recommender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import sparse\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import numpy as np\n",
    "import time\n",
    "import heapq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load original Data, sort by timestamp and save it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df = pd.concat((pd.read_csv('events_train.csv'), pd.read_csv('purchases_train.csv')), ignore_index=True)\n",
    "# df.sort_values(by=['timestamp'], inplace=True)\n",
    "# df.to_csv('unified_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('unified_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unique_counts(df, columns):\n",
    "    print('Unique value counts per column:')\n",
    "    for column in columns:\n",
    "        print('{:<25}{:>}'.format('Unique ' + column + 's:', len(df[column].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe shape: (14803097, 8)\n",
      "Unique value counts per column:\n",
      "Unique event_types:      2\n",
      "Unique customer_ids:     2211386\n",
      "Unique product_ids:      123837\n",
      "Unique titles:           71441\n",
      "Unique category_names:   714\n"
     ]
    }
   ],
   "source": [
    "print('Dataframe shape: {}'.format(df.shape))\n",
    "get_unique_counts(df, ['event_type', 'customer_id', 'product_id', 'title', 'category_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>event_type</th>\n",
       "      <th>product_id</th>\n",
       "      <th>title</th>\n",
       "      <th>category_name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>204685f9-00f0-4a36-bab1-e0b74673c3b1</td>\n",
       "      <td>2019-07-01 00:00:01.101977 UTC</td>\n",
       "      <td>view_item</td>\n",
       "      <td>f130c542-d793-41d0-88f6-d1c52b4fe8f0</td>\n",
       "      <td>Michael Kors Nappa Leather Racer Jacket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>747.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>552445a5-f41d-4b25-b479-4e776d9bee61</td>\n",
       "      <td>2019-07-01 00:00:01.901589 UTC</td>\n",
       "      <td>view_item</td>\n",
       "      <td>29db8f57-49bd-4108-a0bb-0d245278e999</td>\n",
       "      <td>Puma Woven Shorts Girls</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>fac01f48-a03a-41f2-acd5-b5f4e407d9c2</td>\n",
       "      <td>2019-07-01 00:00:08.954928 UTC</td>\n",
       "      <td>view_item</td>\n",
       "      <td>a90bddc4-8eb7-4c99-aa4f-2f2377c246a2</td>\n",
       "      <td>Trendyol Multi-colored Flywheel Detailed Swimwear</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                           customer_id  \\\n",
       "0           0  204685f9-00f0-4a36-bab1-e0b74673c3b1   \n",
       "1           1  552445a5-f41d-4b25-b479-4e776d9bee61   \n",
       "2           2  fac01f48-a03a-41f2-acd5-b5f4e407d9c2   \n",
       "\n",
       "                        timestamp event_type  \\\n",
       "0  2019-07-01 00:00:01.101977 UTC  view_item   \n",
       "1  2019-07-01 00:00:01.901589 UTC  view_item   \n",
       "2  2019-07-01 00:00:08.954928 UTC  view_item   \n",
       "\n",
       "                             product_id  \\\n",
       "0  f130c542-d793-41d0-88f6-d1c52b4fe8f0   \n",
       "1  29db8f57-49bd-4108-a0bb-0d245278e999   \n",
       "2  a90bddc4-8eb7-4c99-aa4f-2f2377c246a2   \n",
       "\n",
       "                                               title category_name   price  \n",
       "0            Michael Kors Nappa Leather Racer Jacket           NaN  747.49  \n",
       "1                            Puma Woven Shorts Girls           NaN   11.92  \n",
       "2  Trendyol Multi-colored Flywheel Detailed Swimwear           NaN   23.85  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.category_name.apply(lambda x: str(x).split('>')[0] if len(str(x).split('>')) > 0 else 'empty').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.price.describe().apply(lambda x: format(x, 'f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purchases.price.plot.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purchases.price.plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_missing_count(df, columns):\n",
    "    print('Missing counts per column:')\n",
    "    for column in columns:\n",
    "        print('{:<25}{:>}'.format('Missing ' + column + 's:', df[column].isna().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing counts per column:\n",
