RandomForest.ipynb

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      "source": [
        "!pip install autogluon.timeseries[all] seaborn[stats]"
      ],
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      "source": [
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import timedelta\n",
        "import calendar\n"
      ],
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      "source": [
        "df = pd.read_csv(\"drive/MyDrive/Colab Data/AutoGluon/PJM_Load_hourly.csv\")\n",
        "df.head(10)"
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              "         Datetime  PJM_Load_MW\n",
              "0   4/1/1998 1:00        22259\n",
              "1   4/1/1998 2:00        21244\n",
              "2   4/1/1998 3:00        20651\n",
              "3   4/1/1998 4:00        20421\n",
              "4   4/1/1998 5:00        20713\n",
              "5   4/1/1998 6:00        22526\n",
              "6   4/1/1998 7:00        25772\n",
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        "df['timestamp'] = pd.to_datetime(df['Datetime'])\n",
        "df['target'] = df['PJM_Load_MW']\n",
        "\n",
        "df = df[['timestamp', 'target']]\n",
        "df.head(10)\n"
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              "            timestamp  target\n",
              "0 1998-04-01 01:00:00   22259\n",
              "1 1998-04-01 02:00:00   21244\n",
              "2 1998-04-01 03:00:00   20651\n",
              "3 1998-04-01 04:00:00   20421\n",
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        "#add seasonality\n",
        "final_data = df\n",
        "final_data['date'] = pd.to_datetime(final_data['timestamp'], format='%Y-%m-%d')\n",
        "final_data['hour'] = final_data['timestamp'].dt.hour\n",
        "final_data['day_of_week'] = final_data['timestamp'].dt.dayofweek\n",
        "final_data['month'] = final_data['timestamp'].dt.month\n",
        "final_data.head(10)"
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          "text": [
            "<ipython-input-36-66520a8ddbda>:3: 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",
            "  final_data['date'] = pd.to_datetime(final_data['timestamp'], format='%Y-%m-%d')\n"
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              "            timestamp  target                date  hour  day_of_week  month\n",
              "0 1998-04-01 01:00:00   22259 1998-04-01 01:00:00     1            2      4\n",
              "1 1998-04-01 02:00:00   21244 1998-04-01 02:00:00     2            2      4\n",
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              "5 1998-04-01 06:00:00   22526 1998-04-01 06:00:00     6            2      4\n",
              "6 1998-04-01 07:00:00   25772 1998-04-01 07:00:00     7            2      4\n",
              "7 1998-04-01 08:00:00   28724 1998-04-01 08:00:00     8            2      4\n",
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              "      <td>22259</td>\n",
              "      <td>1998-04-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1998-04-01 02:00:00</td>\n",
              "      <td>21244</td>\n",
              "      <td>1998-04-01 02:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1998-04-01 03:00:00</td>\n",
              "      <td>20651</td>\n",
              "      <td>1998-04-01 03:00:00</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1998-04-01 04:00:00</td>\n",
              "      <td>20421</td>\n",
              "      <td>1998-04-01 04:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1998-04-01 05:00:00</td>\n",
              "      <td>20713</td>\n",
              "      <td>1998-04-01 05:00:00</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1998-04-01 06:00:00</td>\n",
              "      <td>22526</td>\n",
              "      <td>1998-04-01 06:00:00</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1998-04-01 07:00:00</td>\n",
              "      <td>25772</td>\n",
              "      <td>1998-04-01 07:00:00</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1998-04-01 08:00:00</td>\n",
              "      <td>28724</td>\n",
              "      <td>1998-04-01 08:00:00</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1998-04-01 09:00:00</td>\n",
              "      <td>30075</td>\n",
              "      <td>1998-04-01 09:00:00</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1998-04-01 10:00:00</td>\n",
              "      <td>30689</td>\n",
              "      <td>1998-04-01 10:00:00</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-911afa4b-ff01-4e63-a58b-4cca1e9a1349')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-911afa4b-ff01-4e63-a58b-4cca1e9a1349 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-911afa4b-ff01-4e63-a58b-4cca1e9a1349');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prep for training\n",
        "\n",
        "#remove extra columns\n",
        "finaldf = final_data.drop(['date','timestamp'], axis=1)\n",
        "finaldf = finaldf.reset_index(drop=True)\n",
        "\n",
        "test_length=8760 #365 days\n",
        "\n",
        "end_point = len(finaldf)\n",
        "x = end_point - test_length\n",
        "\n",
        "#set training data params\n",
        "finaldf_train = finaldf.loc[:x - 1, :]\n",
        "finaldf_test = finaldf.loc[x:, :]\n",
        "finaldf_test_x = finaldf_test.loc[:, finaldf_test.columns != 'target']\n",
        "finaldf_test_y = finaldf_test['target']\n",
        "finaldf_train_x = finaldf_train.loc[:, finaldf_train.columns != 'target']\n",
        "finaldf_train_y = finaldf_train['target']\n"
      ],
      "metadata": {
        "id": "mVS3oms9Gb8A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "print(\"Starting Training..\")\n",
        "\n",
        "#original = estimator = RandomForestRegressor(n_jobs=-1, max_depth=5, n_estimators=100, random_state=42, oob_score=True)\n",
        "\n",
        "#tuned parameters\n",
        "estimator = RandomForestRegressor(max_depth=90, max_features=2, min_samples_leaf=5,min_samples_split=12, n_estimators=200)\n",
        "\n",
        "rfe = RFE(estimator, n_features_to_select=4)\n",
        "fit = rfe.fit(finaldf_train_x, finaldf_train_y)\n",
        "y_pred = fit.predict(finaldf_test_x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Mx2Zaz4GjW2",
        "outputId": "171406e6-c26e-4619-a53a-b56f056ac8d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training..\n",
            "CPU times: user 2.86 s, sys: 27 ms, total: 2.89 s\n",
            "Wall time: 2.95 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OAFvGNV-SUal"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#eval\n",
        "y_true = np.array(finaldf_test_y)\n",
        "sumvalue=np.sum(y_true)\n",
        "mape=np.sum(np.abs((y_true - y_pred)))/sumvalue*100\n",
        "accuracy=100-mape\n",
        "print('Accuracy:', round(accuracy,2),'%.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNrUbsufGk8N",
        "outputId": "b6fed961-2d68-4f14-88ef-d6883365b7e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 93.41 %.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Prediction\n",
        "future = pd.date_range(start='2001-12-30', end='2002-01-01', freq='H').to_series()\n",
        "future_df = pd.DataFrame({'timestamp': future})\n",
        "future_df['hour'] = future_df['timestamp'].dt.hour\n",
        "future_df['day_of_week'] = future_df['timestamp'].dt.dayofweek\n",
        "future_df['month'] = future_df['timestamp'].dt.month\n",
        "\n",
        "future_df = future_df.drop(['timestamp'], axis=1)\n",
        "future_df.head(10)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "J1P-ozI_GpMm",
        "outputId": "81e8d1eb-509b-48f5-9c58-00f1c5651a4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     hour  day_of_week  month\n",
              "2001-12-30 00:00:00     0            6     12\n",
              "2001-12-30 01:00:00     1            6     12\n",
              "2001-12-30 02:00:00     2            6     12\n",
              "2001-12-30 03:00:00     3            6     12\n",
              "2001-12-30 04:00:00     4            6     12\n",
              "2001-12-30 05:00:00     5            6     12\n",
              "2001-12-30 06:00:00     6            6     12\n",
              "2001-12-30 07:00:00     7            6     12\n",
              "2001-12-30 08:00:00     8            6     12\n",
              "2001-12-30 09:00:00     9            6     12"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9fac503-6b99-4ced-b6af-9dc327bf6440\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hour</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2001-12-30 00:00:00</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 01:00:00</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 02:00:00</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 03:00:00</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 04:00:00</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 05:00:00</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 06:00:00</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 07:00:00</th>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 08:00:00</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-30 09:00:00</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e9fac503-6b99-4ced-b6af-9dc327bf6440 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-e9fac503-6b99-4ced-b6af-9dc327bf6440');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "future_predictions = fit.predict(future_df)\n",
        "print(future_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcT1UmG2T12p",
        "outputId": "6a6f6e04-2f9f-4e3b-b326-5f88629f54e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[27543.47448226 25535.92165907 24372.45796444 23751.2918577\n",
            " 23419.39877794 23450.08959982 24034.11922114 24955.81143038\n",
            " 26063.92689191 27442.54746103 28428.79621351 28500.83539676\n",
            " 28410.289796   28115.1445037  27689.41009738 27357.04886631\n",
            " 27435.45952109 29094.6936552  32218.02944112 32595.52102032\n",
            " 32516.88345542 32079.5670906  31123.30619703 29469.09377599\n",
            " 27149.09314321 24984.35090664 24014.55610762 23862.23847347\n",
            " 24025.45065069 24463.27179511 26104.59173002 29626.49697129\n",
            " 32089.1936717  32821.36798082 33101.21262068 33142.24377089\n",
            " 32891.92335218 32377.93173946 32181.4307199  31668.11278946\n",
            " 31652.76147418 33128.6938315  35888.14976799 36207.60537037\n",
            " 35551.30494177 34925.37753972 33588.34678073 31187.53952844\n",
            " 29318.66655948]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future_df['predicted'] = future_predictions\n",
        "future_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "zmhUQMHifTYx",
        "outputId": "28b974bd-6037-4b10-c80a-fc71c55b721d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     hour  day_of_week  month     predicted\n",
              "2001-12-30 00:00:00     0            6     12  27543.474482\n",
              "2001-12-30 01:00:00     1            6     12  25535.921659\n",
              "2001-12-30 02:00:00     2            6     12  24372.457964\n",
              "2001-12-30 03:00:00     3            6     12  23751.291858\n",
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              "2001-12-30 05:00:00     5            6     12  23450.089600\n",
              "2001-12-30 06:00:00     6            6     12  24034.119221\n",
              "2001-12-30 07:00:00     7            6     12  24955.811430\n",
              "2001-12-30 08:00:00     8            6     12  26063.926892\n",
              "2001-12-30 09:00:00     9            6     12  27442.547461"
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              "      <th>2001-12-30 04:00:00</th>\n",
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              "      <td>5</td>\n",
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              "      <th>2001-12-30 06:00:00</th>\n",
              "      <td>6</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f3821261-47ec-4998-b0a2-49bbd52c8f22');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from autogluon.timeseries import TimeSeriesDataFrame\n",
        "if not 'item_id' in df:\n",
        "  df.insert(0, \"item_id\", \"H1\")\n",
        "\n",
        "plot_data = TimeSeriesDataFrame.from_data_frame(final_data)\n",
        "plot_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "NGdpd859hb8Z",
        "outputId": "234643a4-a0e0-4ea8-ab1c-e52255f9207b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             target                date  hour  day_of_week  \\\n",
              "item_id timestamp                                                            \n",
              "H1      1998-04-01 01:00:00   22259 1998-04-01 01:00:00     1            2   \n",
              "        1998-04-01 02:00:00   21244 1998-04-01 02:00:00     2            2   \n",
              "        1998-04-01 03:00:00   20651 1998-04-01 03:00:00     3            2   \n",
              "        1998-04-01 04:00:00   20421 1998-04-01 04:00:00     4            2   \n",
              "        1998-04-01 05:00:00   20713 1998-04-01 05:00:00     5            2   \n",
              "\n",
              "                             month  \n",
              "item_id timestamp                   \n",
              "H1      1998-04-01 01:00:00      4  \n",
              "        1998-04-01 02:00:00      4  \n",
              "        1998-04-01 03:00:00      4  \n",
              "        1998-04-01 04:00:00      4  \n",
              "        1998-04-01 05:00:00      4  "
            ],
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              "      <th rowspan=\"5\" valign=\"top\">H1</th>\n",
              "      <th>1998-04-01 01:00:00</th>\n",
              "      <td>22259</td>\n",
              "      <td>1998-04-01 01:00:00</td>\n",
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              "    <tr>\n",
