660 d_661 d_662 d_663 d_664 d_665 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 1 1 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 0 0 2 \n", "4 0 1 0 0 0 0 1 3 0 2 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_666 d_667 d_668 d_669 d_670 d_671 d_672 d_673 d_674 d_675 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 1 0 0 1 0 0 0 0 2 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 1 1 1 0 0 0 0 1 0 0 \n", "4 1 3 1 1 2 0 2 0 7 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_676 d_677 d_678 d_679 d_680 d_681 d_682 d_683 d_684 d_685 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 2 1 1 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 0 2 1 \n", "4 0 0 2 0 1 1 1 1 1 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_686 d_687 d_688 d_689 d_690 d_691 d_692 d_693 d_694 d_695 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 2 1 3 0 3 1 3 2 1 3 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 1 2 0 0 0 0 0 0 0 0 \n", "4 1 0 1 2 0 2 1 3 5 2 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 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d_717 d_718 d_719 d_720 d_721 d_722 d_723 d_724 d_725 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 1 1 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 0 0 0 \n", "4 2 0 0 1 0 0 1 3 2 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_726 d_727 d_728 d_729 d_730 d_731 d_732 d_733 d_734 d_735 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 1 0 0 2 0 1 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 0 6 1 1 1 1 0 \n", "4 0 4 1 0 6 0 0 0 2 2 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_736 d_737 d_738 d_739 d_740 d_741 d_742 d_743 d_744 d_745 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 1 0 1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 2 1 0 3 0 2 3 1 2 1 \n", "4 4 0 1 1 2 1 2 0 2 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 1 0 0 0 0 3 2 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_746 d_747 d_748 d_749 d_750 d_751 d_752 d_753 d_754 d_755 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 1 1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 1 1 0 1 2 0 1 0 3 1 \n", "4 0 0 0 0 2 3 1 0 2 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 2 0 0 2 1 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_756 d_757 d_758 d_759 d_760 d_761 d_762 d_763 d_764 d_765 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 2 0 0 0 0 0 1 1 1 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 2 1 2 6 0 1 1 1 6 \n", "4 0 2 1 4 0 0 2 1 2 2 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 1 0 0 0 0 0 0 4 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_766 d_767 d_768 d_769 d_770 d_771 d_772 d_773 d_774 d_775 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 1 0 0 0 0 1 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 3 0 0 1 4 3 2 0 2 \n", "4 1 0 0 0 0 0 2 0 2 1 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 1 1 1 0 2 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_776 d_777 d_778 d_779 d_780 d_781 d_782 d_783 d_784 d_785 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 2 2 4 2 2 0 1 2 1 2 \n", "4 0 2 2 3 0 2 2 0 1 2 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 0 4 2 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_786 d_787 d_788 d_789 d_790 d_791 d_792 d_793 d_794 d_795 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 1 0 1 0 1 2 0 1 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 7 1 0 1 0 2 5 1 6 1 \n", "4 0 2 1 1 0 1 4 1 1 1 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 2 0 0 1 2 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_796 d_797 d_798 d_799 d_800 d_801 d_802 d_803 d_804 d_805 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 3 4 0 3 2 1 1 \n", "4 2 0 0 0 0 0 0 0 1 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 3 2 1 1 0 1 1 0 1 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_806 d_807 d_808 d_809 d_810 d_811 d_812 d_813 d_814 d_815 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 1 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 2 5 2 0 0 0 0 2 3 3 \n", "4 0 1 1 2 3 1 1 1 3 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 0 0 0 0 0 0 0 1 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_816 d_817 d_818 d_819 d_820 d_821 d_822 d_823 d_824 d_825 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 1 0 1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 1 2 3 1 0 5 3 1 1 2 \n", "4 2 1 1 1 1 2 1 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 1 0 0 0 1 2 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_826 d_827 d_828 d_829 d_830 d_831 d_832 d_833 d_834 d_835 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 1 1 0 0 0 0 1 1 1 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 1 1 1 2 0 1 2 0 2 0 \n", "4 0 1 0 0 0 0 0 0 1 1 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 1 2 0 1 0 1 1 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_836 d_837 d_838 d_839 d_840 d_841 d_842 d_843 d_844 d_845 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 2 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 2 3 7 0 0 2 1 2 3 1 \n", "4 0 1 0 1 1 5 4 1 0 2 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 1 0 0 0 0 0 1 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_846 d_847 d_848 d_849 d_850 d_851 d_852 d_853 d_854 d_855 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 1 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 2 1 3 1 0 0 0 3 6 \n", "4 3 1 3 1 0 0 0 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 1 1 0 0 1 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_856 d_857 d_858 d_859 d_860 d_861 d_862 d_863 d_864 d_865 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 1 1 0 0 1 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 4 0 0 1 1 2 0 0 1 1 \n", "4 0 0 0 1 0 2 1 2 1 1 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 0 1 1 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_866 d_867 d_868 d_869 d_870 d_871 d_872 d_873 d_874 d_875 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 1 0 0 1 0 1 0 1 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 1 0 3 3 0 0 0 2 \n", "4 1 2 2 0 1 0 1 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 1 1 1 1 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 1 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_876 d_877 d_878 d_879 d_880 d_881 d_882 d_883 d_884 d_885 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 1 0 0 0 0 0 0 1 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 1 3 0 0 0 1 1 3 3 0 \n", "4 1 0 0 0 0 4 1 3 0 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 0 1 2 2 0 0 1 1 \n", "30487 0 1 1 0 0 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_886 d_887 d_888 d_889 d_890 d_891 d_892 d_893 d_894 d_895 \\\n", "0 0 0 0 0 0 0 0 0 0 0 \n", "1 0 2 0 0 1 0 0 0 0 2 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 2 0 0 2 0 0 0 0 0 0 \n", "4 0 1 0 3 2 0 2 1 3 5 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 1 0 1 0 0 0 \n", "30487 0 0 0 0 1 2 2 2 3 7 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_896 d_897 d_898 d_899 d_900 d_901 d_902 d_903 d_904 d_905 \\\n", "0 0 0 0 0 0 0 1 0 0 0 \n", "1 0 1 0 0 0 0 0 0 1 1 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 1 3 3 \n", "4 1 2 0 1 0 1 1 0 2 4 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 3 0 1 2 0 1 0 0 \n", "30487 2 3 0 5 2 0 0 2 3 0 \n", 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1 1 0 0 0 1 \n", "1 0 0 1 0 0 0 1 1 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 2 1 0 4 2 1 1 \n", "4 0 0 0 2 0 2 2 0 1 1 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 1 0 1 1 1 0 0 \n", "30487 1 4 6 4 6 0 2 4 3 6 \n", "30488 0 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_936 d_937 d_938 d_939 d_940 d_941 d_942 d_943 d_944 d_945 \\\n", "0 0 0 0 0 0 0 0 1 0 1 \n", "1 0 0 1 0 1 0 0 0 1 1 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 1 1 0 5 0 1 0 1 3 1 \n", "4 5 0 0 1 2 2 3 0 1 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 0 0 0 0 1 0 0 1 \n", "30487 2 2 5 4 4 1 1 1 5 1 \n", "30488 0 0 0 0 1 2 2 0 2 4 \n", "30489 0 0 0 0 0 0 0 0 0 0 \n", "\n", " d_946 d_947 d_948 d_949 d_950 d_951 d_952 d_953 d_954 d_955 \\\n", "0 0 0 2 1 0 0 0 2 0 0 \n", "1 0 0 0 0 0 0 1 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 \n", "3 2 1 4 1 4 0 1 1 4 2 \n", "4 2 3 3 0 1 0 1 3 4 1 \n", "... ... ... ... ... 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d_1137 d_1138 d_1139 \\\n", "0 1 0 0 0 0 1 0 0 0 \n", "1 0 0 0 0 1 0 0 0 0 \n", "2 0 0 0 1 0 0 0 0 0 \n", "3 2 2 0 1 6 10 0 1 1 \n", "4 1 2 0 0 2 1 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 2 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 2 1 2 2 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 3 4 0 4 0 2 0 3 1 \n", "\n", " d_1140 d_1141 d_1142 d_1143 d_1144 d_1145 d_1146 d_1147 d_1148 \\\n", "0 0 0 0 1 0 1 0 1 1 \n", "1 0 0 0 0 0 1 0 0 0 \n", "2 0 0 0 0 0 0 2 0 1 \n", "3 1 2 2 6 1 0 1 3 0 \n", "4 0 3 2 3 0 1 0 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 1 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 6 3 1 1 1 1 2 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 2 6 2 6 1 0 3 1 \n", "\n", " d_1149 d_1150 d_1151 d_1152 d_1153 d_1154 d_1155 d_1156 d_1157 \\\n", "0 1 0 2 0 1 0 0 0 0 \n", "1 0 0 0 0 0 0 0 0 1 \n", "2 0 0 0 1 0 3 0 1 0 \n", "3 5 2 1 4 2 5 2 4 2 \n", "4 1 1 0 0 1 1 1 0 0 \n", "... ... ... ... ... ... ... ... 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d_1179 d_1180 d_1181 d_1182 d_1183 d_1184 \\\n", "0 1 2 0 1 0 0 0 0 0 \n", "1 0 1 0 0 1 1 0 1 0 \n", "2 0 1 0 0 0 0 1 0 0 \n", "3 3 3 4 1 4 2 2 1 1 \n", "4 0 2 3 0 0 1 1 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 4 0 2 0 1 0 0 0 0 \n", "30488 1 3 0 0 1 2 0 0 0 \n", "30489 2 6 3 5 0 1 2 0 4 \n", "\n", " d_1185 d_1186 d_1187 d_1188 d_1189 d_1190 d_1191 d_1192 d_1193 \\\n", "0 1 0 1 0 0 0 0 1 0 \n", "1 0 1 0 0 0 0 0 2 0 \n", "2 0 0 0 0 1 0 0 0 0 \n", "3 4 1 1 1 1 1 2 14 2 \n", "4 1 2 0 0 0 0 3 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 2 0 0 0 0 0 0 0 0 \n", "30487 1 1 0 0 0 2 2 1 0 \n", "30488 2 0 0 3 3 0 1 5 4 \n", "30489 0 0 2 1 0 7 2 4 0 \n", "\n", " d_1194 d_1195 d_1196 d_1197 d_1198 d_1199 d_1200 d_1201 d_1202 \\\n", "0 0 0 0 0 0 1 0 0 0 \n", "1 0 0 0 1 0 0 0 0 1 \n", "2 0 2 0 0 1 0 0 0 0 \n", "3 2 0 2 1 2 3 3 2 2 \n", "4 0 0 1 0 2 1 1 2 2 \n", "... ... ... 