      "Missing customer_ids:    0\n",
      "Missing event_types:     0\n",
      "Missing product_ids:     0\n",
      "Missing titles:          1813\n",
      "Missing category_names:  3435732\n",
      "Missing prices:          44886\n"
     ]
    }
   ],
   "source": [
    "get_missing_count(df, ['customer_id', 'event_type', 'product_id', 'title', 'category_name', 'price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing titles based on equal product_ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing counts per column:\n",
      "Missing titles:          30\n"
     ]
    }
   ],
   "source": [
    "df.loc[df.title.isnull(), 'title'] = df.groupby('product_id')['title'].transform('first')\n",
    "get_missing_count(df, ['title'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing category_names based on equal product_ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing counts per column:\n",
      "Missing category_names:  25034\n"
     ]
    }
   ],
   "source": [
    "df.loc[df.category_name.isnull(), 'category_name'] = df.groupby('product_id')['category_name'].transform('first')\n",
    "get_missing_count(df, ['category_name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing titles based on equal category_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing counts per column:\n",
      "Missing titles:          26\n"
     ]
    }
   ],
   "source": [
    "df.loc[df.title.isnull(), 'title'] = df.groupby('category_name')['title'].transform('first')\n",
    "get_missing_count(df, ['title'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing category_names based on equal titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing counts per column:\n",
      "Missing category_names:  8583\n"
     ]
    }
   ],
   "source": [
    "df.loc[df.category_name.isnull(), 'category_name'] = df.groupby('title')['category_name'].transform('first')\n",
    "get_missing_count(df, ['category_name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing price based on equal titles and category_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing counts per column:\n",
      "Missing prices:          0\n"
     ]
    }
   ],
   "source": [
    "df.loc[df.price.isnull(), 'price'] = df.groupby('title')['price'].transform('first')\n",
    "df.loc[df.price.isnull(), 'price'] = df.groupby('category_name')['price'].transform('first')\n",
    "get_missing_count(df, ['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train len for 85-15% split: 12582632\n"
     ]
    }
   ],
   "source": [
    "train_size = round(df.shape[0]*0.85)\n",
    "print('Train len for 85-15% split:', train_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Map categorical columns to their codes for sparse format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_id_categorical = df[:train_size]['customer_id'].astype(\"category\")\n",
    "product_id_categorical = df[:train_size]['product_id'].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remember category codes for later lookups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_id_lookup = customer_id_categorical.cat.codes\n",
    "product_id_lookup = product_id_categorical.cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Map customers to categories and items for later lookups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "customer_map = dict((y, x) for x, y in enumerate(customer_id_categorical.cat.categories ))\n",
    "item_map = dict((x, y) for x, y in enumerate(product_id_categorical.cat.categories ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    204685f9-00f0-4a36-bab1-e0b74673c3b1\n",
       "1    552445a5-f41d-4b25-b479-4e776d9bee61\n",
       "2    fac01f48-a03a-41f2-acd5-b5f4e407d9c2\n",
       "Name: customer_id, dtype: category\n",
       "Categories (1882180, object): [0000130d-9652-4cfb-8c9c-1b1d97b988ab, 000014d9-5e0c-4a15-8697-20ac41f9a388, 000027e5-4dbd-4b9f-9a56-e4414f325358, 0000461a-6b42-456b-a47c-bc34749e62ce, ..., ffffe78e-28e6-467e-98b4-b6ebe83ca14b, fffff354-dd1f-451b-8c41-f1c039560280, fffffdc5-d457-470f-9939-6bb5df9593ba, fffffed2-4e10-445e-af87-f9b67c84438a]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_id_categorical[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     238211\n",
       "1     626251\n",
       "2    1843948\n",
