From 8ccbb4c062617f7be31eb7840c507cf04c0a9482 Mon Sep 17 00:00:00 2001 From: Sergey Volnov Date: Sun, 26 Mar 2023 14:38:57 +0300 Subject: [PATCH] first commit --- inference_model.py | 266 + main.ipynb | 65596 ++++++++++++++++++++++++++++++++++++++++++ model.pickle | Bin 0 -> 12534785 bytes preprocesser.pickle | Bin 0 -> 723853 bytes requirements.txt | 7 + 5 files changed, 65869 insertions(+) create mode 100644 inference_model.py create mode 100644 main.ipynb create mode 100644 model.pickle create mode 100644 preprocesser.pickle create mode 100644 requirements.txt diff --git a/inference_model.py b/inference_model.py new file mode 100644 index 0000000..803a49b --- /dev/null +++ b/inference_model.py @@ -0,0 +1,266 @@ +import pandas as pd +import numpy as np +from tqdm import tqdm +import os +import time +from datetime import datetime, timedelta +import catboost +import pickle +from sklearn.metrics import mean_absolute_error +from sklearn.base import BaseEstimator, TransformerMixin +import calendar +from datetime import date +import warnings +warnings.filterwarnings("ignore") + +# Creating sales lag features +def create_sales_lag_feats(df, gpby_cols, target_col, lags): + gpby = df.groupby(gpby_cols) + for i in lags: + df['_'.join([target_col, 'lag', str(i)])] = \ + gpby[target_col].shift(i).values + np.random.normal(scale=1, size=(len(df),)) * 0 + return df + +# Creating sales rolling mean features +def create_sales_rmean_feats(df, gpby_cols, target_col, windows, min_periods=2, + shift=1, win_type=None): + gpby = df.groupby(gpby_cols) + for w in windows: + df['_'.join([target_col, 'rmean', str(w)])] = \ + gpby[target_col].shift(shift).rolling(window=w, + min_periods=min_periods, + win_type=win_type).mean().values +\ + np.random.normal(scale=1, size=(len(df),)) * 0 + return df + +# Creating sales rolling median features +def create_sales_rmed_feats(df, gpby_cols, target_col, windows, min_periods=2, + shift=1, win_type=None): + gpby = df.groupby(gpby_cols) + for w in windows: + df['_'.join([target_col, 'rmed', str(w)])] = \ + gpby[target_col].shift(shift).rolling(window=w, + min_periods=min_periods, + win_type=win_type).median().values +\ + np.random.normal(scale=1, size=(len(df),)) * 0 + return df + +# Creating sales exponentially weighted mean features +def create_sales_ewm_feats(df, gpby_cols, target_col, alpha=[0.9], shift=[1]): + gpby = df.groupby(gpby_cols) + for a in alpha: + for s in shift: + df['_'.join([target_col, 'lag', str(s), 'ewm', str(a)])] = \ + gpby[target_col].shift(s).ewm(alpha=a).mean().values + return df + + + +def add_months(sourcedate, months): + month = sourcedate.month - 1 + months + year = sourcedate.year + month // 12 + month = month % 12 + 1 + day = min(sourcedate.day, calendar.monthrange(year,month)[1]) + return date(year, month, day) + + +class Preprocesser(BaseEstimator, TransformerMixin): + def __init__(self, products_info_path, store_info_path): + self.hash_to_numbers = {} + self.numbers_to_hash = {} + self.drop_cols = ['date', 'sales', 'year', 'product_name', 'month'] + self.products_info_path = products_info_path + self.store_info_path = store_info_path + self.cat_cols = ['store', 'item'] + \ + ['inn', 'product_short_name', 'tnved', 'tnved10', 'brand', + 'country', 'region_code', 'city_with_type', 'city_fias_id', 'postal_code'] + self.mean_values = {} + self.min_date = '2021-11-01' + + def fit(self, data): + data = data.copy() + data = data.rename(columns={'dt': 'date', 'gtin': 'item', 'id_sp_': 'store', 'cnt': 'sales'}) + data = data.drop(columns='inn', axis=1) + data = data.dropna(subset='store') + for col in ['item', 'store', 'prid']: + self.hash_to_numbers[col] = {a: b for a, b in zip(np.unique(data[col]), np.arange(data[col].nunique()))} + self.numbers_to_hash[col] = {b: a for a, b in zip(np.unique(data[col]), np.arange(data[col].nunique()))} + + return self + + def get_df(self, data): + data = data.sort_values(by='dt').reset_index(drop=True) + data = data.rename(columns={'dt': 'date', 'gtin': 'item', 'id_sp_': 'store', 'cnt': 'sales'}) + data = data.drop(columns='inn', axis=1) + data = data.dropna(subset='store') + + for col in ['item', 'store', 'prid']: + data.loc[:, col] = data[col].apply(lambda x: self.hash_to_numbers[col][x] if x in self.hash_to_numbers[col] else np.nan) + data.loc[:, col] = data[col].astype('int') + + data['date'] = data['date'].apply(lambda x: x[:-2] + '01') + df = data.groupby(['store', 'item', 'date']).agg(sales = ('sales', 'sum'), + price = ('price', 'mean')).reset_index() + return df + + def add_zero_points(self, df, pred_date=None): + if pred_date is None: + pred_date = df['date'].max() + + i = 0 + all_dates = [] + while True: + cur_month = add_months(datetime.fromisoformat(self.min_date), i).isoformat() + all_dates.append(cur_month) + if cur_month == pred_date: + break + i += 1 + + x = df.groupby(['store', 'item'])['date'].unique() + add = [] + for store_item, now_dates in tqdm(list(x.items())): + for d in all_dates: + if d not in now_dates: + add.append({'store': store_item[0], + 'item': store_item[1], + 'date': d, + 'sales': 0}) + + df = pd.concat([df, pd.DataFrame(add)]) + return df + + def fill_price(self, prices): + L = np.ones(len(prices)) * -1 + R = np.ones(len(prices)) * -1 + for i in range(len(prices)): + if prices[i] == prices[i]: #not is nan + L[i] = prices[i] + elif i > 0: + L[i] = L[i - 1] + + for i in range(len(prices) - 1, -1, -1): + if prices[i] == prices[i]: #not is nan + R[i] = prices[i] + elif i != len(prices) - 1: + R[i] = R[i + 1] + + for i in range(len(prices)): + if prices[i] != prices[i]: + if L[i] == -1: + prices[i] = R[i] + elif R[i] == -1: + prices[i] = L[i] + else: + prices[i] = (L[i] + R[i]) / 2 + return prices + + def fix_prices(self, df): + groups = df.sort_values(by='date').groupby(['store', 'item'])['price'] + + res = [] + for group in tqdm(groups): + res += self.fill_price(group[1].values).tolist() + df.sort_values(by=['store','item', 'date'], axis=0, inplace=True) + df['price'] = res + return df + + def get_product_info(self, path): + products_info = pd.read_csv(path) + + products_info['item'] = products_info['gtin'].apply(lambda x: self.hash_to_numbers['item'][x] if x in + self.hash_to_numbers['item'] else np.nan) + products_info = products_info.dropna(subset='item') + products_info = products_info.drop(columns='gtin') + products_info = products_info.drop_duplicates(subset='item', keep='last') + + products_info['volume'] = products_info['volume'].replace('НЕ КЛАССИФИЦИРОВАНО', np.nan) + products_info['volume'] = products_info['volume'].apply(lambda x: float(x.replace(',', '.').replace(' г', '')) + if x == x else np.nan) + return products_info + + def get_store_info(self, path): + store_info = pd.read_csv(path).drop(columns='inn') + store_info['store'] = store_info['id_sp_'].apply(lambda x: self.hash_to_numbers['store'][x] if x in + self.hash_to_numbers['store'] else np.nan) + store_info = store_info.dropna(subset='store') + store_info = store_info.drop(columns='id_sp_') + store_info = store_info.drop_duplicates(subset='store', keep='last') + return store_info + + def build_features(self, df): + df['date'] = pd.to_datetime(df['date']) + df['month'] = df.date.dt.month + df['year'] = df.date.dt.year + + df = df.merge(self.get_store_info(self.store_info_path), on='store', how='left') + df = df.merge(self.get_product_info(self.products_info_path), on='item', how='left') + + df = create_sales_lag_feats(df, gpby_cols=['store','item'], target_col='sales', + lags=[1, 3, 6, 12]) + + df = create_sales_rmean_feats(df, gpby_cols=['store','item'], + target_col='sales', windows=[2, 3, 6, 12], + min_periods=2, win_type='triang') + + df = create_sales_rmed_feats(df, gpby_cols=['store','item'], + target_col='sales', windows=[2, 3, 6, 12], + min_periods=2, win_type=None) + + df = create_sales_ewm_feats(df, gpby_cols=['store','item'], + target_col='sales', + alpha=[0.95, 0.9, 0.8, 0.7, 0.6, 0.5], + shift=[1, 3, 6, 12]) + + df = create_sales_rmean_feats(df, gpby_cols=['store','item'], + target_col='price', windows=[2, 3, 6, 12], + min_periods=2,) + + df[self.cat_cols] = df[self.cat_cols].fillna('unknown').astype('str') + return df + + def transform(self, data, pred_date=None): + df = self.get_df(data) + df = self.add_zero_points(df, pred_date) + self.mean_values = df.groupby(['store', 'item'])['sales'].mean() + df = self.fix_prices(df) + df = self.build_features(df) + return df + + +def full_solver(model, preprocesser, data, pred_date='2022-12-01'): + cnt = preprocesser.get_df(data).groupby(['store', 'item'])['sales'].count() + df_test = preprocesser.transform(data, pred_date) + pred_mask = df_test['date'] == pred_date + + X_test, y_test = df_test.drop(columns=preprocesser.drop_cols), df_test['sales'] + preds = model.predict(X_test) + + df_test.loc[:, 'store'] = df_test['store'].astype(int) + df_test.loc[:, 'item'] = df_test['item'].astype(int) + mask = df_test.apply(lambda x: cnt[(x['store'], x['item'])], axis=1) < 10 + if mask.sum(): + # print(df_test[mask].apply(lambda x: preprocesser.mean_values[(x['store'], x['item'])], axis=1)) + preds[mask] = df_test[mask].apply(lambda x: preprocesser.mean_values[(x['store'], x['item'])], axis=1) + + preds = np.around(preds) + print('mae:', mean_absolute_error(y_test[~pred_mask], preds[~pred_mask])) + # print('smape:', smape(preds[~pred_mask], y_test[~pred_mask])) + + res = df_test[['store', 'item', 'date']] + res.loc[:, 'preds'] = preds + res.loc[:, 'store'] = res['store'].apply(lambda x: preprocesser.numbers_to_hash['store'][x]) + res.loc[:, 'item'] = res['item'].apply(lambda x: preprocesser.numbers_to_hash['item'][x]) + return res[pred_mask] + + +if __name__ == '__main__': + with open('model.pickle', 'rb') as f: + model = pickle.load(f) + + with open('preprocesser.pickle', 'rb') as f: + preprocesser = pickle.load(f) + + data = pd.read_csv('sample_input.csv') + res = full_solver(model, preprocesser, data, '2022-12-01') + print(res.head()) + diff --git a/main.ipynb b/main.ipynb new file mode 100644 index 0000000..a0f2c3e --- /dev/null +++ b/main.ipynb @@ -0,0 +1,65596 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "2f7219bb-f566-49f5-8281-c840d2fa40a8", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import os\n", + "import time\n", + "from datetime import datetime, timedelta\n", + "import lightgbm\n", + "import plotly.express as px" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "376e3968-709c-42c3-ac1b-c00b52c00eff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "catboost 1.1.1\n", + "cloudpickle 2.2.1\n", + "numpy 1.23.5\n", + "pandas 1.5.2\n", + "pickleshare 0.7.5\n", + "scikit-learn 1.2.0\n", + "tqdm 4.64.1\n" + ] + } + ], + "source": [ + "!pip list | grep 'pandas\\|numpy\\|tqdm\\|skleanr\\|catboost\\|scikit-learn\\|pickle\\|calendra'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b0498a0a-f1c7-4f1b-a673-28953e9f5149", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'solo_shop.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msolo_shop.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m data\u001b[38;5;241m.\u001b[39mhead()\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File 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\u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m 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\u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 852\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 853\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 864\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 865\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'solo_shop.csv'" + ] + } + ], + "source": [ + "data = pd.read_csv('solo_shop.csv').sort_values(by='dt')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf7c03c9-dc52-4b08-9c2e-772e1452d663", + "metadata": {}, + "outputs": [], + "source": [ + "(data['gtin'].value_counts() > 100).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2958ec80-05f0-407e-bfa8-18e5ffab9c05", + "metadata": {}, + "outputs": [], + "source": [ + "data['gtin'].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": 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46732021-11-222C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88BA9F916B567DEF0200B232E5838D1FF38Продажа конечному потребителю в точке продаж785571
79192021-11-222C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88BCED30EC32B77DA7C78E69E75A47CAD0EПродажа конечному потребителю в точке продаж785571
203452021-11-222C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88BNaNПродажа конечному потребителю в точке продаж785573
997752021-11-232C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88B7E9B0DA12006D7A315D35DBAA935F5B9Продажа конечному потребителю в точке продаж785571
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22344792022-11-202C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88B8DC771A98A101E9389504F906C5E422EПродажа конечному потребителю в точке продаж1170851
22316762022-11-202C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88B5AD1A58E9CFDE488E3315293CCF406DBПродажа конечному потребителю в точке продаж1170851
22621542022-11-212C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88BA9F916B567DEF0200B232E5838D1FF38Продажа конечному потребителю в точке продаж1351001
22188272022-11-212C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88B3998CD031AE705195AAF0A63B52C95EFПродажа конечному потребителю в точке продаж1351001
22191142022-11-212C2D3F1352310A924E26F11E384D9784A43BE3249A22A8CCB7AE1F6D1B1477D06B8E111AB5B5C556C0AEA292ACA4D88B68EEEDC60B194BC45E51944B30E5D6E1Продажа конечному потребителю в точке продаж1351001
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'6B8E111AB5B5C556C0AEA292ACA4D88B')]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d3b894dc-fdcc-458e-b41b-9d18f143e5f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2262895, 8)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1884e82b-f74b-4b86-b59f-892d77fb5ccc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "inn gtin \n", + "6B8E111AB5B5C556C0AEA292ACA4D88B 000353B28DA976A5CB2A71139AE83E7B 39319.338018\n", + " 000ED2D0FEEFCB166F2E60DF05368A14 41705.648682\n", + " 001DD284810DEFDD787F4DDFF662EA1F 25957.408877\n", + " 001E37C5AEF7D80112B325441B908ACD 73832.921688\n", + " 002C3D5DDD194A3837BEE8B3857F2C2F 83970.021620\n", + " ... \n", + " FFD98FE9A4F6D9C3FC2C47B73778F9BA 19194.326141\n", + " FFE2D1758B99E61B60C6678E5CDD3547 29298.641196\n", + " 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+ "data = data.drop(columns='inn', axis=1)\n", + "data = data.dropna(subset='store')\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f5bf836d-51f1-432d-aecd-580e2015bacd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateitempridstoretype_operationpricesalesstore_item
1052021-11-018025Продажа конечному потребителю в точке продаж1417141DE3FBFFC682914FBD0A9BC5EC6BAE198_50CD8E20B9B0A...