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d_1221 d_1222 d_1223 d_1224 d_1225 d_1226 d_1227 d_1228 d_1229 \\\n", "0 0 1 0 1 0 1 0 0 1 \n", "1 0 0 0 0 2 0 2 0 0 \n", "2 0 1 0 0 0 0 0 0 0 \n", "3 0 0 2 1 1 3 2 2 2 \n", "4 0 1 2 0 1 3 1 1 3 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 1 1 0 0 0 0 2 2 0 \n", "30487 1 2 1 1 1 0 1 4 0 \n", "30488 3 3 0 2 3 2 1 1 0 \n", "30489 5 4 2 1 1 2 0 0 0 \n", "\n", " d_1230 d_1231 d_1232 d_1233 d_1234 d_1235 d_1236 d_1237 d_1238 \\\n", "0 0 0 1 1 2 0 1 0 0 \n", "1 2 0 0 0 0 0 1 0 0 \n", "2 0 0 0 0 0 1 0 0 0 \n", "3 3 0 2 5 3 2 3 3 1 \n", "4 0 5 0 0 0 0 1 0 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 2 0 1 1 0 1 0 \n", "30487 1 1 1 1 1 1 1 1 1 \n", "30488 1 2 2 1 1 0 1 0 1 \n", "30489 0 0 0 0 0 3 2 0 1 \n", "\n", " d_1239 d_1240 d_1241 d_1242 d_1243 d_1244 d_1245 d_1246 d_1247 \\\n", "0 0 0 0 0 0 0 3 1 2 \n", "1 1 0 3 0 0 0 0 0 0 \n", "2 1 0 1 0 0 0 0 0 0 \n", "3 1 1 2 2 5 3 1 3 6 \n", "4 1 1 0 2 0 0 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 2 0 1 0 0 \n", "30487 2 1 1 1 0 2 0 0 0 \n", "30488 0 0 0 0 0 0 2 0 3 \n", "30489 0 3 2 3 4 0 1 1 0 \n", "\n", " d_1248 d_1249 d_1250 d_1251 d_1252 d_1253 d_1254 d_1255 d_1256 \\\n", "0 2 1 1 0 0 2 0 0 0 \n", "1 1 1 1 0 0 2 1 1 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 5 2 4 1 3 2 4 5 7 \n", "4 1 0 2 0 2 2 0 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 1 1 0 0 1 0 0 \n", "30487 1 0 2 1 2 4 0 3 0 \n", "30488 2 1 0 0 0 0 0 0 0 \n", "30489 1 3 2 8 0 3 0 2 1 \n", "\n", " d_1257 d_1258 d_1259 d_1260 d_1261 d_1262 d_1263 d_1264 d_1265 \\\n", "0 0 1 0 1 0 0 0 1 1 \n", "1 0 1 0 1 1 0 0 0 0 \n", "2 0 1 0 0 0 0 0 0 0 \n", "3 1 0 4 4 1 4 1 0 1 \n", "4 1 0 0 0 1 0 1 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 1 1 1 0 0 0 0 2 0 \n", "30487 1 1 0 0 0 1 3 1 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 0 0 3 2 1 4 1 0 \n", "\n", " d_1266 d_1267 d_1268 d_1269 d_1270 d_1271 d_1272 d_1273 d_1274 \\\n", "0 0 3 1 0 0 0 0 0 1 \n", "1 0 0 1 0 1 1 0 1 0 \n", "2 0 2 1 0 0 0 0 0 0 \n", "3 3 1 3 6 1 2 0 3 0 \n", "4 0 4 0 0 0 2 0 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 0 3 1 0 1 0 0 \n", "30487 2 1 0 3 1 0 1 0 0 \n", "30488 0 0 0 0 0 0 0 0 1 \n", "30489 0 1 1 0 0 4 0 1 8 \n", "\n", " d_1275 d_1276 d_1277 d_1278 d_1279 d_1280 d_1281 d_1282 d_1283 \\\n", "0 0 1 1 1 0 0 0 0 2 \n", "1 0 0 1 0 0 0 2 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 3 8 2 2 0 0 2 4 2 \n", "4 2 2 1 2 0 0 0 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 0 0 0 0 0 0 1 \n", "30487 0 0 0 0 0 1 0 0 3 \n", "30488 0 0 0 0 1 0 0 1 0 \n", "30489 0 2 1 0 1 0 0 0 2 \n", "\n", " d_1284 d_1285 d_1286 d_1287 d_1288 d_1289 d_1290 d_1291 d_1292 \\\n", "0 0 0 2 0 0 0 1 0 0 \n", "1 1 1 0 1 1 0 0 0 0 \n", "2 1 0 0 0 0 1 0 0 0 \n", "3 0 0 2 4 2 3 6 2 0 \n", "4 0 0 0 2 3 2 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 1 1 0 0 1 1 0 0 0 \n", "30487 0 0 4 0 0 0 0 1 0 \n", "30488 1 0 0 1 0 0 2 0 1 \n", "30489 0 4 1 2 2 5 1 0 8 \n", "\n", " d_1293 d_1294 d_1295 d_1296 d_1297 d_1298 d_1299 d_1300 d_1301 \\\n", "0 0 0 0 4 0 0 2 1 0 \n", "1 1 0 0 0 0 0 1 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 3 5 2 0 5 4 1 1 0 \n", "4 1 0 1 1 3 0 0 0 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 1 1 1 2 0 0 0 0 \n", "30488 1 0 0 0 0 1 0 1 0 \n", "30489 0 1 0 3 1 7 6 2 2 \n", "\n", " d_1302 d_1303 d_1304 d_1305 d_1306 d_1307 d_1308 d_1309 d_1310 \\\n", "0 0 0 0 0 1 0 0 0 0 \n", "1 1 1 0 0 0 0 0 1 1 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 1 6 1 2 5 4 3 2 3 \n", "4 2 0 2 0 0 1 1 0 3 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 0 0 0 0 0 0 0 \n", "30487 1 0 0 0 0 0 1 1 0 \n", "30488 0 0 1 0 0 0 0 1 0 \n", "30489 2 0 0 1 0 0 0 0 2 \n", "\n", " d_1311 d_1312 d_1313 d_1314 d_1315 d_1316 d_1317 d_1318 d_1319 \\\n", "0 0 4 0 1 0 1 2 2 0 \n", "1 0 0 0 2 0 1 0 0 0 \n", "2 0 0 0 0 0 1 0 1 0 \n", "3 4 3 2 3 2 0 3 6 1 \n", "4 0 0 0 0 0 2 1 5 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 1 0 0 1 0 0 0 0 0 \n", "30487 2 1 2 0 2 3 2 3 0 \n", "30488 0 0 0 0 0 1 1 1 0 \n", "30489 0 0 3 0 1 1 6 5 0 \n", "\n", " d_1320 d_1321 d_1322 d_1323 d_1324 d_1325 d_1326 d_1327 d_1328 \\\n", "0 0 0 0 2 1 1 1 1 1 \n", "1 0 0 0 0 0 0 0 1 0 \n", "2 1 1 0 0 0 1 0 1 0 \n", "3 2 1 3 3 1 5 0 2 1 \n", "4 0 0 2 0 0 0 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 2 0 0 0 1 0 0 \n", "30487 1 1 1 1 4 7 0 0 1 \n", "30488 1 1 0 1 0 1 0 0 1 \n", "30489 3 1 0 6 0 0 0 1 2 \n", "\n", " d_1329 d_1330 d_1331 d_1332 d_1333 d_1334 d_1335 d_1336 d_1337 \\\n", "0 0 0 1 0 0 1 0 0 0 \n", "1 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 0 2 9 0 0 2 2 3 0 \n", "4 0 0 2 3 1 1 0 1 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 1 0 0 0 0 1 0 0 \n", "30487 0 1 1 0 2 0 0 0 0 \n", "30488 0 0 0 1 0 0 1 0 0 \n", "30489 4 0 1 0 3 1 0 3 5 \n", "\n", " d_1338 d_1339 d_1340 d_1341 d_1342 d_1343 d_1344 d_1345 d_1346 \\\n", "0 1 1 0 1 2 0 0 2 0 \n", "1 0 0 0 0 0 2 1 1 0 \n", "2 1 2 0 0 0 0 0 0 0 \n", "3 0 0 3 2 0 0 1 3 3 \n", "4 1 0 0 0 0 1 2 2 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 3 0 0 0 1 0 0 0 1 \n", "30487 0 0 0 0 0 1 2 3 0 \n", "30488 0 0 0 1 2 0 0 0 0 \n", "30489 0 1 2 0 0 0 0 0 0 \n", "\n", " d_1347 d_1348 d_1349 d_1350 d_1351 d_1352 d_1353 d_1354 d_1355 \\\n", "0 1 3 1 0 0 1 0 0 1 \n", "1 1 1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 0 0 3 0 0 4 5 1 2 \n", "4 0 1 0 0 0 3 3 1 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 1 1 0 0 0 1 1 0 1 \n", "30487 2 1 0 1 2 3 4 4 2 \n", "30488 1 0 1 0 0 1 1 1 0 \n", "30489 0 0 1 0 3 3 1 0 0 \n", "\n", " d_1356 d_1357 d_1358 d_1359 d_1360 d_1361 d_1362 d_1363 d_1364 \\\n", "0 0 0 0 0 0 1 0 0 1 \n", "1 2 0 0 0 1 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 2 0 0 5 0 1 1 0 0 \n", "4 1 0 1 9 6 0 0 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 1 0 \n", "30487 0 3 1 2 1 0 1 2 0 \n", "30488 0 0 0 0 1 0 0 0 1 \n", "30489 0 1 3 2 1 0 3 1 1 \n", "\n", " d_1365 d_1366 d_1367 d_1368 d_1369 d_1370 d_1371 d_1372 d_1373 \\\n", "0 0 1 1 0 1 1 0 0 1 \n", "1 2 0 0 1 0 1 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 0 4 6 1 0 4 4 0 8 \n", "4 2 6 1 0 0 1 1 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 1 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 0 2 1 0 1 0 0 2 \n", "30488 0 0 0 0 0 2 2 1 1 \n", "30489 2 2 5 1 0 2 1 2 6 \n", "\n", " d_1374 d_1375 d_1376 d_1377 d_1378 d_1379 d_1380 d_1381 d_1382 \\\n", "0 1 1 0 0 0 0 0 2 0 \n", "1 1 0 1 0 1 1 1 1 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 2 4 1 2 1 4 0 4 7 \n", "4 2 0 1 0 0 2 2 4 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 0 2 0 4 0 0 \n", "30486 0 0 1 0 0 1 0 1 0 \n", "30487 2 0 0 0 0 0 1 3 0 \n", "30488 1 0 1 0 0 0 1 0 0 \n", "30489 2 0 2 0 0 0 1 1 0 \n", "\n", " d_1383 d_1384 d_1385 d_1386 d_1387 d_1388 d_1389 d_1390 d_1391 \\\n", "0 0 0 0 3 0 2 0 0 0 \n", "1 2 0 1 1 0 1 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 1 5 1 6 5 5 0 1 3 \n", "4 1 0 3 2 7 2 0 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 2 0 0 1 1 0 2 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 1 1 2 1 1 1 2 0 \n", "30488 1 2 0 1 0 1 0 1 0 \n", "30489 4 2 0 1 2 2 0 0 0 \n", "\n", " d_1392 d_1393 d_1394 d_1395 d_1396 d_1397 d_1398 d_1399 d_1400 \\\n", "0 0 3 2 0 2 0 0 2 0 \n", "1 0 0 0 1 0 0 0 1 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 1 15 0 5 0 1 1 6 3 \n", "4 0 1 1 0 0 3 1 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 2 1 0 3 0 0 1 0 1 \n", "30486 0 0 0 0 1 0 0 0 0 \n", "30487 2 1 2 1 1 1 1 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 5 3 1 0 6 3 1 0 \n", "\n", " d_1401 d_1402 d_1403 d_1404 d_1405 d_1406 d_1407 d_1408 d_1409 \\\n", "0 0 0 0 3 0 1 1 1 0 \n", "1 0 0 1 0 1 0 0 0 1 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 2 2 3 1 3 1 8 2 4 \n", "4 2 3 1 0 1 0 0 5 4 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 0 0 0 0 0 3 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 0 2 1 0 1 1 1 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 0 0 0 0 1 2 1 \n", "\n", " d_1410 d_1411 d_1412 d_1413 d_1414 d_1415 d_1416 d_1417 d_1418 \\\n", "0 1 0 1 0 0 2 4 0 0 \n", "1 0 0 1 0 1 0 1 0 1 \n", "2 0 0 0 0 0 0 0 1 0 \n", "3 0 0 2 1 1 5 9 0 2 \n", "4 2 0 2 1 3 1 3 2 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 2 2 4 1 0 1 1 0 0 \n", "30486 0 1 0 0 1 0 0 1 0 \n", "30487 0 0 2 1 0 1 2 1 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 2 0 1 5 1 2 3 0 \n", "\n", " d_1419 d_1420 d_1421 d_1422 d_1423 d_1424 d_1425 d_1426 d_1427 \\\n", "0 0 0 0 2 0 0 1 0 0 \n", "1 0 0 1 4 0 2 3 3 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 2 5 0 7 1 1 3 0 0 \n", "4 0 0 2 4 2 3 2 2 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 1 0 2 0 0 0 0 \n", "30486 0 0 1 0 0 0 0 0 0 \n", "30487 1 1 1 2 0 1 1 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 3 0 2 2 1 1 0 0 \n", "\n", " d_1428 d_1429 d_1430 d_1431 d_1432 d_1433 d_1434 d_1435 d_1436 \\\n", "0 1 1 5 1 1 0 0 0 0 \n", "1 1 0 0 0 0 0 0 0 0 \n", "2 1 0 0 0 0 0 0 1 0 \n", "3 2 10 2 1 0 0 2 4 3 \n", "4 0 1 0 3 0 0 3 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 3 0 0 0 1 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 3 2 0 1 0 1 2 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 1 0 3 0 3 0 2 4 \n", "\n", " d_1437 d_1438 d_1439 d_1440 d_1441 d_1442 d_1443 d_1444 d_1445 \\\n", "0 0 0 0 0 2 1 1 0 2 \n", "1 0 0 0 0 0 0 1 0 2 \n", "2 0 0 0 0 0 0 0 0 1 \n", "3 3 0 2 2 3 0 1 9 2 \n", "4 1 1 0 0 0 1 1 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 0 1 0 0 1 1 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 3 1 3 1 1 0 1 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 2 2 1 2 2 0 0 1 \n", "\n", " d_1446 d_1447 d_1448 d_1449 d_1450 d_1451 d_1452 d_1453 d_1454 \\\n", "0 0 2 1 0 1 0 1 1 0 \n", "1 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 