       "dtype: int32"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_id_lookup[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " '001b46d6-76b8-4bb9-9c2e-34a661137e13': 793,\n",
       " '001b65ad-0ece-4b13-af76-7d667519ae34': 794,\n",
       " '001b6c0a-9c48-4577-a4b0-855f272aacc1': 795,\n",
       " '001b71d3-08d9-498a-8839-0864a1b01203': 796,\n",
       " '001b7200-4986-4a32-9125-001a5c35369c': 797,\n",
       " '001b7732-e85a-4327-8cc8-4b8249e866b1': 798,\n",
       " '001b7bca-4130-4834-b842-a2eabda8040f': 799,\n",
       " '001b9067-ca20-4e3a-abd1-bb87f2a8edc0': 800,\n",
       " '001b9341-8e60-4c1e-ba71-c994f32fc384': 801,\n",
       " '001b952f-b951-449a-863e-2617d25c04f0': 802,\n",
       " '001b9b8a-e42d-4790-aa5c-794b3bf0d257': 803,\n",
       " '001babcd-0227-4401-86cc-62c2f0d08c75': 804,\n",
       " '001bb3c2-c571-4259-86c7-208308f18452': 805,\n",
       " '001bbb44-51e3-4d2a-a934-fd617e35d5fb': 806,\n",
       " '001bd50e-0085-44be-96f1-35b6fbad729f': 807,\n",
       " '001bd570-6497-4253-831c-a62ed950d436': 808,\n",
       " '001bddd9-c6d7-475d-b90b-defa0cabe0b8': 809,\n",
       " '001be7a6-d0f6-4f54-8c47-970a42792085': 810,\n",
       " '001bf316-c0a7-4c9d-a342-b8cd8e099883': 811,\n",
       " '001bf3a5-5a43-4815-8a33-b0f0c8774aef': 812,\n",
       " '001bf434-14c2-485b-9879-4fbff459b04b': 813,\n",
       " '001bfd77-58b4-4a70-9cec-967229b860f3': 814,\n",
       " '001c09aa-86a7-4d86-ad7f-a6118a7c9f73': 815,\n",
       " '001c09b4-86ce-40ef-8299-a24218a82bee': 816,\n",
       " '001c20f4-6803-4743-bce0-2e7a62326c63': 817,\n",
       " '001c237d-1c6e-452e-a293-668e812227d8': 818,\n",
       " '001c2ccf-a4a6-49cc-a2ae-bc55fd61f49b': 819,\n",
       " '001c38cf-2add-4a0c-81f1-0a883fcf71c6': 820,\n",
       " '001c3dfc-44b5-4525-803c-1243a736f255': 821,\n",
       " '001c51ab-9fbf-4849-a3fb-6abbca0b5138': 822,\n",
       " '001c5540-813e-43c3-97fe-61305d0a1e12': 823,\n",
       " '001c5b47-85c8-4b3f-8b1a-11519af2da8d': 824,\n",
       " '001c5da9-2d16-4377-8602-046920991cf3': 825,\n",
       " '001c5fd8-73e6-4ac2-8232-10b82c08cf9c': 826,\n",
       " '001c700d-82f3-461d-acaf-9e10d4582976': 827,\n",
       " '001c72bb-a1dc-43bc-8ffc-17b8de55e6b8': 828,\n",
       " '001c766f-9d78-481e-9e03-c97055e1191a': 829,\n",
       " '001c8217-c3fb-4989-aea1-bfbe4073f53f': 830,\n",
       " '001c853e-0ac7-4734-95d1-eb07e7146239': 831,\n",
       " '001c85b0-be59-49a3-8a21-c8bd4fb11535': 832,\n",
       " '001c8d77-7996-4923-b514-2d8b784d3786': 833,\n",
       " '001c929e-2857-44ce-b873-a95b6b6ec322': 834,\n",
       " '001c92ec-d081-46f0-87e8-6327a3ae1f9c': 835,\n",
       " '001c9e22-ac99-496e-9277-4776da79c822': 836,\n",
       " '001c9ecc-1e92-44b3-9779-d1fb1526dabe': 837,\n",
       " '001ca015-aa77-417a-acec-4b4f44c995ab': 838,\n",
       " '001cd48b-f715-4677-8e31-3630cf74918a': 839,\n",
       " '001ce2c5-5fde-442a-865a-a314b2a99a72': 840,\n",
       " '001ce322-037b-4462-90d0-42b04f9ec720': 841,\n",
       " '001cebfc-8510-4516-8483-b867e67617a3': 842,\n",
       " '001cf52d-43f4-42e2-a95d-d64e7259764f': 843,\n",
       " '001cf771-08cf-491f-85e4-74e09603e7a5': 844,\n",
       " '001d0054-b328-480c-a719-82acee1ef480': 845,\n",
       " '001d00b2-ccd8-41da-9487-55dfe3741757': 846,\n",
       " '001d0f68-cda0-450a-9d37-f555a9886293': 847,\n",
       " '001d18f7-e986-4daa-8745-e42a22bed944': 848,\n",
       " '001d1fee-0176-4fb0-a2b9-ce861934433b': 849,\n",
       " '001d2ac4-34c3-46ac-a270-50f62aae7387': 850,\n",
       " '001d30c7-d75e-4708-9423-86a92dc4ed95': 851,\n",
       " '001d3170-b049-40a6-b081-046cb4fc756f': 852,\n",
       " '001d3719-4be1-4583-a7d5-ba8779cfcc7b': 853,\n",
       " '001d38f1-f741-43d1-86ca-154b1bc2eeac': 854,\n",
       " '001d3f9b-f8fe-4f92-b015-cd5b925813d8': 855,\n",
       " '001d4115-ddef-4c76-89bc-b34c3b225f57': 856,\n",
       " '001d4184-2ff1-4614-a3be-993712a4b9ba': 857,\n",
       " '001d431d-3f16-4156-83e6-b98bc2d3b24e': 858,\n",
       " '001d4ffc-7506-4a82-b3db-54441d6a51b5': 859,\n",
       " '001d5822-4b13-46c7-9357-b9ceaf2a363f': 860,\n",
       " '001d6032-65ba-4903-a0fb-4f3702ad055b': 861,\n",
       " '001d621a-a98d-4a19-98fa-5273f1f3f1a3': 862,\n",
       " '001d63a8-ba49-4892-9df9-903a798b64f8': 863,\n",
       " '001d648e-d958-42f4-9937-7de017e4d1f7': 864,\n",
       " '001d78d7-6251-472f-a650-7317cad2d071': 865,\n",
       " '001d79b2-0222-4bd1-a6a7-c726d9849bbe': 866,\n",
