cnr_hackathon.ipynb

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  "metadata": {
    "colab": {
      "name": "cnr_hackathon.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yaswanthkumargothi/Portfolio/blob/master/cnr_hackathon.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOCUL_c5bH-o"
      },
      "source": [
        "#Wind Power forecasting for the day-ahead energy market\r\n",
        "by ***Compagnie Nationale du Rhône***\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86w98Gg_aqpc"
      },
      "source": [
        "CNR is the French leading producer of exclusively renewable energy (water,wind, sun) and the concessionary of the Rhone river for hydroelectricity production, river navigation and irrigation for agricultural use. This challenge focuses on wind energy production forecast. CNR currently owns around 50 Wind Farms (WF) for a total installed capacity of more than 600 MW. Every day, CNR sells on the energy market its wind energy production for the day ahead. In order to sell the right amount of energy, as well as for legal requirements towards the French Transmission System Operator (TSO) in charge of the electric network stability, CNR needs to know beforehand how much energy the wind farms will produce the day ahead.\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83i-ohTpdWa_"
      },
      "source": [
        "##Challenge goals\r\n",
        "The goal of this challenge is to predict the energy production of six WF owned by CNR. Each WF production will be individually predicted, using meteorological forecasts as input. Predictions will focus on the day-ahead energy production (hourly production forecasts from day D+1 00h to day D+2 00h)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ieyo0WobbGU8"
      },
      "source": [
        "##Project Outline\r\n",
        "\r\n",
        "1.   Data Understanding\r\n",
        "2.   Functions to extract Data\r\n",
        "3.   Missing value imputation\r\n",
        "4.   Feature Engineering\r\n",
        "5.   ML Models\r\n",
        "6.   Facebook Prophet\r\n",
        "7.   Neural Nets\r\n",
        "8.   Conclusion\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WV9rlh6dpygB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcabcf0c-49fb-4ea5-e13a-63c84dfc76ff"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UL8Cz2d7pVIW"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "from datetime import datetime\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8VcAYyocojC"
      },
      "source": [
        "#!pip install ipympl\n",
        "#%matplotlib widget"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gMneic-Bh5jq"
      },
      "source": [
        "###Data\r\n",
        "\r\n",
        "####Data description\r\n",
        "The six WF hourly production data from May the 1st of 2018 to January the 15th of 2019 (8 months and 15 days) is provided. This defines the training dataset, since day-ahead WF hourly production is the prediction target (predictand). Provided hourly WF power production consists in the raw recordings of the DSO (Distribution System Operator), and should therefore be considered as a reference, even if it could contain erroneous or suspect data. The choice is left to competitors to criticize or not this data, using complementary data provided apart.\r\n",
        "\r\n",
        "The test period will run from January the 16^th^ of 2019 to September the 30^rd^ of 2019 (8 months and 15 days). Evaluation will be performed on raw observed hourly WF power production.\r\n",
        "\r\n",
        "For confidentiality purposes, the names of the six considered WF will not appear in the challenge, and we will use generic names instead: WF1, ... , WF6. For similar reasons, locations of the WF will not be provided.\r\n",
        "\r\n",
        "For both training and test periods, predicting variables (predictors) will be given. It consists in hourly forecasted meteorological variables, provided by various Numerical Weather Prediction (NWP) models. NWP models are meteorological models run by several national weather prediction services. For confidentiality reasons, the name of the NWP models will not appear. They will be named with generic names NWP1, ... , NWPn.\r\n",
        "\r\n",
        "We propose to provide also complementary observed data, which could not be used as predicting variables (because these data are not subject to forecasts), but which may help competitors to prepare or criticize WF hourly production data. This consists in hourly wind speeds and wind directions observed at the height of each wind turbine, on each WF. Obviously, complementary data will not be provided for the test period."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rG0kuP4wuRB2"
      },
      "source": [
        "path = '/content/drive/My Drive/cnr/Y_train_sl9m6Jh.csv'\n",
        "y_train=pd.read_csv(path,encoding = \"ISO-8859-1\",delimiter=',')\n",
        "path = '/content/drive/My Drive/cnr/X_train_v2.csv'\n",
        "X_train=pd.read_csv(path,encoding = \"ISO-8859-1\",delimiter=',')\n",
        "#pd.set_option('display.max_rows', 1000)\n",
        "#X_train.isna().sum()\n",
        "path='/content/drive/My Drive/cnr/X_test_v2.csv'\n",
        "X_test=pd.read_csv(path,encoding = \"ISO-8859-1\",delimiter=',')\n",
