{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNArpdTm04Uv"
      },
      "source": [
        "First we need to install [DARTS](https://unit8co.github.io/darts/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3UwoGuzmkSyp"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install darts\n",
        "!pip install sidetable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4tds4uS1Htl"
      },
      "source": [
        "Then mount GDrive where the data is stored:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1ibBCezWjdTu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23bfd388-478e-4ce3-eda4-fccb5495f463"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EIWDE9eHkl1g",
        "outputId": "3a37ddf1-7066-4c81-de0e-206f92820975"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  \\\n",
              "0  2013      3    1     0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7  1023.0   \n",
              "1  2013      3    1     1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1  1023.2   \n",
              "2  2013      3    1     2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1  1023.5   \n",
              "3  2013      3    1     3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4  1024.5   \n",
              "4  2013      3    1     4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0  1025.2   \n",
              "\n",
              "   DEWP  RAIN   wd  WSPM  station           timestamp  \n",
              "0 -18.8   0.0  0.0   4.4        0 2013-03-01 00:00:00  \n",
              "1 -18.2   0.0  1.0   4.7        0 2013-03-01 01:00:00  \n",
              "2 -18.2   0.0  0.0   5.6        0 2013-03-01 02:00:00  \n",
              "3 -19.4   0.0  2.0   3.1        0 2013-03-01 03:00:00  \n",
              "4 -19.5   0.0  1.0   2.0        0 2013-03-01 04:00:00  "
            ],
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              "      <td>6.0</td>\n",
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              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>72.0</td>\n",
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              "      <td>1024.5</td>\n",
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              "      <td>2013-03-01 03:00:00</td>\n",
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              "        document.querySelector('#df-a2d92301-49e2-4478-a7c7-d47f25b5353c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-a2d92301-49e2-4478-a7c7-d47f25b5353c');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import darts\n",
        "import pandas as pd\n",
        "\n",
        "from urllib.request import urlopen\n",
        "from zipfile import ZipFile\n",
        "from io import BytesIO\n",
        "\n",
        "from darts import concatenate\n",
        "\n",
        "from darts import TimeSeries\n",
        "\n",
        "#this zip is a bunch of zips for each station\n",
        "zf = ZipFile(\"/content/drive/MyDrive/PRSA2017_Data_20130301-20170228.zip\")\n",
        "\n",
        "#make a dataframe and unpack all the zips into it\n",
        "df = pd.DataFrame()\n",
        "for file in zf.infolist():\n",
        "    if file.filename.endswith('.csv'):\n",
        "        df = df._append(pd.read_csv(zf.open(file)))\n",
        "\n",
        "# the data format is broken into multiple columsn, so we need to fix that into a single DateTime\n",
        "df['timestamp'] = pd.to_datetime(df[[\"year\", \"month\", \"day\", \"hour\"]])\n",
        "df.drop(columns=['No'], inplace=True)\n",
        "df.sort_values(by=['timestamp', 'station']).head(10)\n",
        "\n",
        "#now turn both wind direction and station name into ints so we can treat them like categorical vars\n",
        "df = df.replace(df['station'].unique(), list(range(len(df['station'].unique()))))\n",
        "df = df.replace(df['wd'].unique(), list(range(len(df['wd'].unique()))))\n",
        "\n",
        "df.head()\n",
        "\n",
        "df.to_csv('/content/drive/MyDrive/beijing_air_data/air_data.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "7s1BMB9C12xf",
        "outputId": "1ef1adb3-8f36-4a51-d247-af455ece7640"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           missing   total  percent\n",
              "year             0  420768      0.0\n",
              "month            0  420768      0.0\n",
              "station          0  420768      0.0\n",
              "WSPM             0  420768      0.0\n",
              "wd               0  420768      0.0\n",
              "RAIN             0  420768      0.0\n",
              "DEWP             0  420768      0.0\n",
              "PRES             0  420768      0.0\n",
              "TEMP             0  420768      0.0\n",
              "O3               0  420768      0.0\n",
              "CO               0  420768      0.0\n",
              "NO2              0  420768      0.0\n",
              "SO2              0  420768      0.0\n",
              "PM10             0  420768      0.0\n",
              "PM2.5            0  420768      0.0\n",
              "hour             0  420768      0.0\n",
              "day              0  420768      0.0\n",
              "timestamp        0  420768      0.0"
            ],
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              "      <th>missing</th>\n",