       " '001d7d0e-d953-4dc3-baa7-b0010dbcfecb': 867,\n",
       " '001d8016-95a7-4d1a-9003-dacdfe0292a9': 868,\n",
       " '001d9c36-f78d-4238-86d8-5380db7b103c': 869,\n",
       " '001dadf2-4e46-4a15-b079-05eeb880aca2': 870,\n",
       " '001dc891-eae6-4413-973a-eb3941f43040': 871,\n",
       " '001de723-343c-4712-b26d-52a43fd31c49': 872,\n",
       " '001df2de-3f62-4834-915f-06b61897d06f': 873,\n",
       " '001dfc04-33e9-4e93-968f-18b198803a7f': 874,\n",
       " '001e0537-8d9e-44a7-a536-e66fe2059b15': 875,\n",
       " '001e1560-c437-4c18-b274-2daa195ce1b5': 876,\n",
       " '001e1bcb-9689-4176-8392-47b7c958f02e': 877,\n",
       " '001e1f73-379b-4f11-8ae3-e7aa325b7746': 878,\n",
       " '001e3a7a-ea84-42b9-afad-516032e2531f': 879,\n",
       " '001e412f-4cc0-417c-bee8-cc37ad49a55c': 880,\n",
       " '001e457f-c62b-4723-9cb3-78dd5dc77024': 881,\n",
       " '001e4679-39c9-4d69-bf43-13fe568fbc5b': 882,\n",
       " '001e72a6-e49d-4092-aeb0-d7ac09d4f955': 883,\n",
       " '001e8448-1cfb-4d5d-bc7e-c0baf570ac99': 884,\n",
       " '001e9036-14c5-451e-89ad-cb4ae7af5561': 885,\n",
       " '001e91d7-5a97-43ec-9de0-13993f34a92d': 886,\n",
       " '001e93f7-fe25-4efc-a418-e95e1cc655ae': 887,\n",
       " '001e9579-93e8-404e-8983-47de2d81bcb5': 888,\n",
       " '001e9620-d6ea-4a33-9c0b-79b8a05b679a': 889,\n",
       " '001e98d9-318f-4c82-ad26-54a97eea8849': 890,\n",
       " '001e9f30-a821-405b-9dfa-04c603e5dee8': 891,\n",
       " '001ea285-5275-483e-9c8d-f090f316abe8': 892,\n",
       " '001ea51a-659e-46ab-98f9-17d84920c089': 893,\n",
       " '001eb2f8-c3c4-41fd-aa1d-a8033988a15b': 894,\n",
       " '001eca2b-6605-4bf9-8dac-23de60f0b383': 895,\n",
       " '001ed1f2-ef93-463e-aa26-2162f2e7543d': 896,\n",
       " '001ed8ae-12d7-45da-af1a-e0c6064679af': 897,\n",
       " '001edc66-265e-4d56-9a0b-da278aeed20a': 898,\n",
       " '001ee02b-d4fd-4694-890d-9bbb8148615a': 899,\n",
       " '001eee7f-7a1a-4aeb-a12c-45646c363a45': 900,\n",
       " '001ef12f-2eeb-450b-a71a-f3415c1674e6': 901,\n",
       " '001ef8c2-e1ff-4ed1-a26b-b0b89e431dd0': 902,\n",
       " '001f01ef-da38-4eb0-8f9a-8c54289e9054': 903,\n",
       " '001f0677-d4d1-4218-a2d1-5c759581f2ce': 904,\n",
       " '001f0bec-5f3c-4d5e-8653-24e144a58f8d': 905,\n",
       " '001f0da5-96b1-45e8-9275-85581a805f09': 906,\n",
       " '001f24d4-80df-41be-ab97-c9dc26a4d257': 907,\n",
       " '001f352b-f3d7-4630-8ea2-6d5615de535e': 908,\n",
       " '001f5066-9e8a-4cbc-bdd3-1f6891f12e5f': 909,\n",
       " '001f6448-84a7-4415-9bd5-febfd862b13c': 910,\n",
       " '001f6b42-72bc-4756-904a-3543846a5560': 911,\n",
       " '001f70b7-3c2a-4df6-acb5-d9941995fcca': 912,\n",
       " '001f83ae-7671-43b0-b09d-fd5b656f1404': 913,\n",
       " '001f8967-7e26-47af-9f6e-d3a2281ea215': 914,\n",
       " '001f8a71-ac68-4f89-9eea-63d9eafed7c7': 915,\n",
       " '001f8e6d-291c-4e75-b6d3-596ae60049b8': 916,\n",
       " '001f9cc6-e0f3-4a96-b3ff-2c5b7f2425b7': 917,\n",
       " '001fa8f5-1b5c-480c-a694-7f44e4a51093': 918,\n",
       " '001fab45-1429-43ea-a0e8-53cc7e7daa2a': 919,\n",
       " '001fb077-5ebd-42e0-ba1f-61a08d0ec799': 920,\n",
       " '001fb444-754f-4e11-9425-6f9c06196c7d': 921,\n",
       " '001fb77d-08e3-4159-9e6e-fe6192ade98c': 922,\n",
       " '001fb7dc-a844-4782-b9b4-09d6a4567f8c': 923,\n",
       " '001fb9e8-c01f-40a2-a872-22720eb9c2fe': 924,\n",
       " '001fbca9-9424-40bd-856a-fdd31053cf88': 925,\n",
       " '001fc11a-7861-4819-ad52-0e263b48c590': 926,\n",
       " '001fca7e-f244-4e4c-bfc0-22700ab137bf': 927,\n",
       " '001fcdbc-3f6b-4eec-8660-85edd4305ec0': 928,\n",
       " '001fdadd-94ef-42dc-becb-4ea3b095ea6a': 929,\n",
       " '001fdff1-b344-4d1c-acc9-43c5e8deeb2f': 930,\n",
       " '001fe58b-e24f-4fd6-b122-57990a3c8270': 931,\n",
       " '001ff8d3-1e11-4dbf-b1ce-10822c33b4de': 932,\n",
       " '001ffddf-523b-41eb-85ac-4de93805d86f': 933,\n",
       " '00200537-708d-4db6-b8cd-91679f682072': 934,\n",
       " '00201443-a6e2-4171-9222-795ee038a847': 935,\n",
       " '00201f10-bc6d-495a-aee8-03c991e3b3b9': 936,\n",
       " '00202161-4ddd-47ea-b467-292e11ed66a3': 937,\n",
       " '00202e22-3e3d-4d31-aed5-7b526ee2af8e': 938,\n",
       " '00203600-dcac-4a28-8fc7-48952c4d5720': 939,\n",
       " '00203671-f20f-4b95-bc26-38391f9a0c35': 940,\n",
       " '00203f13-0e2c-46b2-8191-f7c828f6a5f6': 941,\n",
       " '00205a71-f7e7-4c1b-85f5-fcab8df58ecc': 942,\n",