        "path= '/content/drive/My Drive/cnr/Y_test_random.csv'\n",
        "y_test=pd.read_csv(path,encoding = \"ISO-8859-1\",delimiter=',')\n",
        "path = '/content/drive/My Drive/cnr/WindFarms_complementary_data.csv'\n",
        "complementary=pd.read_csv(path,encoding = \"ISO-8859-1\",delimiter=';')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIGasWJoIE2K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9797cb6b-4ad3-4774-8577-efd57b4928dd"
      },
      "source": [
        "pd.set_option('display.max_rows', 1000)\n",
        "X_train.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                       0\n",
              "WF                       0\n",
              "Time                     0\n",
              "NWP1_00h_D-2_U       17364\n",
              "NWP1_00h_D-2_V       17364\n",
              "NWP1_00h_D-2_T       17364\n",
              "NWP1_06h_D-2_U        8012\n",
              "NWP1_06h_D-2_V        8012\n",
              "NWP1_06h_D-2_T        8012\n",
              "NWP1_12h_D-2_U         282\n",
              "NWP1_12h_D-2_V         282\n",
              "NWP1_12h_D-2_T         282\n",
              "NWP1_18h_D-2_U         282\n",
              "NWP1_18h_D-2_V         282\n",
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              "NWP1_00h_D-1_U         282\n",
              "NWP1_00h_D-1_V         282\n",
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              "NWP1_06h_D-1_U         138\n",
              "NWP1_06h_D-1_V         138\n",
              "NWP1_06h_D-1_T         138\n",
              "NWP1_12h_D-1_U         138\n",
              "NWP1_12h_D-1_V         138\n",
              "NWP1_12h_D-1_T         138\n",
              "NWP1_18h_D-1_U         138\n",
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              "NWP1_18h_D-1_T         138\n",
              "NWP1_00h_D_U           144\n",
              "NWP1_00h_D_V           144\n",
              "NWP1_00h_D_T           144\n",
              "NWP1_06h_D_U          9342\n",
              "NWP1_06h_D_V          9342\n",
              "NWP1_06h_D_T          9342\n",
              "NWP1_12h_D_U         18682\n",
              "NWP1_12h_D_V         18682\n",
              "NWP1_12h_D_T         18682\n",
              "NWP1_18h_D_U         28027\n",
              "NWP1_18h_D_V         28027\n",
              "NWP1_18h_D_T         28027\n",
              "NWP2_00h_D-2_U       29641\n",
              "NWP2_00h_D-2_V       29641\n",
              "NWP2_12h_D-2_U       25051\n",
              "NWP2_12h_D-2_V       25051\n",
              "NWP2_00h_D-1_U       24955\n",
              "NWP2_00h_D-1_V       24955\n",
              "NWP2_12h_D-1_U       25003\n",
              "NWP2_12h_D-1_V       25003\n",
              "NWP2_00h_D_U         24913\n",
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              "NWP3_06h_D-2_U       26471\n",
              "NWP3_06h_D-2_V       26471\n",
              "NWP3_06h_D-2_T       26471\n",
              "NWP3_12h_D-2_U       24913\n",
              "NWP3_12h_D-2_V       24913\n",
              "NWP3_12h_D-2_T       24913\n",
              "NWP3_18h_D-2_U       24955\n",
              "NWP3_18h_D-2_V       24955\n",
              "NWP3_18h_D-2_T       24955\n",
              "NWP3_00h_D-1_U       24913\n",
              "NWP3_00h_D-1_V       24913\n",
              "NWP3_00h_D-1_T       24913\n",
              "NWP3_06h_D-1_U       24913\n",
              "NWP3_06h_D-1_V       24913\n",
              "NWP3_06h_D-1_T       24913\n",
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              "NWP3_12h_D-1_T       24919\n",
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              "NWP3_18h_D-1_T       24955\n",
              "NWP3_00h_D_U         24913\n",
              "NWP3_00h_D_V         24913\n",
              "NWP3_00h_D_T         24913\n",
              "NWP3_06h_D_U         28029\n",
              "NWP3_06h_D_V         28029\n",
              "NWP3_06h_D_T         28029\n",
              "NWP3_12h_D_U         31144\n",
              "NWP3_12h_D_V         31144\n",
              "NWP3_12h_D_T         31144\n",
              "NWP3_18h_D_U         34271\n",
              "NWP3_18h_D_V         34271\n",
              "NWP3_18h_D_T         34271\n",
              "NWP4_00h_D-2_U       35829\n",
              "NWP4_00h_D-2_V       35829\n",
              "NWP4_00h_D-2_CLCT    35829\n",
              "NWP4_12h_D-2_U       17286\n",
              "NWP4_12h_D-2_V       17286\n",
              "NWP4_12h_D-2_CLCT    17286\n",
              "NWP4_00h_D-1_U         282\n",
              "NWP4_00h_D-1_V         282\n",
              "NWP4_00h_D-1_CLCT      282\n",
              "NWP4_12h_D-1_U         138\n",
              "NWP4_12h_D-1_V         138\n",
              "NWP4_12h_D-1_CLCT      138\n",
              "NWP4_00h_D_U           144\n",
              "NWP4_00h_D_V           144\n",
              "NWP4_00h_D_CLCT        144\n",
              "NWP4_12h_D_U         18682\n",
              "NWP4_12h_D_V         18682\n",
              "NWP4_12h_D_CLCT      18682\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "YPeSqEdsgekS",
        "outputId": "85a58122-ea47-4f4f-b59f-4b57fd2c0c10"