1 0 1 0 1 1 \n", "3 0 4 1 1 2 3 8 1 0 \n", "4 0 1 2 1 1 1 1 1 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 0 0 1 0 0 0 \n", "30486 1 0 1 0 0 0 0 0 0 \n", "30487 1 3 2 1 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 0 2 1 8 1 0 7 1 \n", "\n", " d_1455 d_1456 d_1457 d_1458 d_1459 d_1460 d_1461 d_1462 d_1463 \\\n", "0 0 0 2 1 1 0 0 1 0 \n", "1 0 0 0 0 0 0 0 0 1 \n", "2 0 0 1 0 1 1 1 0 0 \n", "3 0 2 11 3 3 1 0 2 1 \n", "4 1 0 2 0 0 2 1 0 4 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 2 1 0 0 1 0 1 \n", "30486 0 0 0 0 0 0 0 0 1 \n", "30487 0 0 0 2 1 0 2 0 1 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 2 4 0 0 1 0 3 5 \n", "\n", " d_1464 d_1465 d_1466 d_1467 d_1468 d_1469 d_1470 d_1471 d_1472 \\\n", "0 3 0 0 0 1 0 0 2 1 \n", "1 0 0 0 0 0 1 0 0 0 \n", "2 0 0 0 0 0 0 1 0 0 \n", "3 2 6 0 0 2 0 3 10 4 \n", "4 1 2 1 0 0 1 1 5 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 4 0 0 0 1 1 4 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 1 2 1 0 1 1 4 4 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 4 2 1 2 3 0 0 2 3 \n", "\n", " d_1473 d_1474 d_1475 d_1476 d_1477 d_1478 d_1479 d_1480 d_1481 \\\n", "0 0 1 0 0 3 3 0 0 0 \n", "1 0 0 0 0 0 0 1 0 0 \n", "2 1 0 0 1 0 0 1 0 0 \n", "3 0 1 1 1 1 4 2 8 2 \n", "4 0 1 2 0 1 1 1 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 1 0 2 1 3 2 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 3 2 1 0 7 2 1 \n", "30488 0 0 0 1 0 1 0 0 0 \n", "30489 1 6 2 0 4 2 0 1 4 \n", "\n", " d_1482 d_1483 d_1484 d_1485 d_1486 d_1487 d_1488 d_1489 d_1490 \\\n", "0 0 0 0 0 1 1 1 0 2 \n", "1 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 3 1 0 0 0 \n", "3 2 2 1 1 12 2 0 0 1 \n", "4 3 3 1 1 4 1 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 2 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 1 0 3 1 1 0 2 1 \n", "30488 0 0 0 0 1 1 1 1 0 \n", "30489 0 1 5 6 7 2 4 1 4 \n", "\n", " d_1491 d_1492 d_1493 d_1494 d_1495 d_1496 d_1497 d_1498 d_1499 \\\n", "0 2 2 0 0 1 0 0 1 3 \n", "1 0 1 0 0 1 0 0 0 0 \n", "2 1 1 1 1 1 0 0 0 0 \n", "3 3 4 1 0 2 1 2 1 3 \n", "4 2 1 2 0 2 1 1 1 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 2 1 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 1 2 4 2 1 3 2 1 \n", "30488 0 1 0 0 0 0 0 0 0 \n", "30489 0 3 0 1 0 0 0 0 0 \n", "\n", " d_1500 d_1501 d_1502 d_1503 d_1504 d_1505 d_1506 d_1507 d_1508 \\\n", "0 0 0 0 1 1 0 1 0 1 \n", "1 0 1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 1 1 0 1 1 \n", "3 10 1 2 0 0 1 0 3 2 \n", "4 2 1 0 1 1 2 0 1 4 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 2 0 0 2 1 0 1 1 2 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 2 1 0 0 0 0 0 0 \n", "30488 0 0 1 0 0 0 2 0 0 \n", "30489 0 0 0 0 1 0 1 1 1 \n", "\n", " d_1509 d_1510 d_1511 d_1512 d_1513 d_1514 d_1515 d_1516 d_1517 \\\n", "0 0 0 1 1 1 2 1 1 1 \n", "1 0 0 1 0 0 0 0 1 0 \n", "2 0 0 0 0 0 1 0 0 0 \n", "3 0 4 0 3 1 10 1 0 0 \n", "4 2 0 2 2 1 3 1 1 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 0 2 4 1 0 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 1 0 2 1 0 \n", "30488 0 0 1 0 0 0 0 0 0 \n", "30489 2 4 0 0 1 1 0 1 4 \n", "\n", " d_1518 d_1519 d_1520 d_1521 d_1522 d_1523 d_1524 d_1525 d_1526 \\\n", "0 0 2 2 2 1 0 0 0 1 \n", "1 0 0 0 0 0 0 0 0 1 \n", "2 0 1 0 0 0 1 0 0 0 \n", "3 4 5 7 0 0 2 0 1 4 \n", "4 0 2 0 1 0 0 1 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 1 0 1 0 1 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 0 1 0 1 1 0 3 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 3 1 4 2 1 0 3 1 \n", "\n", " d_1527 d_1528 d_1529 d_1530 d_1531 d_1532 d_1533 d_1534 d_1535 \\\n", "0 2 0 0 1 2 1 1 1 0 \n", "1 0 0 0 0 0 0 0 0 0 \n", "2 1 0 0 0 0 0 0 1 0 \n", "3 1 7 1 1 0 8 2 0 8 \n", "4 4 0 0 1 0 2 0 1 3 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 0 0 0 1 2 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 3 1 5 1 0 3 2 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 1 0 0 1 2 2 4 3 \n", "\n", " d_1536 d_1537 d_1538 d_1539 d_1540 d_1541 d_1542 d_1543 d_1544 \\\n", "0 1 0 1 0 0 1 0 1 0 \n", "1 0 0 0 0 1 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 2 3 1 1 3 8 4 1 0 \n", "4 2 1 1 2 2 3 3 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 0 0 0 2 0 0 0 \n", "30486 0 0 12 0 0 0 0 0 0 \n", "30487 1 1 0 1 3 0 1 1 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 3 0 4 0 3 5 2 0 \n", "\n", " d_1545 d_1546 d_1547 d_1548 d_1549 d_1550 d_1551 d_1552 d_1553 \\\n", "0 2 1 0 2 0 1 0 0 0 \n", "1 0 0 0 0 0 1 0 0 0 \n", "2 0 0 1 0 0 0 0 0 1 \n", "3 1 3 5 2 3 0 2 1 2 \n", "4 1 1 0 2 0 2 1 2 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 2 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 0 1 0 1 1 2 1 1 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 4 4 1 0 2 4 0 0 \n", "\n", " d_1554 d_1555 d_1556 d_1557 d_1558 d_1559 d_1560 d_1561 d_1562 \\\n", "0 1 1 2 0 0 0 0 0 0 \n", "1 0 0 1 0 1 0 0 1 0 \n", "2 0 1 0 0 1 0 0 0 1 \n", "3 1 6 7 0 2 1 0 3 8 \n", "4 2 3 4 2 0 0 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 1 1 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 2 1 3 0 2 0 0 2 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 5 10 5 5 0 2 3 0 \n", "\n", " d_1563 d_1564 d_1565 d_1566 d_1567 d_1568 d_1569 d_1570 d_1571 \\\n", "0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 1 0 0 0 1 0 \n", "2 0 0 0 0 1 0 1 0 1 \n", "3 4 1 1 1 2 2 6 5 2 \n", "4 0 0 0 0 0 0 0 2 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 1 0 0 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 2 0 0 2 3 0 1 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 1 4 2 0 0 2 0 4 7 \n", "\n", " d_1572 d_1573 d_1574 d_1575 d_1576 d_1577 d_1578 d_1579 d_1580 \\\n", "0 0 0 0 0 0 0 0 0 0 \n", "1 3 0 1 0 0 0 1 0 0 \n", "2 0 0 1 0 1 0 1 0 1 \n", "3 1 0 1 3 4 2 2 2 2 \n", "4 0 0 0 0 0 3 1 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 2 2 2 0 0 0 1 1 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 2 1 1 9 0 0 0 0 0 \n", "\n", " d_1581 d_1582 d_1583 d_1584 d_1585 d_1586 d_1587 d_1588 d_1589 \\\n", "0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 1 0 1 0 0 \n", "2 0 0 2 0 0 0 1 0 0 \n", "3 2 1 0 13 0 0 0 5 3 \n", "4 0 0 2 1 1 0 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 1 1 0 1 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 1 0 0 0 2 1 1 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 0 3 2 2 2 3 1 4 \n", "\n", " d_1590 d_1591 d_1592 d_1593 d_1594 d_1595 d_1596 d_1597 d_1598 \\\n", "0 0 1 1 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 1 1 \n", "2 1 0 0 0 1 0 0 2 0 \n", "3 2 5 1 0 2 1 0 0 12 \n", "4 0 5 1 2 1 0 1 1 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 2 0 0 1 0 2 0 0 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 1 0 1 1 4 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 2 2 0 2 0 3 5 3 1 \n", "\n", " d_1599 d_1600 d_1601 d_1602 d_1603 d_1604 d_1605 d_1606 d_1607 \\\n", "0 0 0 0 1 3 1 1 0 1 \n", "1 0 0 0 1 0 1 1 0 0 \n", "2 0 0 0 1 1 1 0 0 0 \n", "3 0 1 0 2 2 4 1 3 1 \n", "4 0 0 2 1 0 1 0 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 2 0 0 1 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 2 1 2 0 0 0 0 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 8 4 1 0 3 2 5 2 6 \n", "\n", " d_1608 d_1609 d_1610 d_1611 d_1612 d_1613 d_1614 d_1615 d_1616 \\\n", "0 1 0 0 0 1 1 2 0 0 \n", "1 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 1 0 1 0 0 2 \n", "3 1 4 1 4 2 2 0 2 3 \n", "4 1 0 0 3 3 1 1 0 3 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 2 2 0 0 0 2 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 1 0 1 1 1 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 4 1 0 0 0 0 0 0 0 \n", "\n", " d_1617 d_1618 d_1619 d_1620 d_1621 d_1622 d_1623 d_1624 d_1625 \\\n", "0 0 1 0 2 1 0 1 1 0 \n", "1 1 1 1 0 1 0 0 0 0 \n", "2 1 0 0 0 0 1 0 2 0 \n", "3 1 0 2 9 2 2 0 0 10 \n", "4 2 2 3 3 2 1 0 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 1 2 2 2 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 0 0 1 3 0 1 1 \n", "30488 0 1 1 0 2 0 0 1 2 \n", "30489 0 0 0 0 0 0 0 0 0 \n", "\n", " d_1626 d_1627 d_1628 d_1629 d_1630 d_1631 d_1632 d_1633 d_1634 \\\n", "0 0 0 1 1 1 3 1 1 0 \n", "1 0 0 0 2 1 0 0 0 0 \n", "2 0 0 0 0 0 0 1 0 0 \n", "3 2 0 1 0 3 2 4 7 1 \n", "4 2 1 1 0 2 1 1 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 2 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 1 4 1 0 0 0 0 0 \n", "30488 4 0 0 0 0 0 2 2 0 \n", "30489 1 5 1 6 1 0 0 1 6 \n", "\n", " d_1635 d_1636 d_1637 d_1638 d_1639 d_1640 d_1641 d_1642 d_1643 \\\n", "0 0 2 0 1 0 0 0 2 0 \n", "1 0 1 0 0 1 1 0 2 0 \n", "2 0 0 1 1 0 1 0 0 0 \n", "3 0 1 3 3 5 0 0 0 0 \n", "4 2 1 0 0 0 0 0 1 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 4 0 2 1 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 \n", "30488 2 0 1 1 1 1 4 1 5 \n", "30489 4 0 1 3 1 3 4 3 4 \n", "\n", " d_1644 d_1645 d_1646 d_1647 d_1648 d_1649 d_1650 d_1651 d_1652 \\\n", "0 0 0 4 2 1 0 0 0 0 \n", "1 2 1 0 1 0 0 0 2 1 \n", "2 0 0 3 0 0 0 0 0 0 \n", "3 1 0 2 5 0 0 1 1 0 \n", "4 0 1 1 1 2 1 2 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 1 0 2 0 2 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 \n", "30488 0 3 1 4 2 0 5 1 0 \n", "30489 4 4 1 4 0 0 0 1 3 \n", "\n", " d_1653 d_1654 d_1655 d_1656 d_1657 d_1658 d_1659 d_1660 d_1661 \\\n", "0 2 1 0 1 1 0 0 0 1 \n", "1 1 1 0 0 0 1 0 1 0 \n", "2 1 0 0 1 1 1 1 0 1 \n", "3 7 4 1 1 4 0 0 0 5 \n", "4 2 2 1 0 3 3 3 4 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 2 0 0 0 1 0 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 2 0 0 2 2 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 2 4 1 4 0 0 0 0 4 \n", "\n", " d_1662 d_1663 d_1664 d_1665 d_1666 d_1667 d_1668 d_1669 d_1670 \\\n", "0 2 1 1 0 0 2 0 2 2 \n", "1 0 0 1 0 0 1 1 0 0 \n", "2 1 0 0 0 0 0 0 0 0 \n", "3 1 1 0 0 0 11 2 2 1 \n", "4 2 0 2 0 5 2 0 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 1 0 2 1 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 3 0 0 0 0 1 1 1 0 \n", "30488 0 0 0 0 0 0 0 0 0 \n", "30489 0 2 3 0 0 3 7 1 0 \n", "\n", " d_1671 d_1672 d_1673 d_1674 d_1675 d_1676 d_1677 d_1678 d_1679 \\\n", "0 0 0 0 1 1 0 2 0 1 \n", "1 0 3 4 0 0 0 0 1 0 \n", "2 0 1 0 1 0 0 0 0 0 \n", "3 1 2 1 1 2 1 1 0 3 \n", "4 0 3 1 0 1 0 2 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 2 0 0 1 2 0 2 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 0 0 0 0 0 0 0 0 \n", "30488 0 4 1 3 5 3 0 2 3 \n", "30489 5 0 0 0 2 1 3 1 3 \n", "\n", " d_1680 d_1681 d_1682 d_1683 d_1684 d_1685 d_1686 d_1687 d_1688 \\\n", "0 1 2 0 1 0 0 0 2 1 \n", "1 0 0 0 0 0 0 0 1 0 \n", "2 0 2 1 0 3 0 1 2 0 \n", "3 0 2 14 0 0 0 3 3 1 \n", "4 0 1 3 1 1 0 4 0 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 1 1 0 0 1 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 2 2 \n", "30488 2 3 5 0 2 0 3 3 0 \n", "30489 3 1 2 1 2 4 0 0 0 \n", "\n", " d_1689 d_1690 d_1691 d_1692 d_1693 d_1694 d_1695 d_1696 d_1697 \\\n", "0 0 1 1 2 0 0 0 0 0 \n", "1 0 0 1 1 0 0 1 1 0 \n", "2 3 1 0 0 1 0 1 0 0 \n", "3 1 1 1 0 3 3 1 7 3 \n", "4 1 2 4 0 1 0 0 3 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 2 1 0 1 0 1 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 0 0 0 0 0 0 0 0 \n", "30488 1 3 4 0 0 0 0 3 3 \n", "30489 0 0 3 1 0 1 4 4 12 \n", "\n", " d_1698 d_1699 d_1700 d_1701 d_1702 d_1703 d_1704 d_1705 d_1706 \\\n", "0 0 0 1 0 1 0 1 0 3 \n", "1 0 0 0 0 0 0 2 1 0 \n", "2 0 0 2 0 1 0 1 0 1 \n", "3 1 0 0 1 0 1 1 0 0 \n", "4 2 2 0 1 1 0 4 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 1 1 0 1 0 2 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 \n", "30488 0 4 1 0 3 3 0 5 0 \n", "30489 3 4 0 0 2 1 0 1 0 \n", "\n", " d_1707 d_1708 d_1709 d_1710 d_1711 d_1712 d_1713 d_1714 d_1715 \\\n", "0 1 1 0 1 1 2 0 0 0 \n", "1 0 1 0 0 0 0 0 0 0 \n", "2 1 0 1 0 1 0 0 0 1 \n", "3 2 1 4 4 3 0 2 0 0 \n", "4 4 0 2 2 2 1 2 1 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 1 0 0 1 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 \n", "30488 1 0 3 2 2 1 4 0 0 \n", "30489 1 4 2 0 1 1 2 2 7 \n", "\n", " d_1716 d_1717 d_1718 d_1719 d_1720 d_1721 d_1722 d_1723 d_1724 \\\n", "0 0 1 1 0 0 0 0 3 0 \n", "1 0 3 0 0 0 0 0 0 0 \n", "2 2 0 0 0 1 0 1 1 1 \n", "3 1 3 3 0 2 1 2 4 7 \n", "4 1 4 0 2 1 2 0 0 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 4 1 5 0 0 0 0 0 2 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 3 0 \n", "30488 1 0 0 0 0 4 1 0 2 \n", "30489 3 2 10 0 1 4 0 2 6 \n", "\n", " d_1725 d_1726 d_1727 d_1728 d_1729 d_1730 d_1731 d_1732 d_1733 \\\n", "0 1 0 0 0 0 1 1 1 0 \n", "1 0 0 0 1 0 2 1 0 0 \n", "2 1 0 0 0 0 0 0 2 0 \n", "3 0 2 1 0 5 5 2 2 4 \n", "4 0 1 1 2 2 3 1 0 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 0 0 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 0 \n", "30488 1 2 7 0 0 1 4 2 0 \n", "30489 0 0 6 2 4 0 0 0 1 \n", "\n", " d_1734 d_1735 d_1736 d_1737 d_1738 d_1739 d_1740 d_1741 d_1742 \\\n", "0 1 0 2 0 0 0 0 2 0 \n", "1 0 1 1 0 0 0 0 1 1 \n", "2 1 0 0 2 0 0 0 1 0 \n", "3 1 0 0 3 1 0 0 0 3 \n", "4 3 0 1 1 4 0 3 2 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 1 2 1 0 0 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 0 0 0 0 0 0 1 \n", "30488 2 1 3 2 2 1 0 2 0 \n", "30489 3 2 2 4 2 2 3 2 0 \n", "\n", " d_1743 d_1744 d_1745 d_1746 d_1747 d_1748 d_1749 d_1750 d_1751 \\\n", "0 0 0 0 1 1 2 0 0 0 \n", "1 0 1 0 1 1 0 0 0 0 \n", "2 0 1 0 0 2 0 0 0 0 \n", "3 1 3 3 0 0 4 1 1 1 \n", "4 2 1 2 2 1 2 0 1 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 3 1 1 1 0 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 0 0 0 0 0 0 0 0 \n", "30488 3 0 4 3 12 1 0 0 0 \n", "30489 1 0 0 4 2 4 1 1 3 \n", "\n", " d_1752 d_1753 d_1754 d_1755 d_1756 d_1757 d_1758 d_1759 d_1760 \\\n", "0 0 2 0 0 1 1 1 1 0 \n", "1 0 1 0 1 1 0 3 0 0 \n", "2 0 0 0 0 2 0 2 3 0 \n", "3 1 3 3 1 0 3 0 1 3 \n", "4 2 0 2 0 0 0 4 2 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 2 1 0 0 2 0 2 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 1 0 0 1 1 0 0 \n", "30488 4 1 1 2 1 3 2 4 3 \n", "30489 1 0 0 1 2 0 1 0 1 \n", "\n", " d_1761 d_1762 d_1763 d_1764 d_1765 d_1766 d_1767 d_1768 d_1769 \\\n", "0 0 0 0 0 1 2 2 0 1 \n", "1 0 0 0 0 0 1 0 0 0 \n", "2 1 3 1 2 2 3 0 1 1 \n", "3 3 3 2 2 2 4 3 0 5 \n", "4 2 0 0 0 0 0 2 1 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 1 0 1 2 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 1 1 0 1 0 1 0 1 \n", "30488 2 2 2 1 3 2 1 1 0 \n", "30489 0 2 0 1 1 6 1 2 0 \n", "\n", " d_1770 d_1771 d_1772 d_1773 d_1774 d_1775 d_1776 d_1777 d_1778 \\\n", "0 0 0 0 0 1 2 1 0 0 \n", "1 0 0 2 1 0 0 1 1 0 \n", "2 0 0 0 0 2 3 1 1 4 \n", "3 1 3 3 2 0 0 1 1 0 \n", "4 0 1 2 0 1 2 1 2 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 0 2 0 1 1 0 3 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 0 1 0 2 0 1 1 1 \n", "30488 0 0 1 5 1 1 0 4 1 \n", "30489 1 0 3 0 1 3 0 1 0 \n", "\n", " d_1779 d_1780 d_1781 d_1782 d_1783 d_1784 d_1785 d_1786 d_1787 \\\n", "0 0 0 0 1 0 3 0 1 2 \n", "1 2 0 1 0 2 1 1 5 0 \n", "2 3 2 1 2 2 0 1 5 2 \n", "3 2 2 2 3 2 1 2 0 5 \n", "4 2 3 3 0 3 1 5 3 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 1 1 3 3 1 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 1 1 4 0 1 3 1 0 \n", "30488 2 1 2 3 1 1 1 0 1 \n", "30489 5 1 2 2 2 0 2 3 1 \n", "\n", " d_1788 d_1789 d_1790 d_1791 d_1792 d_1793 d_1794 d_1795 d_1796 \\\n", "0 1 0 3 0 0 0 1 0 2 \n", "1 1 0 3 5 0 0 1 0 0 \n", "2 0 1 2 3 0 1 2 1 3 \n", "3 0 1 0 0 0 3 4 0 0 \n", "4 1 2 3 4 0 0 1 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 2 3 0 0 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 1 0 0 0 0 0 1 \n", "30488 3 5 1 0 0 1 2 0 0 \n", "30489 2 0 6 0 0 3 1 0 0 \n", "\n", " d_1797 d_1798 d_1799 d_1800 d_1801 d_1802 d_1803 d_1804 d_1805 \\\n", "0 2 1 0 0 1 2 0 1 0 \n", "1 0 0 0 0 0 0 0 0 0 \n", "2 0 1 1 1 1 0 0 0 0 \n", "3 1 5 3 2 2 0 1 1 0 \n", "4 1 0 0 1 0 0 0 0 2 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 2 1 1 0 0 4 0 6 1 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 2 1 0 2 0 0 2 2 0 \n", "30488 0 0 0 2 1 0 0 0 3 \n", "30489 0 0 0 0 0 1 1 0 2 \n", "\n", " d_1806 d_1807 d_1808 d_1809 d_1810 d_1811 d_1812 d_1813 d_1814 \\\n", "0 1 4 0 0 5 0 0 0 0 \n", "1 0 1 0 0 0 0 0 1 0 \n", "2 0 0 0 1 0 1 0 0 0 \n", "3 2 1 0 2 4 0 0 0 3 \n", "4 0 0 3 0 0 1 2 2 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 1 0 0 0 1 1 0 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 3 0 0 0 0 0 1 3 3 \n", "30488 3 3 2 8 1 1 0 0 0 \n", "30489 1 4 1 8 3 2 0 0 2 \n", "\n", " d_1815 d_1816 d_1817 d_1818 d_1819 d_1820 d_1821 d_1822 d_1823 \\\n", "0 0 0 2 1 2 1 0 0 0 \n", "1 0 1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 2 4 3 1 2 3 0 8 2 \n", "4 1 0 0 0 1 0 0 3 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 0 0 1 0 2 0 1 0 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 0 2 0 0 0 1 1 0 \n", "30488 4 4 2 2 1 1 2 0 1 \n", "30489 2 0 1 3 5 1 0 0 0 \n", "\n", " d_1824 d_1825 d_1826 d_1827 d_1828 d_1829 d_1830 d_1831 d_1832 \\\n", "0 1 1 1 0 0 1 1 1 1 \n", "1 0 0 1 0 0 0 1 0 0 \n", "2 0 0 0 0 1 0 0 0 0 \n", "3 1 2 2 5 2 6 1 0 3 \n", "4 0 1 1 0 3 1 0 4 1 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 0 3 1 1 0 0 0 2 3 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 0 0 1 0 0 2 0 0 2 \n", "30488 0 0 2 0 1 3 1 2 0 \n", "30489 0 0 6 3 2 1 0 2 1 \n", "\n", " d_1833 d_1834 d_1835 d_1836 d_1837 d_1838 d_1839 d_1840 d_1841 \\\n", "0 1 0 0 0 2 2 0 0 1 \n", "1 0 0 0 0 1 0 0 0 0 \n", "2 0 0 0 1 0 1 1 1 1 \n", "3 5 1 1 6 4 3 2 2 3 \n", "4 2 0 0 0 1 1 2 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "30485 1 0 1 3 0 3 0 0 2 \n", "30486 0 0 0 0 0 0 0 0 0 \n", "30487 1 0 0 1 2 0 1 2 1 \n", "30488 0 2 4 1 0 1 0 0 2 \n", "30489 1 2 1 2 0 1 10 0 3 \n", "\n", " d_1842 d_1843 d_1844 d_1845 d_1846 d_1847 d_1848 d_1849 d_1850 \\\n", "0 4 0 0 0 0 1 1 2 0 \n", "1 0 0 0 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store_iditem_idwm_yr_wksell_price
0CA_1HOBBIES_1_001113259.58
1CA_1HOBBIES_1_001113269.58
2CA_1HOBBIES_1_001113278.26
3CA_1HOBBIES_1_001113288.26
4CA_1HOBBIES_1_001113298.26
...............
6841116WI_3FOODS_3_827116171.00
6841117WI_3FOODS_3_827116181.00
6841118WI_3FOODS_3_827116191.00
6841119WI_3FOODS_3_827116201.00
6841120WI_3FOODS_3_827116211.00
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6841121 rows × 4 columns

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" ], "text/plain": [ " store_id item_id wm_yr_wk sell_price\n", "0 CA_1 HOBBIES_1_001 11325 9.58\n", "1 CA_1 HOBBIES_1_001 11326 9.58\n", "2 CA_1 HOBBIES_1_001 11327 8.26\n", "3 CA_1 HOBBIES_1_001 11328 8.26\n", "4 CA_1 HOBBIES_1_001 11329 8.26\n", "... ... ... ... ...\n", "6841116 WI_3 FOODS_3_827 11617 1.00\n", "6841117 WI_3 FOODS_3_827 11618 1.00\n", "6841118 WI_3 FOODS_3_827 11619 1.00\n", "6841119 WI_3 FOODS_3_827 11620 1.00\n", "6841120 WI_3 FOODS_3_827 11621 1.00\n", "\n", "[6841121 rows x 4 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6841121 entries, 0 to 6841120\n", "Data columns (total 4 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 store_id object \n", " 1 item_id object \n", " 2 wm_yr_wk int64 \n", " 3 sell_price float64\n", "dtypes: float64(1), int64(1), object(2)\n", "memory usage: 208.8+ MB\n" ] }, { "data": { "text/html": [ "
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wm_yr_wksell_price
count6.841121e+066.841121e+06
mean1.138294e+044.410952e+00
std1.486100e+023.408814e+00
min1.110100e+041.000000e-02
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" ], "text/plain": [ " wm_yr_wk sell_price\n", "count 6.841121e+06 6.841121e+06\n", "mean 1.138294e+04 4.410952e+00\n", "std 1.486100e+02 3.408814e+00\n", "min 1.110100e+04 1.000000e-02\n", "25% 1.124700e+04 2.180000e+00\n", "50% 1.141100e+04 3.470000e+00\n", "75% 1.151700e+04 5.840000e+00\n", "max 1.162100e+04 1.073200e+02" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices.info()\n", "prices.describe()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datewm_yr_wkweekdaywdaymonthyeardevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WI
02011-01-2911101Saturday112011d_1NaNNaNNaNNaN000
12011-01-3011101Sunday212011d_2NaNNaNNaNNaN000
22011-01-3111101Monday312011d_3NaNNaNNaNNaN000
32011-02-0111101Tuesday422011d_4NaNNaNNaNNaN110
42011-02-0211101Wednesday522011d_5NaNNaNNaNNaN101
.............................................