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" + ], + "text/plain": [ + " store item date sales price\n", + "0 0 195 2022-06-01 2 31385.0\n", + "1 0 195 2022-07-01 3 27828.0\n", + "2 0 195 2022-08-01 2 31385.0\n", + "3 0 195 2022-11-01 9 29995.0\n", + "4 0 280 2022-06-01 3 102000.0" + ] + }, + "execution_count": 284, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = data.groupby(['store', 'item', 'date']).agg(sales = ('sales', 'sum'),\n", + " price = ('price', 'mean')).reset_index()\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "31176cdb-59f5-45c4-9e6b-9f10306ff90e", + "metadata": {}, + "outputs": [], + "source": [ + "all_dates = df['date'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "6b51e9a0-868e-41f2-95fb-ee3ee91f5978", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████| 33315/33315 [00:00<00:00, 35097.25it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(433095, 5)" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add = []\n", + "x = df.groupby(['store', 'item'])['date'].unique()\n", + "for store_item, now_dates in tqdm(list(x.items())):\n", + " for d in all_dates:\n", + " if d not in now_dates:\n", + " add.append({'store': store_item[0],\n", + " 'item': store_item[1],\n", + " 'date': d,\n", + " 'sales': 0})\n", + " \n", + "df = pd.concat([df, pd.DataFrame(add)])\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "15698a0d-c0a8-44ff-92ae-3e196e957074", + "metadata": {}, + "outputs": [], + "source": [ + "df['train_or_test'] = np.where(df['date'] < '2022-10-01', 'train', 'val')" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "id": "d9b189e8-8d23-4b5b-8b02-beed206b8938", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████| 33315/33315 [00:00<00:00, 46435.28it/s]\n" + ] + } + ], + "source": [ + "def fill_price(prices):\n", + " L = np.ones(len(prices)) * -1\n", + " R = np.ones(len(prices)) * -1\n", + " for i in range(len(prices)):\n", + " if prices[i] == prices[i]: #not is nan\n", + " L[i] = prices[i]\n", + " elif i > 0:\n", + " L[i] = L[i - 1]\n", + " \n", + " for i in range(len(prices) - 1, -1, -1):\n", + " if prices[i] == prices[i]: #not is nan\n", + " R[i] = prices[i]\n", + " elif i != len(prices) - 1:\n", + " R[i] = R[i + 1]\n", + " \n", + " for i in range(len(prices)):\n", + " if prices[i] != prices[i]:\n", + " if L[i] == -1:\n", + " prices[i] = R[i]\n", + " elif R[i] == -1:\n", + " prices[i] = L[i]\n", + " else:\n", + " prices[i] = (L[i] + R[i]) / 2\n", + " return prices\n", + "\n", + "groups = df.sort_values(by='date').groupby(['store', 'item'])['price']\n", + "\n", + "res = []\n", + "for group in tqdm(groups):\n", + " res += fill_price(group[1].values).tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "id": "89546954-73aa-42d7-8705-56e7c90b5a1d", + "metadata": {}, + "outputs": [], + "source": [ + "df.sort_values(by=['store','item', 'date'], axis=0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "id": "2bf00dd8-ce6b-448f-95ed-efc8db020b9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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storeitemdatesalespricetrain_or_test
201952021-11-01031385.0train
301952021-12-01031385.0train
701952022-01-01031385.0train
401952022-02-01031385.0train
501952022-03-01031385.0train
\n", + "
" + ], + "text/plain": [ + " store item date sales price train_or_test\n", + "2 0 195 2021-11-01 0 31385.0 train\n", + "3 0 195 2021-12-01 0 31385.0 train\n", + "7 0 195 2022-01-01 0 31385.0 train\n", + "4 0 195 2022-02-01 0 31385.0 train\n", + "5 0 195 2022-03-01 0 31385.0 train" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['price'] = res\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "id": "f6708dae-16ec-4b93-9261-005f07ffa05b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((1, 46), 122 NaN\n", + "103 52200.0\n", + "124 NaN\n", + "123 NaN\n", + "104 58500.0\n", + "105 85307.0\n", + "106 78000.0\n", + "118 NaN\n", + "107 54271.0\n", + "119 NaN\n", + "121 NaN\n", + "108 49985.0\n", + "120 NaN\n", + "Name: price, dtype: float64)\n", + "[52200. 52200. 55350. 55350. 58500. 85307. 78000. 66135.5 54271.\n", + " 52128. 52128. 49985. 49985. ]\n" + ] + } + ], + "source": [ + "for i, el in enumerate(groups):\n", + " if i == 17:\n", + " print(el)\n", + " print(fill_price(el[1].values))\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "id": "1b87a277-21c4-49d9-bde4-400dc4dc0d99", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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storeitemdatesalespricetrain_or_test
1221462021-11-01052200.0train
1031462021-12-01152200.0train
1241462022-01-01055350.0train
1231462022-02-01055350.0train
1041462022-03-01158500.0train
1051462022-04-01285307.0train
1061462022-05-01278000.0train
1181462022-06-01066135.5train
1071462022-07-01254271.0train
1191462022-08-01052128.0train
1211462022-09-01052128.0train
1081462022-10-01549985.0val
1201462022-11-01049985.0val
\n", + "
" + ], + "text/plain": [ + " store item date sales price train_or_test\n", + "122 1 46 2021-11-01 0 52200.0 train\n", + "103 1 46 2021-12-01 1 52200.0 train\n", + "124 1 46 2022-01-01 0 55350.0 train\n", + "123 1 46 2022-02-01 0 55350.0 train\n", + "104 1 46 2022-03-01 1 58500.0 train\n", + "105 1 46 2022-04-01 2 85307.0 train\n", + "106 1 46 2022-05-01 2 78000.0 train\n", + "118 1 46 2022-06-01 0 66135.5 train\n", + "107 1 46 2022-07-01 2 54271.0 train\n", + "119 1 46 2022-08-01 0 52128.0 train\n", + "121 1 46 2022-09-01 0 52128.0 train\n", + "108 1 46 2022-10-01 5 49985.0 val\n", + "120 1 46 2022-11-01 0 49985.0 val" + ] + }, + "execution_count": 292, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['store'] == 1) & (df['item'] == 46)]" + ] + }, + { + "cell_type": "markdown", + "id": "5b5aeaff-12cd-473d-9406-85384286ac71", + "metadata": {}, + "source": [ + "### Date Features" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "id": "042cd36b-9a46-43df-a991-b507a3fea114", + "metadata": {}, + "outputs": [], + "source": [ + "df['date'] = pd.to_datetime(df['date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "id": "899610c9-35ec-4f54-908e-206d2ee89977", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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storeitemdatesalespricetrain_or_testmonthyear
201952021-11-01031385.0train112021
301952021-12-01031385.0train122021
701952022-01-01031385.0train12022
401952022-02-01031385.0train22022
501952022-03-01031385.0train32022
\n", + "