      },
      "source": [
        "pd.set_option('display.max_rows', 1000)\r\n",
        "y_train"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Production</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.02</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.41</td>\n",
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              "      <th>37370</th>\n",
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              "      <td>0.04</td>\n",
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              "    <tr>\n",
              "      <th>37371</th>\n",
              "      <td>37372</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37372</th>\n",
              "      <td>37373</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37373</th>\n",
              "      <td>37374</td>\n",
              "      <td>0.01</td>\n",
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              "    <tr>\n",
              "      <th>37374</th>\n",
              "      <td>37375</td>\n",
              "      <td>0.00</td>\n",
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              "<p>37375 rows × 2 columns</p>\n",
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            "text/plain": [
              "          ID  Production\n",
              "0          1        0.02\n",
              "1          2        0.07\n",
              "2          3        0.22\n",
              "3          4        0.39\n",
              "4          5        0.41\n",
              "...      ...         ...\n",
              "37370  37371        0.04\n",
              "37371  37372        0.33\n",
              "37372  37373        0.13\n",
              "37373  37374        0.01\n",
              "37374  37375        0.00\n",
              "\n",
              "[37375 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoP-3aoeJgJY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3189d363-6dbc-4f54-892d-da8fa6154b2e"
      },
      "source": [
        "pd.set_option('display.max_rows', 1000)\n",
        "\n",
        "X_test.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                       0\n",
              "WF                       0\n",
              "Time                     0\n",
              "NWP1_00h_D-2_U       16739\n",
              "NWP1_00h_D-2_V       16739\n",
              "NWP1_00h_D-2_T       16739\n",
              "NWP1_06h_D-2_U        7833\n",
              "NWP1_06h_D-2_V        7833\n",
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              "NWP1_12h_D-2_U           0\n",
              "NWP1_12h_D-2_V           0\n",
              "NWP1_12h_D-2_T           0\n",
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              "NWP1_18h_D-2_V           0\n",
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              "NWP1_00h_D-1_V           0\n",
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              "NWP1_18h_D-1_U       36529\n",
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              "NWP2_12h_D-2_V       24403\n",
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              "NWP3_12h_D-2_T       24395\n",
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              "NWP3_00h_D-1_U       24395\n",
              "NWP3_00h_D-1_V       24395\n",
              "NWP3_00h_D-1_T       24395\n",
              "NWP3_06h_D-1_U       36529\n",
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              "NWP3_06h_D-1_T       36529\n",
              "NWP3_12h_D-1_U       36529\n",
              "NWP3_12h_D-1_V       36529\n",
              "NWP3_12h_D-1_T       36529\n",
              "NWP3_18h_D-1_U       36529\n",
              "NWP3_18h_D-1_V       36529\n",
              "NWP3_18h_D-1_T       36529\n",
              "NWP3_00h_D_U         36529\n",
              "NWP3_00h_D_V         36529\n",
              "NWP3_00h_D_T         36529\n",
              "NWP3_06h_D_U         36529\n",
              "NWP3_06h_D_V         36529\n",
              "NWP3_06h_D_T         36529\n",
              "NWP3_12h_D_U         36529\n",
              "NWP3_12h_D_V         36529\n",
              "NWP3_12h_D_T         36529\n",
              "NWP3_18h_D_U         36529\n",
              "NWP3_18h_D_V         36529\n",
              "NWP3_18h_D_T         36529\n",
              "NWP4_00h_D-2_U       35017\n",
              "NWP4_00h_D-2_V       35017\n",
              "NWP4_00h_D-2_CLCT    35017\n",
              "NWP4_12h_D-2_U       16817\n",
              "NWP4_12h_D-2_V       16817\n",
              "NWP4_12h_D-2_CLCT    16817\n",
              "NWP4_00h_D-1_U         146\n",
              "NWP4_00h_D-1_V         146\n",
              "NWP4_00h_D-1_CLCT      146\n",
              "NWP4_12h_D-1_U       36529\n",
              "NWP4_12h_D-1_V       36529\n",
              "NWP4_12h_D-1_CLCT    36529\n",
              "NWP4_00h_D_U         36529\n",
              "NWP4_00h_D_V         36529\n",
              "NWP4_00h_D_CLCT      36529\n",
              "NWP4_12h_D_U         36529\n",
              "NWP4_12h_D_V         36529\n",
              "NWP4_12h_D_CLCT      36529\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZOWzoCQ1AeH"
      },
      "source": [