       " '0020774c-0b38-4804-8c3b-72be495fc3f3': 943,\n",
       " '00207bb7-280d-412f-95f2-0b0a0940316b': 944,\n",
       " '00208a56-5d66-45ec-81fa-1e90278e6dc2': 945,\n",
       " '00208d04-1996-4e95-afdc-2a5555fbd0e7': 946,\n",
       " '00209004-5398-4fb5-80bb-bf5176eeef36': 947,\n",
       " '002094e2-d5bb-4b88-a6f0-2266b1517c86': 948,\n",
       " '00209b9f-d3dc-440f-8cc6-fb708cf5d4c5': 949,\n",
       " '0020a380-9062-48f9-839f-bba22265f476': 950,\n",
       " '0020bc9c-17a5-4605-80d7-d93de1a6e780': 951,\n",
       " '0020c0f9-5427-45bc-9b42-509c75d71843': 952,\n",
       " '0020e746-d853-4b2c-a353-aea7f27655fb': 953,\n",
       " '0020e89e-baa0-49b6-b283-742c70d08ee0': 954,\n",
       " '0020eed6-556a-4ecc-8502-12faa2385dc4': 955,\n",
       " '0020f52d-e9e2-4ba7-948e-188042078f95': 956,\n",
       " '0020fed9-2fa8-46a9-8d60-9baa1e982b70': 957,\n",
       " '00210723-6618-447c-bdf4-70deb665bb3a': 958,\n",
       " '0021081a-347a-4722-b381-654ea9d52708': 959,\n",
       " '002108c5-ced5-4eb4-843f-ca6f94245b4e': 960,\n",
       " '00210aae-9680-44bd-bb17-1b152c11bec8': 961,\n",
       " '00210b73-5dab-4fc7-8b4d-2f6d664b0962': 962,\n",
       " '00210d37-9b7e-41c2-9800-29338caf548e': 963,\n",
       " '00211bac-57ca-4e1e-9310-873ae7176304': 964,\n",
       " '0021212e-63cc-4a8e-89d6-361ac67e1b0e': 965,\n",
       " '002139da-6f55-4f8c-944d-96c0e2735f5e': 966,\n",
       " '00214385-c71e-4832-8544-6579e210bd45': 967,\n",
       " '002145d8-e30b-48f3-90d0-65fec9d7d6cc': 968,\n",
       " '0021502a-1616-4734-990c-3a8f99126aba': 969,\n",
       " '002156c2-b0a5-477e-a592-79125997e2d1': 970,\n",
       " '00215980-6a5e-49b3-8be4-bae7d2492d32': 971,\n",
       " '00215cf0-4e28-43a9-acd5-1b95cadbec5a': 972,\n",
       " '002162a0-33bf-4353-bb3e-8437e0075668': 973,\n",
       " '00216db6-3393-4ecb-ab7b-383b7c8c6d3d': 974,\n",
       " '00216f50-6952-42b3-bcc3-b012add1cecd': 975,\n",
       " '00218d28-7807-4de7-b0b9-8084d1c70dcd': 976,\n",
       " '0021afb9-0c51-4a33-890c-fb22aa7f748e': 977,\n",
       " '0021b437-a8df-4494-9a47-187978b191d4': 978,\n",
       " '0021c14a-7f7b-4e81-8c66-f9c3233317c7': 979,\n",
       " '0021c310-bf75-42c5-ad26-ed610928260e': 980,\n",
       " '0021c9ec-9a39-4686-bf91-eff54b4ccb24': 981,\n",
       " '0021cdeb-5b96-402b-8e0c-783e931abcf6': 982,\n",
       " '0021d69b-ce3b-4a98-b383-fb72333e69e4': 983,\n",
       " '0021eadc-cf91-4aa3-96bb-7ec1ab41725b': 984,\n",
       " '0021f0e0-f526-4689-acfa-f3a671723804': 985,\n",
       " '0021f12e-b152-4099-bd03-58f5544172cf': 986,\n",
       " '0022088f-b3aa-4d8d-b332-6618dcd83547': 987,\n",
       " '002208c7-4b4c-4aba-a49f-639c33ebe35b': 988,\n",
       " '002214a8-8947-4b3e-8dd3-01d040d6af31': 989,\n",
       " '0022165f-a1a1-4430-ad2e-f93e59359e66': 990,\n",
       " '002219c6-3132-4418-90f7-05b89be23e29': 991,\n",
       " '002219f6-9812-4a1c-b8c5-8fabfa9ca03a': 992,\n",
       " '002220d4-f993-46e5-b251-0dd3e646a893': 993,\n",
       " '00222178-4c38-4044-a71c-54de785c7e2c': 994,\n",
       " '002225de-ce3e-43af-8dc2-e2f8b754f8ee': 995,\n",
       " '002231bb-e0d1-42e6-9235-f7bc9d4fdc50': 996,\n",
       " '00223c97-52c5-4905-a0ae-7fc9450b3197': 997,\n",
       " '00224720-dfe6-4212-8fc8-6b400a18f914': 998,\n",
       " '0022472b-95b1-4c77-b01c-06c56299a93c': 999,\n",
       " ...}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = df[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['f130c542-d793-41d0-88f6-d1c52b4fe8f0',\n",
       "       '752a7f86-3b60-42b4-945c-9d25a83a9632',\n",
       "       'da087c55-9ccf-4914-a23e-dc2984582c54',\n",
       "       '941e630e-93bc-4326-b57b-c2f2cb42edbc',\n",
       "       'bdd146bd-eba9-4db0-98cf-2a35d8ea3dcd',\n",
       "       '79df83fc-7c78-4417-89a9-6dd4784a2dda',\n",
       "       '23dc13de-88ac-4124-837b-e4016a289d6f',\n",
       "       'a588416e-597c-4ef9-8afa-f315411811a9',\n",
       "       '2809a461-277e-40a1-afdb-9d5aead6dfbc',\n",
       "       '2e7079b4-9773-49c4-966d-c876fc9f3919',\n",
       "       'b5d190c7-32b2-4210-9af7-03d71ba81fc8',\n",
       "       '28c1ba9b-96ed-41ee-b5ab-c05de22ed6b7',\n",
       "       '34957903-0b34-49f7-a243-b6b3fc074fdf',\n",
       "       '046fd932-24cd-45c7-8bc9-899af5204e89',\n",
       "       '035a620f-9789-4be9-921a-2dfdc3dec371',\n",
       "       '7805a29f-6076-4802-a5ed-687ae2680abe',\n",
