3. YandexRecommendationService.ipynb

{
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   "cell_type": "markdown",
   "id": "2a6ec4a0-88c8-4bf7-8915-852c7b6d0413",
   "metadata": {},
   "source": [
    "# 1. Download data and Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00d0c8f7-c1da-4573-ae9b-3e1561949e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip3 install wldhx.yadisk-direct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ec48a32-508e-41ec-a615-e53579e13f1d",
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    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !curl -L $(yadisk-direct https://disk.yandex.ru/d/9yMyAhVNw_tvLA) -o data.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c83edaa-30ff-4def-8c6c-5657a4af31bb",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  data.zip\n",
      "   creating: data/\n",
      "  inflating: data/aspects.csv        \n",
      "  inflating: data/features.csv       \n",
      "  inflating: data/organisations.csv  \n",
      "  inflating: data/reviews.csv        \n",
      "  inflating: data/rubrics.csv        \n",
      "  inflating: data/test_users.csv     \n",
      "  inflating: data/users.csv          \n"
     ]
    }
   ],
   "source": [
    "# !unzip data.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dfe89cdc-cd90-4f2a-b1b3-1824fb158ce0",
   "metadata": {
    "execution": {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import sparse\n",
    "from tqdm.auto import tqdm\n",
    "import typing as tp\n",
    "import plotly\n",
    "import random\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "tqdm.pandas()\n",
    "\n",
    "np.random.seed(2022)\n",
    "random.seed(2022)\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01032d84-0ef2-4259-ac60-efeeba184c89",
   "metadata": {},
   "source": [
    "# 2. Data preparation and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "7ecfba67-59e9-4bc3-968e-7730791e9679",
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "/home/alekseyen/miniconda3/envs/py39/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "reviews_df = pd.read_csv('data/reviews.csv').rename(columns= {'aspects' : 'aspect_ids', 'rating':'review_rating'},)\n",
    "\n",
    "users_df = pd.read_csv('data/users.csv').rename(columns = {'city': 'user_city'})\n",
    "test_users_df = pd.read_csv('data/test_users.csv')\n",
    "organisations_df = pd.read_csv('data/organisations.csv').rename(columns={'city': 'org_city', 'rating': 'org_rating'})\n",
    "\n",
    "rubrics_df = pd.read_csv('data/rubrics.csv')\n",
    "features_df = pd.read_csv('data/features.csv')\n",
    "aspects_df = pd.read_csv('data/aspects.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76ce70cb-402d-44b2-b792-690ca811ffe8",
   "metadata": {
    "execution": {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15 entries, 0 to 14\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   rubric_id    15 non-null     int64 \n",
      " 1   rubric_name  15 non-null     object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 368.0+ bytes\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1006 entries, 0 to 1005\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   feature_id    1006 non-null   int64 \n",
      " 1   feature_name  1006 non-null   object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 15.8+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 243 entries, 0 to 242\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   aspect_id    243 non-null    int64 \n",
      " 1   aspect_name  243 non-null    object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 3.9+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None, None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubrics_df.info(), features_df.info(), aspects_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a1109d-d04b-4e9a-81ef-d711cc6388a9",
   "metadata": {},
   "source": [
    "As assumed sizes reviews_dfof `dict` dataframe is small. But let's imagine it's huge :)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f57b0ada-d9b1-447f-ad3e-28e279a7796f",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "# reviews_df.aspect_ids = pd.to_numeric(reviews_df.aspect_ids)"
   ]
  },
  {
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       "                      user_id                org_id  review_rating    ts  \\\n",
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       "\n",
       "        aspect_ids  \n",
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   "source": [
    "reviews_df.explode('aspect_ids')"
   ]
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   "source": [
    "def apply_func_to_each_columns(df: pd.DataFrame, columns_to_apply: list[str], func: tp.Callable) -> None:\n",
    "    new_df = df.copy()\n",
    "    for column in columns_to_apply:\n",
    "        # rows_without_NAN = ~df[column].isnull()\n",
    "        # df.loc[rows_without_NAN, column] = df.loc[rows_without_NAN, column].apply(func)\n",
    "        new_df[column] = df[column].apply(func)\n",
    "        \n",
    "    return new_df\n",
    "\n",
    "parse_str_to_int_list = lambda line: [int(number) for number in str(line).split(' ') if number.isnumeric()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac2bd73-8f2e-4e82-8bbc-51ffb088f508",
   "metadata": {},
   "source": [
    "1. in `reviews` parse `aspect_ids`"
   ]
  },
  {
   "cell_type": "code",
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   "id": "14f0bdea-20f8-4594-ac38-3e2d9b2abff1",
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   "source": [
    "reviews = apply_func_to_each_columns(reviews_df, ['aspect_ids'], parse_str_to_int_list)\n",
    "\n",
    "reviews.aspect_ids.apply(len).sort_values(ascending=False)\n"
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       "      <td>1206</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
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       "      <td>3064623136350032078</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>744</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>9026371437099015800</td>\n",
       "      <td>5718418596353227955</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1086</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2473092</th>\n",
       "      <td>18375136522233841854</td>\n",
       "      <td>6129601420560555512</td>\n",
       "      <td>5.0</td>\n",
       "      <td>835</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3302014</th>\n",
       "      <td>2515715014433407707</td>\n",
       "      <td>5742705483529187651</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553444</th>\n",
       "      <td>13814904045016888096</td>\n",
       "      <td>15919482492715844882</td>\n",
       "      <td>5.0</td>\n",
       "      <td>425</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      user_id                org_id  review_rating    ts  \\\n",
       "2896631    229425976098127178  17292100939536682647            3.0   679   \n",
       "3672042  16874620654240530563  11427946889796726844            5.0  1156   \n",
       "3918783   2020161878602313605   4176108691529323966            5.0    25   \n",
       "2206782   5313458781801270324  16444928614807344345            5.0  1206   \n",
       "1002351   9818407012068264498   1427695528613762065            5.0  1140   \n",
       "3353762   3064623136350032078   4594011633420927733            5.0   744   \n",
       "2649272   9026371437099015800   5718418596353227955            3.0  1086   \n",
       "2473092  18375136522233841854   6129601420560555512            5.0   835   \n",
       "3302014   2515715014433407707   5742705483529187651            5.0  1013   \n",
       "2553444  13814904045016888096  15919482492715844882            5.0   425   \n",
       "\n",
       "        aspect_id  \n",
       "2896631       NaN  \n",
       "3672042       267  \n",
       "3918783       265  \n",
       "2206782       NaN  \n",
       "1002351       NaN  \n",
       "3353762       NaN  \n",
       "2649272       NaN  \n",
       "2473092       NaN  \n",
       "3302014       NaN  \n",
       "2553444       NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = reviews.explode('aspect_ids').rename(columns={'aspect_ids': 'aspect_id'}) # same function also in spark\n",
    "reviews.sample(10)\n",
    "\n",
    "# or do smth like: https://stackoverflow.com/questions/39954668/how-to-convert-column-with-list-of-values-into-rows-in-pandas-dataframe\n",
    "# https://stackoverflow.com/questions/45846765/efficient-way-to-unnest-explode-multiple-list-columns-in-a-pandas-dataframe\n",
    "\n",
    "# >>> s = df.apply(lambda x: pd.Series(x['samples']), axis=1).stack().reset_index(level=1, drop=True)\n",
    "# >>> s.name = 'sample'\n",
    "# >>> df.drop('samples', axis=1).join(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e2f778-42d1-442e-97fc-b9352d5fe6dc",
   "metadata": {},
   "source": [
    "As u can see instead of `4129696` we got `4585254`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "207c66e4-06ba-4549-9afd-93cf12ee3411",
   "metadata": {},
   "source": [
    "2. in `organisations` parse `features_ids`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7ad2d52-3039-45a4-8bfb-bdc80961649e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:10:57.053126Z",
     "iopub.status.busy": "2022-04-29T13:10:57.052945Z",
     "iopub.status.idle": "2022-04-29T13:10:57.060566Z",
     "shell.execute_reply": "2022-04-29T13:10:57.059658Z",
     "shell.execute_reply.started": "2022-04-29T13:10:57.053102Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3501685156 3501779478 20422 3502045016 3502045...\n",
       "1        1509 1082283206 273469383 10462 11617 35017794...\n",
       "2        10462 11177 11617 11629 1416 1018 11704 11867 ...\n",
       "3        3501618484 2020795524 11629 11617 1018 11704 2...\n",
       "4        11617 10462 11177 1416 11867 3501744275 20282 ...\n",
       "                               ...                        \n",
       "68334    1018 1415 10462 11629 11867 20422 20424 118949...\n",
       "68335    11634 11629 3501481353 11177 3501773763 11867 ...\n",
       "68336    20422 11867 246 3501754799 3501779478 12048 35...\n",
       "68337    3491142672 3501481353 11867 20422 273469383 11...\n",
       "68338    3501498189 3501509030 3501498184 12309 20422 3...\n",
       "Name: features_id, Length: 68339, dtype: object"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organisations_df.features_id"
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "id": "5e921bfd-292b-4832-b902-0a15713c2691",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-04-29T13:10:58.317243Z",
     "shell.execute_reply.started": "2022-04-29T13:10:57.061721Z"
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>org_id</th>\n",
       "      <th>org_city</th>\n",
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       "      <th>org_rating</th>\n",
       "      <th>rubric_id</th>\n",
       "      <th>feature_id</th>\n",
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      "text/plain": [
       "                     org_id org_city  average_bill  org_rating rubric_id  \\\n",
       "65503   3728393500911044816      spb        1000.0    4.577778     30776   \n",
       "14378   4365770885606692016      spb         500.0    4.700000     31495   \n",
       "58402  12458691864921036268      spb         500.0    4.152542     30776   \n",
       "33526  16377585093291514129      msk        1000.0    4.181818     31401   \n",
       "55340   4373450798536241368      msk           NaN    3.969697     30774   \n",
       "36489  16161879390952478621      spb           NaN         NaN     30770   \n",
       "63797   6093159996422930978      msk        1500.0         NaN     30776   \n",
       "29307  15635531236542873290      msk         500.0    3.998634     30771   \n",
       "58990  15627596723391133804      spb         500.0         NaN     30774   \n",
       "62363  17685525691353954335      msk        2000.0    4.386986     30776   \n",
       "\n",
       "       feature_id  \n",
       "65503  3501481353  \n",
       "14378  3501745827  \n",
       "58402  3501513153  \n",
       "33526       20422  \n",
       "55340       20422  \n",
       "36489       11629  \n",
       "63797         256  \n",
       "29307        1415  \n",
       "58990       11177  \n",
       "62363       11629  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organisations = apply_func_to_each_columns(organisations_df, ['features_id', 'rubrics_id'], parse_str_to_int_list)\n",
    "\n",
    "organisations = organisations.explode('features_id') \\\n",
    "                             .explode('rubrics_id') \\\n",
    "                             .rename(columns={'features_id': 'feature_id', 'rubrics_id': 'rubric_id'})\n",
    "\n",
    "organisations.sample(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11e95a7f-2422-4059-8e26-93e92746c1d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:10:58.319555Z",
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     "shell.execute_reply.started": "2022-04-29T13:10:58.319511Z"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1004751, 6), (68339, 6))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organisations.shape, organisations_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496bb315-bd0b-4ad2-9407-ab003e76ec46",
   "metadata": {},
   "source": [
    "So we have `1004751` instead of `68339` len dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2872aa14-c5fe-4578-8ea9-f9a29c45a6e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:10:58.362640Z",
     "iopub.status.busy": "2022-04-29T13:10:58.362404Z",
     "iopub.status.idle": "2022-04-29T13:15:22.490947Z",
     "shell.execute_reply": "2022-04-29T13:15:22.489799Z",
     "shell.execute_reply.started": "2022-04-29T13:10:58.362609Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 50s, sys: 1min 33s, total: 4min 24s\n",
      "Wall time: 4min 24s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "full_review_info = reviews \\\n",
    "    .merge(organisations, on='org_id', how='left') \\\n",
    "    .merge(users_df, on='user_id', how='left') \\\n",
    "    .merge(aspects_df, on='aspect_id', how='left') \\\n",
    "    .merge(features_df, on='feature_id', how='left') \\\n",
    "    .merge(rubrics_df, on='rubric_id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1c445c8-46fb-41bd-a77d-b3b358d86687",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:15:22.514045Z",
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     "shell.execute_reply.started": "2022-04-29T13:15:22.514003Z"
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       "      <td>11704</td>\n",
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       "      <td>wi_fi</td>\n",
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       "    <tr>\n",
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       "      <td>3501749289</td>\n",
       "      <td>msk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>closed_for_visitors</td>\n",
       "      <td>Быстрое питание</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11868192</th>\n",
       "      <td>9312540573052556317</td>\n",
       "      <td>16327095963630245962</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>msk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.697151</td>\n",
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       "      <td>1076799212</td>\n",
       "      <td>spb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mcexpress</td>\n",
       "      <td>Кафе</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44988750</th>\n",
       "      <td>3928830865617394177</td>\n",
       "      <td>9755115000265538221</td>\n",
       "      <td>5.0</td>\n",
       "      <td>733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>spb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.445575</td>\n",
       "      <td>30776</td>\n",
       "      <td>3501481353</td>\n",
       "      <td>spb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>projector</td>\n",
       "      <td>Ресторан</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118900848</th>\n",
       "      <td>16657807763200908305</td>\n",
       "      <td>199252321913224408</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>spb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.380684</td>\n",
       "      <td>31495</td>\n",
       "      <td>3501498389</td>\n",
       "      <td>spb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>free transfer</td>\n",
       "      <td>Кофейня</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117678146</th>\n",
       "      <td>14350758550460120470</td>\n",
       "      <td>7657737654778456349</td>\n",
       "      <td>3.0</td>\n",
       "      <td>535</td>\n",
       "      <td>17</td>\n",
       "      <td>spb</td>\n",
       "      <td>500.0</td>\n",
       "      <td>4.301449</td>\n",
       "      <td>30771</td>\n",
       "      <td>11177</td>\n",
       "      <td>spb</td>\n",
       "      <td>Шаверма</td>\n",
       "      <td>breakfast</td>\n",
       "      <td>Быстрое питание</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116722012</th>\n",
       "      <td>12590011418621898564</td>\n",
       "      <td>8037928576994158448</td>\n",
       "      <td>1.0</td>\n",
       "      <td>928</td>\n",
       "      <td>267</td>\n",
       "      <td>msk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.326752</td>\n",
       "      <td>30771</td>\n",
       "      <td>20424</td>\n",
       "      <td>msk</td>\n",
       "      <td>Чай</td>\n",
       "      <td>price_category</td>\n",
       "      <td>Быстрое питание</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58215430</th>\n",
       "      <td>8234783575766018808</td>\n",
       "      <td>5072738578507807787</td>\n",
       "      <td>5.0</td>\n",
       "      <td>605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>msk</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.506986</td>\n",
       "      <td>30776</td>\n",
       "      <td>3501513153</td>\n",
       "      <td>msk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tag_menu</td>\n",
       "      <td>Ресторан</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        user_id                org_id  review_rating    ts  \\\n",
       "85742529   17913019607399519371   6664350636076147652            1.0   583   \n",
       "97655185   10405407735547291462  14803105372140225878            5.0   943   \n",
       "116707659   1220830570075314678  16327095963630245962            1.0   575   \n",
       "111769041  11576520981468855512   1212502960035640455            2.0   959   \n",
       "11868192    9312540573052556317  16327095963630245962            5.0  1273   \n",
       "44988750    3928830865617394177   9755115000265538221            5.0   733   \n",
       "118900848  16657807763200908305    199252321913224408            5.0  1182   \n",
       "117678146  14350758550460120470   7657737654778456349            3.0   535   \n",
       "116722012  12590011418621898564   8037928576994158448            1.0   928   \n",
       "58215430    8234783575766018808   5072738578507807787            5.0   605   \n",
       "\n",
       "          aspect_id org_city  average_bill  org_rating rubric_id  feature_id  \\\n",
       "85742529        NaN      msk         500.0    4.266667     30775       11704   \n",
       "97655185          4      spb         500.0    4.250000     30770       20424   \n",
       "116707659       267      msk           NaN    3.697151     30771       11177   \n",
       "111769041       NaN      msk         500.0    4.334694     30771  3501749289   \n",
       "11868192        NaN      msk           NaN    3.697151     30774  1076799212   \n",
       "44988750        NaN      spb           NaN    4.445575     30776  3501481353   \n",
       "118900848       NaN      spb           NaN    4.380684     31495  3501498389   \n",
       "117678146        17      spb         500.0    4.301449     30771       11177   \n",
       "116722012       267      msk           NaN    4.326752     30771       20424   \n",
       "58215430        NaN      msk        1000.0    4.506986     30776  3501513153   \n",
       "\n",
       "          user_city aspect_name         feature_name      rubric_name  \n",
       "85742529        msk         NaN                wi_fi         Пиццерия  \n",
       "97655185        spb         Еда       price_category         Бар, паб  \n",
       "116707659       msk         Чай            breakfast  Быстрое питание  \n",
       "111769041       msk         NaN  closed_for_visitors  Быстрое питание  \n",
       "11868192        spb         NaN            mcexpress             Кафе  \n",
       "44988750        spb         NaN            projector         Ресторан  \n",
       "118900848       spb         NaN        free transfer          Кофейня  \n",
       "117678146       spb     Шаверма            breakfast  Быстрое питание  \n",
       "116722012       msk         Чай       price_category  Быстрое питание  \n",
       "58215430        msk         NaN             tag_menu         Ресторан  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_review_info.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c70f89a7-68fc-4f95-af89-87252615a2e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:16:01.009224Z",
     "iopub.status.busy": "2022-04-29T13:16:01.008940Z",
     "iopub.status.idle": "2022-04-29T13:16:01.021905Z",
     "shell.execute_reply": "2022-04-29T13:16:01.020779Z",
     "shell.execute_reply.started": "2022-04-29T13:16:01.009196Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 127825382 entries, 0 to 127825381\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Dtype  \n",
      "---  ------         -----  \n",
      " 0   user_id        uint64 \n",
      " 1   org_id         uint64 \n",
      " 2   review_rating  float64\n",
      " 3   ts             int64  \n",
      " 4   aspect_id      object \n",
      " 5   org_city       object \n",
      " 6   average_bill   float64\n",
      " 7   org_rating     float64\n",
      " 8   rubric_id      object \n",
      " 9   feature_id     object \n",
      " 10  user_city      object \n",
      " 11  aspect_name    object \n",
      " 12  feature_name   object \n",
      " 13  rubric_name    object \n",
      "dtypes: float64(3), int64(1), object(8), uint64(2)\n",
      "memory usage: 14.3+ GB\n"
     ]
    }
   ],
   "source": [
    "full_review_info.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01ef881-3b9a-40b0-897c-daefa308ff09",
   "metadata": {},
   "source": [
    "`14.3+ GB` Looks not okey, but in real task we wouldn't use pandas in production"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0e552c-9ef7-40f4-a11c-5f4b5edd10f5",
   "metadata": {},
   "source": [
    "# 3. Split mechanism, Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96885b53-b97c-4d09-87c4-9cde5c56ca5b",
   "metadata": {},
   "source": [
    "## Metric calc"
   ]
  },
  {
   "attachments": {
    "2ef6f092-55d0-47ed-819e-f7b65bd6a04f.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "40a9b50a-eae6-4dc0-acd3-371aa4ed0589",
   "metadata": {},
   "source": [
    "![image.png](attachment:2ef6f092-55d0-47ed-819e-f7b65bd6a04f.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1324b3dc-4dc2-45be-8d43-d772f31123f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:17:52.786859Z",
     "iopub.status.busy": "2022-04-29T13:17:52.786621Z",
     "iopub.status.idle": "2022-04-29T13:17:52.795042Z",
     "shell.execute_reply": "2022-04-29T13:17:52.794045Z",
     "shell.execute_reply.started": "2022-04-29T13:17:52.786832Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def MNAP(size=20):       \n",
    "    def metric(y_true, predictions, size=size):\n",
    "        predictions = predictions.rename(columns={'target': 'predictions'})\n",
    "        merged = y_true.merge(predictions, on='user_id')\n",
    "    \n",
    "        def score(x, size=size):          \n",
    "            y_true = x[1][1]\n",
    "            predictions = x[1][2][:size]\n",
    "            \n",
    "            weight = 0\n",
    "            inner_weights = [0]\n",
    "            \n",
    "            for n, item in enumerate(predictions):\n",
    "                inner_weight = inner_weights[-1] + (1 if item in y_true else 0)\n",
    "                inner_weights.append(inner_weight)\n",
    "            \n",
    "            for n, item in enumerate(predictions):                \n",
    "                if item in y_true:\n",
    "                    weight += inner_weights[n + 1] / (n + 1)\n",
    "                    \n",
    "            return weight / min(len(y_true), size)\n",
    "    \n",
    "        return np.mean([score(row) for row in merged.iterrows()])\n",
    "    \n",
    "        \n",
    "    return metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a465fcb9-d572-4f88-8c8b-ccfededa686a",
   "metadata": {},
   "source": [
    "## Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7e48de9c-b325-44e9-bd82-9fd6db5d9db5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:16:01.071467Z",
     "iopub.status.busy": "2022-04-29T13:16:01.071172Z",
     "iopub.status.idle": "2022-04-29T13:16:01.078227Z",
     "shell.execute_reply": "2022-04-29T13:16:01.077130Z",
     "shell.execute_reply.started": "2022-04-29T13:16:01.071419Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def review_split(reviews, percentage=0.33, end_ts_values=None):\n",
    "    if end_ts_values is None:\n",
    "        end_ts_values = reviews.ts.max()\n",
    "    \n",
    "    reviews_cut = reviews[reviews.ts < end_ts_values]\n",
    "    ts_percentile = reviews_cut.ts.quantile(0.33)\n",
    "    \n",
    "    reviews_train = reviews_cut[reviews_cut.ts <= ts_percentile]\n",
    "    reviews_test = reviews_cut[reviews_cut.ts >= ts_percentile]    \n",
    "    \n",
    "    return reviews_train, reviews_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d1adad2-a330-4664-86c4-35806cdf2b95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:16:01.079496Z",
     "iopub.status.busy": "2022-04-29T13:16:01.079233Z",
     "iopub.status.idle": "2022-04-29T13:16:37.308916Z",
     "shell.execute_reply": "2022-04-29T13:16:37.308144Z",
     "shell.execute_reply.started": "2022-04-29T13:16:01.079462Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42227002, 14), (85618578, 14))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test = review_split(full_review_info)\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169b8854-ea61-408a-a959-74eeaa609380",
   "metadata": {},
   "source": [
    "## Answering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "03b2f16a-9fd4-4c97-886e-a96fe03e3125",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:16:37.310223Z",
     "iopub.status.busy": "2022-04-29T13:16:37.309967Z",
     "iopub.status.idle": "2022-04-29T13:16:37.690240Z",
     "shell.execute_reply": "2022-04-29T13:16:37.689345Z",
     "shell.execute_reply.started": "2022-04-29T13:16:37.310184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "msk    19029\n",
       "spb     3456\n",
       "Name: user_city, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_users = test_users_df.merge(users_df, on='user_id', how='left')\n",
    "test_users.user_city.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93718f9d-ca6d-43c2-883c-fc3ed23ba8ff",
   "metadata": {},
   "source": [
    "So, for each user in test we know his destination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "db70ddcd-b643-44a2-a4c0-3f88f4bb60be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:41:47.388198Z",
     "iopub.status.busy": "2022-04-29T13:41:47.387893Z",
     "iopub.status.idle": "2022-04-29T13:41:49.007850Z",
     "shell.execute_reply": "2022-04-29T13:41:49.006819Z",
     "shell.execute_reply.started": "2022-04-29T13:41:47.388146Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_test = test_users_df \\\n",
    "    .merge(reviews[['user_id', 'org_id']], on='user_id', how='left') \\\n",
    "    .groupby('user_id') \\\n",
    "    .agg({'org_id': list}) \\\n",
    "    .rename(columns={'org_id': 'y_true'}) \\\n",
    "    .reset_index()\n",
    "\n",
    "y_test.y_true = y_test.y_true.apply(lambda x: list(map(int, x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0d3e7877-f7c4-4237-a4d9-ac9759bad571",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:41:49.037938Z",
     "iopub.status.busy": "2022-04-29T13:41:49.037559Z",
     "iopub.status.idle": "2022-04-29T13:41:49.045343Z",
     "shell.execute_reply": "2022-04-29T13:41:49.044344Z",
     "shell.execute_reply.started": "2022-04-29T13:41:49.037902Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    uint64\n",
       "y_true     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87d37230-7ec7-4b2c-bb76-5a42286c77db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:16:39.007664Z",
     "iopub.status.busy": "2022-04-29T13:16:39.007448Z",
     "iopub.status.idle": "2022-04-29T13:16:39.013165Z",
     "shell.execute_reply": "2022-04-29T13:16:39.012435Z",
     "shell.execute_reply.started": "2022-04-29T13:16:39.007630Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_answer(df, name):\n",
    "    ans = df.copy()\n",
    "    ans['target'] = ans['target'].apply(lambda orgs: ' '.join(map(str, orgs)))\n",
    "    ans.to_csv(f'{name}.csv', index=None)\n",
    "    \n",
    "    del ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bbf6cec3-caa8-4c94-9459-00972a8a4000",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:17:56.799294Z",
     "iopub.status.busy": "2022-04-29T13:17:56.799010Z",
     "iopub.status.idle": "2022-04-29T13:17:56.805030Z",
     "shell.execute_reply": "2022-04-29T13:17:56.803713Z",
     "shell.execute_reply.started": "2022-04-29T13:17:56.799246Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "N_PREDICTION_ORG = 20\n",
    "\n",
    "mnap_metric_calc = MNAP(N_PREDICTION_ORG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "22e45142-3b8b-4b0a-b5ea-8f15e2ea2a23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:18:34.569034Z",
     "iopub.status.busy": "2022-04-29T13:18:34.568672Z",
     "iopub.status.idle": "2022-04-29T13:18:34.577687Z",
     "shell.execute_reply": "2022-04-29T13:18:34.576239Z",
     "shell.execute_reply.started": "2022-04-29T13:18:34.568994Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_example = pd.DataFrame(\n",
    "{\n",
    "    'user_id': [1175872067657083, 3960211634564871],\n",
    "    'target': [[5326072468960064989,  9293289382222853816,  3361065379355406105, 380393201136864076, 16397358758595904644],\n",
    "               [16996585379035144871, 14585266011799937129, 13220328679916389818, 13882403132669515214, 18021456929242079630]\n",
    "              ]\n",
    "}\n",
    ")\n",
    "\n",
    "y_test_example = pd.DataFrame(\n",
    "{\n",
    "    'user_id': [1175872067657083, 3960211634564871],\n",
    "    'y_true': [[3099370727209496142, 5326072468960064989, 14037457789472617093, 4433870819289787251, 11006800892303205642],\n",
    "               [10868679817509284366, 13220328679916389818]\n",
    "              ]\n",
    "}\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1987f855-922e-4bc1-a3de-80f2648b1685",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:18:34.730633Z",
     "iopub.status.busy": "2022-04-29T13:18:34.730465Z",
     "iopub.status.idle": "2022-04-29T13:18:34.741390Z",
     "shell.execute_reply": "2022-04-29T13:18:34.740685Z",
     "shell.execute_reply.started": "2022-04-29T13:18:34.730622Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18333333333333335"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnap_metric_calc(y_test_example, pred_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f8bd75-d477-4093-a818-a626f7bf83ee",
   "metadata": {},
   "source": [
    "# 4. baselines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0da2ea8a-4185-483f-9f76-8c740c098fce",
   "metadata": {},
   "source": [
    "Simple baselines without using any Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e9389b-d10d-42eb-942e-8e91f7ece64b",
   "metadata": {},
   "source": [
    "## Random recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "84a22e84-534b-4e90-9b5f-9c6331004b75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:18:49.643723Z",
     "iopub.status.busy": "2022-04-29T13:18:49.643469Z",
     "iopub.status.idle": "2022-04-29T13:19:23.179470Z",
     "shell.execute_reply": "2022-04-29T13:19:23.178214Z",
     "shell.execute_reply.started": "2022-04-29T13:18:49.643694Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45619, 22720)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msk_orgs = full_review_info[full_review_info['org_city'] == 'msk']['org_id'].unique()\n",
    "spb_orgs = full_review_info[full_review_info['org_city'] == 'spb']['org_id'].unique()\n",
    "\n",
    "len(msk_orgs), len(spb_orgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "66ff1ea2-0c10-44ab-a1b4-b0dccf9e93b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:19:23.181428Z",
     "iopub.status.busy": "2022-04-29T13:19:23.181230Z",
     "iopub.status.idle": "2022-04-29T13:19:24.036869Z",
     "shell.execute_reply": "2022-04-29T13:19:24.036075Z",
     "shell.execute_reply.started": "2022-04-29T13:19:23.181398Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import functools\n",
    "# functools.partial\n",
    "\n",
    "random_choose_org = lambda x: np.random.choice(spb_orgs, N_PREDICTION_ORG, replace=False) if x['user_city'] == 'msk' else np.random.choice(msk_orgs, N_PREDICTION_ORG, replace=False)\n",
    "\n",
    "target = test_users.apply(random_choose_org, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "115c5527-c63b-41d8-b73a-5cdd6719a15c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:19:24.038413Z",
     "iopub.status.busy": "2022-04-29T13:19:24.038184Z",
     "iopub.status.idle": "2022-04-29T13:19:24.045467Z",
     "shell.execute_reply": "2022-04-29T13:19:24.044419Z",
     "shell.execute_reply.started": "2022-04-29T13:19:24.038384Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "result_random = test_users[['user_id']].copy()\n",
    "result_random['target'] = target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b5d92426-a31b-4335-b09f-f96d4680179f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:19:24.047500Z",
     "iopub.status.busy": "2022-04-29T13:19:24.047219Z",
     "iopub.status.idle": "2022-04-29T13:19:24.054921Z",
     "shell.execute_reply": "2022-04-29T13:19:24.053781Z",
     "shell.execute_reply.started": "2022-04-29T13:19:24.047427Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22485, 2), (22485, 2))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape, result_random.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c4d6f96b-050b-4202-b750-ec269f097630",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:22:18.797535Z",
     "iopub.status.busy": "2022-04-29T13:22:18.797260Z",
     "iopub.status.idle": "2022-04-29T13:22:18.828040Z",
     "shell.execute_reply": "2022-04-29T13:22:18.827225Z",
     "shell.execute_reply.started": "2022-04-29T13:22:18.797498Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>target</th>\n",
       "      <th>y_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>[14905610313433618054, 9675524123436578073, 11...</td>\n",
       "      <td>[1435674414900395919, 2645993823236004281, 123...</td>\n",
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       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>17064760978885603717</td>\n",
       "      <td>[13941432392042473131, 7870406827290866253, 10...</td>\n",
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       "      <td>[3189471572895390546, 8754130794036124532, 747...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>14715457714563610142</td>\n",
       "      <td>[10421132470345218153, 13547277892034259676, 1...</td>\n",
       "      <td>[14471364305557264390, 2966606192252052550, 12...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    user_id  \\\n",
       "16868  14633171782851322853   \n",
       "17568   3031978000135456738   \n",
       "20053  17908969283618987579   \n",
       "16572    612933854916714492   \n",
       "17003   8031530490302329253   \n",
       "2284   12869715996591675718   \n",
       "222    17064760978885603717   \n",
       "10849  17240385623564938799   \n",
       "22166  14447881977804036742   \n",
       "2051   14715457714563610142   \n",
       "\n",
       "                                                  target  \\\n",
       "16868  [12454525412902198085, 11218019101392238711, 4...   \n",
       "17568  [5696833996459062817, 15788148632233890150, 54...   \n",
       "20053  [9243025813592328873, 14017993842420089842, 13...   \n",
       "16572  [12981183737349217927, 7256833316788929358, 15...   \n",
       "17003  [1161546094295717314, 12368228128599732964, 74...   \n",
       "2284   [14905610313433618054, 9675524123436578073, 11...   \n",
       "222    [13941432392042473131, 7870406827290866253, 10...   \n",
       "10849  [10295641935307348321, 3799567149887107179, 16...   \n",
       "22166  [9917483987007302684, 15743962532501255514, 74...   \n",
       "2051   [10421132470345218153, 13547277892034259676, 1...   \n",
       "\n",
       "                                                  y_true  \n",
       "16868  [13730233750976174314, 2787156097552998474, 11...  \n",
       "17568  [11006800892303205642, 10382281594135798475, 1...  \n",
       "20053  [3432151593601609433, 7824224429364335211, 756...  \n",
       "16572  [14299038106324100783, 3290745739241311464, 15...  \n",
       "17003  [1352900198295267165, 2195361217477256866, 111...  \n",
       "2284   [1435674414900395919, 2645993823236004281, 123...  \n",
       "222    [11598849612575836677, 8628711277418501758, 79...  \n",
       "10849         [2004179479792092234, 6721526179323412826]  \n",
       "22166  [3189471572895390546, 8754130794036124532, 747...  \n",
       "2051   [14471364305557264390, 2966606192252052550, 12...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_random.merge(y_test, on='user_id').sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d55f5001-e914-48a6-8807-17b8fd2af0d6",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-04-29T13:21:19.830755Z",
     "iopub.status.idle": "2022-04-29T13:21:35.071757Z",
     "shell.execute_reply": "2022-04-29T13:21:35.070825Z",
     "shell.execute_reply.started": "2022-04-29T13:21:19.830966Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "0.004379863195415152"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "mnap_metric_calc(y_test, result_random) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6cc79173-6e38-4c08-8802-b6b8eb6b6a65",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-04-29T13:19:39.741581Z",
     "shell.execute_reply.started": "2022-04-29T13:19:39.151754Z"
    },
    "tags": []
   },
   "outputs": [
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       "                    user_id                                             target\n",
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       "\n",
       "[22485 rows x 2 columns]"
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     "execution_count": 40,
     "metadata": {},
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   ],
   "source": [
    "make_answer(result_random, 'result_random')\n",
    "result_random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9327ed7-fdd9-4f61-82b4-29f7366d25f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T13:22:53.500641Z",
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     "shell.execute_reply.started": "2022-04-29T13:22:53.500609Z"
    }
   },
   "source": [
    "## Recommend most popular places with high rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "c06ea5f5-e04b-4a63-b140-9609d849dc31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:22:30.942895Z",
     "iopub.status.busy": "2022-04-29T15:22:30.942518Z",
     "iopub.status.idle": "2022-04-29T15:23:43.532352Z",
     "shell.execute_reply": "2022-04-29T15:23:43.531422Z",
     "shell.execute_reply.started": "2022-04-29T15:22:30.942862Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Msk good org reviews ratio 0.874799915272791\n",
      "SPB good org reviews ratio 0.89232099201243\n"
     ]
    }
   ],
   "source": [
    "msk_orgs = full_review_info[(full_review_info['org_city'] == 'msk') & (full_review_info['org_rating'] > 4)]['org_id']\n",
    "spb_orgs = full_review_info[(full_review_info['org_city'] == 'spb') & (full_review_info['org_rating'] > 4)]['org_id']\n",
    "\n",
    "print(f'Msk good org reviews ratio {len(msk_orgs) / (full_review_info[full_review_info[\"org_city\"] == \"msk\"].shape[0])}')\n",
    "print(f'SPB good org reviews ratio {len(spb_orgs) / (full_review_info[full_review_info[\"org_city\"] == \"spb\"].shape[0])}')\n",
    "\n",
    "msk_orgs = msk_orgs.value_counts().index[:N_PREDICTION_ORG].to_list()\n",
    "spb_orgs = spb_orgs.value_counts().index[:N_PREDICTION_ORG].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "5c6b5afb-47f4-42c4-b451-5e3fed86323f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:23:43.534251Z",
     "iopub.status.busy": "2022-04-29T15:23:43.533970Z",
     "iopub.status.idle": "2022-04-29T15:23:43.541232Z",
     "shell.execute_reply": "2022-04-29T15:23:43.540236Z",
     "shell.execute_reply.started": "2022-04-29T15:23:43.534179Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.default_rng()\n",
    "rng.choice([1, 2, 3, 1], size = 3, replace=False)\n",
    "# Interesting details about np.random.choices withoud duplication\n",
    "# https://ymd_h.gitlab.io/ymd_blog/posts/numpy_random_choice/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "0afe0ce4-cded-4ced-84e4-33b2b9ed74b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:23:43.542924Z",
     "iopub.status.busy": "2022-04-29T15:23:43.542696Z",
     "iopub.status.idle": "2022-04-29T15:23:45.089967Z",
     "shell.execute_reply": "2022-04-29T15:23:45.088964Z",
     "shell.execute_reply.started": "2022-04-29T15:23:43.542889Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "model_id": "fe072de5032c425481509a47451aed07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/22485 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  0%|          | 0/22485 [00:00<?, ?it/s]"
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   ],
   "source": [
    "# random_choose_org = lambda x: np.random.choice(spb_orgs, size = N_PREDICTION_ORG, replace=False) if x['user_city'] == 'msk' else np.random.choice(msk_orgs, N_PREDICTION_ORG, replace=False)\n",
    "random_choose_org = lambda x: np.random.choice(spb_orgs, size = N_PREDICTION_ORG, replace=False) if x['user_city'] == 'msk' else np.random.choice(msk_orgs, N_PREDICTION_ORG, replace=False)\n",
    "\n",
    "target = test_users.progress_apply(random_choose_org, axis=1)\n",
    "target = target.progress_apply(lambda x: list(map(int, x)))\n",
    "\n",
    "result_top_N = test_users[['user_id']].copy()\n",
    "result_top_N['target'] = target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "242399c2-8d6e-4271-86aa-e52458f5e448",
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    "execution": {
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     "iopub.status.idle": "2022-04-29T15:23:46.423384Z",
     "shell.execute_reply": "2022-04-29T15:23:46.421515Z",
     "shell.execute_reply.started": "2022-04-29T15:23:45.091913Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "14988    0\n",
       "14996    0\n",
       "14995    0\n",
       "14994    0\n",
       "        ..\n",
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       "Length: 22485, dtype: int64"
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     "execution_count": 206,
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   ],
   "source": [
    "result_top_N.merge(y_test, on='user_id').apply(lambda x: sum([i in x['y_true'] for i in x['target']]), axis=1).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "d886ed0d-1a24-49f8-8e09-399b88b50e61",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-04-29T15:16:11.934748Z",
     "iopub.status.idle": "2022-04-29T15:16:14.224458Z",
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     "shell.execute_reply.started": "2022-04-29T15:16:11.934926Z"
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0831595696295419"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnap_metric_calc(y_test, result_top_N) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "431d3fff-7ac9-4d2e-ba0f-dfd896721cea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:24:16.291920Z",
     "iopub.status.busy": "2022-04-29T15:24:16.291636Z",
     "iopub.status.idle": "2022-04-29T15:24:16.575604Z",
     "shell.execute_reply": "2022-04-29T15:24:16.574533Z",
     "shell.execute_reply.started": "2022-04-29T15:24:16.291882Z"
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       "                    user_id                                             target\n",
       "0      11859048046981233908  [1732348878070501632, 15092837550836111360, 53...\n",
       "1      15587804372050325574  [11006800892303206400, 11710076672100157440, 1...\n",
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       "4      10945514241898471401  [1352900198295267072, 17483411528870660096, 17...\n",
       "...                     ...                                                ...\n",
       "22480  13566530607351820237  [1352900198295267072, 1732348878070501632, 532...\n",
       "22481  11335509666663005941  [1732348878070501632, 3099370727209496064, 171...\n",
       "22482   9661327606538591652  [15120040193672648704, 1876636121854267392, 15...\n",
       "22483  14022370448741367741  [12309025298401949696, 10276373697321246720, 1...\n",
       "22484  17587673573341943884  [14133244540333873152, 336748997849365888, 153...\n",
       "\n",
       "[22485 rows x 2 columns]"
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     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_answer(result_top_N, 'result_top_N')\n",
    "result_top_N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "037931c3-3e16-432e-ab8d-32c83afb7893",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Recommend from each category"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2d0cc2-9237-4672-af73-c790afb592dc",
   "metadata": {},
   "source": [
    "todo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f93ceed-b259-4355-b3a8-c54974f7d7ad",
   "metadata": {},
   "source": [
    "# 5. [Model] Collaborative filtering & ALS\n",
    "\n",
    "1. Make interaction matrix for MSK places\n",
    "2. Make interaction matrix for SPB places\n",
    "3. use `implicit.als`\n",
    "4. `recommend_all` and transformer into appropirate format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "b5379f06-bb69-4762-8670-4dc3ccdedfe8",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-04-29T15:36:35.846442Z",
     "shell.execute_reply.started": "2022-04-29T15:36:35.635997Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import implicit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "ffde6756-a7e7-4768-84b6-4b9439651152",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:56:46.092848Z",
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    }
   },
   "outputs": [],
   "source": [
    "def filter_reviews(reviews, users=None, orgs=None): \n",
    "    '''\n",
    "    filter_reviews(reviews, users=None, orgs=None)\n",
    "    Оставляет в выборке только отзывы, оставленные заданными пользователями на заданные организации. \n",
    "\n",
    "    '''\n",
    "    if users is not None: \n",
    "        reviews = reviews.merge(users, on='user_id', how='inner')\n",
    "        clear_df(reviews)\n",
    "        \n",
    "    if orgs is not None:\n",
    "        reviews = reviews.merge(orgs, on='org_id', how='inner')\n",
    "        clear_df(reviews)\n",
    "        \n",
    "    return reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0782ae-4164-4588-a520-a5856cb276ce",
   "metadata": {},
   "source": [
    "## Interaction matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "96c50433-18c1-4f09-9291-efacd47efd6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:56:47.492228Z",
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     "shell.execute_reply.started": "2022-04-29T15:56:47.492183Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def reduce_reviews(reviews, min_user_reviews=5, min_org_reviews=13):\n",
    "    \n",
    "    inner_reviews = reviews[reviews['user_city'] == reviews['org_city']]\n",
    "    outer_reviews = reviews[reviews['user_city'] != reviews['org_city']]\n",
    "\n",
    "    tourist_users = outer_reviews['user_id'].unique()\n",
    "    inner_reviews = filter_reviews(inner_reviews, users=tourist_users)\n",
    "    \n",
    "    top_users = count_unique(inner_reviews, 'user_id')\n",
    "    top_users = top_users[top_users['count'] >= min_user_reviews]\n",
    "        \n",
    "    top_orgs = count_unique(inner_reviews, 'org_id')\n",
    "    top_orgs = top_orgs[top_orgs['count'] >= min_org_reviews]\n",
    "        \n",
    "    inner_reviews = filter_reviews(inner_reviews, users=top_users, orgs=top_orgs)\n",
    "    outer_reviews = filter_reviews(outer_reviews, users=top_users)\n",
    "    \n",
    "    reviews = pd.concat([inner_reviews, outer_reviews])\n",
    "    users = reviews['user_id'].unique()\n",
    "    orgs = reviews['org_id'].unique()\n",
    "    \n",
    "    \n",
    "    return (\n",
    "        (\n",
    "            inner_reviews,\n",
    "            inner_reviews['org_id'].unique()\n",
    "        ),\n",
    "        (\n",
    "            outer_reviews,\n",
    "            outer_reviews['org_id'].unique()\n",
    "        ),\n",
    "        inner_reviews['user_id'].unique()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "16839c48-47da-4259-a2c9-4dae230fd4f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:56:47.714183Z",
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     "shell.execute_reply.started": "2022-04-29T15:56:47.714161Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_mappings(df, column):\n",
    "    '''\n",
    "    create_mappings(df, column)\n",
    "        Создаёт маппинг между оригинальными ключами словаря и новыми порядковыми.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        df : pandas.DataFrame\n",
    "            DataFrame с данными.\n",
    "            \n",
    "        column : str\n",
    "            Название колонки, содержащей нужны ключи. \n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        code_to_idx : dict\n",
    "            Словарь с маппингом: \"оригинальный ключ\" -> \"новый ключ\".\n",
    "        \n",
    "        idx_to_code : dict\n",
    "            Словарь с маппингом: \"новый ключ\" -> \"оригинальный ключ\".\n",
    "    '''\n",
    "    \n",
    "    code_to_idx = {}\n",
    "    idx_to_code = {}\n",
    "    \n",
    "    for idx, code in enumerate(df[column].to_list()):\n",
    "        code_to_idx[code] = idx\n",
    "        idx_to_code[idx] = code\n",
    "        \n",
    "    return code_to_idx, idx_to_code\n",
    "\n",
    "\n",
    "def map_ids(row, mapping):\n",
    "    '''\n",
    "    Вспомогательная функция\n",
    "    '''\n",
    "    \n",
    "    return mapping[row]\n",
    "\n",
    "\n",
    "def interaction_matrix(reviews, test_users, min_user_reviews=5, min_org_reviews=12): \n",
    "    '''\n",
    "    Parameters\n",
    "    ---------\n",
    "    min_user_reviews : int, default=5\n",
    "        Minumal user feedbacks to include him in matrix\n",
    "\n",
    "    min_org_reviews : int, default=12\n",
    "        Same but with orgs\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    InteractionMatrix : scipy.sparse.csr_matrix\n",
    "             ---------------------------------------------------\n",
    "            | TRAIN USERS, INNER ORGS | TRAIN USERS, OUTER ORGS |\n",
    "            |                         |                         |\n",
    "             ---------------------------------------------------\n",
    "            |  TEST USERS, INNER ORGS |  TEST USERS, OUTER ORGS |\n",
    "            |                         |                         |\n",
    "             ---------------------------------------------------\n",
    "\n",
    "    splitting : tuple\n",
    "            1. Number users in train\n",
    "            2. Number users in test\n",
    "\n",
    "    splitting: tuple\n",
    "            1. (idx_to_uid, uid_to_idx) - map ind to user_id\n",
    "            2. (idx_to_oid, oid_to_idx) - map ind to org_id\n",
    "    '''\n",
    "    \n",
    "    \n",
    "    info = reduce_reviews(reviews, min_user_reviews, min_org_reviews)\n",
    "    \n",
    "    (inner_reviews, inner_orgs), (outer_reviews, outer_orgs), train_users = info\n",
    "    \n",
    "    # удалим из обучающей выборки пользователей, которые есть в тестовой\n",
    "    test_users = test_users[['user_id']]\n",
    "    \n",
    "    train_users = (\n",
    "        pd.merge(train_users, test_users, indicator=True, how='outer')\n",
    "        .query('_merge==\"left_only\"')\n",
    "        .drop('_merge', axis=1)\n",
    "    )\n",
    "    \n",
    "    inner_reviews = filter_reviews(inner_reviews, train_users)\n",
    "    outer_reviews = filter_reviews(outer_reviews, train_users)\n",
    "    \n",
    "    # оставляем отзывы, оставленные тестовыми пользователями\n",
    "    test_reviews = filter_reviews(reviews, test_users, pd.concat([inner_orgs, outer_orgs]))\n",
    "    \n",
    "    # получаем полный набор маппингов\n",
    "    all_users = pd.concat([train_users, test_users])\n",
    "    all_orgs = pd.concat([inner_orgs, outer_orgs])\n",
    "    \n",
    "    uid_to_idx, idx_to_uid = create_mappings(all_users, 'user_id')\n",
    "    oid_to_idx, idx_to_oid = create_mappings(all_orgs, 'org_id')\n",
    "    \n",
    "    # собираем матрицу взаимодействий \n",
    "    reviews = pd.concat([inner_reviews, outer_reviews, test_reviews])    \n",
    "        \n",
    "    I = reviews['user_id'].apply(map_ids, args=[uid_to_idx]).values\n",
    "    J = reviews['org_id'].apply(map_ids, args=[oid_to_idx]).values\n",
    "    values = reviews['rating']\n",
    "        \n",
    "    interactions = sparse.coo_matrix(\n",
    "        (values, (I, J)), \n",
    "        shape=(len(all_users), len(all_orgs)), \n",
    "        dtype=np.float64\n",
    "    ).tocsr()\n",
    "    \n",
    "    \n",
    "    return (\n",
    "        interactions, \n",
    "        (len(train_users), len(inner_orgs)), \n",
    "        (\n",
    "            (idx_to_uid, uid_to_idx),\n",
    "            (idx_to_oid, oid_to_idx)\n",
    "        )\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0a8fd8-717c-420e-8db4-20fe59ff0a23",
   "metadata": {},
   "source": [
    "## ALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "b64bab83-f9f8-405e-8181-d9186b9545d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:56:49.036322Z",
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     "iopub.status.idle": "2022-04-29T15:56:49.045046Z",
     "shell.execute_reply": "2022-04-29T15:56:49.043832Z",
     "shell.execute_reply.started": "2022-04-29T15:56:49.036214Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_predictions(interactions, X_test, N):\n",
    "    \n",
    "    predictions = X_test[['user_id']].copy()\n",
    "    predictions['target'] = pd.Series(dtype=object)\n",
    "    predictions = predictions.set_index('user_id')\n",
    "    \n",
    "    interactions, (train_users_len, inner_orgs_len), mappings = interactions\n",
    "    (idx_to_uid, uid_to_idx), (idx_to_oid, oid_to_idx) = mappings\n",
    "\n",
    "    base_model = implicit.als.AlternatingLeastSquares(\n",
    "        factors=5, \n",
    "        iterations=75, \n",
    "        regularization=0.05, \n",
    "        random_state=42\n",
    "    )\n",
    "    \n",
    "    base_model.fit(interactions.T)\n",
    "    \n",
    "    orgs_to_filter = list(np.arange(inner_orgs_len))\n",
    "\n",
    "    recommendations = base_model.recommend_all(\n",
    "        interactions,\n",
    "        N=N,\n",
    "        filter_already_liked_items=True,\n",
    "        filter_items=orgs_to_filter,\n",
    "        show_progress=True\n",
    "    )\n",
    "    \n",
    "    for user_id in tqdm(X_test['user_id'].values, leave=False):\n",
    "        predictions.loc[user_id, 'target'] = list(\n",
    "            map(\n",
    "                lambda org_idx: idx_to_oid[org_idx], \n",
    "                recommendations[uid_to_idx[user_id]]\n",
    "            )\n",
    "        )\n",
    "        \n",
    "    return predictions.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "a9d5eb15-d8a6-4f27-89c1-08859595cb95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T15:56:49.046303Z",
     "iopub.status.busy": "2022-04-29T15:56:49.046042Z",
     "iopub.status.idle": "2022-04-29T15:57:04.509889Z",
     "shell.execute_reply": "2022-04-29T15:57:04.508412Z",
     "shell.execute_reply.started": "2022-04-29T15:56:49.046256Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Can only merge Series or DataFrame objects, a <class 'numpy.ndarray'> was passed",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/tmp/ipykernel_29681/377663443.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m msk_interactions = interaction_matrix(\n\u001B[0m\u001B[1;32m      2\u001B[0m     \u001B[0mtrain\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mtrain\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'user_city'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0;34m'msk'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m     \u001B[0mtest_users\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mtest_users\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'user_city'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0;34m'msk'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m )\n\u001B[1;32m      5\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/var/tmp/ipykernel_29681/2734951725.py\u001B[0m in \u001B[0;36minteraction_matrix\u001B[0;34m(reviews, test_users, min_user_reviews, min_org_reviews)\u001B[0m\n\u001B[1;32m     70\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     71\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 72\u001B[0;31m     \u001B[0minfo\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mreduce_reviews\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mreviews\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmin_user_reviews\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmin_org_reviews\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     73\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     74\u001B[0m     \u001B[0;34m(\u001B[0m\u001B[0minner_reviews\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minner_orgs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mouter_reviews\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mouter_orgs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtrain_users\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0minfo\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/var/tmp/ipykernel_29681/711607501.py\u001B[0m in \u001B[0;36mreduce_reviews\u001B[0;34m(reviews, min_user_reviews, min_org_reviews)\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      6\u001B[0m     \u001B[0mtourist_users\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mouter_reviews\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'user_id'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0munique\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 7\u001B[0;31m     \u001B[0minner_reviews\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mfilter_reviews\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minner_reviews\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0musers\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtourist_users\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      8\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      9\u001B[0m     \u001B[0mtop_users\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mcount_unique\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minner_reviews\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'user_id'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/var/tmp/ipykernel_29681/3953000426.py\u001B[0m in \u001B[0;36mfilter_reviews\u001B[0;34m(reviews, users, orgs)\u001B[0m\n\u001B[1;32m      6\u001B[0m     '''\n\u001B[1;32m      7\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0musers\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 8\u001B[0;31m         \u001B[0mreviews\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mreviews\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmerge\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0musers\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mon\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'user_id'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mhow\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'inner'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      9\u001B[0m         \u001B[0mclear_df\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mreviews\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     10\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/frame.py\u001B[0m in \u001B[0;36mmerge\u001B[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[1;32m   8193\u001B[0m         \u001B[0;32mfrom\u001B[0m \u001B[0mpandas\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreshape\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmerge\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mmerge\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   8194\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 8195\u001B[0;31m         return merge(\n\u001B[0m\u001B[1;32m   8196\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   8197\u001B[0m             \u001B[0mright\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/reshape/merge.py\u001B[0m in \u001B[0;36mmerge\u001B[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[1;32m     72\u001B[0m     \u001B[0mvalidate\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mNone\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     73\u001B[0m ) -> \"DataFrame\":\n\u001B[0;32m---> 74\u001B[0;31m     op = _MergeOperation(\n\u001B[0m\u001B[1;32m     75\u001B[0m         \u001B[0mleft\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     76\u001B[0m         \u001B[0mright\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/reshape/merge.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[1;32m    597\u001B[0m     ):\n\u001B[1;32m    598\u001B[0m         \u001B[0m_left\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_validate_operand\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mleft\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 599\u001B[0;31m         \u001B[0m_right\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_validate_operand\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mright\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    600\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mleft\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0morig_left\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_left\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    601\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mright\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0morig_right\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_right\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/reshape/merge.py\u001B[0m in \u001B[0;36m_validate_operand\u001B[0;34m(obj)\u001B[0m\n\u001B[1;32m   2146\u001B[0m             \u001B[0;32mreturn\u001B[0m \u001B[0mobj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mto_frame\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2147\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 2148\u001B[0;31m         raise TypeError(\n\u001B[0m\u001B[1;32m   2149\u001B[0m             \u001B[0;34mf\"Can only merge Series or DataFrame objects, a {type(obj)} was passed\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2150\u001B[0m         )\n",
      "\u001B[0;31mTypeError\u001B[0m: Can only merge Series or DataFrame objects, a <class 'numpy.ndarray'> was passed"
     ]
    }
   ],
   "source": [
    "msk_interactions = interaction_matrix(\n",
    "    train[train['user_city'] == 'msk'],\n",
    "    test_users[test_users['user_city'] == 'msk'],\n",
    ")\n",
    "\n",
    "spb_interactions = interaction_matrix(\n",
    "    train[train['user_city'] == 'spb'],\n",
    "    test_users[test_users['user_city'] == 'spb'],\n",
    ")       \n",
    "        \n",
    "test_msk_users = test_users[test_users['user_city'] == 'msk']\n",
    "test_spb_users = test_users[test_users['user_city'] == 'spb']\n",
    "\n",
    "msk_predictions = make_predictions(msk_interactions, test_msk_users, N)\n",
    "spb_predictions = make_predictions(spb_interactions, test_spb_users, N)\n",
    "\n",
    "predictions = pd.concat([msk_predictions, spb_predictions])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bda9dc19-dda4-4b59-8978-ad4efc640962",
   "metadata": {},
   "outputs": [],
   "source": [
    "todo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd9bc22-2ebe-4bf4-9d07-137ba72c64f9",
   "metadata": {},
   "source": [
    "# 7. [Model] Gradient boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b306b57-7073-4f2e-8910-3312ae028d51",
   "metadata": {},
   "source": [
    "## Augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf87e98-f0e1-4326-8943-74f7b5f7e292",
   "metadata": {},
   "source": [
    "todo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e550b8b1-13e2-4f08-b5a8-463c3b5611a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T16:01:36.759169Z",
     "iopub.status.busy": "2022-04-29T16:01:36.758791Z",
     "iopub.status.idle": "2022-04-29T16:01:36.764683Z",
     "shell.execute_reply": "2022-04-29T16:01:36.763425Z",
     "shell.execute_reply.started": "2022-04-29T16:01:36.759127Z"
    },
    "tags": []
   },
   "source": [
    "## xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6beb66-5f32-43a4-b14e-79a7a847f4fa",
   "metadata": {},
   "source": [
    "todo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1eb1b35-0d08-4ca9-88f8-823620d85c2d",
   "metadata": {},
   "source": [
    "# 8. [Model] LightFM's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a79dd00-fb66-4255-8207-996f13c1daa6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import lightfm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c806aaf0-7471-4e61-ab58-4987942292b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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