booking_recsys.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mike-a-yen/Kaggle/blob/master/booking_recsys.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2QRSzelkKPnd"
      },
      "source": [
        "# Booking.com RecSys Challenge 2021\n",
        "\n",
        "- Given a sequence of stops on a trip, predict the next stop.\n",
        "\n",
        "https://www.bookingchallenge.com/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gS7lpf_fzrL"
      },
      "source": [
        "# Pip install"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeqHVjyNfK_U",
        "outputId": "339f7e1d-f3d6-4a76-ccaa-e1fa671062a3"
      },
      "source": [
        "!pip install torch pytorch_lightning sklearn wget tqdm plotly torchmetrics wandb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.18)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n",
            "Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n",
            "Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n",
            "Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (1.6.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (21.2.0)\n",
            "Requirement already satisfied: async-timeout<4.0,>=3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (3.0.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (5.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard!=2.5.0,>=2.2.0->pytorch_lightning) (3.5.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4E7zowfYfp1p"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WulB_NW0dw9b",
        "outputId": "a6e64064-3ee4-416d-c4e8-fa34af3a6d6a"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Aug  9 22:33:32 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.42.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P0    33W / 250W |   1017MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVXRN_tlfZDd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc391fe4-20eb-4ef2-ee78-cf711d6620eb"
      },
      "source": [
        "from pathlib import Path\n",
        "from collections import Counter, namedtuple\n",
        "from typing import Optional\n",
        "import zipfile\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.utils as utils\n",
        "import torchmetrics\n",
        "from tqdm import tqdm\n",
        "import wandb\n",
        "import wget\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.callbacks import EarlyStopping\n",
        "from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger\n",
        "\n",
        "%matplotlib notebook\n",
        "%load_ext tensorboard"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The tensorboard extension is already loaded. To reload it, use:\n",
            "  %reload_ext tensorboard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1XaPD5hf4ci"
      },
      "source": [
        "DATA_DIR = Path('./data').resolve()\n",
        "DATA_DIR.mkdir(exist_ok=True, parents=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHyRk_x4gOik"
      },
      "source": [
        "## Utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGqJBq7cgQ2z"
      },
      "source": [
        "class CityIndex:\n",
        "    \"\"\"\n",
        "    city_index = CityIndex.from_list(train_df.city_id.tolist(), max_size=10_000)\n",
        "    \"\"\"\n",
        "    def __init__(self, max_size: int = None) -> None:\n",
        "        self.max_size = max_size\n",
        "        self.counter = Counter()\n",
        "        self.specials = []\n",
        "        self.index = None\n",
        "        self.reverse_index = []\n",
        "        self._add_special('<pad>')\n",
        "        self._add_special('<unk>')\n",
        "        self._add_special('<bos>')\n",
        "        self._add_special('<eos>')\n",
        "\n",
        "    def __call__(self, ids, reverse=False):\n",
        "        if reverse:\n",
        "            return [self.reverse_index[i] for i in ids]\n",
        "        return [self.index.get(i, self.unk_id) for i in ids]\n",
        "\n",
        "    @classmethod\n",
        "    def from_list(cls, city_ids, **kwargs):\n",
        "        index = CityIndex(**kwargs)\n",
        "        index.update(city_ids)\n",
        "        index.build_index()\n",
        "        return index\n",
        "\n",
        "    def _add_special(self, city_id):\n",
        "        self.counter[city_id] = float('inf')\n",
        "        self.specials.append(city_id)\n",
        "\n",
        "    def update(self, city_id):\n",
        "        \"\"\"Update counter with one or many cities.\"\"\"\n",
        "        if isinstance(city_id, int):\n",
        "            self.counter.update([city_id])\n",
        "        else:\n",
        "            self.counter.update(city_id)\n",
        "\n",
        "    def __getitem__(self, city_id):\n",
        "        return self.index.get(city_id, self.unk_id)\n",
        "\n",
        "    def build_index(self):\n",
        "        self.index = dict()\n",
        "        self.reverse_index = []\n",
        "        size = self.max_size if self.max_size is not None else len(self.counter)\n",
        "        for i, (city_id, count) in enumerate(self.counter.most_common(size)):\n",
        "            self.index[city_id] = i\n",
        "            while i >= len(self.reverse_index):\n",
        "                self.reverse_index.append(None)\n",
        "            self.reverse_index[i] = city_id\n",
        "\n",
        "    @property\n",
        "    def size(self):\n",
        "        return len(self.index)\n",
        "\n",
        "    @property\n",
        "    def pad_id(self):\n",
        "        return self.index['<pad>']\n",
        "\n",
        "    @property\n",
        "    def unk_id(self):\n",
        "        return self.index['<unk>']\n",