              "      <th>total</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>station</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WSPM</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wd</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAIN</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEWP</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRES</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEMP</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>O3</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CO</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NO2</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SO2</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PM10</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PM2.5</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hour</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <td>0</td>\n",
              "      <td>420768</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        document.querySelector('#df-234e9e2f-7546-4e2c-8f9c-36f37b2eb522 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-234e9e2f-7546-4e2c-8f9c-36f37b2eb522');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    height: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "import sidetable\n",
        "\n",
        "df.stb.missing()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hH4LHWZL1wZL",
        "outputId": "bf78ccf8-e1ea-49fc-dbf3-9269a38704b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-f8d0f37f2283>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  ts_df['ts_ind'] = delta_index\n"
          ]
        }
      ],
      "source": [
        "#get the variables that we're interested in\n",
        "ts_df = df[['PM2.5', 'timestamp', 'station', 'wd', 'WSPM', 'TEMP', 'PRES', 'DEWP', 'RAIN', 'O3', 'CO', 'NO2', 'SO2']]\n",
        "\n",
        "#create an indexed timestamp\n",
        "d = ts_df.timestamp - pd.to_datetime('2013-03-01 00:00:00')\n",
        "\n",
        "delta_index = []\n",
        "for delta in d:\n",
        "    delta_index.append(int(delta.total_seconds()/3600))\n",
        "\n",
        "#store our friendly new time stamp\n",
        "ts_df['ts_ind'] = delta_index\n",
        "\n",
        "#get just the PM2.5, and ts_ind for the target that we want to predict\n",
        "target_series = TimeSeries.from_group_dataframe(ts_df[[\"PM2.5\", 'ts_ind', 'station']], group_cols=['station'], time_col='ts_ind')\n",
        "#now everything other than PM2.5 for the covs\n",
        "cov_series = TimeSeries.from_group_dataframe(ts_df.drop([\"PM2.5\",'timestamp'], axis=1), group_cols=['station'], time_col='ts_ind')\n",
        "\n",
        "# Training is a little complex because some models use historical values, and some don't\n",
        "# 80% for historical data, 10% for future values, 10% held back to test\n",
        "future_split_point = int(len(cov_series[0]) * 0.8)\n",
        "val_split_point = int(len(cov_series[0]) * 0.9)\n",
        "\n",
        "# since some of our models use past/future and others don't we need to get a little funky here.\n",
        "# also, we need to make a list of time series instances, one for each station, so we do that here\n",
        "past_target_series = []\n",
        "future_target_series = []\n",
        "test_target_series = []\n",
        "\n",
        "for ts in target_series:\n",
        "    past_target_series.append(ts.slice(0, future_split_point)) #up to our 'present'\n",
        "    future_target_series.append(ts.slice(0, val_split_point)) #up to our testing\n",
        "    test_target_series.append(ts.slice(future_split_point, len(ts))) #hold out for testing\n",
        "\n",
        "#now we do the same with our covariates\n",
        "past_cov_series = []\n",
        "future_cov_series = []\n",
        "test_cov_series = []\n",
        "\n",
        "for ts in cov_series:\n",
        "    past_cov_series.append(ts.slice(0, future_split_point)) #up to our 'present'\n",
        "    future_cov_series.append(ts.slice(0, val_split_point)) #up to our testing\n",
        "    test_cov_series.append(ts.slice(val_split_point, len(ts))) #hold out for testing\n",
        "\n",
        "\n",
        "non_static_cov = []\n",
        "\n",
        "#TCN doesn't use static covariates so we just have the station ID as covars in the time series\n",
        "for i in range(0, len(cov_series)):\n",
        "    temp_series = TimeSeries.from_series(pd.DataFrame({'station':[i] * len(cov_series[i])}))\n",
        "    non_static_cov.append(concatenate([cov_series[i], temp_series], axis=1))"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name          | Type              | Params\n",
            "----------------------------------------------------\n",
            "0 | criterion     | MSELoss           | 0     \n",
            "1 | train_metrics | MetricCollection  | 0     \n",
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            "3 | dropout       | MonteCarloDropout | 0     \n",
            "4 | res_blocks    | ModuleList        | 464   \n",
            "----------------------------------------------------\n",
            "464       Trainable params\n",
            "0         Non-trainable params\n",
            "464       Total params\n",
            "0.002     Total estimated model params size (MB)\n"
          ]
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        {
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
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          "data": {
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
            ],