" + ], + "text/plain": [ + " store item date sales price train_or_test month year\n", + "2 0 195 2021-11-01 0 31385.0 train 11 2021\n", + "3 0 195 2021-12-01 0 31385.0 train 12 2021\n", + "7 0 195 2022-01-01 0 31385.0 train 1 2022\n", + "4 0 195 2022-02-01 0 31385.0 train 2 2022\n", + "5 0 195 2022-03-01 0 31385.0 train 3 2022" + ] + }, + "execution_count": 294, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# df['dayofmonth'] = df.date.dt.day\n", + "# df['dayofyear'] = df.date.dt.dayofyear\n", + "# df['dayofweek'] = df.date.dt.dayofweek\n", + "df['month'] = df.date.dt.month\n", + "df['year'] = df.date.dt.year\n", + "# df['weekofyear'] = df.date.dt.weekofyear\n", + "# df['is_month_start'] = (df.date.dt.is_month_start).astype(int)\n", + "# df['is_month_end'] = (df.date.dt.is_month_end).astype(int)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "id": "3aa57bfb-a665-4c59-8d3d-a9cb020bd89c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33315" + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(['store'])['item'].nunique().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "id": "6ae22d79-9490-4c4c-8164-e202e9e7ee18", + "metadata": {}, + "outputs": [], + "source": [ + "df.sort_values(by=['store','item','date'], axis=0, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "03002bdd-3e6a-4370-8ad0-3c5f0acc413e", + "metadata": {}, + "source": [ + "### Add background information" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "id": "b977a884-a31f-4f97-b869-5b00e0d1a364", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(433095, 8)" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "id": "80ff39e1-8117-4245-b6c6-3b8fb2fe2f4f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_product_info(path):\n", + " products_info = pd.read_csv(path)\n", + " \n", + " products_info['item'] = products_info['gtin'].apply(lambda x: preprocesser.hash_to_numbers['item'][x] if x in \n", + " preprocesser.hash_to_numbers['item'] else np.nan)\n", + " products_info = products_info.dropna(subset='item')\n", + " products_info = products_info.drop(columns='gtin')\n", + " products_info = products_info.drop_duplicates(subset='item', keep='last')\n", + " \n", + " products_info['volume'] = products_info['volume'].replace('НЕ КЛАССИФИЦИРОВАНО', np.nan)\n", + " products_info['volume'] = products_info['volume'].apply(lambda x: float(x.replace(',', '.').replace(' г', ''))\n", + " if x == x else np.nan)\n", + " return products_info\n", + "\n", + "def get_store_info(path):\n", + " store_info = pd.read_csv(path).drop(columns='inn')\n", + " store_info['store'] = store_info['id_sp_'].apply(lambda x: preprocesser.hash_to_numbers['store'][x] if x in \n", + " preprocesser.hash_to_numbers['store'] else np.nan)\n", + " store_info = store_info.dropna(subset='store')\n", + " store_info = store_info.drop(columns='id_sp_')\n", + " store_info = store_info.drop_duplicates(subset='store', keep='last')\n", + " return store_info" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "id": "3a2df936-3699-42ff-b485-4554f79e1b57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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region_codecity_with_typecity_fias_idpostal_codestore
3864г Саратовbf465fda-7834-47d5-986b-ccdb584a85a6410005.0181.0
4877NaNNaN111622.010.0
5764г Саратовbf465fda-7834-47d5-986b-ccdb584a85a6410012.0239.0
37447г Кудрово98cd19c4-e172-4fa3-a81d-7caedeec669b188692.0167.0
72955г Омск140e31da-27bf-4519-9ea0-6185d681d44e644024.078.0
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" + ], + "text/plain": [ + " region_code city_with_type city_fias_id \\\n", + "38 64 г Саратов bf465fda-7834-47d5-986b-ccdb584a85a6 \n", + "48 77 NaN NaN \n", + "57 64 г Саратов bf465fda-7834-47d5-986b-ccdb584a85a6 \n", + "374 47 г Кудрово 98cd19c4-e172-4fa3-a81d-7caedeec669b \n", + "729 55 г Омск 140e31da-27bf-4519-9ea0-6185d681d44e \n", + "\n", + " postal_code store \n", + "38 410005.0 181.0 \n", + "48 111622.0 10.0 \n", + "57 410012.0 239.0 \n", + "374 188692.0 167.0 \n", + "729 644024.0 78.0 " + ] + }, + "execution_count": 300, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "store_info = get_store_info('data/Справочник торговых точек.csv')\n", + "store_info.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "id": "a47a0f00-369f-44b1-ab78-1a47c7ff195c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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storeitemdatesalespricetrain_or_testmonthyearregion_codecity_with_typecity_fias_idpostal_codeinnproduct_nameproduct_short_nametnvedtnved10brandcountryvolume
001952021-11-01031385.0train11202177NaNNaN117588.05A96B9812186BDEEBE3642E0F01487757F2ED3710B9F591DB3FC05A6892F1C92B8AF2335E04E7BAD5168D9B39FABAC216D2580183CEF6C8AF1CC72E1C6E6FBC4FAEA36DABA0E46CD081A7FA49DAD8FE060491AB8FECDD08CA4C04B3E6D659407NaN50.0
101952021-12-01031385.0train12202177NaNNaN117588.05A96B9812186BDEEBE3642E0F01487757F2ED3710B9F591DB3FC05A6892F1C92B8AF2335E04E7BAD5168D9B39FABAC216D2580183CEF6C8AF1CC72E1C6E6FBC4FAEA36DABA0E46CD081A7FA49DAD8FE060491AB8FECDD08CA4C04B3E6D659407NaN50.0
201952022-01-01031385.0train1202277NaNNaN117588.05A96B9812186BDEEBE3642E0F01487757F2ED3710B9F591DB3FC05A6892F1C92B8AF2335E04E7BAD5168D9B39FABAC216D2580183CEF6C8AF1CC72E1C6E6FBC4FAEA36DABA0E46CD081A7FA49DAD8FE060491AB8FECDD08CA4C04B3E6D659407NaN50.0
301952022-02-01031385.0train2202277NaNNaN117588.05A96B9812186BDEEBE3642E0F01487757F2ED3710B9F591DB3FC05A6892F1C92B8AF2335E04E7BAD5168D9B39FABAC216D2580183CEF6C8AF1CC72E1C6E6FBC4FAEA36DABA0E46CD081A7FA49DAD8FE060491AB8FECDD08CA4C04B3E6D659407NaN50.0
401952022-03-01031385.0train3202277NaNNaN117588.05A96B9812186BDEEBE3642E0F01487757F2ED3710B9F591DB3FC05A6892F1C92B8AF2335E04E7BAD5168D9B39FABAC216D2580183CEF6C8AF1CC72E1C6E6FBC4FAEA36DABA0E46CD081A7FA49DAD8FE060491AB8FECDD08CA4C04B3E6D659407NaN50.0
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" + ], + "text/plain": [ + " store item date sales price train_or_test month year \\\n", + "0 0 195 2021-11-01 0 31385.0 train 11 2021 \n", + "1 0 195 2021-12-01 0 31385.0 train 12 2021 \n", + "2 0 195 2022-01-01 0 31385.0 train 1 2022 \n", + "3 0 195 2022-02-01 0 31385.0 train 2 2022 \n", + "4 0 195 2022-03-01 0 31385.0 train 3 2022 \n", + "\n", + " region_code city_with_type city_fias_id postal_code \\\n", + "0 77 NaN NaN 117588.0 \n", + "1 77 NaN NaN 117588.0 \n", + "2 77 NaN NaN 117588.0 \n", + "3 77 NaN NaN 117588.0 \n", + "4 77 NaN NaN 117588.0 \n", + "\n", + " inn product_name \\\n", + "0 5A96B9812186BDEEBE3642E0F0148775 7F2ED3710B9F591DB3FC05A6892F1C92 \n", + "1 5A96B9812186BDEEBE3642E0F0148775 7F2ED3710B9F591DB3FC05A6892F1C92 \n", + "2 5A96B9812186BDEEBE3642E0F0148775 7F2ED3710B9F591DB3FC05A6892F1C92 \n", + "3 5A96B9812186BDEEBE3642E0F0148775 7F2ED3710B9F591DB3FC05A6892F1C92 \n", + "4 5A96B9812186BDEEBE3642E0F0148775 7F2ED3710B9F591DB3FC05A6892F1C92 \n", + "\n", + " product_short_name tnved \\\n", + "0 B8AF2335E04E7BAD5168D9B39FABAC21 6D2580183CEF6C8AF1CC72E1C6E6FBC4 \n", + "1 B8AF2335E04E7BAD5168D9B39FABAC21 6D2580183CEF6C8AF1CC72E1C6E6FBC4 \n", + "2 B8AF2335E04E7BAD5168D9B39FABAC21 6D2580183CEF6C8AF1CC72E1C6E6FBC4 \n", + "3 B8AF2335E04E7BAD5168D9B39FABAC21 6D2580183CEF6C8AF1CC72E1C6E6FBC4 \n", + "4 B8AF2335E04E7BAD5168D9B39FABAC21 6D2580183CEF6C8AF1CC72E1C6E6FBC4 \n", + "\n", + " tnved10 brand country \\\n", + "0 FAEA36DABA0E46CD081A7FA49DAD8FE0 60491AB8FECDD08CA4C04B3E6D659407 NaN \n", + "1 FAEA36DABA0E46CD081A7FA49DAD8FE0 60491AB8FECDD08CA4C04B3E6D659407 NaN \n", + "2 FAEA36DABA0E46CD081A7FA49DAD8FE0 60491AB8FECDD08CA4C04B3E6D659407 NaN \n", + "3 FAEA36DABA0E46CD081A7FA49DAD8FE0 60491AB8FECDD08CA4C04B3E6D659407 NaN \n", + "4 FAEA36DABA0E46CD081A7FA49DAD8FE0 60491AB8FECDD08CA4C04B3E6D659407 NaN \n", + "\n", + " volume \n", + "0 50.0 \n", + "1 50.0 \n", + "2 50.0 \n", + "3 50.0 \n", + "4 50.0 " + ] + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.merge(store_info, on='store', how='left')\n", + "df = df.merge(products_info, on='item', how='left')\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "id": "38e81398-fe9f-4ee9-8dff-26773c9465b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(433095, 20)" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "id": "44fcad74-d71f-4f67-861c-77ac07dc21c4", + "metadata": {}, + "source": [ + "### Monthwise aggregated sales values" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "id": "a98825d2-daf7-4ad3-9f90-56cdc68fc650", + "metadata": {}, + "outputs": [], + "source": [ + "def create_sales_agg_monthwise_features(df, gpby_cols, target_col, agg_funcs):\n", + " '''\n", + " Creates various sales agg features with