        "pd.set_option('display.max_rows', 1000)\n",
        "X_test.head();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QkqIXFSkDwr"
      },
      "source": [
        "####Here is a description of all data provided in the input csv files:\r\n",
        "* **ID**: This is the unique ID of each row in the csv files. One ID correspond \r\n",
        "to a couple Time / WF. The ID of the test set are consecutive to the ID of the training set.    \r\n",
        "* **WF**: The considered Wind Farm. WF ranges from WF1 to WF6. It is crucial for the competitors to be aware that this prediction problem is totally dependent to the WF considered. In other words, the statistical link between input variables and wind power production is completely different from one WF to another. Consequently, it could be judicious to train specific prediction algorithms for each WF, instead of training a unique algorithm which could be unable to model the behavior of each WF.     \r\n",
        "* **Time (UTC)**: date and hour of the target timestep, i.e. corresponding to the observed Power production. Time zone is Coordinated Universal Time (UTC).\r\n",
        "Meteorological variables: Numerical Weather Predictions are provided by meteorological centers several times a day (updates), typically at 00h UTC, 06h UTC, 12h UTC and 18h UTC. We call these sets of forecasts “Runs”. Consequently, if the input file contains forecasts arising from several runs, this implies that a single NWP is associated with several forecasts for the same forecasting time. There|fore, the information on the hour of run is provided.\r\n",
        "The format of the header of the csv files for the meteorological variables is the following:     \r\n",
        "                    NWPi_HourOfTheRun_DayOfTheRun_Variable   \r\n",
        "With NWPi the considered Numerical Weather Prediction model (meteorological model);    \r\n",
        "\r\n",
        "   HourOfTheRun the hour (UTC) of the considered run. According to the NWP, it could be 00h, 06h, 12h and 18h (case of NWP with 4 runs per day) or only 00h and 12h (case of NWP with 2 runs per day);\r\n",
        "DayOfTheRun the day of the considered run. We provide in the csv files predictions from the D-2 day runs (the day before yesterday), D-1 day runs (yesterday) and D day runs;    \r\n",
        "\r\n",
        "Variables the different meteorological variables forecasted by the NWP. These are essentially U, V and T:        \r\n",
        "\r\n",
        "* **U and V components of the wind at 100m (or10m) height (m/s)**: these are the zonal and meridional velocities of the wind, respectively. Both are given at a height of 100m above ground for NWP1, NWP2 and NWP3. U and V are given at a height of 10m for NWP4. Even if these variables are given at hourly timestep, we draw competitors attention on the fact that the temporal representativity of the given values is for a 10-minutes window ranging from H-10 min to H.\r\n",
        "Additional remark: since wind power production is principally driven by the wind speed impacting turbines, it could be useful for the competitors to derive wind speed (and wind direction) from U and V. This can be done using a simple trigonometric calculation of the magnitude and direction of a vector with U and V components. The choice is let to the competitors.     \r\n",
        "* **Temperature of air (°C), abbreviated T**: this is the averaged temperature over the entire hour (from H-1 to H). Wind power production is sensitive to air temperature since it affects the air density. This variable is provided only for NWP1 and NWP3.\r\n",
        "* **Total cloud cover (%), abbreviated CLCT**: this is the total cloud cover of the sky, ranging from 0% (clear sky, no cloud) to 100% (fully clouded sky). The value is an instant value at hour H. This variable is provided only for NWP4.\r\n",
        "* **Other variables**: We may provide other meteorological variables in the future, before the start of the challenge. If so, we will provide an update of this technical description, including the definition of these new variables.\r\n",
        "Important remark 1: Some of the NWP provide hourly forecasts, which means that predicted variables are available on every timestep in the csv files, but others provide only three hourly forecasts. For the corresponding columns, blanks are let for timesteps that are not subject to forecasts. The choice is let to competitors to reconstruct or not the missing values to get hourly time series for all the NWP.\r\n",
        "\r\n",
        "* **Important remark 2**: As stated before, day-ahead predictions must be computed with data that are available on day D at 09h00 UTC, and data provided in test sets will be restricted in that way. Practically, this means that at 09hUTC competitors will have access to 00h UTC runs only (and to runs from the D-1 day), because 06hUTC runs are not available at this time. This is an important remark to account for in the construction of the methodology, even if this does not mean that the inputs unavailable in the test set are not usefull for the training.\r\n",
        "\r\n",