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              "model_id": "8fcea85e81c94fefb49d058962ab627a"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ff78ba496f94a5cb9eebf67b744268c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TCNModel(kernel_size=3, num_filters=3, num_layers=None, dilation_base=2, weight_norm=False, dropout=0.01, input_chunk_length=128, output_chunk_length=6, pl_trainer_kwargs={'accelerator': 'gpu', 'devices': [0], 'callbacks': [<pytorch_lightning.callbacks.early_stopping.EarlyStopping object at 0x79d536763d30>]})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "from darts.models import TCNModel\n",
        "from darts.dataprocessing.transformers import Scaler\n",
        "from darts.utils.likelihood_models import QuantileRegression\n",
        "\n",
        "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
        "\n",
        "early_stop = EarlyStopping(\n",
        "    monitor=\"train_loss\",\n",
        "    patience=5,\n",
        "    min_delta=0.001,\n",
        "    mode='min',\n",
        ")\n",
        "\n",
        "model = TCNModel(input_chunk_length=128,\n",
        "                 output_chunk_length=6,\n",
        "                 dropout=0.01,\n",
        "                 pl_trainer_kwargs={\n",
        "                    \"accelerator\": \"gpu\",\n",
        "                    \"devices\": [0],\n",
        "                    \"callbacks\": [early_stop]\n",
        "                  })\n",
        "\n",
        "model.fit(past_target_series, past_covariates=non_static_cov, val_series=future_target_series, val_past_covariates=non_static_cov, epochs=40)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UrnjMWaDyJQb"
      },
      "outputs": [],
      "source": [
        "model.save(\"/content/drive/MyDrive/tcn_model/12_20\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from darts.models import TCNModel\n",
        "\n",
        "tcn_model = TCNModel.load(\"/content/drive/MyDrive/tcn_model/12_20\")"
      ],
      "metadata": {
        "id": "0HSWeBYesQ7q"
      },
      "execution_count": null,
      "outputs": []
    },
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "   | Name                              | Type                             | Params\n",
            "----------------------------------------------------------------------------------------\n",
            "0  | train_metrics                     | MetricCollection                 | 0     \n",
            "1  | val_metrics                       | MetricCollection                 | 0     \n",
            "2  | input_embeddings                  | _MultiEmbedding                  | 0     \n",
            "3  | static_covariates_vsn             | _VariableSelectionNetwork        | 528   \n",
            "4  | encoder_vsn                       | _VariableSelectionNetwork        | 15.1 K\n",
            "5  | decoder_vsn                       | _VariableSelectionNetwork        | 6.6 K \n",
            "6  | static_context_grn                | _GatedResidualNetwork            | 1.1 K \n",
            "7  | static_context_hidden_encoder_grn | _GatedResidualNetwork            | 1.1 K \n",
            "8  | static_context_cell_encoder_grn   | _GatedResidualNetwork            | 1.1 K \n",
            "9  | static_context_enrichment         | _GatedResidualNetwork            | 1.1 K \n",
            "10 | lstm_encoder                      | LSTM                             | 2.2 K \n",
            "11 | lstm_decoder                      | LSTM                             | 2.2 K \n",
            "12 | post_lstm_gan                     | _GateAddNorm                     | 576   \n",
            "13 | static_enrichment_grn             | _GatedResidualNetwork            | 1.4 K \n",
            "14 | multihead_attn                    | _InterpretableMultiHeadAttention | 676   \n",
            "15 | post_attn_gan                     | _GateAddNorm                     | 576   \n",
            "16 | feed_forward_block                | _GatedResidualNetwork            | 1.1 K \n",
            "17 | pre_output_gan                    | _GateAddNorm                     | 576   \n",
            "18 | output_layer                      | Linear                           | 289   \n",
            "----------------------------------------------------------------------------------------\n",
            "36.2 K    Trainable params\n",
            "0         Non-trainable params\n",
            "36.2 K    Total params\n",
            "0.145     Total estimated model params size (MB)\n"
          ]
        },
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        {
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        {
          "data": {
            "text/plain": [
              "TFTModel(hidden_size=16, lstm_layers=1, num_attention_heads=4, full_attention=False, feed_forward=GatedResidualNetwork, dropout=0.1, hidden_continuous_size=8, categorical_embedding_sizes=None, add_relative_index=False, loss_fn=None, likelihood=None, norm_type=LayerNorm, use_static_covariates=True, input_chunk_length=6, output_chunk_length=6, n_epochs=20, pl_trainer_kwargs={'accelerator': 'gpu', 'devices': [0], 'callbacks': [<pytorch_lightning.callbacks.early_stopping.EarlyStopping object at 0x7bd9fb777b80>]})"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from darts.models import TFTModel\n",
        "\n",
        "early_stop = EarlyStopping(\n",
        "    monitor=\"train_loss\",\n",
        "    patience=5,\n",
        "    min_delta=0.05,\n",
        "    mode='min',\n",
        ")\n",
        "\n",
        "model = TFTModel(\n",
        "    input_chunk_length=6,\n",