given agg functions \n", + " '''\n", + " gpby = df.groupby(gpby_cols)\n", + " newdf = df[gpby_cols].drop_duplicates().reset_index(drop=True)\n", + " for agg_name, agg_func in agg_funcs.items():\n", + " aggdf = gpby[target_col].agg(agg_func).reset_index()\n", + " aggdf.rename(columns={target_col:target_col+'_'+agg_name}, inplace=True)\n", + " newdf = newdf.merge(aggdf, on=gpby_cols, how='left')\n", + " return newdf" + ] + }, + { + "cell_type": "markdown", + "id": "3384f8ad-2176-4be6-9f1a-abfec8dae2ef", + "metadata": { + "tags": [] + }, + "source": [ + "### Features constructed from previous sales values" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "id": "15e9dbe4-d35f-45fc-82cc-c1602e721332", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating sales lag features\n", + "def create_sales_lag_feats(df, gpby_cols, target_col, lags):\n", + " gpby = df.groupby(gpby_cols)\n", + " for i in lags:\n", + " df['_'.join([target_col, 'lag', str(i)])] = \\\n", + " gpby[target_col].shift(i).values + np.random.normal(scale=1, size=(len(df),)) * 0\n", + " return df\n", + "\n", + "# Creating sales rolling mean features\n", + "def create_sales_rmean_feats(df, gpby_cols, target_col, windows, min_periods=2, \n", + " shift=1, win_type=None):\n", + " gpby = df.groupby(gpby_cols)\n", + " for w in windows:\n", + " df['_'.join([target_col, 'rmean', str(w)])] = \\\n", + " gpby[target_col].shift(shift).rolling(window=w, \n", + " min_periods=min_periods,\n", + " win_type=win_type).mean().values +\\\n", + " np.random.normal(scale=1, size=(len(df),)) * 0\n", + " return df\n", + "\n", + "# Creating sales rolling median features\n", + "def create_sales_rmed_feats(df, gpby_cols, target_col, windows, min_periods=2, \n", + " shift=1, win_type=None):\n", + " gpby = df.groupby(gpby_cols)\n", + " for w in windows:\n", + " df['_'.join([target_col, 'rmed', str(w)])] = \\\n", + " gpby[target_col].shift(shift).rolling(window=w, \n", + " min_periods=min_periods,\n", + " win_type=win_type).median().values +\\\n", + " np.random.normal(scale=1, size=(len(df),)) * 0\n", + " return df\n", + "\n", + "# Creating sales exponentially weighted mean features\n", + "def create_sales_ewm_feats(df, gpby_cols, target_col, alpha=[0.9], shift=[1]):\n", + " gpby = df.groupby(gpby_cols)\n", + " for a in alpha:\n", + " for s in shift:\n", + " df['_'.join([target_col, 'lag', str(s), 'ewm', str(a)])] = \\\n", + " gpby[target_col].shift(s).ewm(alpha=a).mean().values\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "id": "0487a55e-7773-4669-896b-2c28f6f38fd3", + "metadata": {}, + "source": [ + "### OHE of categorical features" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "id": "42fbb824-87ed-444f-978e-cac300d91544", + "metadata": {}, + "outputs": [], + "source": [ + "def one_hot_encoder(df, ohe_cols=['store','item','dayofmonth','dayofweek','month','weekofyear']):\n", + " '''\n", + " One-Hot Encoder function\n", + " '''\n", + " print('Creating OHE features..\\nOld df shape:{}'.format(df.shape))\n", + " df = pd.get_dummies(df, columns=ohe_cols)\n", + " print('New df shape:{}'.format(df.shape))\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "id": "80eb8c77-d9f7-43e7-bafe-ce8ce97223d0", + "metadata": {}, + "source": [ + "### Log Sales" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "id": "904b580f-7201-4378-8868-1db8f9ebbfef", + "metadata": {}, + "outputs": [], + "source": [ + "# df['sales'] = np.log1p(df.sales.values)\n", + "# df.sample(2)" + ] + }, + { + "cell_type": "markdown", + "id": "c177e88f-1b93-4b02-8944-b2b36e14d05a", + "metadata": {}, + "source": [ + "### Building features" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "id": "ac788562-8366-4911-ab79-1ed6b05be1ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(433095, 20)" + ] + }, + "execution_count": 307, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "id": "74951b72-54a8-4625-a3aa-2377514922e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train shape:(366465, 60), Val shape:(66630, 60)\n" + ] + } + ], + "source": [ + "# Converting sales of validation period to nan so as to resemble test period\n", + "train = df.loc[df.train_or_test.isin(['train','val']), :]\n", + "y_val = train.loc[train.train_or_test=='val', 'sales'].values.reshape((-1))\n", + "y_train = train.loc[train.train_or_test=='train', 'sales'].values.reshape((-1))\n", + "train.loc[train.train_or_test=='val', 'sales'] = np.nan\n", + "\n", + "# # Creating sales lag, rolling mean, rolling median, ohe features of the above train set\n", + "train = create_sales_lag_feats(train, gpby_cols=['store','item'], target_col='sales', \n", + " lags=[1, 3, 6, 12])\n", + "\n", + "train = create_sales_rmean_feats(train, gpby_cols=['store','item'], \n", + " target_col='sales', windows=[1, 2, 3, 6, 12], \n", + " min_periods=1, win_type='triang')\n", + "\n", + "train = create_sales_rmed_feats(train, gpby_cols=['store','item'], \n", + " target_col='sales', windows=[2, 3, 6, 12], \n", + " min_periods=2, win_type=None)\n", + "\n", + "train = create_sales_ewm_feats(train, gpby_cols=['store','item'], \n", + " target_col='sales', \n", + " alpha=[0.95, 0.9, 0.8, 0.7, 0.6, 0.5], \n", + " shift=[1, 3, 6, 12])\n", + "\n", + "train = create_sales_rmean_feats(train, gpby_cols=['store','item'], \n", + " target_col='price', windows=[2, 3, 6, 12], \n", + " min_periods=2,)\n", + "\n", + "\n", + "# # Creating sales monthwise aggregated values\n", + "# agg_df = create_sales_agg_monthwise_features(df.loc[df.train_or_test=='train', :], \n", + "# gpby_cols=['store','item','month'], \n", + "# target_col='sales', \n", + "# agg_funcs={'mean':np.mean, \n", + "# 'median':np.median, 'max':np.max, \n", + "# 'min':np.min, 'std':np.std})\n", + "\n", + "# # Joining agg_df with train\n", + "# train = train.merge(agg_df, on=['store','item','month'], how='left')\n", + "\n", + "# One-Hot Encoding \n", + "# train = one_hot_encoder(train, ohe_cols=['store', 'item', 'month']) \n", + "#,'dayofmonth','weekofyear'\n", + "\n", + "# Final train and val datasets\n", + "val = train.loc[train.train_or_test=='val', :]\n", + "train = train.loc[train.train_or_test=='train', :]\n", + "print('Train shape:{}, Val shape:{}'.format(train.shape, val.shape))" + ] + }, + { + "cell_type": "code", + "execution_count": 309, + "id": "5ed444c1-28f2-4a20-a826-69109d6c8a9a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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storeitemdatesalespricetrain_or_testmonthyearregion_codecity_with_type...sales_lag_6_ewm_0.6sales_lag_12_ewm_0.6sales_lag_1_ewm_0.5sales_lag_3_ewm_0.5sales_lag_6_ewm_0.5sales_lag_12_ewm_0.5price_rmean_2price_rmean_3price_rmean_6price_rmean_12
381992017022022-04-010.077300.0train4202223г Сочи...0.7402199.274828e-1140.6508640.6090490.7244024.825246e-8669675.062050.055950.00000079751.893939
3338741786492022-07-014.034271.0train7202247г Гатчина...0.7434042.000007e+000.0220480.0891520.7130902.000122e+0035699.535699.535937.58333341078.090909
33094917613992022-07-011.066585.0train7202247NaN...1.6662010.000000e+000.0490940.1985161.7104970.000000e+0054713.554713.552734.91666750229.154545
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3 rows × 60 columns