              "      <th>1998-04-01 02:00:00</th>\n",
              "      <td>21244</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998-04-01 03:00:00</th>\n",
              "      <td>20651</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1998-04-01 04:00:00</th>\n",
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              "    <tr>\n",
              "      <th>1998-04-01 05:00:00</th>\n",
              "      <td>20713</td>\n",
              "      <td>1998-04-01 05:00:00</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8397f59b-517d-4986-9d82-b36ecc13611a 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-8397f59b-517d-4986-9d82-b36ecc13611a');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fix_plot_data = plot_data.to_regular_index(freq='H')\n",
        "print(f\"Data has frequency '{fix_plot_data.freq}'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5mwRSH-NhPws",
        "outputId": "96938735-3f0d-4ec1-a512-4990ffd7b998"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data has frequency 'H'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Does it have any missing data? '{fix_plot_data.isnull().values.any()}'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHAZqp0QhToE",
        "outputId": "eb58afa0-3329-40e4-f093-53f61309ca8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Does it have any missing data? 'True'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if fix_plot_data.isnull().values.any():\n",
        "  fix_plot_data = fix_plot_data.fill_missing_values()\n",
        "\n",
        "print(f\"Does it have any missing data? '{fix_plot_data.isnull().values.any()}'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20Cmw-WrhVPd",
        "outputId": "7b8374f0-fa5e-4b13-8821-402755f0f537"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Does it have any missing data? 'False'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sb\n",
        "import matplotlib.pyplot as plt\n",
        "from autogluon.timeseries import TimeSeriesDataFrame\n",
        "\n",
        "sb.set_style(\"darkgrid\")\n",
        "# TimeSeriesDataFrame can also be loaded directly from a file\n",
        "test_data = TimeSeriesDataFrame.from_path(\"drive/MyDrive/Colab Data/AutoGluon/test_energy.csv\")\n",
        "\n",
        "\n",
        "\n",
        "plt.figure(figsize=(20, 3))\n",
        "\n",
        "item_id = \"H1\"\n",
        "y_past = fix_plot_data.loc[item_id][\"target\"]\n",
        "y_pred = future_df[\"predicted\"]\n",
        "y_test = test_data.loc[\"H1\"][\"target\"][-48:]\n",
        "\n",
        "plt.plot(y_past[-200:], label=\"Past time series values\")\n",
        "plt.plot(y_pred, label=\"Predicted forecast\")\n",
        "plt.plot(y_test, label=\"Future time series values\")\n",
        "\n",
        "# plt.fill_between(\n",
        "#     y_pred.index, y_pred[\"0.1\"], y_pred[\"0.9\"], color=\"red\", alpha=0.1, label=f\"10%-90% confidence interval\"\n",
        "# )\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "M9zmr3KucSoX",
        "outputId": "4370c8ec-0d57-42f8-f9df-72b161f3a841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.model_selection import GridSearchCV\n",
        "# # Create the parameter grid based on the results of random search\n",
        "\n",
        "# param_grid = {\n",
        "#     'bootstrap': [True],\n",
        "#     'max_depth': [80, 90, 100, 110],\n",
        "#     'max_features': [2, 3],\n",
        "#     'min_samples_leaf': [3, 4, 5],\n",
        "#     'min_samples_split': [8, 10, 12],\n",
        "#     'n_estimators': [100, 200, 300, 1000]\n",
        "# }\n",
        "# # Create a based model\n",
        "# rf = RandomForestRegressor()\n",
        "\n",
        "# # Instantiate the grid search model\n",
        "# grid_search = GridSearchCV(estimator = rf, param_grid = param_grid,\n",
        "#                           cv = 3, n_jobs = -1, verbose = 2)"
      ],
      "metadata": {
        "id": "9EmWo_WMSV-a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %%time\n",
        "# #tune parameters\n",
        "# grid_search.fit(finaldf_train_x, finaldf_train_y)\n",
        "# grid_search.best_params_"
      ],
      "metadata": {
        "id": "HT3DNCwFZLKa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# grid_search.best_score_"
      ],
      "metadata": {
        "id": "j8OhkSNtTFEv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# grid_search.best_estimator_\n",
        "\n",
        "##Result was: RandomForestRegressor(max_depth=90, max_features=2, min_samples_leaf=5,min_samples_split=12, n_estimators=200)"
      ],
      "metadata": {
        "id": "YZ4atHBMTpw8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}