       "       '75046ab2-a978-44a4-afbc-5ae30ce7f449',\n",
       "       'a6eb2400-c46b-4e1f-9ce0-aa469d95fc8b',\n",
       "       '51d65dad-a8be-4553-8034-2c50c5ccb67b',\n",
       "       '3072b612-5b82-4d74-8fa2-35d4de1256a8',\n",
       "       'e2eeb1d4-87ab-4981-b553-14f62710f022',\n",
       "       '591db297-8ff5-4c2d-8902-4e5ff5c041df'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['customer_id'] == '204685f9-00f0-4a36-bab1-e0b74673c3b1'].product_id.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create occurence sparse matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cooDf = sparse.coo_matrix((np.ones(train_size), \n",
    "    (customer_id_lookup, product_id_lookup)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1882180, 118612)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cooDf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transform coo_matrix to transposed lil_matrix for counting column occurences fast "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lilDf = cooDf.tolil().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118612, 1882180)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lilDf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_counts = [i.nnz for i in lilDf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "del lilDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get 10 most popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_popular_idxs = heapq.nlargest(10, range(len(column_counts)), column_counts.__getitem__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[78118, 105143, 7219, 44905, 23061, 95360, 50429, 48582, 60728, 82888]\n"
     ]
    }
   ],
   "source": [
    "print(most_popular_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print total user interactions with popular items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparse density - number of unique user interactions with items:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Item id: 78118 - Interactions: 27500',\n",
       " 'Item id: 105143 - Interactions: 27314',\n",
       " 'Item id: 7219 - Interactions: 24698',\n",
       " 'Item id: 44905 - Interactions: 24655',\n",
       " 'Item id: 23061 - Interactions: 23024',\n",
       " 'Item id: 95360 - Interactions: 21887',\n",
       " 'Item id: 50429 - Interactions: 18969',\n",
       " 'Item id: 48582 - Interactions: 16833',\n",
       " 'Item id: 60728 - Interactions: 16397',\n",
       " 'Item id: 82888 - Interactions: 15351']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Sparse density - number of unique user interactions with items:')\n",
    "list(map(lambda x: 'Item id: {} - Interactions: {}'.format(x, len(cooDf.getcol(x).nonzero()[0])), most_popular_idxs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_popular_item_ids = list(map(lambda x: item_map[x], most_popular_idxs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a8f0292d-5fca-42b9-b0d3-b38e7efa416b',\n",
       " 'e33cea18-ee3f-4e37-83a7-1d88c54d83b8',\n",
       " '0f943312-7141-4606-abfa-81fd63a5498f',\n",
       " '608f32cd-210c-4af5-99d8-6182a9678cf5',\n",
       " '31ae211d-57a9-4bfa-b95f-40776af03c3f',\n",
       " 'ce1bc0cc-5e6c-4587-9d7a-82a6e32648a5',\n",
       " '6cee8dc2-0528-49f4-891a-f89a8114cfd6',\n",
       " '68d71059-0dd3-4817-b266-ebeb0d555c38',\n",
       " '8369aebc-fba5-4957-b3fb-7da05f327dff',\n",
       " 'b345fd30-281d-4b32-8f88-76fc84bea664']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_popular_item_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a8f0292d-5fca-42b9-b0d3-b38e7efa416b (43209, 8)\n",
      "e33cea18-ee3f-4e37-83a7-1d88c54d83b8 (42252, 8)\n",
      "0f943312-7141-4606-abfa-81fd63a5498f (39279, 8)\n",
      "608f32cd-210c-4af5-99d8-6182a9678cf5 (39636, 8)\n",
      "31ae211d-57a9-4bfa-b95f-40776af03c3f (42247, 8)\n",
      "ce1bc0cc-5e6c-4587-9d7a-82a6e32648a5 (33184, 8)\n",
      "6cee8dc2-0528-49f4-891a-f89a8114cfd6 (31303, 8)\n",
      "68d71059-0dd3-4817-b266-ebeb0d555c38 (27118, 8)\n",
      "8369aebc-fba5-4957-b3fb-7da05f327dff (28081, 8)\n",
      "b345fd30-281d-4b32-8f88-76fc84bea664 (24353, 8)\n"
     ]
    }
   ],
   "source": [
    "tmp = df[:train_size]\n",
    "for id in most_popular_item_ids:\n",
    "    print(id, tmp[tmp.product_id == id].shape)\n",
    "del tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KNN_model:\n",
    "    def __init__(self,\n",