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" + ], + "text/plain": [ + " store item date sales price train_or_test month year \\\n", + "38199 20 1702 2022-04-01 0.0 77300.0 train 4 2022 \n", + "333874 178 649 2022-07-01 4.0 34271.0 train 7 2022 \n", + "330949 176 1399 2022-07-01 1.0 66585.0 train 7 2022 \n", + "\n", + " region_code city_with_type ... sales_lag_6_ewm_0.6 \\\n", + "38199 23 г Сочи ... 0.740219 \n", + "333874 47 г Гатчина ... 0.743404 \n", + "330949 47 NaN ... 1.666201 \n", + "\n", + " sales_lag_12_ewm_0.6 sales_lag_1_ewm_0.5 sales_lag_3_ewm_0.5 \\\n", + "38199 9.274828e-114 0.650864 0.609049 \n", + "333874 2.000007e+00 0.022048 0.089152 \n", + "330949 0.000000e+00 0.049094 0.198516 \n", + "\n", + " sales_lag_6_ewm_0.5 sales_lag_12_ewm_0.5 price_rmean_2 price_rmean_3 \\\n", + "38199 0.724402 4.825246e-86 69675.0 62050.0 \n", + "333874 0.713090 2.000122e+00 35699.5 35699.5 \n", + "330949 1.710497 0.000000e+00 54713.5 54713.5 \n", + "\n", + " price_rmean_6 price_rmean_12 \n", + "38199 55950.000000 79751.893939 \n", + "333874 35937.583333 41078.090909 \n", + "330949 52734.916667 50229.154545 \n", + "\n", + "[3 rows x 60 columns]" + ] + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.sample(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "id": "d5da053c-a4b1-4f6e-b615-3d695a52e71f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((366465, 54), (66630, 54))" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drop_cols = ['date', 'sales', 'train_or_test', 'year', 'product_name', 'month']\n", + "X_train = train.drop(columns=drop_cols)\n", + "X_val = val.drop(columns=drop_cols)\n", + "X_train.shape, X_val.shape" + ] + }, + { + "cell_type": "markdown", + "id": "a95967f1-ad0e-4da2-9cd1-6e564da69cb2", + "metadata": { + "tags": [] + }, + "source": [ + "## Train catboost" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "id": "30d83574-cea3-4bd5-984b-90ca0bef0c38", + "metadata": {}, + "outputs": [], + "source": [ + "from catboost import CatBoostRegressor\n", + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "def smape(preds, target):\n", + " '''\n", + " Function to calculate SMAPE\n", + " '''\n", + " n = len(preds)\n", + " masked_arr = ~((preds==0)&(target==0))\n", + " preds, target = preds[masked_arr], target[masked_arr]\n", + " num = np.abs(preds-target)\n", + " denom = np.abs(preds)+np.abs(target)\n", + " smape_val = (200*np.sum(num/denom))/n\n", + " return smape_val" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "id": "c4f410fd-0524-4582-a286-5e03e4955514", + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = ['store', 'item'] + \\\n", + " ['inn', 'product_short_name', 'tnved', 'tnved10', 'brand', \n", + " 'country', 'region_code', 'city_with_type', 'city_fias_id', 'postal_code']\n", + "\n", + "params_cat = {'n_estimators' : 1000,\n", + " 'learning_rate': .03,\n", + " 'depth' : 3,\n", + " 'use_best_model': True,\n", + " 'cat_features' : cat_cols,\n", + " # 'text_features': text_cols,\n", + " # 'train_dir' : '/home/jovyan/work/catboost',\n", + " # 'border_count' : 64,\n", + " 'l2_leaf_reg' : 1,\n", + " # 'bagging_temperature' : 2,\n", + " # 'rsm' : .1,\n", + " # 'loss_function': 'MultiClass',\n", + " # 'auto_class_weights' : 'Balanced', #try not balanced\n", + " 'random_state': 42,\n", + " 'eval_metric' : 'MAE'\n", + " }\n", + "\n", + "cat_model = CatBoostRegressor(**params_cat)" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "id": "846379c9-1ab6-4004-bd74-d986343b7db4", + "metadata": {}, + "outputs": [], + "source": [ + "X_train[cat_cols] = X_train[cat_cols].fillna('unknown').astype('str')\n", + "X_val[cat_cols] = X_val[cat_cols].fillna('unknown').astype('str')" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "id": "54c16633-1445-477e-a631-187cd284e0a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0:\tlearn: 1.9186353\ttest: 1.7634101\tbest: 1.7634101 (0)\ttotal: 140ms\tremaining: 2m 19s\n", + "100:\tlearn: 1.5993968\ttest: 1.5548603\tbest: 1.5548603 (100)\ttotal: 5.42s\tremaining: 48.2s\n", + "200:\tlearn: 1.5154855\ttest: 1.5161940\tbest: 1.5157129 (194)\ttotal: 10.3s\tremaining: 41.1s\n", + "300:\tlearn: 1.4728283\ttest: 1.5127866\tbest: 1.5116569 (276)\ttotal: 15.3s\tremaining: 35.4s\n", + "400:\tlearn: 1.4486499\ttest: 1.5135461\tbest: 1.5114578 (324)\ttotal: 20.2s\tremaining: 30.1s\n", + "500:\tlearn: 1.4349350\ttest: 1.5125139\tbest: 1.5114578 (324)\ttotal: 24.8s\tremaining: 24.7s\n", + "Stopped by overfitting detector (200 iterations wait)\n", + "\n", + "bestTest = 1.511457849\n", + "bestIteration = 324\n", + "\n", + "Shrink model to first 325 iterations.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 314, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_model.fit(X_train, y_train, verbose=100, eval_set=(X_val, y_val), early_stopping_rounds=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "0621c010-90a3-4d29-91dd-a473eddad5e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18.2319587628866, 198.96907216494844)" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp = df.groupby(['store', 'item'])['sales'].mean()\n", + "preds = X_val.apply(lambda el: mp[(el['store'], el['item'])] if (el['store'], el['item']) in mp else 0, axis=1)\n", + "mean_absolute_error(y_val, preds), smape(preds, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "id": "695c4fc1-83c0-483b-ab87-20127e8dc678", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.464006776661614, 60.76750868739798)" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = cat_model.predict(big_X_val)\n", + "mean_absolute_error(big_y_val, pred), smape(pred, big_y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "id": "f3e0a228-888f-4c3f-be37-af99b8db8fc0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.5114588488939953, 103.53749890327455)" + ] + }, + "execution_count": 315, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = cat_model.predict(X_val)\n", + "mean_absolute_error(y_val, pred), smape(pred, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "id": "47441f93-d722-4494-98fb-daa7220b52a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.0327480114062735, 133.26429536244936)" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp = df.groupby(['store', 'item'])['sales'].mean()\n", + "preds = X_val.apply(lambda el: mp[(el['store'], el['item'])] if (el['store'], el['item']) in mp else 0, axis=1)\n", + "mean_absolute_error(y_val, preds), smape(preds, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "id": "0ac4a1ce-8a98-43d2-b48a-6e3cb0988810", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.5917755933909224, 173.1597072678853)" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp = df.groupby(['item'])['sales'].mean()\n", + "preds = X_val.apply(lambda el: mp[el['item']], axis=1)\n", + "mean_absolute_error(y_val, preds), smape(preds, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "id": "2d0a3fee-4c2b-4749-a787-f03fba171d6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.5399093360997136, 182.09267252672777)" + ] + }, + "execution_count": 248, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds = cat_model.predict(X_val)\n", + "mean_absolute_error(y_val, preds), smape(preds, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 465, + "id": "4e228dfe-4b23-41f6-9aed-04d194719411", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open('model.pickle', 'wb') as f:\n", + " pickle.dump(cat_model, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "ce02e7b9-4389-4cb7-be76-5d8188e3b66e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Feature IdImportances
0sales_rmean_319.737363
1sales_lag_113.473987
2sales_lag_38.142901
3price7.672493
4sales_rmean_127.383541
5item6.773528
6sales_rmed_36.770380
7price_rmean_24.814952
8store4.241851
9sales_rmean_22.816378
10sales_lag_1_ewm_0.72.778737
11sales_lag_1_ewm_0.52.066457