        "* **Observed Power Production (MW or MW.h)**: this is the observed total amount of energy injected by the WF to the electric network over the entire hour H-1 to H (MW.h). Equivalently, we can consider that this is the mean power output of the WF observed between H-1 and H (MW)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuzTfaEmniQp"
      },
      "source": [
        "complementary.head();\r\n",
        "complementary.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbABTOpXojxH"
      },
      "source": [
        "####WF complementary data\r\n",
        "We provide complementary data in the .zip supplementary files. These data may be used by the competitors to prepare or criticize WF hourly production data, but they are not predictors. The file WindFarms_complementary_data.csv contains the following hourly variables:\r\n",
        ">     \r\n",
        "• Average power output for each wind turbine of the WF (MW)    \r\n",
        "• Cumulated energy produced by each wind turbine (MWh). This value could differ from the hourly average power output when the considered turbine has not been operational during the entire hour.    \r\n",
        "• Observed average wind direction at hub (nacelle) height for each wind turbine (°, from 0 to 359)     \r\n",
        "• Observed average wind speed at hub (nacelle) height for each wind turbine (m/s)     \r\n",
        "• Observed average nacelle direction for each wind turbine (°, from 0 to 359)     \r\n",
        "• Observed average rotational speed of each wind turbine (s-1)\r\n",
        "Metric        "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68dIc-mBHnfF"
      },
      "source": [
        "X_train['Time']=pd.to_datetime(X_train['Time'],format='%d/%m/%Y %H:%M')\n",
        "X_test['Time']=pd.to_datetime(X_test['Time'],format='%d/%m/%Y %H:%M')\n",
        "\n",
        "train_aug=X_test[X_test['Time']< pd.Timestamp(2019,5,2)]\n",
        "\n",
        "train_1yr=pd.concat([X_train,train_aug],axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbpaRuIaCtJm"
      },
      "source": [
        "id_train=train_aug['ID']\n",
        "y_aug=y_test[y_test['ID'].isin(id_train)]\n",
        "y_1yr=pd.concat([y_train,y_aug])\n",
        "#y_test.iloc[:32858,:]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yr-yxJqT6xcV"
      },
      "source": [
        "test_aug=X_test[(X_test['Time']>= pd.Timestamp(2019,5,1)) & (X_test['Time']< pd.Timestamp(2019,5,2))]\n",
        "\n",
        "id_test=test_aug['ID']\n",
        "y_aug_test=y_test[y_test['ID'].isin(id_test)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-loa4pmQeOqM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c497d261-dc9e-4eb2-c2e4-b339bb5cd849"
      },
      "source": [
        "y_aug.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYzKVJfv32E-"
      },
      "source": [
        "train_1yr.isna().sum();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rr34YGr52BJH"
      },
      "source": [
        "#function to extract data for particular wind farm and weather forecasting model \n",
        "def wind_farm_weather(wf_num,X_train,weather):\n",
        "  wind_farm=X_train[X_train[\"WF\"]==wf_num]\n",
        "  a=wind_farm.iloc[:,:3]\n",
        "  if weather == 'nwp1d1':\n",
        "    b=wind_farm.iloc[:,15:27]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp1d2':\n",
        "    wf_nwpd = wind_farm.iloc[:,:15]\n",
        "  elif weather == 'nwp1d':\n",
        "    b=wind_farm.iloc[:,27:39]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp2d2':\n",
        "    b=wind_farm.iloc[:,39:43]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp2d1':\n",
        "    b=wind_farm.iloc[:,43:47]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp2d':\n",
        "    b=wind_farm.iloc[:,47:51]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp3d2':\n",
        "    b=wind_farm.iloc[:,51:63]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp3d1':\n",
        "    b=wind_farm.iloc[:,63:75]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp3d':\n",
        "    b=wind_farm.iloc[:,75:87]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp4d2':\n",
        "    b=wind_farm.iloc[:,87:93]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp4d1':\n",
        "    b=wind_farm.iloc[:,93:99]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  elif weather == 'nwp4d':\n",
        "    b=wind_farm.iloc[:,99:105]\n",
        "    wf_nwpd= pd.concat([a, b.reindex(a.index)], axis=1)\n",
        "  \n",
        "    \n",
        "  return wf_nwpd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60LN-VBV7ZPb"
      },
      "source": [
        "wf1_nwp1d1=wind_farm_weather('WF1',train_1yr,'nwp1d1')\n",
        "wf2_nwp1d1=wind_farm_weather('WF2',train_1yr,'nwp1d1')\n",
        "wf3_nwp1d1=wind_farm_weather('WF3',train_1yr,'nwp1d1')\n",
        "wf4_nwp1d1=wind_farm_weather('WF4',train_1yr,'nwp1d1')\n",
        "wf5_nwp1d1=wind_farm_weather('WF5',train_1yr,'nwp1d1')\n",
        "wf6_nwp1d1=wind_farm_weather('WF6',train_1yr,'nwp1d1')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmtzRCFH1-rJ"
      },
      "source": [
        "wf1_nwp1d2=wind_farm_weather('WF1',train_1yr,'nwp1d2')\n",
        "wf2_nwp1d2=wind_farm_weather('WF2',train_1yr,'nwp1d2')\n",
        "wf3_nwp1d2=wind_farm_weather('WF3',train_1yr,'nwp1d2')\n",
        "wf4_nwp1d2=wind_farm_weather('WF4',train_1yr,'nwp1d2')\n",