    "                 n_neighbors, \n",
    "                 metric, \n",
    "                 coo, \n",
    "                 customer_map, \n",
    "                 product_map, \n",
    "                 N_popular_ids, \n",
    "                 discard_already_seen=False,\n",
    "                 cutoff_low_similarity=False,\n",
    "                 cutoff_threshold=0.2,\n",
    "                 cutoff_count=0,\n",
    "                 normalize_coo=False\n",
    "                ):\n",
    "        self.n_neighbors = n_neighbors\n",
    "        self.model = NearestNeighbors(n_neighbors, metric=metric, n_jobs=-1)\n",
    "        self.coo = coo\n",
    "        self.customer_map = customer_map\n",
    "        self.product_map = product_map\n",
    "        self.N_popular_ids = N_popular_ids\n",
    "        self.discard_already_seen = discard_already_seen\n",
    "        self.cutoff_low_similarity = cutoff_low_similarity\n",
    "        self.cutoff_threshold = cutoff_threshold\n",
    "        self.cutoff_count = cutoff_count\n",
    "        if normalize_coo == True:\n",
    "            self.coo.data = np.log1p(self.coo.data)\n",
    "        \n",
    "            \n",
    "        \n",
    "    def fit(self):\n",
    "        self.model.fit(self.coo)\n",
    "        \n",
    "    def translate_relevances_to_product_ids(self, relevances):\n",
    "        return list(map(lambda x: self.product_map[x[0]], relevances))\n",
    "    \n",
    "    def fill_with_popularity(self, recommendations):\n",
    "        rec_len = len(recommendations)\n",
    "        return recommendations + self.N_popular_ids[:10-rec_len]\n",
    "    \n",
    "    def find_first_smaller_index(self, relevances):\n",
    "        try:\n",
    "            idx_first_smaller = next(idx for idx, value in enumerate(relevances) \n",
    "                                     if value[1] < self.cutoff_threshold)\n",
    "            return idx_first_smaller\n",
    "        except StopIteration:\n",
    "            return len(relevances)\n",
    "            \n",
    "            \n",
    "    def predict_10(self, customer_id):\n",
    "        if customer_id in self.customer_map:\n",
    "            customer_index = self.customer_map[customer_id]\n",
    "            customer_row = self.coo.getrow(customer_index)\n",
    "\n",
    "            # get neighbors\n",
    "            neighbors = self.model.kneighbors(customer_row, n_neighbors=self.n_neighbors)\n",
    "            \n",
    "            # sum their items based on distance            \n",
    "            item_relevances = {}\n",
    "            for dist, idx in zip(neighbors[0][0], neighbors[1][0]):           \n",
    "                nb_items = self.coo.getrow(idx).nonzero()[1]\n",
    "                relevance = 1 - dist\n",
    "                for nb_item in nb_items:\n",
    "                    item_relevances[nb_item] = item_relevances.get(nb_item, 0) + relevance\n",
    "                    \n",
    "            if self.discard_already_seen == True:\n",
    "                # remove items already interacted with\n",
    "                own_items = customer_row.nonzero()[1]\n",
    "                for i in own_items:\n",
    "                    item_relevances.pop(i, None)\n",
    "                \n",
    "            \n",
    "            # Sorted by relevance\n",
    "            sorted_relevances = sorted(item_relevances.items(), key=lambda kv: kv[1], reverse=True)\n",
    "            \n",
    "            # Cutoff items with low relevance and thus fill missing spots with popularity\n",
    "            if self.cutoff_low_similarity == True:\n",
    "                if self.cutoff_count > 0:\n",
    "                    sorted_relevances = sorted_relevances[:10-self.cutoff_count]\n",
    "                else:\n",
    "                    first_smaller_idx = self.find_first_smaller_index(sorted_relevances)\n",
    "                    sorted_relevances = sorted_relevances[:first_smaller_idx]\n",
    "            \n",
    "            product_recommendations = self.translate_relevances_to_product_ids(sorted_relevances[:10])\n",
    "            product_recommendations = self.fill_with_popularity(product_recommendations)\n",
    "    \n",
    "            return product_recommendations\n",
    "            # return top 10\n",