12sales_lag_1_ewm_0.81.853374
13sales_rmed_21.683506
14sales_rmed_61.082603
15sales_lag_1_ewm_0.90.923286
16sales_lag_60.896040
17sales_lag_1_ewm_0.60.697879
18postal_code0.663713
19sales_lag_1_ewm_0.950.658503
20price_rmean_60.604549
21sales_lag_12_ewm_0.50.551600
22price_rmean_120.545497
23price_rmean_30.539916
24sales_lag_3_ewm_0.950.521122
\n", + "
" + ], + "text/plain": [ + " Feature Id Importances\n", + "0 sales_rmean_3 19.737363\n", + "1 sales_lag_1 13.473987\n", + "2 sales_lag_3 8.142901\n", + "3 price 7.672493\n", + "4 sales_rmean_12 7.383541\n", + "5 item 6.773528\n", + "6 sales_rmed_3 6.770380\n", + "7 price_rmean_2 4.814952\n", + "8 store 4.241851\n", + "9 sales_rmean_2 2.816378\n", + "10 sales_lag_1_ewm_0.7 2.778737\n", + "11 sales_lag_1_ewm_0.5 2.066457\n", + "12 sales_lag_1_ewm_0.8 1.853374\n", + "13 sales_rmed_2 1.683506\n", + "14 sales_rmed_6 1.082603\n", + "15 sales_lag_1_ewm_0.9 0.923286\n", + "16 sales_lag_6 0.896040\n", + "17 sales_lag_1_ewm_0.6 0.697879\n", + "18 postal_code 0.663713\n", + "19 sales_lag_1_ewm_0.95 0.658503\n", + "20 price_rmean_6 0.604549\n", + "21 sales_lag_12_ewm_0.5 0.551600\n", + "22 price_rmean_12 0.545497\n", + "23 price_rmean_3 0.539916\n", + "24 sales_lag_3_ewm_0.95 0.521122" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from catboost import Pool\n", + "\n", + "fi = cat_model.get_feature_importance(Pool(X_val, y_val, cat_features=cat_cols),\n", + " prettified=True)\n", + "fi.head(25)" + ] + }, + { + "cell_type": "markdown", + "id": "8ccc52aa-834c-4714-8b42-4b2d5d19378e", + "metadata": {}, + "source": [ + "## Inference" + ] + }, + { + "cell_type": "code", + "execution_count": 450, + "id": "08ff6de9-aa9f-4607-8ebf-ec956501d49f", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "import calendar\n", + "from datetime import date\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 460, + "id": "b6b92126-e594-44d5-8d77-226cf7c5f08a", + "metadata": {}, + "outputs": [], + "source": [ + "def add_months(sourcedate, months):\n", + " month = sourcedate.month - 1 + months\n", + " year = sourcedate.year + month // 12\n", + " month = month % 12 + 1\n", + " day = min(sourcedate.day, calendar.monthrange(year,month)[1])\n", + " return date(year, month, day)\n", + "\n", + "\n", + "class Preprocesser(BaseEstimator, TransformerMixin):\n", + " def __init__(self, products_info_path, store_info_path):\n", + " self.hash_to_numbers = {}\n", + " self.numbers_to_hash = {}\n", + " self.drop_cols = ['date', 'sales', 'year', 'product_name', 'month']\n", + " self.products_info_path = products_info_path\n", + " self.store_info_path = store_info_path\n", + " self.cat_cols = ['store', 'item'] + \\\n", + " ['inn', 'product_short_name', 'tnved', 'tnved10', 'brand', \n", + " 'country', 'region_code', 'city_with_type', 'city_fias_id', 'postal_code']\n", + " self.mean_values = {}\n", + " self.min_date = '2021-11-01'\n", + " \n", + " def fit(self, data):\n", + " data = data.copy()\n", + " data = data.rename(columns={'dt': 'date', 'gtin': 'item', 'id_sp_': 'store', 'cnt': 'sales'})\n", + " data = data.drop(columns='inn', axis=1)\n", + " data = data.dropna(subset='store')\n", + " for col in ['item', 'store', 'prid']:\n", + " self.hash_to_numbers[col] = {a: b for a, b in zip(np.unique(data[col]), np.arange(data[col].nunique()))}\n", + " self.numbers_to_hash[col] = {b: a for a, b in zip(np.unique(data[col]), np.arange(data[col].nunique()))}\n", + " \n", + " return self\n", + " \n", + " def get_df(self, data):\n", + " data = data.sort_values(by='dt').reset_index(drop=True)\n", + " data = data.rename(columns={'dt': 'date', 'gtin': 'item', 'id_sp_': 'store', 'cnt': 'sales'})\n", + " data = data.drop(columns='inn', axis=1)\n", + " data = data.dropna(subset='store')\n", + " \n", + " for col in ['item', 'store', 'prid']:\n", + " data.loc[:, col] = data[col].apply(lambda x: self.hash_to_numbers[col][x] if x in self.hash_to_numbers[col] else np.nan)\n", + " data.loc[:, col] = data[col].astype('int')\n", + " \n", + " data['date'] = data['date'].apply(lambda x: x[:-2] + '01')\n", + " df = data.groupby(['store', 'item', 'date']).agg(sales = ('sales', 'sum'),\n", + " price = ('price', 'mean')).reset_index()\n", + " return df\n", + " \n", + " def add_zero_points(self, df, pred_date=None):\n", + " if pred_date is None:\n", + " pred_date = df['date'].max()\n", + " \n", + " i = 0\n", + " all_dates = []\n", + " while True:\n", + " cur_month = add_months(datetime.fromisoformat(self.min_date), i).isoformat() \n", + " all_dates.append(cur_month)\n", + " if cur_month == pred_date:\n", + " break\n", + " i += 1\n", + " \n", + " x = df.groupby(['store', 'item'])['date'].unique()\n", + " add = []\n", + " for store_item, now_dates in tqdm(list(x.items())):\n", + " for d in all_dates:\n", + " if d not in now_dates:\n", + " add.append({'store': store_item[0],\n", + " 'item': store_item[1],\n", + " 'date': d,\n", + " 'sales': 0})\n", + "\n", + " df = pd.concat([df, pd.DataFrame(add)])\n", + " return df\n", + " \n", + " def fill_price(self, prices):\n", + " L = np.ones(len(prices)) * -1\n", + " R = np.ones(len(prices)) * -1\n", + " for i in range(len(prices)):\n", + " if prices[i] == prices[i]: #not is nan\n", + " L[i] = prices[i]\n", + " elif i > 0:\n", + " L[i] = L[i - 1]\n", + "\n", + " for i in range(len(prices) - 1, -1, -1):\n", + " if prices[i] == prices[i]: #not is nan\n", + " R[i] = prices[i]\n", + " elif i != len(prices) - 1:\n", + " R[i] = R[i + 1]\n", + "\n", + " for i in range(len(prices)):\n", + " if prices[i] != prices[i]:\n", + " if L[i] == -1:\n", + " prices[i] = R[i]\n", + " elif R[i] == -1:\n", + " prices[i] = L[i]\n", + " else:\n", + " prices[i] = (L[i] + R[i]) / 2\n", + " return prices\n", + " \n", + " def fix_prices(self, df):\n", + " groups = df.sort_values(by='date').groupby(['store', 'item'])['price']\n", + "\n", + " res = []\n", + " for group in tqdm(groups):\n", + " res += self.fill_price(group[1].values).tolist()\n", + " df.sort_values(by=['store','item', 'date'], axis=0, inplace=True)\n", + " df['price'] = res\n", + " return df\n", + " \n", + " def get_product_info(self, path):\n", + " products_info = pd.read_csv(path)\n", + "\n", + " products_info['item'] = products_info['gtin'].apply(lambda x: self.hash_to_numbers['item'][x] if x in \n", + " self.hash_to_numbers['item'] else np.nan)\n", + " products_info = products_info.dropna(subset='item')\n", + " products_info = products_info.drop(columns='gtin')\n", + " products_info = products_info.drop_duplicates(subset='item', keep='last')\n", + "\n", + " products_info['volume'] = products_info['volume'].replace('НЕ КЛАССИФИЦИРОВАНО', np.nan)\n", + " products_info['volume'] = products_info['volume'].apply(lambda x: float(x.replace(',', '.').replace(' г', ''))\n", + " if x == x else np.nan)\n", + " return products_info\n", + "\n", + " def get_store_info(self, path):\n", + " store_info = pd.read_csv(path).drop(columns='inn')\n", + " store_info['store'] = store_info['id_sp_'].apply(lambda x: self.hash_to_numbers['store'][x] if x in \n", + " self.hash_to_numbers['store'] else np.nan)\n", + " store_info = store_info.dropna(subset='store')\n", + " store_info = store_info.drop(columns='id_sp_')\n", + " store_info = store_info.drop_duplicates(subset='store', keep='last')\n", + " return store_info\n", + " \n", + " def build_features(self, df):\n", + " df['date'] = pd.to_datetime(df['date'])\n", + " df['month'] = df.date.dt.month\n", + " df['year'] = df.date.dt.year\n", + " \n", + " df = df.merge(self.get_store_info(self.store_info_path), on='store', how='left')\n", + " df = df.merge(self.get_product_info(self.products_info_path), on='item', how='left')\n", + " \n", + " df = create_sales_lag_feats(df, gpby_cols=['store','item'], target_col='sales', \n", + " lags=[1, 3, 6, 12])\n", + "\n", + " df = create_sales_rmean_feats(df, gpby_cols=['store','item'], \n", + " target_col='sales', windows=[2, 3, 6, 12], \n", + " min_periods=2, win_type='triang')\n", + "\n", + " df = create_sales_rmed_feats(df, gpby_cols=['store','item'], \n", + " target_col='sales', windows=[2, 3, 6, 12], \n", + " min_periods=2, win_type=None)\n", + "\n", + " df = create_sales_ewm_feats(df, gpby_cols=['store','item'], \n", + " target_col='sales', \n", + " alpha=[0.95, 0.9, 0.8, 0.7, 0.6, 0.5], \n", + " shift=[1, 3, 6, 12])\n", + " \n", + " df = create_sales_rmean_feats(df, gpby_cols=['store','item'], \n", + " target_col='price', windows=[2, 3, 6, 12], \n", + " min_periods=2,)\n", + " \n", + " df[self.cat_cols] = df[self.cat_cols].fillna('unknown').astype('str')\n", + " return df\n", + " \n", + " def transform(self, data, pred_date=None):\n", + " df = self.get_df(data)\n", + " df = self.add_zero_points(df, pred_date)\n", + " self.mean_values = df.groupby(['store', 'item'])['sales'].mean()\n", + " df = self.fix_prices(df) \n", + " df = self.build_features(df)\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 475, + "id": "089fad55-18ea-4018-ae06-ce52c6ffd8c4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtgtinpridinnid_sp_type_operationpricecnt
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136762022-02-19F9638C01CB7835E6DC133B33533D405391ECAC3DC5225241188E04925B2C97CA6B8E111AB5B5C556C0AEA292ACA4D88BDB144114FDED5796EC868E55DA982839Продажа конечному потребителю в точке продаж539141
114452021-12-28D2ADE4420267691085492905CD39F5526B8E111AB5B5C556C0AEA292ACA4D88B6B8E111AB5B5C556C0AEA292ACA4D88BDB144114FDED5796EC868E55DA982839Продажа конечному потребителю в точке продаж785571
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" + ], + "text/plain": [ + " dt gtin \\\n", + "2916 2021-12-12 3F52CBC77CDFFF9C463FCACC791FE08C \n", + "13676 2022-02-19 F9638C01CB7835E6DC133B33533D4053 \n", + "11445 2021-12-28 D2ADE4420267691085492905CD39F552 \n", + "\n", + " prid inn \\\n", + "2916 7D1B71037D27D5597BEA1D5077663541 6B8E111AB5B5C556C0AEA292ACA4D88B \n", + "13676 91ECAC3DC5225241188E04925B2C97CA 6B8E111AB5B5C556C0AEA292ACA4D88B \n", + "11445 6B8E111AB5B5C556C0AEA292ACA4D88B 6B8E111AB5B5C556C0AEA292ACA4D88B \n", + "\n", + " id_sp_ \\\n", + "2916 DB144114FDED5796EC868E55DA982839 \n", + "13676 DB144114FDED5796EC868E55DA982839 \n", + "11445 DB144114FDED5796EC868E55DA982839 \n", + "\n", + " type_operation price cnt \n", + "2916 Продажа конечному потребителю в точке продаж 19985 1 \n", + "13676 Продажа конечному потребителю в точке продаж 53914 1 \n", + "11445 Продажа конечному потребителю в точке продаж 78557 1 " + ] + }, + "execution_count": 475, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_data = pd.read_csv('data/solo_shop.csv')\n", + "sample_data = pd.read_csv('data/sample_input.csv')\n", + "# sample_data.loc[sample_data['id_sp_'] == 'DB144114FDED5796EC868E55DA982839', 'id_sp_'] = np.nan\n", + "sample_data.sample(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 476, + "id": "0f815a55-aa23-4f37-bed2-feedb17888c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Preprocesser(products_info_path='Справочник продукции.csv',\n",
+       "             store_info_path='Справочник торговых точек.csv')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Preprocesser(products_info_path='Справочник продукции.csv',\n", + " store_info_path='Справочник торговых точек.csv')" + ] + }, + "execution_count": 476, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocesser = Preprocesser('Справочник продукции.csv', 'Справочник торговых точек.csv')\n", + "preprocesser.fit(first_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 498, + "id": "270477b5-8342-456e-b90d-ff22ae667a07", + "metadata": {}, + "outputs": [], + "source": [ + "def full_solver(model, preprocesser, data, pred_date='2022-12-01'):\n", + " cnt = preprocesser.get_df(data).groupby(['store', 'item'])['sales'].count()\n", + " df_test = preprocesser.transform(data, pred_date)\n", + " pred_mask = df_test['date'] == pred_date\n", + " \n", + " X_test, y_test = df_test.drop(columns=preprocesser.drop_cols), df_test['sales'] \n", + " preds = model.predict(X_test)\n", + " \n", + " df_test.loc[:, 'store'] = df_test['store'].astype(int)\n", + " df_test.loc[:, 'item'] = df_test['item'].astype(int)\n", + " mask = df_test.apply(lambda x: cnt[(x['store'], x['item'])], axis=1) < 10\n", + " if mask.sum():\n", + " # print(df_test[mask].apply(lambda x: preprocesser.mean_values[(x['store'], x['item'])], axis=1))\n", + " preds[mask] = df_test[mask].apply(lambda x: preprocesser.mean_values[(x['store'], x['item'])], axis=1) \n", + " \n", + " preds = np.around(preds)\n", + " print('mae:', mean_absolute_error(y_test[~pred_mask], preds[~pred_mask]))\n", + " print('smape:', smape(preds[~pred_mask], y_test[~pred_mask]))\n", + " \n", + " res = df_test[['store', 'item', 'date', 'sales']]\n", + " res.loc[:, 'preds'] = preds\n", + " res.loc[:, 'store'] = res['store'].apply(lambda x: preprocesser.numbers_to_hash['store'][x])\n", + " res.loc[:, 'item'] = res['item'].apply(lambda x: preprocesser.numbers_to_hash['item'][x])\n", + " return res[pred_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 499, + "id": "0728e787-253b-4bb4-9bd7-9d6f875dcb8f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████| 353850/353850 [00:09<00:00, 36473.91it/s]\n", + "100%|███████████████████████████████| 353850/353850 [00:07<00:00, 45729.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mae: 0.4042466205077481\n", + "smape: 48.88772680727258\n" + ] + }, + { + "data": { + "text/html": [ + "
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storeitemdatesalespreds
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storeitemdatepreds
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" + ], + "text/plain": [ + " store item \\\n", + "13 02DCBDBCDDEED5C6F2745F98B66441EA 0044BE7AA4BE1481389D6F23EE8AEAF2 \n", + "27 02DCBDBCDDEED5C6F2745F98B66441EA 00607B2F926E755BB8E4761E1B79BD9C \n", + "41 02DCBDBCDDEED5C6F2745F98B66441EA 008F0DE7FC1ED920B16F346ECE148020 \n", + "55 02DCBDBCDDEED5C6F2745F98B66441EA 0129331E8C601F46FB6959F801E7D62B \n", + "69 02DCBDBCDDEED5C6F2745F98B66441EA 023185562958EDC5AE67B5D77FC32904 \n", + "\n", + " date preds \n", + "13 2022-12-01 0.0 \n", + "27 2022-12-01 0.0 \n", + "41 2022-12-01 0.0 \n", + "55 2022-12-01 0.0 \n", + "69 2022-12-01 0.0 " + ] + }, + "execution_count": 478, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = full_solver(cat_model, preprocesser, first_data)\n", + "res.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 486, + "id": "2e47944a-b249-4ef2-9d29-2814f542e8a3", + "metadata": {}, + "outputs": [], + "source": [ + "res.to_csv('data/full_data_predictions.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 464, + "id": "c767bfee-9df0-43e7-b6aa-53c3a3481abf", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open('preprocesser.pickle', 'wb') as f:\n", + " pickle.dump(preprocesser, f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7bfa3d2-177e-49f6-93ec-0e22b66f70c6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/model.pickle b/model.pickle new file mode 100644 index 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