19642016-06-1511620Wednesday562016d_1965NaNNaNNaNNaN011
19652016-06-1611620Thursday662016d_1966NaNNaNNaNNaN000
19662016-06-1711620Friday762016d_1967NaNNaNNaNNaN000
19672016-06-1811621Saturday162016d_1968NaNNaNNaNNaN000
19682016-06-1911621Sunday262016d_1969NBAFinalsEndSportingFather's dayCultural000
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1969 rows × 14 columns

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" ], "text/plain": [ " date wm_yr_wk weekday wday month year d \\\n", "0 2011-01-29 11101 Saturday 1 1 2011 d_1 \n", "1 2011-01-30 11101 Sunday 2 1 2011 d_2 \n", "2 2011-01-31 11101 Monday 3 1 2011 d_3 \n", "3 2011-02-01 11101 Tuesday 4 2 2011 d_4 \n", "4 2011-02-02 11101 Wednesday 5 2 2011 d_5 \n", "... ... ... ... ... ... ... ... \n", "1964 2016-06-15 11620 Wednesday 5 6 2016 d_1965 \n", "1965 2016-06-16 11620 Thursday 6 6 2016 d_1966 \n", "1966 2016-06-17 11620 Friday 7 6 2016 d_1967 \n", "1967 2016-06-18 11621 Saturday 1 6 2016 d_1968 \n", "1968 2016-06-19 11621 Sunday 2 6 2016 d_1969 \n", "\n", " event_name_1 event_type_1 event_name_2 event_type_2 snap_CA snap_TX \\\n", "0 NaN NaN NaN NaN 0 0 \n", "1 NaN NaN NaN NaN 0 0 \n", "2 NaN NaN NaN NaN 0 0 \n", "3 NaN NaN NaN NaN 1 1 \n", "4 NaN NaN NaN NaN 1 0 \n", "... ... ... ... ... ... ... \n", "1964 NaN NaN NaN NaN 0 1 \n", "1965 NaN NaN NaN NaN 0 0 \n", "1966 NaN NaN NaN NaN 0 0 \n", "1967 NaN NaN NaN NaN 0 0 \n", "1968 NBAFinalsEnd Sporting Father's day Cultural 0 0 \n", "\n", " snap_WI \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 1 \n", "... ... \n", "1964 1 \n", "1965 0 \n", "1966 0 \n", "1967 0 \n", "1968 0 \n", "\n", "[1969 rows x 14 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calendar" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 1969 entries, 0 to 1968\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 date 1969 non-null object\n", " 1 wm_yr_wk 1969 non-null int64 \n", " 2 weekday 1969 non-null object\n", " 3 wday 1969 non-null int64 \n", " 4 month 1969 non-null int64 \n", " 5 year 1969 non-null int64 \n", " 6 d 1969 non-null object\n", " 7 event_name_1 162 non-null object\n", " 8 event_type_1 162 non-null object\n", " 9 event_name_2 5 non-null object\n", " 10 event_type_2 5 non-null object\n", " 11 snap_CA 1969 non-null int64 \n", " 12 snap_TX 1969 non-null int64 \n", " 13 snap_WI 1969 non-null int64 \n", "dtypes: int64(7), object(7)\n", "memory usage: 215.5+ KB\n" ] }, { "data": { "text/html": [ "
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wm_yr_wkwdaymonthyearsnap_CAsnap_TXsnap_WI
count1969.0000001969.0000001969.0000001969.0000001969.0000001969.0000001969.000000
mean11347.0863383.9974616.3255462013.2884710.3301170.3301170.330117
std155.2770432.0011413.4168641.5801980.4703740.4703740.470374
min11101.0000001.0000001.0000002011.0000000.0000000.0000000.000000
25%11219.0000002.0000003.0000002012.0000000.0000000.0000000.000000
50%11337.0000004.0000006.0000002013.0000000.0000000.0000000.000000
75%11502.0000006.0000009.0000002015.0000001.0000001.0000001.000000
max11621.0000007.00000012.0000002016.0000001.0000001.0000001.000000
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" ], "text/plain": [ " wm_yr_wk wday month year snap_CA \\\n", "count 1969.000000 1969.000000 1969.000000 1969.000000 1969.000000 \n", "mean 11347.086338 3.997461 6.325546 2013.288471 0.330117 \n", "std 155.277043 2.001141 3.416864 1.580198 0.470374 \n", "min 11101.000000 1.000000 1.000000 2011.000000 0.000000 \n", "25% 11219.000000 2.000000 3.000000 2012.000000 0.000000 \n", "50% 11337.000000 4.000000 6.000000 2013.000000 0.000000 \n", "75% 11502.000000 6.000000 9.000000 2015.000000 1.000000 \n", "max 11621.000000 7.000000 12.000000 2016.000000 1.000000 \n", "\n", " snap_TX snap_WI \n", "count 1969.000000 1969.000000 \n", "mean 0.330117 0.330117 \n", "std 0.470374 0.470374 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 0.000000 \n", "75% 1.000000 1.000000 \n", "max 1.000000 1.000000 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calendar.info()\n", "calendar.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Sales time series \n", "\n", "At the beginning of the sales time series of each item, there could be some zeros, meaning the product was not available. So it's necessary to extract the 1st week when the item went in stock, and discard the 0 sales before that time point." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sales.iloc[:3][sales.columns[6:]].T.plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "del sales, prices, calendar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess Sales Data " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sales = pd.read_csv(SALES_CSV)\n", "prices = pd.read_csv(PRICES_CSV)\n", "calendar = pd.read_csv(CALENDAR_CSV)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flatten Sales data " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1HOBBIES_1_002_CA_1_evaluationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_10
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4HOBBIES_1_005_CA_1_evaluationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CAd_10
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59181086FOODS_3_824_WI_3_evaluationFOODS_3_824FOODS_3FOODSWI_3WId_19410
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59181089FOODS_3_827_WI_3_evaluationFOODS_3_827FOODS_3FOODSWI_3WId_19411
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59181090 rows × 8 columns

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" ], "text/plain": [ " id item_id dept_id cat_id \\\n", "0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES \n", "1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES \n", "2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES \n", "3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES \n", "4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES \n", "... ... ... ... ... \n", "59181085 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS \n", "59181086 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS \n", "59181087 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS \n", "59181088 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS \n", "59181089 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS \n", "\n", " store_id state_id d sales \n", "0 CA_1 CA d_1 0 \n", "1 CA_1 CA d_1 0 \n", "2 CA_1 CA d_1 0 \n", "3 CA_1 CA d_1 0 \n", "4 CA_1 CA d_1 0 \n", "... ... ... ... ... \n", "59181085 WI_3 WI d_1941 1 \n", "59181086 WI_3 WI d_1941 0 \n", "59181087 WI_3 WI d_1941 2 \n", "59181088 WI_3 WI d_1941 0 \n", "59181089 WI_3 WI d_1941 1 \n", "\n", "[59181090 rows x 8 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "id_cols = ['id','item_id','dept_id','cat_id','store_id','state_id']\n", "sales = pd.melt(sales, id_vars=id_cols, var_name='d', value_name='sales')\n", "sales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add placeholding rows for test prediction " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iditem_iddept_idcat_idstore_idstate_iddsales
0HOBBIES_1_001_CA_1_evaluationHOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_10.0
1HOBBIES_1_002_CA_1_evaluationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_10.0
2HOBBIES_1_003_CA_1_evaluationHOBBIES_1_003HOBBIES_1HOBBIESCA_1CAd_10.0
3HOBBIES_1_004_CA_1_evaluationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CAd_10.0
4HOBBIES_1_005_CA_1_evaluationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CAd_10.0
...........................
60034805FOODS_3_823_WI_3_evaluationFOODS_3_823FOODS_3FOODSWI_3WId_1969NaN
60034806FOODS_3_824_WI_3_evaluationFOODS_3_824FOODS_3FOODSWI_3WId_1969NaN
60034807FOODS_3_825_WI_3_evaluationFOODS_3_825FOODS_3FOODSWI_3WId_1969NaN
60034808FOODS_3_826_WI_3_evaluationFOODS_3_826FOODS_3FOODSWI_3WId_1969NaN
60034809FOODS_3_827_WI_3_evaluationFOODS_3_827FOODS_3FOODSWI_3WId_1969NaN
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60034810 rows × 8 columns

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" ], "text/plain": [ " id item_id dept_id cat_id \\\n", "0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES \n", "1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES \n", "2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES \n", "3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES \n", "4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES \n", "... ... ... ... ... \n", "60034805 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS \n", "60034806 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS \n", "60034807 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS \n", "60034808 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS \n", "60034809 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS \n", "\n", " store_id state_id d sales \n", "0 CA_1 CA d_1 0.0 \n", "1 CA_1 CA d_1 0.0 \n", "2 CA_1 CA d_1 0.0 \n", "3 CA_1 CA d_1 0.0 \n", "4 CA_1 CA d_1 0.0 \n", "... ... ... ... ... \n", "60034805 WI_3 WI d_1969 NaN \n", "60034806 WI_3 WI d_1969 NaN \n", "60034807 WI_3 WI d_1969 NaN \n", "60034808 WI_3 WI d_1969 NaN \n", "60034809 WI_3 WI d_1969 NaN \n", "\n", "[60034810 rows x 8 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def add_test_rows(sales: pd.DataFrame, id_cols) -> pd.DataFrame:\n", " index_df = sales[id_cols].drop_duplicates()\n", " test_df = pd.DataFrame()\n", " for d in range(1, 29):\n", " df = index_df.copy()\n", " df['d'] = f'd_{END_TRAIN + d}'\n", " df[TARGET_COL] = np.nan\n", " test_df = pd.concat([test_df, df], axis=0)\n", "\n", " sales = pd.concat([sales, test_df]).reset_index(drop=True)\n", " return sales\n", "\n", "sales = add_test_rows(sales, id_cols)\n", "sales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove the leading 0-Sales entries before products went in-stock " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Extract in-stock week from Prices data " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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store_iditem_idinstock_week
0CA_1FOODS_1_00111101
1CA_1FOODS_1_00211101
2CA_1FOODS_1_00311101
3CA_1FOODS_1_00411206
4CA_1FOODS_1_00511101
............
30485WI_3HOUSEHOLD_2_51211101
30486WI_3HOUSEHOLD_2_51311311
30487WI_3HOUSEHOLD_2_51411101
30488WI_3HOUSEHOLD_2_51511352
30489WI_3HOUSEHOLD_2_51611101
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30490 rows × 3 columns

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" ], "text/plain": [ " store_id item_id instock_week\n", "0 CA_1 FOODS_1_001 11101\n", "1 CA_1 FOODS_1_002 11101\n", "2 CA_1 FOODS_1_003 11101\n", "3 CA_1 FOODS_1_004 11206\n", "4 CA_1 FOODS_1_005 11101\n", "... ... ... ...\n", "30485 WI_3 HOUSEHOLD_2_512 11101\n", "30486 WI_3 HOUSEHOLD_2_513 11311\n", "30487 WI_3 HOUSEHOLD_2_514 11101\n", "30488 WI_3 HOUSEHOLD_2_515 11352\n", "30489 WI_3 HOUSEHOLD_2_516 11101\n", "\n", "[30490 rows x 3 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "instock = prices.groupby(['store_id', 'item_id'])['wm_yr_wk'].agg(['min']).reset_index()\n", "instock.columns = list(instock)[:2] + ['instock_week']\n", "instock" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iditem_iddept_idcat_idstore_idstate_iddsalesinstock_week
0HOBBIES_1_001_CA_1_evaluationHOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_10.011325
1HOBBIES_1_002_CA_1_evaluationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_10.011121
2HOBBIES_1_003_CA_1_evaluationHOBBIES_1_003HOBBIES_1HOBBIESCA_1CAd_10.011401
3HOBBIES_1_004_CA_1_evaluationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CAd_10.011106
4HOBBIES_1_005_CA_1_evaluationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CAd_10.011117
..............................
60034805FOODS_3_823_WI_3_evaluationFOODS_3_823FOODS_3FOODSWI_3WId_1969NaN11101
60034806FOODS_3_824_WI_3_evaluationFOODS_3_824FOODS_3FOODSWI_3WId_1969NaN11101
60034807FOODS_3_825_WI_3_evaluationFOODS_3_825FOODS_3FOODSWI_3WId_1969NaN11101
60034808FOODS_3_826_WI_3_evaluationFOODS_3_826FOODS_3FOODSWI_3WId_1969NaN11331
60034809FOODS_3_827_WI_3_evaluationFOODS_3_827FOODS_3FOODSWI_3WId_1969NaN11405
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60034810 rows × 9 columns

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" ], "text/plain": [ " id item_id dept_id cat_id \\\n", "0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES \n", "1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES \n", "2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES \n", "3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES \n", "4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES \n", "... ... ... ... ... \n", "60034805 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS \n", "60034806 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS \n", "60034807 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS \n", "60034808 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS \n", "60034809 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS \n", "\n", " store_id state_id d sales instock_week \n", "0 CA_1 CA d_1 0.0 11325 \n", "1 CA_1 CA d_1 0.0 11121 \n", "2 CA_1 CA d_1 0.0 11401 \n", "3 CA_1 CA d_1 0.0 11106 \n", "4 CA_1 CA d_1 0.0 11117 \n", "... ... ... ... ... ... \n", "60034805 WI_3 WI d_1969 NaN 11101 \n", "60034806 WI_3 WI d_1969 NaN 11101 \n", "60034807 WI_3 WI d_1969 NaN 11101 \n", "60034808 WI_3 WI d_1969 NaN 11331 \n", "60034809 WI_3 WI d_1969 NaN 11405 \n", "\n", "[60034810 rows x 9 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sales = pd.merge(sales, instock, on=['store_id', 'item_id'], how='left')\n", "sales" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "del instock, prices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Extract current week from Calendar data " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iditem_iddept_idcat_idstore_idstate_iddsalesinstock_weekwm_yr_wk
0HOBBIES_1_001_CA_1_evaluationHOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_10.01132511101
1HOBBIES_1_002_CA_1_evaluationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_10.01112111101
2HOBBIES_1_003_CA_1_evaluationHOBBIES_1_003HOBBIES_1HOBBIESCA_1CAd_10.01140111101
3HOBBIES_1_004_CA_1_evaluationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CAd_10.01110611101
4HOBBIES_1_005_CA_1_evaluationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CAd_10.01111711101
.................................