    "        else:\n",
    "            return self.fill_with_popularity([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1_settings = {\n",
    "    'n_neighbors': 10,\n",
    "    'discard_already_seen': True,\n",
    "    'cutoff_low_similarity': False,\n",
    "    'cutoff_threshold': 0.2,\n",
    "    'normalize_coo': False\n",
    "}\n",
    "\n",
    "model_2_settings = {\n",
    "    'n_neighbors': 20,\n",
    "    'discard_already_seen': True,\n",
    "    'cutoff_low_similarity': True,\n",
    "    'cutoff_threshold': 0.4,\n",
    "    'normalize_coo': False\n",
    "}\n",
    "\n",
    "model_3_settings = {\n",
    "    'n_neighbors': 5,\n",
    "    'discard_already_seen': True,\n",
    "    'cutoff_low_similarity': True,\n",
    "    'cutoff_threshold': 0.15,\n",
    "    'normalize_coo': True\n",
    "}\n",
    "\n",
    "model_4_settings = {\n",
    "    'n_neighbors': 20,\n",
    "    'discard_already_seen': False,\n",
    "    'cutoff_low_similarity': True,\n",
    "    'cutoff_threshold': 0.4,\n",
    "    'normalize_coo': False\n",
    "}\n",
    "\n",
    "model_5_settings = {\n",
    "    'n_neighbors': 20,\n",
    "    'discard_already_seen': False,\n",
    "    'cutoff_low_similarity': False,\n",
    "    'cutoff_threshold': 0.2,\n",
    "    'normalize_coo': True\n",
    "}\n",
    "\n",
    "models = [model_1_settings, model_2_settings, model_3_settings, model_4_settings, model_5_settings]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_6_settings = {\n",
    "    'n_neighbors': 15,\n",
    "    'discard_already_seen': False,\n",
    "    'cutoff_low_similarity': True,\n",
    "    'cutoff_threshold': 1,\n",
    "    'normalize_coo': False\n",
    "}\n",
    "\n",
    "model_7_settings = {\n",
    "    'n_neighbors': 15,\n",
    "    'discard_already_seen': False,\n",
    "    'cutoff_low_similarity': True,\n",
    "    'cutoff_threshold': 1.3,\n",
    "    'normalize_coo': False\n",
    "}\n",
    "\n",
    "model_8_settings = {\n",
    "    'n_neighbors': 10,\n",
    "    'discard_already_seen': False,\n",
    "    'cutoff_low_similarity': True,\n",
    "    'cutoff_count': 3,\n",
    "    'normalize_coo': False\n",
    "}\n",
    "\n",
    "models2 = [model_6_settings, model_7_settings, model_8_settings]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dcg_at_k(r, k, method=0):\n",
    "    r = np.asfarray(r)[:k]\n",
    "    if r.size:\n",
    "        if method == 0:\n",
    "            return r[0] + np.sum(r[1:] / np.log2(np.arange(2, r.size + 1)))\n",
    "        elif method == 1:\n",
    "            return np.sum(r / np.log2(np.arange(2, r.size + 2)))\n",
    "        else:\n",
    "            raise ValueError('method must be 0 or 1.')\n",
    "    return 0.\n",
    "\n",
    "def ndcg_at_k(r, k, method=0):\n",
    "    dcg_max = dcg_at_k(sorted(r, reverse=True), k, method)\n",
    "#             print('Relevances:\\n', sorted_relevances[:10])\n",
    "    if not dcg_max:\n",
    "        return 0.\n",
    "    return dcg_at_k(r, k, method) / dcg_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing on: 400 users\n"
     ]
    }
   ],
   "source": [
    "first_N_users = test_df[:1075].customer_id.unique()\n",
    "\n",
    "print('Testing on: {} users'.format(len(first_N_users)))\n",
    "\n",
    "for i, model in enumerate(models2):\n",
    "    f = open(\"model_{}.txt\".format(i+6), \"a\")\n",
    "    \n",
    "    neigh = KNN_model(\n",
    "        metric='cosine',\n",
    "        coo=cooDf, \n",
    "        customer_map=customer_map, \n",
    "        product_map=item_map,\n",
    "        N_popular_ids=most_popular_item_ids,\n",
    "        **model\n",
    "    )\n",
    "    neigh.fit()\n",
    "    \n",
    "    total_ndcg = 0\n",
    "    count = 0\n",
    "    for customer in first_N_users:\n",
    "        nbs = neigh.predict_10(customer)\n",
    "        \n",
    "        checked_products = set(test_df[test_df.customer_id == customer].product_id.unique())\n",
    "        relevances = list(map(lambda x: 1 if x in checked_products else 0, nbs))\n",
    "\n",
    "        ndcg = ndcg_at_k(relevances, 10)\n",
    "        \n",
    "        total_ndcg += ndcg\n",
    "        count += 1\n",
    "    \n",
    "    f.write(\"NDCG: {}\".format(total_ndcg / count))\n",
    "    f.close()"
   ]
  }
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