        "    \n",
        "    @property\n",
        "    def bos_id(self):\n",
        "        return self.index['<bos>']\n",
        "    \n",
        "    @property\n",
        "    def eos_id(self):\n",
        "        return self.index['<eos>']\n",
        "\n",
        "    @property\n",
        "    def special_ids(self):\n",
        "        return [self.index(i) for i in self.specials]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNUoRCZogJK5"
      },
      "source": [
        "def concatenate_groups(x):\n",
        "    return x.tolist()\n",
        "\n",
        "\n",
        "def collect_trip_df(df, city_index):\n",
        "    df.sort_values('checkin', ascending=True, inplace=True)\n",
        "    trip_df = df.groupby('utrip_id').agg(\n",
        "        cities=('city_id', concatenate_groups),\n",
        "        countries=('hotel_country', concatenate_groups),\n",
        "        booker_countries=('booker_country', concatenate_groups),\n",
        "        trip_start=('checkin', 'min'),\n",
        "        trip_end=('checkout', 'max'),\n",
        "        checkins=('checkin', concatenate_groups),\n",
        "        checkouts=('checkout', concatenate_groups)\n",
        "    )\n",
        "\n",
        "    trip_df['destinations'] = trip_df.cities.apply(lambda x: [city_index.bos_id] + city_index(x))\n",
        "    trip_df['country_destinations'] = trip_df.countries.apply(lambda x: [city_index.bos_id] + city_index(x))\n",
        "    trip_df['checkin_months'] = trip_df.checkins.apply(lambda x: [city_index.bos_id] + [i.month for i in x])\n",
        "    trip_df['days_from_trip_start'] = trip_df.apply(\n",
        "        lambda row: [-1] + [np.log( (i - row.trip_start).days + 1 ) for i in row.checkins], axis=1)\n",
        "    trip_df['days_till_trip_end'] = trip_df.apply(\n",
        "        lambda row: [-1] + [np.log( (i - row.trip_end).days + 1 ) for i in row.checkins], axis=1)\n",
        "    trip_df['days_from_last_stop'] = trip_df.checkins.apply(\n",
        "        lambda x: [-1, 0] + [np.log( (x[i] - x[i-1]).days + 1 ) for i in range(1, len(x))])\n",
        "    trip_df['checkin_dayofweek'] = trip_df.checkins.apply(\n",
        "        lambda x: [7] + [i.dayofweek for i in x])\n",
        "    trip_df['num_stops'] = trip_df.cities.apply(\n",
        "        lambda x: [-1] + [np.log(i + 1) for i, _ in enumerate(x)])\n",
        "    trip_df['stop_length_days'] = trip_df.apply(\n",
        "        lambda row: [0] + [np.log( (co - ci).days + 1 ) for ci, co in zip(row.checkins, row.checkouts)], axis=1)\n",
        "    trip_df['international_stop'] = trip_df.apply(\n",
        "        lambda row: [0] + [int(row.booker_countries[0] != row.countries[0])] \\\n",
        "            + [int(row.countries[i] != row.countries[i - 1]) for i in range(1, len(row.countries))],\n",
        "        axis=1)\n",
        "    return trip_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNhF3NmXggyl"
      },
      "source": [
        "## Lightning Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7sJ9_1tgiKd"
      },
      "source": [
        "sequential_features = [\n",
        "            'destinations',\n",
        "            'country_destinations',\n",
        "            'checkin_months',\n",
        "            'days_from_trip_start',\n",
        "            'days_till_trip_end',\n",
        "            'days_from_last_stop',\n",
        "            'checkin_dayofweek',\n",
        "            'num_stops',\n",
        "            'stop_length_days',\n",
        "            'international_stop'\n",
        "        ]\n",
        "pred_sample = namedtuple('pred_sample', sequential_features + ['maxlen'])\n",
        "float_features = [\n",
        "                'days_from_trip_start',\n",
        "                'days_till_trip_end',\n",
        "                'days_from_last_stop',\n",
        "                'num_stops',\n",
        "                'stop_length_days',\n",
        "                'international_stop'\n",
        "]\n",
        "\n",
        "\n",
        "class TripLMDataset(utils.data.Dataset):\n",
        "    def __init__(self, df, mode: str) -> None:\n",
        "        self.df = df\n",
        "        self.mode = mode\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        sample = self.df.iloc[idx]\n",
        "        X, Y, maxlen = [], None, 0\n",
        "        for feature in sequential_features:\n",
        "            data = torch.tensor(sample[feature])\n",
        "            # when training trim and shift the inputs to create labels\n",
        "            # when testing do nothing\n",
        "            x = data if self.mode == 'test' else data[:-1]\n",
        "            if feature == 'destinations':\n",
        "                Y = torch.tensor( sample[feature][1:] + [0]) if self.mode == 'test' else data[1:]\n",
        "                maxlen = len(x)\n",
        "            X.append(x)\n",
        "        X.append(maxlen)\n",
        "        return pred_sample(*X) , Y\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.df.shape[0]\n",
        "\n",
        "\n",
        "pred_batch = namedtuple('pred_batch', \n",
        "                        [\n",
        "                         'input_ids',\n",
        "                         'country_ids',\n",
        "                         'checkin_months',\n",
        "                         'days_from_trip_start',\n",
        "                         'days_till_trip_end',\n",
        "                         'days_from_last_stop',\n",
        "                         'checkin_dayofweek',\n",
        "                         'num_stops',\n",
        "                         'stop_length_days',\n",
        "                         'international_stop',\n",