60034805FOODS_3_823_WI_3_evaluationFOODS_3_823FOODS_3FOODSWI_3WId_1969NaN1110111621
60034806FOODS_3_824_WI_3_evaluationFOODS_3_824FOODS_3FOODSWI_3WId_1969NaN1110111621
60034807FOODS_3_825_WI_3_evaluationFOODS_3_825FOODS_3FOODSWI_3WId_1969NaN1110111621
60034808FOODS_3_826_WI_3_evaluationFOODS_3_826FOODS_3FOODSWI_3WId_1969NaN1133111621
60034809FOODS_3_827_WI_3_evaluationFOODS_3_827FOODS_3FOODSWI_3WId_1969NaN1140511621
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60034810 rows × 10 columns

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" ], "text/plain": [ " id item_id dept_id cat_id \\\n", "0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES \n", "1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES \n", "2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES \n", "3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES \n", "4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES \n", "... ... ... ... ... \n", "60034805 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS \n", "60034806 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS \n", "60034807 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS \n", "60034808 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS \n", "60034809 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS \n", "\n", " store_id state_id d sales instock_week wm_yr_wk \n", "0 CA_1 CA d_1 0.0 11325 11101 \n", "1 CA_1 CA d_1 0.0 11121 11101 \n", "2 CA_1 CA d_1 0.0 11401 11101 \n", "3 CA_1 CA d_1 0.0 11106 11101 \n", "4 CA_1 CA d_1 0.0 11117 11101 \n", "... ... ... ... ... ... ... \n", "60034805 WI_3 WI d_1969 NaN 11101 11621 \n", "60034806 WI_3 WI d_1969 NaN 11101 11621 \n", "60034807 WI_3 WI d_1969 NaN 11101 11621 \n", "60034808 WI_3 WI d_1969 NaN 11331 11621 \n", "60034809 WI_3 WI d_1969 NaN 11405 11621 \n", "\n", "[60034810 rows x 10 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sales = pd.merge(sales, calendar[['wm_yr_wk', 'd']], on=['d'], how='left')\n", "sales" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "del calendar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Remove the leading 0-Sales entries " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "sales = sales.query(\"wm_yr_wk >= instock_week\").reset_index(drop=True)\n", "sales['instock_week'] = (sales['instock_week'] - sales['instock_week'].min()).astype(np.int16) # minimize values to reduce memory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert IDs to Pandas Category for later use in lightgbm \n", "\n", "Also reduce memory usage." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "for col in id_cols:\n", " sales[col] = sales[col].astype('category')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save flattened Sales data " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mem. usage decreased to 1002.86 Mb (35.3% reduction)\n" ] } ], "source": [ "\n", "reduce_mem_usage(sales)\n", "sales.to_pickle(SALES_DATA_FILE)\n", "del sales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess Weekly Price Data " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "prices = pd.read_csv(PRICES_CSV)\n", "calendar = pd.read_csv(CALENDAR_CSV)\n", "sales = pd.read_pickle(SALES_DATA_FILE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add summary statistical features to price series per store / item " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "for feat in ['max', 'min', 'mean', 'std']:\n", " prices[f'price_{feat}'] = prices.groupby(['store_id', 'item_id'])['sell_price'].transform(feat)\n", "prices['price_norm'] = prices['sell_price'] / prices['price_max']\n", "\n", "prices['price_unique'] = prices.groupby(['store_id', 'item_id'])['sell_price'].transform('nunique')\n", "prices['item_unique'] = prices.groupby(['store_id', 'sell_price'])['item_id'].transform('nunique')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add momentum features " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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store_iditem_idwm_yr_wksell_priceprice_maxprice_minprice_meanprice_stdprice_normprice_uniqueitem_uniqueprice_growthprice_deviation_monthprice_deviation_year
0CA_1HOBBIES_1_001113259.589.588.268.2857140.1521391.00000033NaN1.1270591.145166
1CA_1HOBBIES_1_001113269.589.588.268.2857140.1521391.000000331.0000001.1270591.145166
2CA_1HOBBIES_1_001113278.269.588.268.2857140.1521390.862213350.8622130.9717650.987377
3CA_1HOBBIES_1_001113288.269.588.268.2857140.1521390.862213351.0000001.0000000.987377
4CA_1HOBBIES_1_001113298.269.588.268.2857140.1521390.862213351.0000001.0000000.987377
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6841116WI_3FOODS_3_827116171.001.001.001.0000000.0000001.00000011421.0000001.0000001.000000
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6841121 rows × 14 columns

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" ], "text/plain": [ " store_id item_id wm_yr_wk sell_price price_max price_min \\\n", "0 CA_1 HOBBIES_1_001 11325 9.58 9.58 8.26 \n", "1 CA_1 HOBBIES_1_001 11326 9.58 9.58 8.26 \n", "2 CA_1 HOBBIES_1_001 11327 8.26 9.58 8.26 \n", "3 CA_1 HOBBIES_1_001 11328 8.26 9.58 8.26 \n", "4 CA_1 HOBBIES_1_001 11329 8.26 9.58 8.26 \n", "... ... ... ... ... ... ... \n", "6841116 WI_3 FOODS_3_827 11617 1.00 1.00 1.00 \n", "6841117 WI_3 FOODS_3_827 11618 1.00 1.00 1.00 \n", "6841118 WI_3 FOODS_3_827 11619 1.00 1.00 1.00 \n", "6841119 WI_3 FOODS_3_827 11620 1.00 1.00 1.00 \n", "6841120 WI_3 FOODS_3_827 11621 1.00 1.00 1.00 \n", "\n", " price_mean price_std price_norm price_unique item_unique \\\n", "0 8.285714 0.152139 1.000000 3 3 \n", "1 8.285714 0.152139 1.000000 3 3 \n", "2 8.285714 0.152139 0.862213 3 5 \n", "3 8.285714 0.152139 0.862213 3 5 \n", "4 8.285714 0.152139 0.862213 3 5 \n", "... ... ... ... ... ... \n", "6841116 1.000000 0.000000 1.000000 1 142 \n", "6841117 1.000000 0.000000 1.000000 1 142 \n", "6841118 1.000000 0.000000 1.000000 1 142 \n", "6841119 1.000000 0.000000 1.000000 1 142 \n", "6841120 1.000000 0.000000 1.000000 1 142 \n", "\n", " price_growth price_deviation_month price_deviation_year \n", "0 NaN 1.127059 1.145166 \n", "1 1.000000 1.127059 1.145166 \n", "2 0.862213 0.971765 0.987377 \n", "3 1.000000 1.000000 0.987377 \n", "4 1.000000 1.000000 0.987377 \n", "... ... ... ... \n", "6841116 1.000000 1.000000 1.000000 \n", "6841117 1.000000 1.000000 1.000000 \n", "6841118 1.000000 1.000000 1.000000 \n", "6841119 1.000000 1.000000 1.000000 \n", "6841120 1.000000 1.000000 1.000000 \n", "\n", "[6841121 rows x 14 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Add month/year columns from Calendar data for temporary use\n", "prices = pd.merge(prices, calendar[['wm_yr_wk', 'month', 'year']].drop_duplicates(subset=['wm_yr_wk']), on=['wm_yr_wk'], how='left')\n", "\n", "# Add momentum columns\n", "prices['price_growth'] = prices['sell_price'] / prices.groupby(['store_id', 'item_id'])['sell_price'].transform(lambda x: x.shift(1))\n", "prices['price_deviation_month'] = prices['sell_price'] / prices.groupby(['store_id', 'item_id', 'month'])['sell_price'].transform('mean')\n", "prices['price_deviation_year'] = prices['sell_price'] / prices.groupby(['store_id', 'item_id', 'year'])['sell_price'].transform('mean')\n", "\n", "# Drop month/year columns\n", "prices = prices.drop(['month', 'year'], axis=1)\n", "prices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Re-index weekly Prices to flattend Sales index (id and d) " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iddsell_priceprice_maxprice_minprice_meanprice_stdprice_normprice_uniqueitem_uniqueprice_growthprice_deviation_monthprice_deviation_year
0HOBBIES_1_008_CA_1_evaluationd_10.460.500.420.4763120.0197640.920000416NaN0.9688640.949452
1HOBBIES_1_009_CA_1_evaluationd_11.561.771.561.7647870.0327310.88135629NaN0.8859260.896552
2HOBBIES_1_010_CA_1_evaluationd_13.173.172.972.9813480.0463501.000000220NaN1.0642241.044376
3HOBBIES_1_012_CA_1_evaluationd_15.986.525.986.4695040.1159910.917178371NaN0.9220350.959180
4HOBBIES_1_015_CA_1_evaluationd_10.700.720.680.7065960.0113390.972222316NaN0.9901601.001752
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47735392FOODS_3_823_WI_3_evaluationd_19692.982.982.482.8015600.1716001.00000052061.01.0318561.022790
47735393FOODS_3_824_WI_3_evaluationd_19692.482.682.002.5079790.2531650.92537341351.00.9860051.111908
47735394FOODS_3_825_WI_3_evaluationd_19693.984.383.984.1159570.1885910.90867631501.00.9576521.000000
47735395FOODS_3_826_WI_3_evaluationd_19691.281.281.281.2800000.0000001.0000001441.01.0000001.000000
47735396FOODS_3_827_WI_3_evaluationd_19691.001.001.001.0000000.0000001.00000011421.01.0000001.000000
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47735397 rows × 13 columns

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" ], "text/plain": [ " id d sell_price price_max \\\n", "0 HOBBIES_1_008_CA_1_evaluation d_1 0.46 0.50 \n", "1 HOBBIES_1_009_CA_1_evaluation d_1 1.56 1.77 \n", "2 HOBBIES_1_010_CA_1_evaluation d_1 3.17 3.17 \n", "3 HOBBIES_1_012_CA_1_evaluation d_1 5.98 6.52 \n", "4 HOBBIES_1_015_CA_1_evaluation d_1 0.70 0.72 \n", "... ... ... ... ... \n", "47735392 FOODS_3_823_WI_3_evaluation d_1969 2.98 2.98 \n", "47735393 FOODS_3_824_WI_3_evaluation d_1969 2.48 2.68 \n", "47735394 FOODS_3_825_WI_3_evaluation d_1969 3.98 4.38 \n", "47735395 FOODS_3_826_WI_3_evaluation d_1969 1.28 1.28 \n", "47735396 FOODS_3_827_WI_3_evaluation d_1969 1.00 1.00 \n", "\n", " price_min price_mean price_std price_norm price_unique \\\n", "0 0.42 0.476312 0.019764 0.920000 4 \n", "1 1.56 1.764787 0.032731 0.881356 2 \n", "2 2.97 2.981348 0.046350 1.000000 2 \n", "3 5.98 6.469504 0.115991 0.917178 3 \n", "4 0.68 0.706596 0.011339 0.972222 3 \n", "... ... ... ... ... ... \n", "47735392 2.48 2.801560 0.171600 1.000000 5 \n", "47735393 2.00 2.507979 0.253165 0.925373 4 \n", "47735394 3.98 4.115957 0.188591 0.908676 3 \n", "47735395 1.28 1.280000 0.000000 1.000000 1 \n", "47735396 1.00 1.000000 0.000000 1.000000 1 \n", "\n", " item_unique price_growth price_deviation_month \\\n", "0 16 NaN 0.968864 \n", "1 9 NaN 0.885926 \n", "2 20 NaN 1.064224 \n", "3 71 NaN 0.922035 \n", "4 16 NaN 0.990160 \n", "... ... ... ... \n", "47735392 206 1.0 1.031856 \n", "47735393 135 1.0 0.986005 \n", "47735394 150 1.0 0.957652 \n", "47735395 44 1.0 1.000000 \n", "47735396 142 1.0 1.000000 \n", "\n", " price_deviation_year \n", "0 0.949452 \n", "1 0.896552 \n", "2 1.044376 \n", "3 0.959180 \n", "4 1.001752 \n", "... ... \n", "47735392 1.022790 \n", "47735393 1.111908 \n", "47735394 1.000000 \n", "47735395 1.000000 \n", "47735396 1.000000 \n", "\n", "[47735397 rows x 13 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "on_cols = ['store_id', 'item_id', 'wm_yr_wk']\n", "prices = pd.merge(sales[FLAT_INDEX_COLS + on_cols], prices, on=on_cols, how='left').drop(on_cols, axis=1)\n", "prices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save preprocessed Prices data " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mem. usage decreased to 1776.68 Mb (63.2% reduction)\n" ] } ], "source": [ "reduce_mem_usage(prices)\n", "prices.to_pickle(PRICES_DATA_FILE)\n", "del prices, sales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess Calendar data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Re-Index Calendar to the flattened Sales index (id and d) " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "prices = pd.read_csv(CALENDAR_CSV)\n", "sales = pd.read_pickle(SALES_DATA_FILE)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "cal_index_cols = [\"date\", \"d\"]\n", "cal_event_cols = [\n", " \"event_name_1\",\n", " \"event_type_1\",\n", " \"event_name_2\",\n", " \"event_type_2\",\n", " \"snap_CA\",\n", " \"snap_TX\",\n", " \"snap_WI\",\n", "]\n", "\n", "calendar = pd.merge(sales[FLAT_INDEX_COLS], calendar[cal_index_cols+cal_event_cols], on=['d'], how='left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert event column types to category to save memory " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "for col in cal_event_cols:\n", " calendar[col] = calendar[col].astype('category')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert date column and split to more features " ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0HOBBIES_1_008_CA_1_evaluationd_1NaNNaNNaNNaN00029410551
1HOBBIES_1_009_CA_1_evaluationd_1NaNNaNNaNNaN00029410551
2HOBBIES_1_010_CA_1_evaluationd_1NaNNaNNaNNaN00029410551
3HOBBIES_1_012_CA_1_evaluationd_1NaNNaNNaNNaN00029410551
4HOBBIES_1_015_CA_1_evaluationd_1NaNNaNNaNNaN00029410551
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47735392FOODS_3_823_WI_3_evaluationd_1969NBAFinalsEndSportingFather's dayCultural000192465361