        "wf5_nwp1d2=wind_farm_weather('WF5',train_1yr,'nwp1d2')\n",
        "wf6_nwp1d2=wind_farm_weather('WF6',train_1yr,'nwp1d2')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIOEht3pWeNq"
      },
      "source": [
        "wf1_nwp4d1=wind_farm_weather('WF1',train_1yr,'nwp4d1')\n",
        "wf2_nwp4d1=wind_farm_weather('WF2',train_1yr,'nwp4d1')\n",
        "wf3_nwp4d1=wind_farm_weather('WF3',train_1yr,'nwp4d1')\n",
        "wf4_nwp4d1=wind_farm_weather('WF4',train_1yr,'nwp4d1')\n",
        "wf5_nwp4d1=wind_farm_weather('WF5',train_1yr,'nwp4d1')\n",
        "wf6_nwp4d1=wind_farm_weather('WF6',train_1yr,'nwp4d1')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dv-C1o9dtGAU"
      },
      "source": [
        "wf1_nwp1d1_test=wind_farm_weather('WF1',X_test,'nwp1d1')\n",
        "wf2_nwp1d1_test=wind_farm_weather('WF2',X_test,'nwp1d1')\n",
        "wf3_nwp1d1_test=wind_farm_weather('WF3',X_test,'nwp1d1')\n",
        "wf4_nwp1d1_test=wind_farm_weather('WF4',X_test,'nwp1d1')\n",
        "wf5_nwp1d1_test=wind_farm_weather('WF5',X_test,'nwp1d1')\n",
        "wf6_nwp1d1_test=wind_farm_weather('WF6',X_test,'nwp1d1')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6uh6fsj42V5t"
      },
      "source": [
        "wf1_nwp1d2_test=wind_farm_weather('WF1',X_test,'nwp1d2')\n",
        "wf2_nwp1d2_test=wind_farm_weather('WF2',X_test,'nwp1d2')\n",
        "wf3_nwp1d2_test=wind_farm_weather('WF3',X_test,'nwp1d2')\n",
        "wf4_nwp1d2_test=wind_farm_weather('WF4',X_test,'nwp1d2')\n",
        "wf5_nwp1d2_test=wind_farm_weather('WF5',X_test,'nwp1d2')\n",
        "wf6_nwp1d2_test=wind_farm_weather('WF6',X_test,'nwp1d2')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VUWPQ_lW-Rg"
      },
      "source": [
        "wf1_nwp4d1_test=wind_farm_weather('WF1',X_test,'nwp4d1')\n",
        "wf2_nwp4d1_test=wind_farm_weather('WF2',X_test,'nwp4d1')\n",
        "wf3_nwp4d1_test=wind_farm_weather('WF3',X_test,'nwp4d1')\n",
        "wf4_nwp4d1_test=wind_farm_weather('WF4',X_test,'nwp4d1')\n",
        "wf5_nwp4d1_test=wind_farm_weather('WF5',X_test,'nwp4d1')\n",
        "wf6_nwp4d1_test=wind_farm_weather('WF6',X_test,'nwp4d1')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XKcutVIs8Qu"
      },
      "source": [
        "def reset_indx(wf_nwpd_test):\n",
        "  wf_nwpd_test=wf_nwpd_test.reset_index(drop=True)\n",
        " # wf_nwpd_test=wf_nwpd_test.drop('index',axis=1)\n",
        "  return wf_nwpd_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUjKgvNptU8C"
      },
      "source": [
        "wf1_nwp1d1_test=reset_indx(wf1_nwp1d1_test)\n",
        "wf2_nwp1d1_test=reset_indx(wf2_nwp1d1_test)\n",
        "wf3_nwp1d1_test=reset_indx(wf3_nwp1d1_test)\n",
        "wf4_nwp1d1_test=reset_indx(wf4_nwp1d1_test)\n",
        "wf5_nwp1d1_test=reset_indx(wf5_nwp1d1_test)\n",
        "wf6_nwp1d1_test=reset_indx(wf6_nwp1d1_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7laihB6v2k9q"
      },
      "source": [
        "wf1_nwp1d2_test=reset_indx(wf1_nwp1d2_test)\n",
        "wf2_nwp1d2_test=reset_indx(wf2_nwp1d2_test)\n",
        "wf3_nwp1d2_test=reset_indx(wf3_nwp1d2_test)\n",
        "wf4_nwp1d2_test=reset_indx(wf4_nwp1d2_test)\n",
        "wf5_nwp1d2_test=reset_indx(wf5_nwp1d2_test)\n",
        "wf6_nwp1d2_test=reset_indx(wf6_nwp1d2_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnz2PHvuXfgM"
      },
      "source": [
        "wf1_nwp4d1_test=reset_indx(wf1_nwp4d1_test)\n",
        "wf2_nwp4d1_test=reset_indx(wf2_nwp4d1_test)\n",
        "wf3_nwp4d1_test=reset_indx(wf3_nwp4d1_test)\n",
        "wf4_nwp4d1_test=reset_indx(wf4_nwp4d1_test)\n",
        "wf5_nwp4d1_test=reset_indx(wf5_nwp4d1_test)\n",
        "wf6_nwp4d1_test=reset_indx(wf6_nwp4d1_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ei7Bf0WaIyeP"
      },
      "source": [
        "#wf1_nwp1d1_test.reset_index(inplace=True)\n",
        "wf3_nwp1d2_test.head();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_ZFYIYS7rmM"
      },
      "source": [
        "#rows with all columns null values\n",
        "#print(wf1_nwp1d1.iloc[:,3:].isna().all(axis=1).sum())\n",
        "wf1_nwp1d2.isna().sum();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kp5A1ei15gGX"
      },
      "source": [
        "#imputation\n",
        "from sklearn.experimental import enable_iterative_imputer  \n",
        "from sklearn.impute import IterativeImputer\n",
        "\n",
        "def impute_missing(wf_nwpd,wf_nwpd_test):\n",
        "  wf_nwpd_trunc=wf_nwpd.iloc[23:,:]\n",
        "  wf_nwpd_trunc.reset_index(inplace=True)\n",
        "  wf_nwpd_trunc=wf_nwpd_trunc.drop('index',axis=1)\n",
        "  \n",
        "  imputer = IterativeImputer(max_iter=20)\n",
        "  imputer.fit(wf_nwpd_trunc.iloc[:,3:])\n",
        "  wf_nwpd_imputed=imputer.transform(wf_nwpd_trunc.iloc[:,3:])\n",
        "\n",
        "  wf_nwpd_imputed_test=imputer.transform(wf_nwpd_test.iloc[:,3:])\n",
        "\n",
        "  wf_nwpd_imputed_df=pd.DataFrame(wf_nwpd_imputed,columns=wf_nwpd.iloc[:,3:].columns)\n",
        "  wf_nwpd_imputed_test=pd.DataFrame(wf_nwpd_imputed_test,columns=wf_nwpd_test.iloc[:,3:].columns)\n",
        "  return wf_nwpd_trunc,wf_nwpd_imputed_df,wf_nwpd_imputed_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2V4i_8k4ir-"
      },
      "source": [
        "wf1_nwp1d1_trunc,wf1_nwp1d1_imputed_df,wf1_nwp1d1_imputed_test=impute_missing(wf1_nwp1d1,wf1_nwp1d1_test)\n",
        "wf2_nwp1d1_trunc,wf2_nwp1d1_imputed_df,wf2_nwp1d1_imputed_test=impute_missing(wf2_nwp1d1,wf2_nwp1d1_test)\n",
        "wf3_nwp1d1_trunc,wf3_nwp1d1_imputed_df,wf3_nwp1d1_imputed_test=impute_missing(wf3_nwp1d1,wf3_nwp1d1_test)\n",
        "wf4_nwp1d1_trunc,wf4_nwp1d1_imputed_df,wf4_nwp1d1_imputed_test=impute_missing(wf4_nwp1d1,wf4_nwp1d1_test)\n",
        "wf5_nwp1d1_trunc,wf5_nwp1d1_imputed_df,wf5_nwp1d1_imputed_test=impute_missing(wf5_nwp1d1,wf5_nwp1d1_test)\n",
        "wf6_nwp1d1_trunc,wf6_nwp1d1_imputed_df,wf6_nwp1d1_imputed_test=impute_missing(wf6_nwp1d1,wf6_nwp1d1_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXxxJq1T3HPH"
      },
      "source": [
        "wf1_nwp1d2_trunc,wf1_nwp1d2_imputed_df,wf1_nwp1d2_imputed_test=impute_missing(wf1_nwp1d2,wf1_nwp1d2_test)\n",
        "wf2_nwp1d2_trunc,wf2_nwp1d2_imputed_df,wf2_nwp1d2_imputed_test=impute_missing(wf2_nwp1d2,wf2_nwp1d2_test)\n",
        "wf3_nwp1d2_trunc,wf3_nwp1d2_imputed_df,wf3_nwp1d2_imputed_test=impute_missing(wf3_nwp1d2,wf3_nwp1d2_test)\n",
        "wf4_nwp1d2_trunc,wf4_nwp1d2_imputed_df,wf4_nwp1d2_imputed_test=impute_missing(wf4_nwp1d2,wf4_nwp1d2_test)\n",
        "wf5_nwp1d2_trunc,wf5_nwp1d2_imputed_df,wf5_nwp1d2_imputed_test=impute_missing(wf5_nwp1d2,wf5_nwp1d2_test)\n",
        "wf6_nwp1d2_trunc,wf6_nwp1d2_imputed_df,wf6_nwp1d2_imputed_test=impute_missing(wf6_nwp1d2,wf6_nwp1d2_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHvO7_oyYtPt"
      },
      "source": [
        "wf1_nwp4d1_trunc,wf1_nwp4d1_imputed_df,wf1_nwp4d1_imputed_test=impute_missing(wf1_nwp4d1,wf1_nwp4d1_test)\n",
        "wf2_nwp4d1_trunc,wf2_nwp4d1_imputed_df,wf2_nwp4d1_imputed_test=impute_missing(wf2_nwp4d1,wf2_nwp4d1_test)\n",
        "wf3_nwp4d1_trunc,wf3_nwp4d1_imputed_df,wf3_nwp4d1_imputed_test=impute_missing(wf3_nwp4d1,wf3_nwp4d1_test)\n",
        "wf4_nwp4d1_trunc,wf4_nwp4d1_imputed_df,wf4_nwp4d1_imputed_test=impute_missing(wf4_nwp4d1,wf4_nwp4d1_test)\n",
        "wf5_nwp4d1_trunc,wf5_nwp4d1_imputed_df,wf5_nwp4d1_imputed_test=impute_missing(wf5_nwp4d1,wf5_nwp4d1_test)\n",
        "wf6_nwp4d1_trunc,wf6_nwp4d1_imputed_df,wf6_nwp4d1_imputed_test=impute_missing(wf6_nwp4d1,wf6_nwp4d1_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es6FUNXdbltt"
      },
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvqjd-0EqGL-"
      },
      "source": [
        "#new variables\n",
        "def new_features(wf1_nwpd1_imputed_df):\n",
        "  speed_list=[]\n",
        "  complete_dir =[]\n",
        "  for i in range(len(wf1_nwpd1_imputed_df.columns)):\n",
        "    if i%3==0:\n",
        "      individual_dir=[]\n",
        "      speed=np.linalg.norm(wf1_nwpd1_imputed_df.iloc[:,i:i+2],axis=1)\n",
        "      speed_list.append(speed)\n",
        "      df=wf1_nwpd1_imputed_df.iloc[:,i:i+2]\n",
        "      for i,row in df.iterrows():\n",
        "        x=row[0]\n",
        "        y=row[1]\n",
        "        #direction calculations\n",
        "        if x>0:\n",
        "          dir=np.arctan(y/x)\n",
        "        elif y>0 and x<0:\n",
        "          dir=np.arctan(y/x)+np.pi\n",
        "        elif y<0 and x<0:\n",
        "          dir= np.arctan(y/x)-np.pi\n",
        "        elif y>0 and x==0:\n",
        "          dir = np.pi/2\n",
        "        elif y<0 and x==0:\n",
        "          dir = -np.pi/2\n",
        "        elif y==0 and x==0:\n",
        "          dir = 0\n",
        "        individual_dir.append(dir)\n",
        "      complete_dir.append(individual_dir)\n",
        "  return speed_list, complete_dir     "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKzGmFMO_38R"
      },
      "source": [
        "def df_extrafeatures(wf_nwpd,wf_nwpd_imputed_df):\n",
        "  speed_ls,complete_dir=new_features(wf_nwpd_imputed_df)\n",
        "  #speed\n",
        "  speeddf=pd.DataFrame(speed_ls)\n",
        "  speeddf=speeddf.T\n",
        "  speeddf.columns=['NWP1_00h_D-1_speed','NWP1_06h_D-1_speed','NWP1_12h_D-1_speed','NWP1_18h_D-1_speed']\n",
        "  #direction\n",
        "  directiondf=pd.DataFrame(complete_dir)\n",
        "  directiondf=directiondf.T\n",
        "  directiondf.columns=['NWP1_00h_D-1_dir','NWP1_06h_D-1_dir','NWP1_12h_D-1_dir','NWP1_18h_D-1_dir']\n",
        "  #merging all the dataframes\n",
        "  wf_nwpd_features=pd.concat([wf_nwpd.iloc[:,:3],wf_nwpd_imputed_df,speeddf,directiondf],axis=1)\n",
        "  return wf_nwpd_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILxBMF6tbzJk"
      },
      "source": [
        "def df_extrafeatures_4(wf_nwpd,wf_nwpd_imputed_df):\n",
        "  speed_ls,complete_dir=new_features(wf_nwpd_imputed_df)\n",
        "  #speed\n",
        "  speeddf=pd.DataFrame(speed_ls)\n",
        "  speeddf=speeddf.T\n",
        "  speeddf.columns=['NWP4_00h_D-1_speed','NWP1_12h_D-1_speed']\n",
        "  #direction\n",
        "  directiondf=pd.DataFrame(complete_dir)\n",
        "  directiondf=directiondf.T\n",
        "  directiondf.columns=['NWP4_00h_D-1_dir','NWP4_12h_D-1_dir']\n",
        "  #merging all the dataframes\n",
        "  wf_nwpd_features=pd.concat([wf_nwpd.iloc[:,:3],wf_nwpd_imputed_df,speeddf,directiondf],axis=1)\n",