        "                         'attention_mask'\n",
        "                         ]\n",
        ")\n",
        "\n",
        "\n",
        "class BatchManager:\n",
        "    def __init__(self, pad_id: int) -> None:\n",
        "        self.pad_id = pad_id\n",
        "        self.fields = list(pred_sample._fields)\n",
        "        self.fields.pop(self.fields.index('maxlen'))\n",
        "        self.field_dtypes = [torch.int64 if f not in float_features else torch.float32 for f in self.fields]\n",
        "\n",
        "    def __call__(self, batch):\n",
        "        N = len(batch)\n",
        "        maxlen = max([sample[0].maxlen for sample in batch])\n",
        "        input_ids = torch.zeros((N, maxlen), dtype=torch.int64) + self.pad_id\n",
        "        X = [torch.zeros((N, maxlen), dtype=dtype) for field, dtype in zip(self.fields, self.field_dtypes)]\n",
        "        mask = torch.zeros((N, maxlen), dtype=torch.int64)\n",
        "        Y = torch.zeros((N, maxlen), dtype=torch.int64) + self.pad_id\n",
        "        for i, (sample, y) in enumerate(batch):\n",
        "            size = sample.maxlen\n",
        "            for j, feature in enumerate(self.fields):\n",
        "                x = getattr(sample, feature)\n",
        "                X[j][i, -size:] = x\n",
        "            mask[i, -size:] = 1\n",
        "            Y[i, -size:] = y\n",
        "        X.append(mask)\n",
        "        return pred_batch(*X), Y\n",
        "\n",
        "\n",
        "class DataModule(pl.LightningDataModule):\n",
        "    def __init__(self, data_dir: Path, batch_size: int = 32, subsample: int = 0):\n",
        "        super().__init__()\n",
        "        self.wget_url = \"https://035105f7-ae32-47b6-a25b-87af7924c7ea.filesusr.com/archives/3091be_233bace50f3f48fba40547a89443a96e.zip?dn=booking_dataset.zip\"\n",
        "        self.data_dir = data_dir\n",
        "        self.batch_size = batch_size\n",
        "        self.subsample = subsample\n",
        "\n",
        "    def prepare_data(self):\n",
        "        zip_cache = self.data_dir / 'booking_dataset.zip'\n",
        "        if not zip_cache.exists():\n",
        "            print('Downloading data...')\n",
        "            wget.download(self.wget_url, str(zip_cache))\n",
        "            with zipfile.ZipFile(zip_cache, 'r') as zip_ref:\n",
        "                zip_ref.extractall(self.data_dir)\n",
        "\n",
        "    def setup(self, stage: Optional[str] = None):\n",
        "        train_df = pd.read_csv(self.data_dir / 'train_set.csv').sort_values(by=['utrip_id','checkin'])\n",
        "        train_df['checkin'] = pd.to_datetime(train_df.checkin)\n",
        "        train_df['checkout'] = pd.to_datetime(train_df.checkout)\n",
        "        if self.subsample > 0:\n",
        "            train_df = train_df.iloc[0: self.subsample].copy()\n",
        "        test_df = pd.read_csv(self.data_dir / 'test_set.csv').sort_values(by=['utrip_id','checkin'])\n",
        "        test_df['checkin'] = pd.to_datetime(test_df.checkin)\n",
        "        test_df['checkout'] = pd.to_datetime(test_df.checkout)\n",
        "\n",
        "        print('Building index...')\n",
        "        self.city_index = CityIndex.from_list(\n",
        "            train_df.city_id.tolist() + train_df.hotel_country.tolist(),\n",
        "            max_size=25_000\n",
        "        )\n",
        "\n",
        "        print('Preparing trips...')\n",
        "        train_trip_df = collect_trip_df(train_df, self.city_index)\n",
        "        train_trip_df, val_trip_df = train_test_split(train_trip_df, test_size=0.1)\n",
        "        test_trip_df = collect_trip_df(test_df, self.city_index)\n",
        "\n",
        "        self.train_ds = TripLMDataset(train_trip_df, mode='train')\n",
        "        self.val_ds = TripLMDataset(val_trip_df, mode='val')\n",
        "        self.test_ds = TripLMDataset(test_trip_df, mode='test')\n",
        "        self.batch_manager = BatchManager(self.city_index.pad_id)\n",
        "        print('Done.')\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return self._make_dataloader(self.train_ds, shuffle=True)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return self._make_dataloader(self.val_ds, False)\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        return self._make_dataloader(self.test_ds, False)\n",
        "\n",
        "    def predict_dataloader(self):\n",
        "        return self._make_dataloader(self.test_ds, False)\n",
        "\n",
        "    def _make_dataloader(self, ds, shuffle = False):\n",
        "        return utils.data.DataLoader(ds, collate_fn=self.batch_manager, batch_size=self.batch_size, shuffle=shuffle, pin_memory=True, num_workers=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xx2GuS1sgoTr",
        "outputId": "34060104-424b-4641-9945-520bc42b1bab"
      },
      "source": [
        "data_module = DataModule(DATA_DIR, batch_size=128, subsample=0)\n",
        "data_module.prepare_data()\n",
        "data_module.setup()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building index...\n",
            "Preparing trips...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: RuntimeWarning:\n",
            "\n",
            "invalid value encountered in log\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: RuntimeWarning:\n",
            "\n",
            "divide by zero encountered in log\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iqk3y0ONhOZg"
      },
      "source": [
        "## Lightning Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLeJtRhvhPed"
      },
      "source": [
        "class TripModel(nn.Module):\n",
        "    def __init__(self, num_cities, hidden_size, num_layers, numeric_features, dropout: float = 0.1, max_trip_size: int = 128, mode: str = 'GRU'):\n",
        "        super().__init__()\n",
        "        self.num_cities = num_cities\n",
        "        self.hidden_size = hidden_size\n",
        "        self.numeric_features = numeric_features\n",
        "\n",
        "        self.embedding_layer = nn.Embedding(num_cities, hidden_size)\n",
        "        self.months_layer = nn.Embedding(13, hidden_size)\n",
        "        self.dayofweek_layer = nn.Embedding(8, hidden_size)\n",
        "        if len(self.numeric_features) > 0:\n",
        "            self.numeric_lin = nn.Linear(len(self.numeric_features), hidden_size)\n",
        "        if mode == 'GRU':\n",
        "            self.encoder = nn.ModuleList([\n",
        "                nn.GRU((2 + (len(self.numeric_features) > 0)) * hidden_size, hidden_size, 1, dropout=0.1, batch_first=True),\n",
        "                nn.GRU(hidden_size, hidden_size, num_layers, dropout=0.1, batch_first=True)\n",
        "            ])\n",
        "        elif mode == 'LSTM':\n",
        "            self.encoder = nn.ModuleList([\n",
        "                nn.LSTM((2 + (len(self.numeric_features) > 0)) * hidden_size, hidden_size, 1, dropout=0.1, batch_first=True),\n",
        "                nn.LSTM(hidden_size, hidden_size, num_layers, dropout=0.1, batch_first=True)\n",
        "            ])\n",
        "        self.drop = nn.ModuleList([nn.Dropout(dropout) for _ in self.encoder])\n",
        "        self.lin = nn.Linear(hidden_size, num_cities, bias=False)\n",
        "\n",
        "    def forward(self, X):\n",
        "        months = self.months_layer(X.checkin_months)\n",
        "        days = self.dayofweek_layer(X.checkin_dayofweek)\n",
        "        time_emb = months + days\n",
        "        loc_emb = self.embedding_layer(X.input_ids) + self.embedding_layer(X.country_ids)\n",
        "        emb = torch.cat((loc_emb, time_emb), dim=-1)\n",
        "        if len(self.numeric_features) > 0:\n",
        "            trip_details = torch.stack([getattr(X, f) for f in self.numeric_features], dim=-1)\n",
        "            detail_emb = self.numeric_lin(trip_details)\n",
        "            emb = torch.cat((emb, detail_emb), dim=-1)\n",
        "        for (layer, drop) in zip(self.encoder, self.drop):\n",
        "            emb, *_ = layer(emb)\n",
        "            emb = drop(emb)\n",
        "        return self.lin(emb)\n",
        "\n",
        "\n",
        "class LightningTripModel(pl.LightningModule):\n",
        "    def __init__(self, *args, **kwargs):\n",
        "        super().__init__()\n",
        "        self.model = TripModel(*args, **kwargs)\n",
        "        self.metrics = nn.ModuleDict({\n",
        "            'top4_accuracy': torchmetrics.Accuracy(top_k=4, ignore_index=0)\n",
        "        })\n",
        "        self.eval_metric = torchmetrics.Accuracy(top_k=4, ignore_index=0)\n",
        "        self.lr = 0.01\n",
        "        self.loss_fn = nn.CrossEntropyLoss(ignore_index=0)\n",
        "\n",
        "    def forward(self, X):\n",
        "        return self.model.forward(X)\n",
        "\n",
        "    def forward_with_loss(self, xb, yb):\n",
        "        logits = self.forward(xb)\n",
        "        cost = self.loss_fn(logits.view(-1, self.model.num_cities), yb.view(-1))\n",
        "        return logits, cost\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        xb, yb = batch\n",
        "        y_hat, cost = self.forward_with_loss(xb, yb)\n",
        "        return cost\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        xb, yb = batch\n",
        "        logits, cost = self.forward_with_loss(xb, yb)\n",
        "        self.log(\"val_loss\", cost)\n",
        "        probs = logits.softmax(dim=-1)\n",
        "        for name, metric in self.metrics.items():\n",
        "            metric(probs.view(-1, self.model.num_cities), yb.view(-1))\n",
        "        self.eval_metric(probs[:, -1], yb[:, -1])  # accuracy on last destination in trip\n",
        "        return cost\n",
        "\n",
        "    def validation_epoch_end(self, outs):\n",
        "        for name, metric in self.metrics.items():\n",
        "            self.log(f'{name}', metric.compute())\n",
        "        self.log(f'eval_top4_accuracy', self.eval_metric.compute())\n",
        "\n",
        "    def predict_step(self, batch, batch_idx):\n",
        "        xb, yb = batch\n",
        "        logits = self.forward(xb)\n",
        "        return logits\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        return torch.optim.Adam(self.parameters(), lr=self.lr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9ZpqkK_hbaW"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XizNaikbCrOb"
      },
      "source": [
        "config = {\n",
        "    'model': {\n",
        "        'num_cities': data_module.city_index.size,\n",
        "        'hidden_size': 128,\n",
        "        'num_layers': 2,\n",
        "        'numeric_features': ['days_from_trip_start', 'days_from_last_stop', 'num_stops', 'stop_length_days', 'international_stop'],\n",
        "        'dropout': 0.1,\n",
        "        'mode': 'GRU'\n",
        "    },\n",
        "    'trainer': {\n",
        "        'max_epochs': 8,\n",
        "        'auto_lr_find': True,\n",
        "        'gpus': torch.cuda.device_count(),\n",
        "        'fast_dev_run': False\n",
        "    }\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9MJS6KdDSW4",
        "outputId": "03f9bf7a-2d31-4fa7-caf9-4a24cc8ed03d"
      },
      "source": [
        "pl_model = LightningTripModel(**config['model'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:65: UserWarning:\n",
            "\n",
            "dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ck9tpInChcN8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "b123c1aa-f3c0-4b60-e476-191d2eae092a"
      },
      "source": [
        "use_wandb = True\n",
        "if use_wandb:\n",
        "    run = wandb.init(project='booking-recsys', tags=[config['model']['mode']], config=config)\n",
        "    logger = WandbLogger(experiment=run)\n",
        "else:\n",
        "    logger = TensorBoardLogger(DATA_DIR / 'tb_logs', name=mode)\n",
        "\n",
        "callbacks = [\n",
        "             EarlyStopping(monitor=\"val_loss\")\n",
        "]\n",
        "trainer = pl.Trainer(logger=logger, callbacks=callbacks, **config['trainer'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmike-a-yen\u001b[0m (use `wandb login --relogin` to force relogin)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.11.2 is available!  To upgrade, please run:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.11.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">effortless-serenity-9</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/mike-a-yen/booking-recsys\" target=\"_blank\">https://wandb.ai/mike-a-yen/booking-recsys</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/mike-a-yen/booking-recsys/runs/15ns0x67\" target=\"_blank\">https://wandb.ai/mike-a-yen/booking-recsys/runs/15ns0x67</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210809_223645-15ns0x67</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gASD3ueXhrM-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389,
          "referenced_widgets": [
            "933b25fb09014fe8a010c19233370618",
            "3c508f50787c45d2825f339959f3f9d7",
            "f6ecad80ab59480b9b9fc77bc883f6e6",
            "5bc0d58c73814219acf4e1bb6a0c8b73",
            "b6721adc85414ed897423c8888287ec3",
            "30cb47935e8d43e2aa39fd5e99cf23e6",
            "31d910a4e3cb4156b4e99adc46fa4542",
            "e8abf817b1cd4546b9b60a0ae9e72ce0"
          ]
        },
        "outputId": "03dac3ae-d609-44d7-9236-015a6468e815"
      },
      "source": [
        "if config['trainer']['auto_lr_find']:\n",
        "    results = trainer.tune(pl_model, datamodule=data_module)['lr_find']\n",
        "    fig = results.plot(suggest=True)\n",
        "    fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning:\n",
            "\n",
            "DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n",
            "\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name        | Type             | Params\n",
            "-------------------------------------------------\n",
            "0 | model       | TripModel        | 6.8 M \n",
            "1 | metrics     | ModuleDict       | 0     \n",
            "2 | eval_metric | Accuracy         | 0     \n",
            "3 | loss_fn     | CrossEntropyLoss | 0     \n",
            "-------------------------------------------------\n",
            "6.8 M     Trainable params\n",
            "0         Non-trainable params\n",
            "6.8 M     Total params\n",
            "27.196    Total estimated model params size (MB)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "933b25fb09014fe8a010c19233370618",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Finding best initial lr', style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Restoring states from the checkpoint file at /content/lr_find_temp_model.ckpt\n",
            "Restored all states from the checkpoint file at /content/lr_find_temp_model.ckpt\n",
            "Learning rate set to 0.003311311214825908\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
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              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
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              "    }\n",
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              "\n",
              "    this.context = undefined;\n",
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              "\n",
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              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
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              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
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              "\n",
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              "\n",
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              "\n",
              "\n",
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              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
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              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
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              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
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              "\n",
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              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
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              "\n",
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              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
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              "\n",
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              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
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              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
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              "\n",
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              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
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              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
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              "        var button = $('<button/>');\n",
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              "                        'ui-button-icon-only');\n",
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              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
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              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id='02b2c711-2e38-4843-863c-d5b942b39dc4'></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k59bOqK-hx4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591,
          "referenced_widgets": [
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      "source": [
        "trainer.fit(pl_model, data_module)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning:\n",
            "\n",
            "DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n",
            "\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name        | Type             | Params\n",
            "-------------------------------------------------\n",
            "0 | model       | TripModel        | 6.8 M \n",
            "1 | metrics     | ModuleDict       | 0     \n",
            "2 | eval_metric | Accuracy         | 0     \n",
            "3 | loss_fn     | CrossEntropyLoss | 0     \n",
            "-------------------------------------------------\n",
            "6.8 M     Trainable params\n",
            "0         Non-trainable params\n",
            "6.8 M     Total params\n",
            "27.196    Total estimated model params size (MB)\n"
          ],
          "name": "stderr"
        },
        {
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          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
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            "\n",
            "\n",
            "\n",
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning:\n",
            "\n",
            "DataModule.teardown has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.teardown.\n",
            "\n"
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          "name": "stderr"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKC4I5SofHb7"
      },
      "source": [
        "# Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeywPrp9fR5C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1906e25b-29a4-4135-ae44-f0e5ac873247"
      },
      "source": [
        "gt_df = pd.read_csv(DATA_DIR / 'ground_truth.csv')\n",
        "gt_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .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>utrip_id</th>\n",
              "      <th>city_id</th>\n",
              "      <th>hotel_country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1038944_1</td>\n",
              "      <td>54085</td>\n",
              "      <td>Sokovia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1068715_1</td>\n",
              "      <td>29319</td>\n",
              "      <td>Cobra Island</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1075528_1</td>\n",
              "      <td>55763</td>\n",
              "      <td>Bozatta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1110462_4</td>\n",
              "      <td>11930</td>\n",
              "      <td>Alvonia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1132565_1</td>\n",
              "      <td>58659</td>\n",
              "      <td>Axphain</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    utrip_id  city_id hotel_country\n",
              "0  1038944_1    54085       Sokovia\n",
              "1  1068715_1    29319  Cobra Island\n",
              "2  1075528_1    55763       Bozatta\n",
              "3  1110462_4    11930       Alvonia\n",
              "4  1132565_1    58659       Axphain"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkmrZY6GkNtm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "6c269cf8-fa7a-45f9-92a3-b350651a8c74"
      },
      "source": [
        "test_df = pd.read_csv(DATA_DIR / 'test_set.csv')\n",
        "test_df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>user_id</th>\n",
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              "      <th>checkout</th>\n",
              "      <th>device_class</th>\n",
              "      <th>affiliate_id</th>\n",
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              "      <th>utrip_id</th>\n",
              "      <th>city_id</th>\n",
              "      <th>hotel_country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000066</td>\n",
              "      <td>2016-07-21</td>\n",
              "      <td>2016-07-23</td>\n",
              "      <td>desktop</td>\n",
              "      <td>9924</td>\n",
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              "      <td>1000066_2</td>\n",
              "      <td>56430</td>\n",
              "      <td>Urkesh</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>9924</td>\n",
              "      <td>Gondal</td>\n",
              "      <td>1000066_2</td>\n",
              "      <td>41971</td>\n",
              "      <td>Urkesh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000066</td>\n",
              "      <td>2016-07-25</td>\n",
              "      <td>2016-07-28</td>\n",
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              "      <td>9924</td>\n",
              "      <td>Gondal</td>\n",
              "      <td>1000066_2</td>\n",
              "      <td>5797</td>\n",
              "      <td>Urkesh</td>\n",
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              "      <td>1000066</td>\n",
              "      <td>2016-07-28</td>\n",
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              "      <td>2436</td>\n",
              "      <td>Gondal</td>\n",
              "      <td>1000066_2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000270</td>\n",
              "      <td>2016-02-08</td>\n",
              "      <td>2016-02-09</td>\n",
              "      <td>mobile</td>\n",
              "      <td>9452</td>\n",
              "      <td>The Devilfire Empire</td>\n",
              "      <td>1000270_1</td>\n",
              "      <td>50075</td>\n",
              "      <td>The Devilfire Empire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000270</td>\n",
              "      <td>2016-02-09</td>\n",
              "      <td>2016-02-10</td>\n",
              "      <td>desktop</td>\n",
              "      <td>116</td>\n",
              "      <td>The Devilfire Empire</td>\n",
              "      <td>1000270_1</td>\n",
              "      <td>29207</td>\n",
              "      <td>Cobra Island</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1000270</td>\n",
              "      <td>2016-02-10</td>\n",
              "      <td>2016-02-19</td>\n",
              "      <td>desktop</td>\n",
              "      <td>9924</td>\n",
              "      <td>The Devilfire Empire</td>\n",
              "      <td>1000270_1</td>\n",
              "      <td>44768</td>\n",
              "      <td>Cobra Island</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1000270</td>\n",
              "      <td>2016-02-19</td>\n",
              "      <td>2016-02-20</td>\n",
              "      <td>desktop</td>\n",
              "      <td>9924</td>\n",
              "      <td>The Devilfire Empire</td>\n",
              "      <td>1000270_1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1000441</td>\n",
              "      <td>2016-05-29</td>\n",
              "      <td>2016-06-01</td>\n",
              "      <td>desktop</td>\n",
              "      <td>9924</td>\n",
              "      <td>Bartovia</td>\n",
              "      <td>1000441_1</td>\n",
              "      <td>47759</td>\n",
              "      <td>Osterlich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1000441</td>\n",
              "      <td>2016-06-01</td>\n",
              "      <td>2016-06-02</td>\n",
              "      <td>desktop</td>\n",
              "      <td>9924</td>\n",
              "      <td>Bartovia</td>\n",
              "      <td>1000441_1</td>\n",
              "      <td>46411</td>\n",
              "      <td>Osterlich</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id     checkin    checkout  ...   utrip_id  city_id         hotel_country\n",
              "0  1000066  2016-07-21  2016-07-23  ...  1000066_2    56430                Urkesh\n",
              "1  1000066  2016-07-23  2016-07-25  ...  1000066_2    41971                Urkesh\n",
              "2  1000066  2016-07-25  2016-07-28  ...  1000066_2     5797                Urkesh\n",
              "3  1000066  2016-07-28  2016-07-31  ...  1000066_2        0                   NaN\n",
              "4  1000270  2016-02-08  2016-02-09  ...  1000270_1    50075  The Devilfire Empire\n",
              "5  1000270  2016-02-09  2016-02-10  ...  1000270_1    29207          Cobra Island\n",
              "6  1000270  2016-02-10  2016-02-19  ...  1000270_1    44768          Cobra Island\n",
              "7  1000270  2016-02-19  2016-02-20  ...  1000270_1        0                   NaN\n",
              "8  1000441  2016-05-29  2016-06-01  ...  1000441_1    47759             Osterlich\n",
              "9  1000441  2016-06-01  2016-06-02  ...  1000441_1    46411             Osterlich\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5lcSlOLAc1l"
      },
      "source": [
        "def predict_on_ds(ds, pl_model, data_module, topk: int = 4):\n",
        "    dl = utils.data.DataLoader(\n",
        "        ds, collate_fn=data_module.batch_manager, batch_size=128, shuffle=False,\n",
        "        num_workers=4\n",
        "    )\n",
        "    city_predictions = []\n",
        "    special_token_ids = torch.tensor([data_module.city_index[i] for i in data_module.city_index.specials], device=pl_model.device)\n",
        "    with torch.no_grad():\n",
        "        for xb, yb in tqdm(dl, desc='Predicting'):\n",
        "            xb_device = pred_batch(*[getattr(xb, f).to(pl_model.device) for f in xb._fields])\n",
        "            logits = pl_model(xb_device)[:, -2]\n",
        "            logit_mask = torch.zeros_like(logits)\n",
        "            logit_mask[:, special_token_ids] -= 1e9\n",
        "            logits += logit_mask\n",
        "            probs = logits.softmax(dim=-1)\n",
        "            preds = torch.topk(probs, topk, dim=-1)\n",
        "            prediction_index = preds.indices.tolist()\n",
        "            city_predictions += [data_module.city_index(row, reverse=True) for row in prediction_index]\n",
        "    return {'predictions': city_predictions, 'utrip_id': ds.df.index.tolist()}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2WTlfyBBVPy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "daa8a27c-5c03-4864-88e0-3a6864d9a66a"
      },
      "source": [
        "results = predict_on_ds(data_module.test_ds, pl_model, data_module)\n",
        "\n",
        "# make the results df\n",
        "payload = {'utrip_id': results['utrip_id']}\n",
        "for i in range(4):\n",
        "    payload[f'city_id_{i+1}'] = [row[i] for row in results['predictions']]\n",
        "df = pd.DataFrame(payload)\n",
        "df = df.merge(gt_df, how='left', left_on='utrip_id', right_on='utrip_id')\n",
        "df['is_correct'] = df.apply(lambda row: row.city_id in [row[f'city_id_{i+1}'] for i in range(4)], axis=1).astype(int)\n",
        "\n",
        "# compute accuracy\n",
        "acc = df.is_correct.mean()\n",
        "print(f'Top 4 Accuracy: {100 * acc:0.2f}%')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicting: 100%|██████████| 553/553 [02:17<00:00,  4.01it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Top 4 Accuracy: 47.08%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>utrip_id</th>\n",
              "      <th>city_id_1</th>\n",
              "      <th>city_id_2</th>\n",
              "      <th>city_id_3</th>\n",
              "      <th>city_id_4</th>\n",
              "      <th>city_id</th>\n",
              "      <th>hotel_country</th>\n",
              "      <th>is_correct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000066_2</td>\n",
              "      <td>5797</td>\n",
              "      <td>39200</td>\n",
              "      <td>30018</td>\n",
              "      <td>46854</td>\n",
              "      <td>11543</td>\n",
              "      <td>Urkesh</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000270_1</td>\n",
              "      <td>27404</td>\n",
              "      <td>32821</td>\n",
              "      <td>38677</td>\n",
              "      <td>21328</td>\n",
              "      <td>29207</td>\n",
              "      <td>Cobra Island</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000441_1</td>\n",
              "      <td>35160</td>\n",
              "      <td>13260</td>\n",
              "      <td>8935</td>\n",
              "      <td>56421</td>\n",
              "      <td>35160</td>\n",
              "      <td>Osterlich</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100048_1</td>\n",
              "      <td>26235</td>\n",
              "      <td>13530</td>\n",
              "      <td>45030</td>\n",
              "      <td>13150</td>\n",
              "      <td>33394</td>\n",
              "      <td>Alvonia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000543_1</td>\n",
              "      <td>44869</td>\n",
              "      <td>52818</td>\n",
              "      <td>25390</td>\n",
              "      <td>67353</td>\n",
              "      <td>52818</td>\n",
              "      <td>Elbonia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    utrip_id  city_id_1  city_id_2  ...  city_id  hotel_country  is_correct\n",
              "0  1000066_2       5797      39200  ...    11543         Urkesh           0\n",
              "1  1000270_1      27404      32821  ...    29207   Cobra Island           0\n",
              "2  1000441_1      35160      13260  ...    35160      Osterlich           1\n",
              "3   100048_1      26235      13530  ...    33394        Alvonia           0\n",
              "4  1000543_1      44869      52818  ...    52818        Elbonia           1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iARN7zMpgZkD"
      },
      "source": [
        "trainer.logger.experiment.log({'final_top4_accuracy': acc})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGmRNhc4Evf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667,
          "referenced_widgets": [
            "60e2ca08d03245a49f65402eaa5e5466",
            "032e6ab625894889b7ee8a3d0b0b60f6",
            "3264eaec9f8b43149eda5b3f82511574",
            "e27ef840aa9d4fe9879cc30c7a47657d",
            "79dacd1e809e44d29fa4f3004b4d8529",
            "6634acc921ab4199a3cc5697251fa191",
            "5565a78ac040482b95ada48a0989cfbb",
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        "outputId": "666b2c20-eae9-47dc-d8c5-d47c001615a1"
      },
      "source": [
        "trainer.logger.experiment.finish()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 697<br/>Program ended successfully."
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          "metadata": {
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        {
          "output_type": "display_data",
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              "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find user logs for this run at: <code>/content/wandb/run-20210809_223645-15ns0x67/logs/debug.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "data": {
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              "Find internal logs for this run at: <code>/content/wandb/run-20210809_223645-15ns0x67/logs/debug-internal.log</code>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "<tr><td>val_loss</td><td>4.78485</td></tr><tr><td>top4_accuracy</td><td>0.35258</td></tr><tr><td>eval_top4_accuracy</td><td>0.46999</td></tr><tr><td>epoch</td><td>7</td></tr><tr><td>trainer/global_step</td><td>12248</td></tr><tr><td>_runtime</td><td>645</td></tr><tr><td>_timestamp</td><td>1628549251</td></tr><tr><td>_step</td><td>8</td></tr><tr><td>final_top4_accuracy</td><td>0.47083</td></tr></table>"
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              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>val_loss</td><td>█▄▂▁▁▁▁▁</td></tr><tr><td>top4_accuracy</td><td>▁▄▅▆▇▇██</td></tr><tr><td>eval_top4_accuracy</td><td>▁▄▅▆▇▇██</td></tr><tr><td>epoch</td><td>▁▂▃▄▅▆▇█</td></tr><tr><td>trainer/global_step</td><td>▁▂▃▄▅▆▇██</td></tr><tr><td>_runtime</td><td>▁▂▃▃▄▅▆▆█</td></tr><tr><td>_timestamp</td><td>▁▂▃▃▄▅▆▆█</td></tr><tr><td>_step</td><td>▁▂▃▄▅▅▆▇█</td></tr><tr><td>final_top4_accuracy</td><td>▁</td></tr></table><br/>"
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              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">effortless-serenity-9</strong>: <a href=\"https://wandb.ai/mike-a-yen/booking-recsys/runs/15ns0x67\" target=\"_blank\">https://wandb.ai/mike-a-yen/booking-recsys/runs/15ns0x67</a><br/>\n",
              "                "
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}