47735393FOODS_3_824_WI_3_evaluationd_1969NBAFinalsEndSportingFather's dayCultural000192465361
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47735396FOODS_3_827_WI_3_evaluationd_1969NBAFinalsEndSportingFather's dayCultural000192465361
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47735397 rows × 16 columns

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" ], "text/plain": [ " id d event_name_1 event_type_1 \\\n", "0 HOBBIES_1_008_CA_1_evaluation d_1 NaN NaN \n", "1 HOBBIES_1_009_CA_1_evaluation d_1 NaN NaN \n", "2 HOBBIES_1_010_CA_1_evaluation d_1 NaN NaN \n", "3 HOBBIES_1_012_CA_1_evaluation d_1 NaN NaN \n", "4 HOBBIES_1_015_CA_1_evaluation d_1 NaN NaN \n", "... ... ... ... ... \n", "47735392 FOODS_3_823_WI_3_evaluation d_1969 NBAFinalsEnd Sporting \n", "47735393 FOODS_3_824_WI_3_evaluation d_1969 NBAFinalsEnd Sporting \n", "47735394 FOODS_3_825_WI_3_evaluation d_1969 NBAFinalsEnd Sporting \n", "47735395 FOODS_3_826_WI_3_evaluation d_1969 NBAFinalsEnd Sporting \n", "47735396 FOODS_3_827_WI_3_evaluation d_1969 NBAFinalsEnd Sporting \n", "\n", " event_name_2 event_type_2 snap_CA snap_TX snap_WI day week month \\\n", "0 NaN NaN 0 0 0 29 4 1 \n", "1 NaN NaN 0 0 0 29 4 1 \n", "2 NaN NaN 0 0 0 29 4 1 \n", "3 NaN NaN 0 0 0 29 4 1 \n", "4 NaN NaN 0 0 0 29 4 1 \n", "... ... ... ... ... ... ... ... ... \n", "47735392 Father's day Cultural 0 0 0 19 24 6 \n", "47735393 Father's day Cultural 0 0 0 19 24 6 \n", "47735394 Father's day Cultural 0 0 0 19 24 6 \n", "47735395 Father's day Cultural 0 0 0 19 24 6 \n", "47735396 Father's day Cultural 0 0 0 19 24 6 \n", "\n", " year week_of_month weekday weekend \n", "0 0 5 5 1 \n", "1 0 5 5 1 \n", "2 0 5 5 1 \n", "3 0 5 5 1 \n", "4 0 5 5 1 \n", "... ... ... ... ... \n", "47735392 5 3 6 1 \n", "47735393 5 3 6 1 \n", "47735394 5 3 6 1 \n", "47735395 5 3 6 1 \n", "47735396 5 3 6 1 \n", "\n", "[47735397 rows x 16 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calendar['date'] = pd.to_datetime(calendar['date'])\n", "calendar['day'] = calendar['date'].dt.day.astype(np.int8)\n", "calendar['week'] = calendar['date'].dt.isocalendar().week.astype(np.int8)\n", "calendar['month'] = calendar['date'].dt.month.astype(np.int8)\n", "calendar['year'] = (calendar['date'].dt.year - calendar['date'].dt.year.min()).astype(np.int8)\n", "calendar['week_of_month'] = calendar['day'].apply(lambda day: ceil(day/7)).astype(np.int8)\n", "calendar['weekday'] = calendar['date'].dt.dayofweek.astype(np.int8)\n", "calendar['weekend'] = (calendar['weekday'] >= 5).astype(np.int8)\n", "\n", "calendar.drop('date', axis=1, inplace=True)\n", "calendar\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save preprocessed Calendar data " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mem. usage decreased to 1458.01 Mb (0.0% reduction)\n" ] } ], "source": [ "reduce_mem_usage(calendar)\n", "calendar.to_pickle(CALENDAR_DATA_FILE)\n", "del calendar, sales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additional Cleaning " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iditem_iddept_idcat_idstore_idstate_iddsalesinstock_week
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4HOBBIES_1_015_CA_1_evaluationHOBBIES_1_015HOBBIES_1HOBBIESCA_1CA14.00
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47735396FOODS_3_827_WI_3_evaluationFOODS_3_827FOODS_3FOODSWI_3WI1969NaN304
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47735397 rows × 9 columns

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" ], "text/plain": [ " id item_id dept_id cat_id \\\n", "0 HOBBIES_1_008_CA_1_evaluation HOBBIES_1_008 HOBBIES_1 HOBBIES \n", "1 HOBBIES_1_009_CA_1_evaluation HOBBIES_1_009 HOBBIES_1 HOBBIES \n", "2 HOBBIES_1_010_CA_1_evaluation HOBBIES_1_010 HOBBIES_1 HOBBIES \n", "3 HOBBIES_1_012_CA_1_evaluation HOBBIES_1_012 HOBBIES_1 HOBBIES \n", "4 HOBBIES_1_015_CA_1_evaluation HOBBIES_1_015 HOBBIES_1 HOBBIES \n", "... ... ... ... ... \n", "47735392 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS \n", "47735393 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS \n", "47735394 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS \n", "47735395 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS \n", "47735396 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS \n", "\n", " store_id state_id d sales instock_week \n", "0 CA_1 CA 1 12.0 0 \n", "1 CA_1 CA 1 2.0 0 \n", "2 CA_1 CA 1 0.0 0 \n", "3 CA_1 CA 1 0.0 0 \n", "4 CA_1 CA 1 4.0 0 \n", "... ... ... ... ... ... \n", "47735392 WI_3 WI 1969 NaN 0 \n", "47735393 WI_3 WI 1969 NaN 0 \n", "47735394 WI_3 WI 1969 NaN 0 \n", "47735395 WI_3 WI 1969 NaN 230 \n", "47735396 WI_3 WI 1969 NaN 304 \n", "\n", "[47735397 rows x 9 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 47735397 entries, 0 to 47735396\n", "Data columns (total 9 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 id category\n", " 1 item_id category\n", " 2 dept_id category\n", " 3 cat_id category\n", " 4 store_id category\n", " 5 state_id category\n", " 6 d int16 \n", " 7 sales float16 \n", " 8 instock_week int16 \n", "dtypes: category(6), float16(1), int16(2)\n", "memory usage: 638.7 MB\n" ] } ], "source": [ "sales = pd.read_pickle(SALES_DATA_FILE)\n", "\n", "# Convert 'd' column to int\n", "sales['d'] = sales['d'].apply(lambda x: x[2:]).astype(np.int16)\n", "\n", "# Drop wm_yr_wk\n", "sales.drop('wm_yr_wk', axis=1, inplace=True)\n", "\n", "\n", "sales\n", "sales.info()\n", "\n", "sales.to_pickle(SALES_DATA_FILE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create lags and rolling mean / standard deviation " ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "sales = pd.read_pickle(SALES_DATA_FILE)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "sales_lags_rolling = sales[FLAT_INDEX_COLS + [TARGET_COL]].copy()\n", "del sales" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "# Lags from 28 days to 28 + 15 days\n", "n_lag_days = 15\n", "sales_lags_rolling = sales_lags_rolling.assign(**{\n", " f'{TARGET_COL}_lag_{lag_i}': sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(lag_i)).astype(np.float16)\n", " for lag_i in range(SHIFT_DAY, SHIFT_DAY + n_lag_days)\n", " })" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# Rollings on 28-day shift with different window lengths\n", "win_lens = [7, 14, 30, 60, 180]\n", "for window in win_lens:\n", " sales_lags_rolling['rolling_mean_'+str(window)] = sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(SHIFT_DAY).rolling(window).mean()).astype(np.float16)\n", " sales_lags_rolling['rolling_std_'+str(window)] = sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(SHIFT_DAY).rolling(window).std()).astype(np.float16)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# Rollings on sliding shifts with different window lengths\n", "shift_days = [1, 7, 14]\n", "win_lens = [7, 14, 30, 60]\n", "for shift_d in shift_days:\n", " for window in win_lens:\n", " col = f\"rolling_mean_shift{shift_d}_win{window}\"\n", " sales_lags_rolling[col] = sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(shift_d).rolling(window).mean()).astype(np.float16)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mem. usage decreased to 3643.16 Mb (0.0% reduction)\n" ] } ], "source": [ "reduce_mem_usage(sales_lags_rolling)\n", "sales_lags_rolling.to_pickle(SALES_LAG_ROLL_STATS_FILE)\n", "del sales_lags_rolling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create mean / std for aggregation level 2-12 (Guide Table 1) " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "sales = pd.read_pickle(SALES_DATA_FILE)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Empty result dataframe\n", "sales_agg_stats = sales[FLAT_INDEX_COLS].copy()\n", "\n", "# remove validation labels\n", "val_index = sales.query(f\"d > {1941 - 28}\").index\n", "sales.loc[val_index, TARGET_COL] = np.nan\n", "\n", "# Level columns according to Table 1 in the Guide document\n", "level_cols = [\n", " [\"state_id\"],\n", " [\"store_id\"],\n", " [\"cat_id\"],\n", " [\"dept_id\"],\n", " [\"state_id\", \"cat_id\"],\n", " [\"state_id\", \"dept_id\"],\n", " [\"store_id\", \"cat_id\"],\n", " [\"store_id\", \"dept_id\"],\n", " [\"item_id\"],\n", " [\"item_id\", \"state_id\"],\n", " [\"item_id\", \"store_id\"],\n", "]\n", "\n", "# Create mean / std of sales for each level\n", "for level_i, level_col in enumerate(level_cols, start=2):\n", " col = f\"level{level_i}_\" + \"_\".join(level_col)\n", " sales_agg_stats[col + '_mean'] = sales.groupby(level_col)[\"sales\"].transform(\"mean\").astype(np.float16)\n", " sales_agg_stats[col + '_std'] = sales.groupby(level_col)[\"sales\"].transform(\"std\").astype(np.float16)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mem. usage decreased to 2185.39 Mb (0.0% reduction)\n" ] } ], "source": [ "reduce_mem_usage(sales_agg_stats)\n", "sales_agg_stats.to_pickle(SALES_AGG_STATS_FILE)\n", "del sales_agg_stats, sales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combine Sales, Prices and Calendar " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "sales = pd.read_pickle(SALES_DATA_FILE)\n", "prices = pd.read_pickle(PRICES_DATA_FILE)\n", "calendar = pd.read_pickle(CALENDAR_DATA_FILE)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id', 'd',\n", " 'sales', 'instock_week'],\n", " dtype='object')" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Index(['id', 'd', 'sell_price', 'price_max', 'price_min', 'price_mean',\n", " 'price_std', 'price_norm', 'price_unique', 'item_unique',\n", " 'price_growth', 'price_deviation_month', 'price_deviation_year'],\n", " dtype='object')" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Index(['id', 'd', 'event_name_1', 'event_type_1', 'event_name_2',\n", " 'event_type_2', 'snap_CA', 'snap_TX', 'snap_WI', 'day', 'week', 'month',\n", " 'year', 'week_of_month', 'weekday', 'weekend'],\n", " dtype='object')" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sales.columns\n", "prices.columns\n", "calendar.columns" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(47735397, 9)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "(47735397, 13)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "(47735397, 16)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sales.shape\n", "prices.shape\n", "calendar.shape" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "data_processed = pd.concat([sales, prices[prices.columns[2:]], calendar[calendar.columns[2:]]], axis=1)\n", "data_processed.to_pickle(SALES_PRICES_CALENDAR_DATA_FILE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modeling \n", "Run Global definitions before run this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hyperparameters " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# data = pd.read_pickle(SALES_PRICES_CALENDAR_DATA_FILE)\n", "# IDs_ALL = {\n", "# \"store_id\": data['store_id'].unique().tolist(),\n", "# \"cat_id\": data['cat_id'].unique().tolist(),\n", "# \"dept_id\": data['dept_id'].unique().tolist(),\n", "# }\n", "IDs_ALL = {\n", " \"store_id\": [\n", " \"CA_1\",\n", " \"CA_2\",\n", " \"CA_3\",\n", " \"CA_4\",\n", " \"TX_1\",\n", " \"TX_2\",\n", " \"TX_3\",\n", " \"WI_1\",\n", " \"WI_2\",\n", " \"WI_3\",\n", " ],\n", " \"cat_id\": [\"HOBBIES\", \"HOUSEHOLD\", \"FOODS\"],\n", " \"dept_id\": [\n", " \"HOBBIES_1\",\n", " \"HOBBIES_2\",\n", " \"HOUSEHOLD_1\",\n", " \"HOUSEHOLD_2\",\n", " \"FOODS_1\",\n", " \"FOODS_2\",\n", " \"FOODS_3\",\n", " ],\n", "}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# sales_lag_rolling_stats_cols = pd.read_pickle(SALES_LAG_ROLL_STATS_FILE).columns\n", "# sales_agg_stats_cols = pd.read_pickle(SALES_AGG_STATS_FILE).columns\n", "sales_lag_rolling_stats_cols = [\n", " \"id\",\n", " \"d\",\n", " \"sales\",\n", " \"sales_lag_28\",\n", " \"sales_lag_29\",\n", " \"sales_lag_30\",\n", " \"sales_lag_31\",\n", " \"sales_lag_32\",\n", " \"sales_lag_33\",\n", " \"sales_lag_34\",\n", " \"sales_lag_35\",\n", " \"sales_lag_36\",\n", " \"sales_lag_37\",\n", " \"sales_lag_38\",\n", " \"sales_lag_39\",\n", " \"sales_lag_40\",\n", " \"sales_lag_41\",\n", " \"sales_lag_42\",\n", " \"rolling_mean_7\",\n", " \"rolling_std_7\",\n", " \"rolling_mean_14\",\n", " \"rolling_std_14\",\n", " \"rolling_mean_30\",\n", " \"rolling_std_30\",\n", " \"rolling_mean_60\",\n", " \"rolling_std_60\",\n", " \"rolling_mean_180\",\n", " \"rolling_std_180\",\n", " \"rolling_mean_shift1_win7\",\n", " \"rolling_mean_shift1_win14\",\n", " \"rolling_mean_shift1_win30\",\n", " \"rolling_mean_shift1_win60\",\n", " \"rolling_mean_shift7_win7\",\n", " \"rolling_mean_shift7_win14\",\n", " \"rolling_mean_shift7_win30\",\n", " \"rolling_mean_shift7_win60\",\n", " \"rolling_mean_shift14_win7\",\n", " \"rolling_mean_shift14_win14\",\n", " \"rolling_mean_shift14_win30\",\n", " \"rolling_mean_shift14_win60\",\n", "]\n", "sales_agg_stats_cols = [\n", " \"id\",\n", " \"d\",\n", " \"level2_state_id_mean\",\n", " \"level2_state_id_std\",\n", " \"level3_store_id_mean\",\n", " \"level3_store_id_std\",\n", " \"level4_cat_id_mean\",\n", " \"level4_cat_id_std\",\n", " \"level5_dept_id_mean\",\n", " \"level5_dept_id_std\",\n", " \"level6_state_id_cat_id_mean\",\n", " \"level6_state_id_cat_id_std\",\n", " \"level7_state_id_dept_id_mean\",\n", " \"level7_state_id_dept_id_std\",\n", " \"level8_store_id_cat_id_mean\",\n", " \"level8_store_id_cat_id_std\",\n", " \"level9_store_id_dept_id_mean\",\n", " \"level9_store_id_dept_id_std\",\n", " \"level10_item_id_mean\",\n", " \"level10_item_id_std\",\n", " \"level11_item_id_state_id_mean\",\n", " \"level11_item_id_state_id_std\",\n", " \"level12_item_id_store_id_mean\",\n", " \"level12_item_id_store_id_std\",\n", "]\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "loop_cols_L3_store = ['store_id']\n", "loop_cols_L8_store_cat = ['store_id', 'cat_id']\n", "loop_cols_L9_store_dept = ['store_id', 'dept_id']\n", "\n", "drop_cols_L3_store = ['state_id', 'store_id']\n", "drop_cols_L8_store_cat = ['state_id', 'store_id', 'cat_id']\n", "drop_cols_L9_store_dept = ['state_id', 'store_id', 'cat_id', 'dept_id']\n", "\n", "rec_lag_rolling_cols = sales_lag_rolling_stats_cols[3:]\n", "nonrec_lag_rolling_cols = [f'sales_lag_{i}' for i in range(28, 43)] + [f'rolling_{stat}_{i}' for i in (7, 14, 30, 60, 180) for stat in ('mean', 'std')]\n", "\n", "rec_agg_stats_cols_L3_store = [col for col in sales_agg_stats_cols if col.split('_')[0] in ('level4', 'level5', 'level10')]\n", "rec_agg_stats_cols_L8_store_cat = [col for col in sales_agg_stats_cols if col.split('_')[0] in ('level9', 'level12')]\n", "rec_agg_stats_cols_L9_store_dept = [col for col in sales_agg_stats_cols if col.split('_')[0] in ('level12',)]\n", "\n", "nonrec_agg_stats_cols_L3_store = [col for col in sales_agg_stats_cols if col.split('_')[0] in ('level9', 'level11')]\n", "nonrec_agg_stats_cols_L8_store_cat = [col for col in sales_agg_stats_cols if col.split('_')[0] in ('level9', 'level12')]\n", "nonrec_agg_stats_cols_L9_store_dept = [col for col in sales_agg_stats_cols if col.split('_')[0] in ('level12',)]\n", "\n", "rec_start_d_L3 = 0\n", "rec_start_d_L8 = 700\n", "rec_start_d_L9 = 700\n", "rec_num_leaves_L3 = 2*11 - 1\n", "rec_num_leaves_L8 = 2**8 - 1\n", "rec_num_leaves_L9 = 2**8 - 1\n", "rec_min_data_in_leaf_L3 = 2**12 - 1\n", "rec_min_data_in_leaf_L8 = 2**8 - 1\n", "rec_min_data_in_leaf_L9 = 2**8 - 1\n", "\n", "nonrec_start_d = 710\n", "nonrec_num_leaves = 2**8 - 1\n", "nonrec_min_data_in_leaf = 2**8 - 1" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "feature_hyperparas = {\n", " \"rec_L3_store\": {\n", " \"loop_cols\": loop_cols_L3_store,\n", " \"drop_cols\": drop_cols_L3_store,\n", " \"lag_rolling_cols\": rec_lag_rolling_cols,\n", " \"agg_stats_cols\": rec_agg_stats_cols_L3_store,\n", " \"start_d\": rec_start_d_L3,\n", " \"num_leaves\": rec_num_leaves_L3,\n", " \"min_data_in_leaf\": rec_min_data_in_leaf_L3,\n", " },\n", "\n", " \"rec_L8_store_cat\": {\n", " \"loop_cols\": loop_cols_L8_store_cat,\n", " \"drop_cols\": drop_cols_L8_store_cat,\n", " \"lag_rolling_cols\": rec_lag_rolling_cols,\n", " \"agg_stats_cols\": rec_agg_stats_cols_L8_store_cat,\n", " \"start_d\": rec_start_d_L8,\n", " \"num_leaves\": rec_num_leaves_L8,\n", " \"min_data_in_leaf\": rec_min_data_in_leaf_L8,\n", " },\n", "\n", " \"rec_L9_store_dept\": {\n", " \"loop_cols\": loop_cols_L9_store_dept,\n", " \"drop_cols\": drop_cols_L9_store_dept,\n", " \"lag_rolling_cols\": rec_lag_rolling_cols,\n", " \"agg_stats_cols\": rec_agg_stats_cols_L9_store_dept,\n", " \"start_d\": rec_start_d_L9,\n", " \"num_leaves\": rec_num_leaves_L9,\n", " \"min_data_in_leaf\": rec_min_data_in_leaf_L9,\n", " },\n", "\n", " \"nonrec_L3_store\": {\n", " \"loop_cols\": loop_cols_L3_store,\n", " \"drop_cols\": drop_cols_L3_store,\n", " \"lag_rolling_cols\": nonrec_lag_rolling_cols,\n", " \"agg_stats_cols\": nonrec_agg_stats_cols_L3_store,\n", " \"start_d\": nonrec_start_d,\n", " \"num_leaves\": nonrec_num_leaves,\n", " \"min_data_in_leaf\": nonrec_min_data_in_leaf,\n", " },\n", "\n", " \"nonrec_L8_store_cat\": {\n", " \"loop_cols\": loop_cols_L8_store_cat,\n", " \"drop_cols\": drop_cols_L8_store_cat,\n", " \"lag_rolling_cols\": nonrec_lag_rolling_cols,\n", " \"agg_stats_cols\": nonrec_agg_stats_cols_L8_store_cat,\n", " \"start_d\": nonrec_start_d,\n", " \"num_leaves\": nonrec_num_leaves,\n", " \"min_data_in_leaf\": nonrec_min_data_in_leaf,\n", " },\n", "\n", " \"nonrec_L9_store_dept\": {\n", " \"loop_cols\": loop_cols_L9_store_dept,\n", " \"drop_cols\": drop_cols_L9_store_dept,\n", " \"lag_rolling_cols\": nonrec_lag_rolling_cols,\n", " \"agg_stats_cols\": nonrec_agg_stats_cols_L9_store_dept,\n", " \"start_d\": nonrec_start_d,\n", " \"num_leaves\": nonrec_num_leaves,\n", " \"min_data_in_leaf\": nonrec_min_data_in_leaf,\n", " },\n", "}\n", "\n", "lgb_hyperparas = {\n", " \"boosting\": \"gbdt\",\n", " \"objective\": \"tweedie\",\n", " \"tweedie_variance_power\": 1.1,\n", " \"metric\": \"rmse\",\n", " \"bagging_fraction\": 0.5,\n", " \"bagging_freq\": 1,\n", " \"learning_rate\": 0.015,\n", " \"num_leaves\": 2 ** 8 - 1,\n", " \"min_data_in_leaf\": 2 ** 8 - 1,\n", " \"feature_fraction\": 0.5,\n", " \"max_bin\": 100,\n", " \"num_iterations\": 3000,\n", " \"boost_from_average\": False,\n", " \"verbosity\": -1, # FATAL, suppress lgb warnings\n", " \"seed\": 1995,\n", " \"num_threads\": 10\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Models per Agg Level and IDs " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def get_data_for_modeling(loop_ids: tuple, hyperparas: dict) -> tuple:\n", " \"\"\"\n", " Read data and Filter / Merge features per aggregation level and IDs.\n", " \"\"\"\n", "\n", " global ID_D_TARGET_COLS\n", " global SALES_PRICES_CALENDAR_DATA_FILE, SALES_LAG_ROLL_STATS_FILE, SALES_AGG_STATS_FILE\n", "\n", " print(\"Load SALES_PRICES_CALENDAR_DATA_FILE ...\")\n", " data = pd.read_pickle(SALES_PRICES_CALENDAR_DATA_FILE)\n", " \n", " # Cut off from starting day\n", " print(\"Cut off from starting day ...\")\n", " data = data[data['d'] >= hyperparas['start_d']]\n", " \n", " # Filter data by agg level ids\n", " print(\"Create agg id filter mask ...\")\n", " id_mask = True\n", " for loop_col, loop_val in zip(hyperparas['loop_cols'], loop_ids):\n", " id_mask &= (data[loop_col] == loop_val)\n", " data = data[id_mask]\n", "\n", " # Drop aggregated id columns\n", " data = data.drop(hyperparas['drop_cols'], axis=1)\n", "\n", " # Lags / rollings features\n", " print(\"Load lag rolling stats ...\")\n", " df_lags_rolling = pd.read_pickle(SALES_LAG_ROLL_STATS_FILE)[hyperparas['lag_rolling_cols']]\n", " df_lags_rolling = df_lags_rolling[df_lags_rolling.index.isin(data.index)]\n", "\n", " # Agg mean/std features\n", " print(\"Load agg stats ...\")\n", " df_agg_mean_std = pd.read_pickle(SALES_AGG_STATS_FILE)[hyperparas['agg_stats_cols']]\n", " df_agg_mean_std = df_agg_mean_std[df_agg_mean_std.index.isin(data.index)]\n", "\n", " # Merge data\n", " print(\"Merge data with stats ...\")\n", " data = pd.concat([data, df_lags_rolling, df_agg_mean_std], axis=1).reset_index(drop=True)\n", " features = [col for col in data.columns if col not in ID_D_TARGET_COLS]\n", "\n", " return data, features\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def get_train_val_test_data(loop_ids, hyperparas):\n", " global END_TRAIN, SHIFT_DAY, TARGET_COL, FLAT_INDEX_COLS\n", " \n", " data, features = get_data_for_modeling(loop_ids, hyperparas)\n", "\n", " train_mask = data['d'] <= END_TRAIN\n", " valid_mask = (data['d'] > END_TRAIN - SHIFT_DAY) & train_mask\n", " test_mask = ~train_mask\n", "\n", " train = lgb.Dataset(data[train_mask][features], label=data[train_mask][TARGET_COL])\n", "\n", " X_valid = data[valid_mask][features]\n", " valid = lgb.Dataset(X_valid, label=data[valid_mask][TARGET_COL])\n", " id_valid = data[valid_mask][FLAT_INDEX_COLS]\n", " id_valid['id'] = id_valid['id'].str.replace('evaluation', 'validation')\n", "\n", " test = data[test_mask]\n", " X_test = test[features]\n", " id_test = test[FLAT_INDEX_COLS]\n", "\n", " return train, valid, X_valid, id_valid, X_test, id_test\n", "\n", "def train_one_model(train, valid, hyperparas: dict, lgb_hyperparas=lgb_hyperparas) -> None:\n", " \"\"\"\n", " At a particular aggregation level, train one model for a particular id group (e.g. store_id and cat_id).\n", " \"\"\"\n", " # Update parameters\n", " lgb_hyperparas = lgb_hyperparas.copy()\n", " lgb_hyperparas['num_leaves'] = hyperparas['num_leaves']\n", " lgb_hyperparas['min_data_in_leaf'] = hyperparas['min_data_in_leaf']\n", "\n", " # Train\n", " random.seed(42)\n", " np.random.seed(42)\n", " warnings.filterwarnings('ignore')\n", " model = lgb.train(lgb_hyperparas, train, valid_sets=[valid, train], callbacks=[lgb.log_evaluation(period=100)])\n", " warnings.filterwarnings('default')\n", "\n", " # Feature importace\n", " featimp = pd.DataFrame({'feature': model.feature_name(), 'importance': model.feature_importance()}).sort_values('importance', ascending=False)\n", "\n", " return model, featimp\n", "\n", "def predict_one_model(X_test: pd.DataFrame, id_test: pd.DataFrame, model):\n", " global TARGET_COL\n", " \n", " id_test[TARGET_COL] = model.predict(X_test)\n", " return id_test\n", "\n", "def train_predict_one_level(agg_level, feature_hyperparas=feature_hyperparas):\n", " global IDs_ALL\n", " hyperparas = feature_hyperparas[agg_level]\n", " loop_ids_all = list(product(*(IDs_ALL[col] for col in hyperparas['loop_cols'])))\n", " results = {}\n", "\n", " for loop_ids in loop_ids_all:\n", " print(f'\\n=== Train / Predict {agg_level} - {loop_ids} ===')\n", "\n", " print(\"Get data and split to Train / Validation / Test ...\")\n", " train, valid, X_valid, id_valid, X_test, id_test = get_train_val_test_data(loop_ids, hyperparas)\n", "\n", " print(\"Train model ...\")\n", " model, featimp = train_one_model(train, valid, hyperparas)\n", "\n", " print(\"Predict ...\")\n", " val_pred = predict_one_model(X_valid, id_valid, model)\n", " test_pred = predict_one_model(X_test, id_test, model)\n", "\n", " # Save results\n", " print(\"Dump model and results ...\")\n", " loop_ids_str = '_'.join(list(loop_ids))\n", " results[loop_ids_str] = {\n", " 'model': model,\n", " 'feature_importance': featimp,\n", " 'val_pred': val_pred,\n", " 'test_pred': test_pred,\n", " }\n", " joblib.dump(results, f'{agg_level}.pkl') # overwrite at each iteration\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_predict_one_level('rec_L3_store')\n", "train_predict_one_level('rec_L8_store_cat')\n", "train_predict_one_level('rec_L9_store_dept')\n", "train_predict_one_level('nonrec_L3_store')\n", "train_predict_one_level('nonrec_L8_store_cat')\n", "train_predict_one_level('nonrec_L9_store_dept')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combine Predictions to Submission " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process results/rec_L3_store.pkl\n", "Process results/rec_L8_store_cat.pkl\n", "Process results/rec_L9_store_dept.pkl\n", "Process results/nonrec_L3_store.pkl\n", "Process results/nonrec_L8_store_cat.pkl\n", "Process results/nonrec_L9_store_dept.pkl\n" ] } ], "source": [ "result_files = [RESULTS_DIR + f for f in ('rec_L3_store.pkl', 'rec_L8_store_cat.pkl', 'rec_L9_store_dept.pkl', 'nonrec_L3_store.pkl', 'nonrec_L8_store_cat.pkl', 'nonrec_L9_store_dept.pkl')]\n", "\n", "def combine_predictions(result_files=result_files):\n", " sample = pd.read_csv(SAMPLE_SUBMISSION_CSV).set_index('id')\n", "\n", " pred_list = []\n", " for result_f in result_files:\n", " print(f\"Process {result_f}\")\n", " result = joblib.load(result_f)\n", " val_pred = pd.concat((r['val_pred'] for r in result.values()), axis=0)\n", " test_pred = pd.concat((r['test_pred'] for r in result.values()), axis=0)\n", " val_pred = val_pred.pivot(index='id', columns='d', values=TARGET_COL)\n", " test_pred = test_pred.pivot(index='id', columns='d', values=TARGET_COL)\n", " val_pred.columns = [f'F{i}' for i in range(1, 29)]\n", " test_pred.columns = [f'F{i}' for i in range(1, 29)]\n", " pred = pd.concat([val_pred, test_pred], axis=0)\n", " pred = pred.reindex(sample.index)\n", " pred_list.append(pred)\n", "\n", " pred = sum(pred_list) / len(pred_list)\n", " pred.to_csv(SUBMISSION_FILE, index=True)\n", "\n", "combine_predictions()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "f6b0d3faf30ba3d9ead12cafe30eabbf1d71a53f9577af15dc3e1a12414ee7e5" }, "kernelspec": { "display_name": "Python 3.9.7 64-bit ('ml': conda)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }