basket_novelty.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T04:02:41.039993Z",
     "start_time": "2020-10-11T04:02:39.877997Z"
    },
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "\n",
    "tables = {\n",
    "    'orders': '/kaggle/input/test-recsys/kaggle_tab_1345/tab_1_orders.csv',\n",
    "    'products': list(sorted(glob.glob('/kaggle/input/test-recsys/sbermarket_tab_2*/*'))),\n",
    "    'categories': '/kaggle/input/test-recsys/kaggle_tab_1345/tab_3_categories.csv',\n",
    "    'users': '/kaggle/input/test-recsys/kaggle_tab_1345/tab_4_user_profiles.csv',\n",
    "    'product_properties': '/kaggle/input/test-recsys/kaggle_tab_1345/tab_5_product_properties.csv',\n",
    "    'cities': '/kaggle/input/test-recsys/tab_6_city.csv',\n",
    "    'sample_susmission': '/kaggle/input/test-recsys/sample_submission.csv',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "products = pd.concat(list(map(lambda x: pd.read_csv(x)[['user_id', 'product_id', 'order_id']], tables['products'])))\n",
    "\n",
    "products = products.sort_values(['user_id', 'order_id'], ascending=[True, False])\n",
    "last_orders = products[['user_id', 'order_id']].groupby('user_id').head(1)\n",
    "\n",
    "last_order_products = products.loc[products['order_id'].isin(last_orders['order_id'])].drop(columns = 'order_id').groupby('user_id')['product_id'].apply(list).reset_index()\n",
    "prev_order_products = products.loc[~products['order_id'].isin(last_orders['order_id'])].drop(columns = 'order_id').groupby('user_id')['product_id'].unique().reset_index()\n",
    "\n",
    "last_order_products['number_of_products'] = np.array(list(map(len,last_order_products.product_id.values)))\n",
    "prev_order_products['number_of_products'] = np.array(list(map(len,prev_order_products.product_id.values)))\n",
    "\n",
    "final_table = pd.merge(prev_order_products, last_order_products, how='inner', on='user_id')\n",
    "list_of_intersection = [len(list(set(a) & set(b))) for a, b in zip(list(final_table['product_id_x']), list(final_table['product_id_y']))]\n",
    "final_table['percent'] = np.array(list_of_intersection)/final_table['number_of_products_x'].to_numpy()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(final_table[final_table['percent'] != 0]['percent'].sort_values().to_numpy(), bins=200)\n",
    "plt.xlabel('%', fontsize=15)\n",
    "plt.ylabel('Number of users', fontsize=15)\n",
    "plt.grid()\n",
    "clear_output()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}