        "    output_chunk_length=6,\n",
        "    n_epochs=20,\n",
        "    pl_trainer_kwargs={\n",
        "                    \"accelerator\": \"gpu\",\n",
        "                    \"devices\": [0],\n",
        "                    \"callbacks\": [early_stop]\n",
        "                  }\n",
        ")\n",
        "\n",
        "# future_covariates are mandatory for `TFTModel`\n",
        "model.fit(past_target_series,\n",
        "          past_covariates=cov_series,\n",
        "          future_covariates=cov_series,\n",
        "          val_series=future_target_series,\n",
        "          val_future_covariates=future_cov_series,\n",
        "          val_past_covariates=cov_series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QwAjPdm4khDz"
      },
      "outputs": [],
      "source": [
        "model.save(\"/content/drive/MyDrive/tft_model/11_11\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from darts.models import NHiTSModel\n",
        "\n",
        "early_stop = EarlyStopping(\n",
        "    monitor=\"train_loss\",\n",
        "    patience=5,\n",
        "    min_delta=0.05,\n",
        "    mode='min',\n",
        ")\n",
        "\n",
        "model_nhits = NHiTSModel(\n",
        "    input_chunk_length=48,\n",
        "    output_chunk_length=6,\n",
        "    num_blocks=3,\n",
        "    num_layers=4,\n",
        "    layer_widths=512,\n",
        "    n_epochs=100,\n",
        "    nr_epochs_val_period=1,\n",
        "    batch_size=800,\n",
        "    pl_trainer_kwargs={\n",
        "                    \"accelerator\": \"gpu\",\n",
        "                    \"devices\": [0],\n",
        "                    \"callbacks\": [early_stop]\n",
        "                  })\n",
        "\n",
        "model_nhits.fit(past_target_series,\n",
        "                past_covariates=cov_series,\n",
        "                val_series=future_target_series,\n",
        "                val_past_covariates=cov_series)"
      ],
      "metadata": {
        "colab": {
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        "id": "9d_hmDNoyiuy",
        "outputId": "57242a79-43f4-4f4e-dd9c-595d64317b06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name          | Type             | Params\n",
            "---------------------------------------------------\n",
            "0 | criterion     | MSELoss          | 0     \n",
            "1 | train_metrics | MetricCollection | 0     \n",
            "2 | val_metrics   | MetricCollection | 0     \n",
            "3 | stacks        | ModuleList       | 10.3 M\n",
            "---------------------------------------------------\n",
            "10.0 M    Trainable params\n",
            "270 K     Non-trainable params\n",
            "10.3 M    Total params\n",
            "41.108    Total estimated model params size (MB)\n"
          ]
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          },
          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NHiTSModel(num_stacks=3, num_blocks=3, num_layers=4, layer_widths=512, pooling_kernel_sizes=None, n_freq_downsample=None, dropout=0.1, activation=ReLU, MaxPool1d=True, input_chunk_length=48, output_chunk_length=6, n_epochs=100, nr_epochs_val_period=1, batch_size=800, pl_trainer_kwargs={'accelerator': 'gpu', 'devices': [0], 'callbacks': [<pytorch_lightning.callbacks.early_stopping.EarlyStopping object at 0x79d4b24a5e10>]})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vTXJ0Nr96FMW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_nhits.save(\"/content/drive/MyDrive/nhits_model/12_20\")"
      ],
      "metadata": {
        "id": "w2uxSCSl1Zv0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from darts.models import NBEATSModel\n",
        "\n",
        "model_nbeats = NBEATSModel(\n",
        "    input_chunk_length=30,\n",
        "    output_chunk_length=7,\n",
        "    generic_architecture=False,\n",
        "    num_blocks=3,\n",
        "    num_layers=4,\n",
        "    layer_widths=512,\n",
        "    n_epochs=100,\n",
        "    nr_epochs_val_period=1,\n",
        "    batch_size=800,\n",
        "    model_name=\"nbeats_interpretable_run\",\n",
        "    # input_chunk_length=10,\n",
        "    # output_chunk_length=10,\n",
        "    # n_epochs=1,\n",
        "    # generic_architecture=False\n",
        ")\n",
        "\n",
        "model_nbeats.fit(series=past_target_series,\n",
        "                 past_covariates=past_cov_series,\n",
        "                 val_series=future_target_series,\n",
        "                 val_past_covariates=cov_series,\n",
        "                 epochs=20)\n",
        "\n",
        "model_nbeats.save('/content/drive/MyDrive/nbeats_model/11_16')"
      ],
      "metadata": {
        "id": "JG3ClbXX6GcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from darts.models import DLinearModel\n",
        "\n",
        "from darts.metrics.metrics import rmse, mape\n",
        "\n",
        "dlin_model = DLinearModel(\n",
        "    input_chunk_length=24,\n",
        "    output_chunk_length=6,\n",
        "    n_epochs=20,\n",
        ")\n",
        "dlin_model.fit(past_target_series, past_covariates=past_cov_series, future_covariates=future_cov_series)\n",
        "\n",
        "dlin_model.save('/content/drive/MyDrive/dlinear_model/12_11')"
      ],
      "metadata": {
        "id": "V5NQ_N2t6Vzt"
      },
      "execution_count": null,
      "outputs": []
    }
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