LSTM_1step_daily.ipynb

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      "name": "LSTM_1step_daily.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BexZygSl10H4"
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      "source": [
        "references:\n",
        "\n",
        "\n",
        "*   Multivariate Time Series Forecasting with LSTMs in Keras (machinelearningmastery.com)\n",
        "*   https://www.youtube.com/watch?v=4FmVIpcwl4k&t=1117s\n",
        "\n",
        "\n",
        "*   BOOK: Hands-on time series analysis\n",
        "*   List item\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAgMuAzNKeiL"
      },
      "source": [
        "from numpy.random import seed\n",
        "seed(1)\n",
        "import tensorflow as tf\n",
        "tf.compat.v1.set_random_seed(2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GY3HgVB7T8D"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.constraints import maxnorm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "import tensorflow as tf\n",
        "from keras.models import load_model\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (20, 20)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tj0hXjtjrJYT"
      },
      "source": [
        "from math import sqrt\n",
        "from numpy import concatenate\n",
        "from matplotlib import pyplot\n",
        "from pandas import read_csv\n",
        "from pandas import DataFrame\n",
        "from pandas import concat\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout\n",
        "from keras.layers import LSTM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmNbWyCJ7n0a"
      },
      "source": [
        "df = pd.read_csv('/content/clean_daily_df_human_logic_supervised.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
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          "height": 225
        },
        "outputId": "486927b0-84f7-4556-e92f-6e506f129f45"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_7</th>\n",
              "      <th>clients_day_t_7</th>\n",
              "      <th>Holiday_Food_Related_t_7</th>\n",
              "      <th>consommation_t_7</th>\n",
              "      <th>pdl_total_daily_load_t_7</th>\n",
              "      <th>max_pdl_daily_load_t_7</th>\n",
              "      <th>weekend_t_7</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_1</th>\n",
              "      <th>clients_day_t_1</th>\n",
              "      <th>Holiday_Food_Related_t_1</th>\n",
              "      <th>consommation_t_1</th>\n",
              "      <th>pdl_total_daily_load_t_1</th>\n",
              "      <th>max_pdl_daily_load_t_1</th>\n",
              "      <th>weekend_t_1</th>\n",
              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>clients_day_t</th>\n",
              "      <th>Holiday_Food_Related_t</th>\n",
              "      <th>consommation_t</th>\n",
              "      <th>pdl_total_daily_load_t</th>\n",
              "      <th>weekend_t</th>\n",
              "      <th>max_pdl_daily_load_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>08/01/2015</td>\n",
              "      <td>8</td>\n",
              "      <td>3970</td>\n",
              "      <td>1</td>\n",
              "      <td>7068.81</td>\n",
              "      <td>16580</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7468.64</td>\n",
              "      <td>20928</td>\n",
              "      <td>1319.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7464.41</td>\n",
              "      <td>20809</td>\n",
              "      <td>0</td>\n",
              "      <td>1379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>09/01/2015</td>\n",
              "      <td>12</td>\n",
              "      <td>3970</td>\n",
              "      <td>0</td>\n",
              "      <td>7074.65</td>\n",
              "      <td>22006</td>\n",
              "      <td>1439.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7464.41</td>\n",
              "      <td>20809</td>\n",
              "      <td>1379.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7464.41</td>\n",
              "      <td>20803</td>\n",
              "      <td>0</td>\n",
              "      <td>1370.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10/01/2015</td>\n",
              "      <td>12</td>\n",
              "      <td>3970</td>\n",
              "      <td>1</td>\n",
              "      <td>7074.65</td>\n",
              "      <td>18629</td>\n",
              "      <td>1127.0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7464.41</td>\n",
              "      <td>20803</td>\n",
              "      <td>1370.0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7446.13</td>\n",
              "      <td>18059</td>\n",
              "      <td>1</td>\n",
              "      <td>1181.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11/01/2015</td>\n",
              "      <td>15</td>\n",
              "      <td>3971</td>\n",
              "      <td>0</td>\n",
              "      <td>7075.31</td>\n",
              "      <td>18060</td>\n",
              "      <td>1099.0</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7446.13</td>\n",
              "      <td>18059</td>\n",
              "      <td>1181.0</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7058.37</td>\n",
              "      <td>17478</td>\n",
              "      <td>1</td>\n",
              "      <td>1093.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12/01/2015</td>\n",
              "      <td>15</td>\n",
              "      <td>3971</td>\n",
              "      <td>0</td>\n",
              "      <td>7083.30</td>\n",
              "      <td>20122</td>\n",
              "      <td>1288.0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7058.37</td>\n",
              "      <td>17478</td>\n",
              "      <td>1093.0</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>3979</td>\n",
              "      <td>0</td>\n",
              "      <td>7217.96</td>\n",
              "      <td>18556</td>\n",
              "      <td>0</td>\n",
              "      <td>1205.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "         time  MIN_TEMPERATURE_C_t_7  ...  weekend_t  max_pdl_daily_load_t\n",
              "0  08/01/2015                      8  ...          0                1379.0\n",
              "1  09/01/2015                     12  ...          0                1370.0\n",
              "2  10/01/2015                     12  ...          1                1181.0\n",
              "3  11/01/2015                     15  ...          1                1093.0\n",
              "4  12/01/2015                     15  ...          0                1205.0\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
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        "outputId": "d2b40a37-42f5-41aa-bb0a-14aafa977c2d"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_7</th>\n",
              "      <th>clients_day_t_7</th>\n",
              "      <th>Holiday_Food_Related_t_7</th>\n",
              "      <th>consommation_t_7</th>\n",
              "      <th>pdl_total_daily_load_t_7</th>\n",
              "      <th>max_pdl_daily_load_t_7</th>\n",
              "      <th>weekend_t_7</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_1</th>\n",
              "      <th>clients_day_t_1</th>\n",
              "      <th>Holiday_Food_Related_t_1</th>\n",
              "      <th>consommation_t_1</th>\n",
              "      <th>pdl_total_daily_load_t_1</th>\n",
              "      <th>max_pdl_daily_load_t_1</th>\n",
              "      <th>weekend_t_1</th>\n",
              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>clients_day_t</th>\n",
              "      <th>Holiday_Food_Related_t</th>\n",
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              "      <th>max_pdl_daily_load_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2027</th>\n",
              "      <td>27/07/2020</td>\n",
              "      <td>24</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>12627</td>\n",
              "      <td>924.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>11167</td>\n",
              "      <td>781.0</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13174</td>\n",
              "      <td>0</td>\n",
              "      <td>1010.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2028</th>\n",
              "      <td>28/07/2020</td>\n",
              "      <td>25</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>12536</td>\n",
              "      <td>935.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13174</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13097</td>\n",
              "      <td>0</td>\n",
              "      <td>986.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2029</th>\n",
              "      <td>29/07/2020</td>\n",
              "      <td>25</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13369</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13097</td>\n",
              "      <td>986.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>12000</td>\n",
              "      <td>0</td>\n",
              "      <td>873.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2030</th>\n",
              "      <td>30/07/2020</td>\n",
              "      <td>26</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13587</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>12000</td>\n",
              "      <td>873.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>11604</td>\n",
              "      <td>0</td>\n",
              "      <td>762.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2031</th>\n",
              "      <td>31/07/2020</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13226</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>11604</td>\n",
              "      <td>762.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>1</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>8935</td>\n",
              "      <td>0</td>\n",
              "      <td>528.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            time  MIN_TEMPERATURE_C_t_7  ...  weekend_t  max_pdl_daily_load_t\n",
              "2027  27/07/2020                     24  ...          0                1010.0\n",
              "2028  28/07/2020                     25  ...          0                 986.0\n",
              "2029  29/07/2020                     25  ...          0                 873.0\n",
              "2030  30/07/2020                     26  ...          0                 762.0\n",
              "2031  31/07/2020                     27  ...          0                 528.0\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
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        "outputId": "ea014927-e065-4ec9-83b6-5c8023a955a0"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2032 entries, 0 to 2031\n",
            "Data columns (total 22 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   time                      2032 non-null   object \n",
            " 1   MIN_TEMPERATURE_C_t_7     2032 non-null   int64  \n",
            " 2   clients_day_t_7           2032 non-null   int64  \n",
            " 3   Holiday_Food_Related_t_7  2032 non-null   int64  \n",
            " 4   consommation_t_7          2032 non-null   float64\n",
            " 5   pdl_total_daily_load_t_7  2032 non-null   int64  \n",
            " 6   max_pdl_daily_load_t_7    2032 non-null   float64\n",
            " 7   weekend_t_7               2032 non-null   int64  \n",
            " 8   MIN_TEMPERATURE_C_t_1     2032 non-null   int64  \n",
            " 9   clients_day_t_1           2032 non-null   int64  \n",
            " 10  Holiday_Food_Related_t_1  2032 non-null   int64  \n",
            " 11  consommation_t_1          2032 non-null   float64\n",
            " 12  pdl_total_daily_load_t_1  2032 non-null   int64  \n",
            " 13  max_pdl_daily_load_t_1    2032 non-null   float64\n",
            " 14  weekend_t_1               2032 non-null   int64  \n",
            " 15  MIN_TEMPERATURE_C_t       2032 non-null   int64  \n",
            " 16  clients_day_t             2032 non-null   int64  \n",
            " 17  Holiday_Food_Related_t    2032 non-null   int64  \n",
            " 18  consommation_t            2032 non-null   float64\n",
            " 19  pdl_total_daily_load_t    2032 non-null   int64  \n",
            " 20  weekend_t                 2032 non-null   int64  \n",
            " 21  max_pdl_daily_load_t      2032 non-null   float64\n",
            "dtypes: float64(6), int64(15), object(1)\n",
            "memory usage: 349.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DX4Wjmy08aWn"
      },
      "source": [
        "time = pd.to_datetime(df['time'], dayfirst=True).dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3QqtUCsyKLa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "f8a8878c-608f-4094-8f7d-fa8d964e86d5"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>MIN_TEMPERATURE_C_t_7</th>\n",
              "      <th>clients_day_t_7</th>\n",
              "      <th>Holiday_Food_Related_t_7</th>\n",
              "      <th>consommation_t_7</th>\n",
              "      <th>pdl_total_daily_load_t_7</th>\n",
              "      <th>max_pdl_daily_load_t_7</th>\n",
              "      <th>weekend_t_7</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_1</th>\n",
              "      <th>clients_day_t_1</th>\n",
              "      <th>Holiday_Food_Related_t_1</th>\n",
              "      <th>consommation_t_1</th>\n",
              "      <th>pdl_total_daily_load_t_1</th>\n",
              "      <th>max_pdl_daily_load_t_1</th>\n",
              "      <th>weekend_t_1</th>\n",
              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>clients_day_t</th>\n",
              "      <th>Holiday_Food_Related_t</th>\n",
              "      <th>consommation_t</th>\n",
              "      <th>pdl_total_daily_load_t</th>\n",
              "      <th>weekend_t</th>\n",
              "      <th>max_pdl_daily_load_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20.633366</td>\n",
              "      <td>4761.092028</td>\n",
              "      <td>0.113681</td>\n",
              "      <td>7498.069537</td>\n",
              "      <td>13720.760827</td>\n",
              "      <td>1082.264365</td>\n",
              "      <td>0.285433</td>\n",
              "      <td>20.678150</td>\n",
              "      <td>4764.689961</td>\n",
              "      <td>0.112697</td>\n",
              "      <td>7494.061850</td>\n",
              "      <td>13700.614665</td>\n",
              "      <td>1081.028637</td>\n",
              "      <td>0.285433</td>\n",
              "      <td>20.685531</td>\n",
              "      <td>4765.288878</td>\n",
              "      <td>0.113189</td>\n",
              "      <td>7493.201004</td>\n",
              "      <td>13694.712598</td>\n",
              "      <td>0.285433</td>\n",
              "      <td>1080.639365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.917364</td>\n",
              "      <td>435.595628</td>\n",
              "      <td>0.317502</td>\n",
              "      <td>1189.333584</td>\n",
              "      <td>4953.021415</td>\n",
              "      <td>209.215731</td>\n",
              "      <td>0.451732</td>\n",
              "      <td>5.910439</td>\n",
              "      <td>434.079536</td>\n",
              "      <td>0.316300</td>\n",
              "      <td>1193.029654</td>\n",
              "      <td>4944.948179</td>\n",
              "      <td>209.136835</td>\n",
              "      <td>0.451732</td>\n",
              "      <td>5.910212</td>\n",
              "      <td>433.824788</td>\n",
              "      <td>0.316902</td>\n",
              "      <td>1193.678924</td>\n",
              "      <td>4943.474583</td>\n",
              "      <td>0.451732</td>\n",
              "      <td>209.429624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>3970.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4664.350000</td>\n",
              "      <td>3197.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3972.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4664.350000</td>\n",
              "      <td>3197.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3972.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4664.350000</td>\n",
              "      <td>3197.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>404.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>4355.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6750.780000</td>\n",
              "      <td>10185.750000</td>\n",
              "      <td>936.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4356.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6747.520000</td>\n",
              "      <td>10185.750000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4356.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6747.520000</td>\n",
              "      <td>10174.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>935.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>4795.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7383.670000</td>\n",
              "      <td>12869.000000</td>\n",
              "      <td>1073.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>4799.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7383.670000</td>\n",
              "      <td>12855.000000</td>\n",
              "      <td>1071.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>4801.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7381.705000</td>\n",
              "      <td>12855.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1071.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>5189.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8125.475000</td>\n",
              "      <td>17240.500000</td>\n",
              "      <td>1243.717949</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>5189.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8125.475000</td>\n",
              "      <td>17198.750000</td>\n",
              "      <td>1243.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>5189.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8125.475000</td>\n",
              "      <td>17174.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1242.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>5354.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10771.150000</td>\n",
              "      <td>27936.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>5354.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10771.150000</td>\n",
              "      <td>27936.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>5354.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10771.150000</td>\n",
              "      <td>27936.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       MIN_TEMPERATURE_C_t_7  ...  max_pdl_daily_load_t\n",
              "count            2032.000000  ...           2032.000000\n",
              "mean               20.633366  ...           1080.639365\n",
              "std                 5.917364  ...            209.429624\n",
              "min                 8.000000  ...            404.000000\n",
              "25%                16.000000  ...            935.000000\n",
              "50%                20.000000  ...           1071.000000\n",
              "75%                26.000000  ...           1242.250000\n",
              "max                34.000000  ...           1628.000000\n",
              "\n",
              "[8 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVuqU0u0dmbG",
        "outputId": "97f162d4-00be-439f-bfd4-4581b156fdf1"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['time', 'MIN_TEMPERATURE_C_t_7', 'clients_day_t_7',\n",
              "       'Holiday_Food_Related_t_7', 'consommation_t_7',\n",
              "       'pdl_total_daily_load_t_7', 'max_pdl_daily_load_t_7', 'weekend_t_7',\n",
              "       'MIN_TEMPERATURE_C_t_1', 'clients_day_t_1', 'Holiday_Food_Related_t_1',\n",
              "       'consommation_t_1', 'pdl_total_daily_load_t_1',\n",
              "       'max_pdl_daily_load_t_1', 'weekend_t_1', 'MIN_TEMPERATURE_C_t',\n",
              "       'clients_day_t', 'Holiday_Food_Related_t', 'consommation_t',\n",
              "       'pdl_total_daily_load_t', 'weekend_t', 'max_pdl_daily_load_t'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YVVz0bEduD6"
      },
      "source": [
        "df_input=df[['MIN_TEMPERATURE_C_t_7',\n",
        "        'clients_day_t_7','Holiday_Food_Related_t_7', 'consommation_t_7',\n",
        "       'pdl_total_daily_load_t_7', 'max_pdl_daily_load_t_7', 'weekend_t_7',\n",
        "\n",
        "       'MIN_TEMPERATURE_C_t_1', 'clients_day_t_1', 'Holiday_Food_Related_t_1',\n",
        "       'consommation_t_1', 'pdl_total_daily_load_t_1',\n",
        "       'max_pdl_daily_load_t_1', 'weekend_t_1',\n",
        "       \n",
        "        'MIN_TEMPERATURE_C_t','clients_day_t', 'Holiday_Food_Related_t',\n",
        "         'weekend_t', 'max_pdl_daily_load_t']].dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59aKcM4mdUQU"
      },
      "source": [
        "the add of weekend feature makes the seasonal weekend decrease more visible, so increases performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdnkSpWaENYQ"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "data_scaled = scaler.fit_transform(df_input)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCY48qWVESHY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90e43b84-4fba-411f-a3b3-ac00f6a5bf75"
      },
      "source": [
        "data_scaled[-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.73076923, 0.88078035, 0.        , 0.17276642, 0.4053923 ,\n",
              "       0.50081699, 0.        , 0.73076923, 0.88060781, 0.        ,\n",
              "       0.17276642, 0.3398278 , 0.29248366, 0.        , 0.76923077,\n",
              "       0.88060781, 1.        , 0.        , 0.10130719])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEZuMY99zfLU"
      },
      "source": [
        "features=data_scaled[:,:-1]\n",
        "target=data_scaled[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6x_iIIrkomf7",
        "outputId": "bea11e15-fa61-4183-ce2a-82ac93de1794"
      },
      "source": [
        "features.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2032, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpuzlDtTotWA",
        "outputId": "76ce3a18-3c9d-4a22-9206-d58453bfcf89"
      },
      "source": [
        "target.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2032,)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDaUCPH6njFx",
        "outputId": "85fa2155-ba33-4260-dd0d-1f768c187c9a"
      },
      "source": [
        "# split into train and test sets\n",
        "\n",
        "n_train_days = -30*7\n",
        "\n",
        "train_X,train_y = features[:n_train_days, :],target[:n_train_days]\n",
        "test_X, test_y = features[n_train_days:, :],target[n_train_days:]\n",
        "\n",
        "# reshape input to be 3D\n",
        "train_X = train_X.reshape((train_X.shape[0], train_X.shape[1],1))\n",
        "test_X = test_X.reshape((test_X.shape[0],test_X.shape[1],1))\n",
        "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1822, 18, 1) (1822,) (210, 18, 1) (210,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZthsjPVnjHh",
        "outputId": "cf4a5a92-60e4-438b-832b-c86cff32825c"
      },
      "source": [
        "def create_model(layers):\n",
        "    model = Sequential()\n",
        "    for i, nodes in enumerate(layers):\n",
        "        if i==0:\n",
        "            model.add(LSTM(units=nodes,input_shape= (train_X.shape[1],1), kernel_constraint=maxnorm(3)))\n",
        "            model.add(Dropout(0.3))\n",
        "        else:\n",
        "            model.add(LSTM(units=nodes, kernel_constraint=maxnorm(3)))\n",
        "            model.add(Dropout(0.3))\n",
        "    model.add(Dense(1)) \n",
        "    \n",
        "    model.compile(loss='mse', optimizer='adam')\n",
        "    return model\n",
        "\n",
        "model = KerasClassifier(build_fn=create_model, verbose=0) #build_fn is the name of the \n",
        "#function to call to get your model.   \n",
        "tscv = TimeSeriesSplit(n_splits=4)\n",
        "layers = [[100], [100, 50], [100, 50, 25]]\n",
        "batches=[16,32]\n",
        "epochs=[100,200]\n",
        "param_grid = dict(layers=layers, batch_size = batches , epochs=epochs)\n",
        "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_grid,scoring='r2')\n",
        "grid_result=gsearch.fit(train_X, train_y)\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_5 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_7 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_9 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_11 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_13 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_15 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_17 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_19 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_25 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_27 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_29 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_31 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_33 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_35 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_37 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_39 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_45 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_47 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_49 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_51 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_53 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_55 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_57 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_59 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_65 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_67 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_69 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_71 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_73 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_75 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_77 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_79 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best: -9.754916 using {'batch_size': 16, 'epochs': 100, 'layers': [100]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2wZgmBbYLOS",
        "outputId": "696ecbe0-b389-404f-f39a-f686118b5626"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=100,input_shape= (train_X.shape[1],1),kernel_constraint=maxnorm(3.5)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "es = tf.keras.callbacks.EarlyStopping(monitor='val_loss',patience=20,mode='min',verbose=1)\n",
        "mc = tf.keras.callbacks.ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, \n",
        "                     save_best_only=True)\n",
        "model.compile(loss='mae', optimizer='adam')\n",
        "history = model.fit(train_X, train_y, epochs=100, batch_size=16,validation_data=(test_X,test_y),\n",
        "                    verbose=0,callbacks=[es,mc], shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.18890, saving model to best_model.h5\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.18890 to 0.16677, saving model to best_model.h5\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.16677\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.16677 to 0.15215, saving model to best_model.h5\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.15215\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.15215 to 0.13592, saving model to best_model.h5\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.13592\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.13592\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.13592\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.13592\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.13592 to 0.12972, saving model to best_model.h5\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.12972\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.12972 to 0.12720, saving model to best_model.h5\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.12720 to 0.10170, saving model to best_model.h5\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.10170\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.10170 to 0.10025, saving model to best_model.h5\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.10025\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.10025 to 0.10003, saving model to best_model.h5\n",
            "\n",
            "Epoch 00019: val_loss improved from 0.10003 to 0.09416, saving model to best_model.h5\n",
            "\n",
            "Epoch 00020: val_loss improved from 0.09416 to 0.09252, saving model to best_model.h5\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.09252 to 0.08826, saving model to best_model.h5\n",
            "\n",
            "Epoch 00022: val_loss improved from 0.08826 to 0.08445, saving model to best_model.h5\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.08445 to 0.07392, saving model to best_model.h5\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.07392\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.07392\n",
            "\n",
            "Epoch 00026: val_loss improved from 0.07392 to 0.06859, saving model to best_model.h5\n",
            "\n",
            "Epoch 00027: val_loss improved from 0.06859 to 0.06408, saving model to best_model.h5\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.06408\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.06408\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.06408\n",
            "\n",
            "Epoch 00031: val_loss improved from 0.06408 to 0.05773, saving model to best_model.h5\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.05773\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.05773\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.05773\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.05773\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.05773\n",
            "\n",
            "Epoch 00037: val_loss improved from 0.05773 to 0.05722, saving model to best_model.h5\n",
            "\n",
            "Epoch 00038: val_loss improved from 0.05722 to 0.05598, saving model to best_model.h5\n",
            "\n",
            "Epoch 00039: val_loss improved from 0.05598 to 0.05432, saving model to best_model.h5\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.05432\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.05432\n",
            "\n",
            "Epoch 00042: val_loss improved from 0.05432 to 0.05304, saving model to best_model.h5\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.05304\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.05304\n",
            "Epoch 00062: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2DtZsevRZXZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "outputId": "78a6b4b4-eb02-4292-a2d8-ad9a391ec389"
      },
      "source": [
        "# plot history\n",
        "pyplot.plot(history.history['loss'], label='train')\n",
        "pyplot.plot(history.history['val_loss'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_KMCaeInjNn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34a07006-5b32-436d-814a-15e425fff95e"
      },
      "source": [
        "  # load the saved model\n",
        "model = load_model('best_model.h5')\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 100)               40800     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 40,901\n",
            "Trainable params: 40,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hsk_n93VtEES"
      },
      "source": [
        "# make a prediction\n",
        "yhat = model.predict(test_X) #shape=(14,1)\n",
        "test_X = test_X.reshape((test_X.shape[0], test_X.shape[1]))\n",
        "# invert scaling for forecast\n",
        "#inv_yhat = concatenate((test_X[:, :-1],yhat), axis=1)\n",
        "inv_yhat = concatenate((test_X,yhat), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,-1]\n",
        "# invert scaling for actual\n",
        "test_y = test_y.reshape((len(test_y), 1))\n",
        "#inv_y = concatenate((test_X[:, :-1],test_y), axis=1)\n",
        "inv_y = concatenate((test_X,test_y), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CejIp8s7rvO6"
      },
      "source": [
        "def timeseries_evaluation_metrics_func(y_true, y_pred):\n",
        "  def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "  def smape(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return 2/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum((np.abs(y_true)+np.abs(y_pred)))*100\n",
        "  def mase(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    naive_pred=y_true[1:]\n",
        "    return 1/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum(np.abs(y_true[:len(y_true)-1]-naive_pred))*(y_true.shape[0]-1)\n",
        "  #def owe(y_true,y_pred):\n",
        "   # return (mase(y_true,y_pred)+smape(y_true,y_pred))/2\n",
        "\n",
        "  print('Evaluation metric results:-')\n",
        "  print(f'MSE is : {metrics.mean_squared_error(y_true, y_pred)}')\n",
        "  print(f'MAE is : {metrics.mean_absolute_error(y_true, y_pred)}')\n",
        "  print(f'RMSE is : {np.sqrt(metrics.mean_squared_error(y_true, y_pred))}')\n",
        "  print(f'MAPE is : {mean_absolute_percentage_error(y_true, y_pred)} %')\n",
        "  #mape.append(mean_absolute_percentage_error(y_true, y_pred))\n",
        "  #rmse.append(np.sqrt(metrics.mean_squared_error(y_true, y_pred)))\n",
        "  print(f'R2 is : {metrics.r2_score(y_true, y_pred)}',end='\\n\\n')\n",
        "  print('SMAPE is : '+str(smape(y_true,y_pred))+'\\n')\n",
        "  print('MASE is : '+str(mase(y_true,y_pred)))\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-yxUOktrvTD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eefa44e6-7122-410f-bbbd-647a50d6406b"
      },
      "source": [
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "\n",
        "timeseries_evaluation_metrics_func(inv_y, inv_yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 10183.677457647149\n",
            "MAE is : 64.92198991775516\n",
            "RMSE is : 100.91420840321322\n",
            "MAPE is : 5.997747444600328 %\n",
            "R2 is : 0.7379070098752432\n",
            "\n",
            "SMAPE is : 0.025940714884420325\n",
            "\n",
            "MASE is : 0.7445100627056696\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "TUIag9jQ7IzX",
        "outputId": "b0784840-17d2-46b8-ecc4-836fa202d5d5"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[train_X.shape[0]:len(time)],inv_y,label='actual')\n",
        "plt.plot(time[train_X.shape[0]:len(time)],inv_yhat,label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "creouAuRJ2qH"
      },
      "source": [
        "# peak period"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Owa5qPllJ4_7",
        "outputId": "e414545f-7db3-49b7-d51c-eae52cbc4b4f"
      },
      "source": [
        "timeseries_evaluation_metrics_func(inv_y[:90], inv_yhat[:90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 10611.318209070872\n",
            "MAE is : 72.91998900837372\n",
            "RMSE is : 103.01125282740169\n",
            "MAPE is : 5.7271598219768745 %\n",
            "R2 is : 0.3647643322889653\n",
            "\n",
            "SMAPE is : 0.06157051319996055\n",
            "\n",
            "MASE is : 0.7283814839220268\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smzoQvxrrvUh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "c6ad5a53-8772-4380-8677-a365e80e028b"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[train_X.shape[0]:train_X.shape[0]+90],inv_y[:90],label='actual')\n",
        "plt.plot(time[train_X.shape[0]:train_X.shape[0]+90],inv_yhat[:90],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-cLLUdcLPSC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}