        "  return wf_nwpd_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYnu_w80mpqS"
      },
      "source": [
        "wf1_nwp1d1_features=df_extrafeatures(wf1_nwp1d1_trunc,wf1_nwp1d1_imputed_df)\n",
        "wf2_nwp1d1_features=df_extrafeatures(wf2_nwp1d1_trunc,wf2_nwp1d1_imputed_df)\n",
        "wf3_nwp1d1_features=df_extrafeatures(wf3_nwp1d1_trunc,wf3_nwp1d1_imputed_df)\n",
        "wf4_nwp1d1_features=df_extrafeatures(wf4_nwp1d1_trunc,wf4_nwp1d1_imputed_df)\n",
        "wf5_nwp1d1_features=df_extrafeatures(wf5_nwp1d1_trunc,wf5_nwp1d1_imputed_df)\n",
        "wf6_nwp1d1_features=df_extrafeatures(wf6_nwp1d1_trunc,wf6_nwp1d1_imputed_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mvh3pdiF46mL"
      },
      "source": [
        "wf1_nwp1d2_features=df_extrafeatures(wf1_nwp1d2_trunc,wf1_nwp1d2_imputed_df)\n",
        "wf2_nwp1d2_features=df_extrafeatures(wf2_nwp1d2_trunc,wf2_nwp1d2_imputed_df)\n",
        "wf3_nwp1d2_features=df_extrafeatures(wf3_nwp1d2_trunc,wf3_nwp1d2_imputed_df)\n",
        "wf4_nwp1d2_features=df_extrafeatures(wf4_nwp1d2_trunc,wf4_nwp1d2_imputed_df)\n",
        "wf5_nwp1d2_features=df_extrafeatures(wf5_nwp1d2_trunc,wf5_nwp1d2_imputed_df)\n",
        "wf6_nwp1d2_features=df_extrafeatures(wf6_nwp1d2_trunc,wf6_nwp1d2_imputed_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4-yEYMRa1eN"
      },
      "source": [
        "wf1_nwp4d1_features=df_extrafeatures_4(wf1_nwp4d1_trunc,wf1_nwp4d1_imputed_df)\n",
        "wf2_nwp4d1_features=df_extrafeatures_4(wf2_nwp4d1_trunc,wf2_nwp4d1_imputed_df)\n",
        "wf3_nwp4d1_features=df_extrafeatures_4(wf3_nwp4d1_trunc,wf3_nwp4d1_imputed_df)\n",
        "wf4_nwp4d1_features=df_extrafeatures_4(wf4_nwp4d1_trunc,wf4_nwp4d1_imputed_df)\n",
        "wf5_nwp4d1_features=df_extrafeatures_4(wf5_nwp4d1_trunc,wf5_nwp4d1_imputed_df)\n",
        "wf6_nwp4d1_features=df_extrafeatures_4(wf6_nwp4d1_trunc,wf6_nwp4d1_imputed_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJvELkGVxDlb"
      },
      "source": [
        "wf1_nwp1d1_test_features=df_extrafeatures(wf1_nwp1d1_test,wf1_nwp1d1_imputed_test)\n",
        "wf2_nwp1d1_test_features=df_extrafeatures(wf2_nwp1d1_test,wf2_nwp1d1_imputed_test)\n",
        "wf3_nwp1d1_test_features=df_extrafeatures(wf3_nwp1d1_test,wf3_nwp1d1_imputed_test)\n",
        "wf4_nwp1d1_test_features=df_extrafeatures(wf4_nwp1d1_test,wf4_nwp1d1_imputed_test)\n",
        "wf5_nwp1d1_test_features=df_extrafeatures(wf5_nwp1d1_test,wf5_nwp1d1_imputed_test)\n",
        "wf6_nwp1d1_test_features=df_extrafeatures(wf6_nwp1d1_test,wf6_nwp1d1_imputed_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbjIa9PO5FKF"
      },
      "source": [
        "wf1_nwp1d2_test_features=df_extrafeatures(wf1_nwp1d2_test,wf1_nwp1d2_imputed_test)\n",
        "wf2_nwp1d2_test_features=df_extrafeatures(wf2_nwp1d2_test,wf2_nwp1d2_imputed_test)\n",
        "wf3_nwp1d2_test_features=df_extrafeatures(wf3_nwp1d2_test,wf3_nwp1d2_imputed_test)\n",
        "wf4_nwp1d2_test_features=df_extrafeatures(wf4_nwp1d2_test,wf4_nwp1d2_imputed_test)\n",
        "wf5_nwp1d2_test_features=df_extrafeatures(wf5_nwp1d2_test,wf5_nwp1d2_imputed_test)\n",
        "wf6_nwp1d2_test_features=df_extrafeatures(wf6_nwp1d2_test,wf6_nwp1d2_imputed_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9A038wKYcwUj"
      },
      "source": [
        "wf1_nwp4d1_test_features=df_extrafeatures_4(wf1_nwp4d1_test,wf1_nwp4d1_imputed_test)\n",
        "wf2_nwp4d1_test_features=df_extrafeatures_4(wf2_nwp4d1_test,wf2_nwp4d1_imputed_test)\n",
        "wf3_nwp4d1_test_features=df_extrafeatures_4(wf3_nwp4d1_test,wf3_nwp4d1_imputed_test)\n",
        "wf4_nwp4d1_test_features=df_extrafeatures_4(wf4_nwp4d1_test,wf4_nwp4d1_imputed_test)\n",
        "wf5_nwp4d1_test_features=df_extrafeatures_4(wf5_nwp4d1_test,wf5_nwp4d1_imputed_test)\n",
        "wf6_nwp4d1_test_features=df_extrafeatures_4(wf6_nwp4d1_test,wf6_nwp4d1_imputed_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbd-cUWN_8Hl"
      },
      "source": [
        "wf6_nwp4d1_test_features.head();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OELgKcozcXQZ"
      },
      "source": [
        "def hour_day(wf_nwpd_features):\n",
        "  wf_nwpd_features['week']=wf_nwpd_features['Time'].dt.week\n",
        "  wf_nwpd_features['day']=wf_nwpd_features['Time'].dt.day\n",
        "  wf_nwpd_features['hour']=wf_nwpd_features['Time'].dt.hour\n",
        "  wf_nwpd_features['month']=wf_nwpd_features['Time'].dt.month\n",
        "  return wf_nwpd_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmL9jApokTZD"
      },
      "source": [
        "wf1_nwp1d1_features=hour_day(wf1_nwp1d1_features)\n",
        "wf2_nwp1d1_features=hour_day(wf2_nwp1d1_features)\n",
        "wf3_nwp1d1_features=hour_day(wf3_nwp1d1_features)\n",
        "wf4_nwp1d1_features=hour_day(wf4_nwp1d1_features)\n",
        "wf5_nwp1d1_features=hour_day(wf5_nwp1d1_features)\n",
        "wf6_nwp1d1_features=hour_day(wf6_nwp1d1_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0RTvtbO5-NP"
      },
      "source": [
        "wf1_nwp1d2_features=hour_day(wf1_nwp1d2_features)\n",
        "wf2_nwp1d2_features=hour_day(wf2_nwp1d2_features)\n",
        "wf3_nwp1d2_features=hour_day(wf3_nwp1d2_features)\n",
        "wf4_nwp1d2_features=hour_day(wf4_nwp1d2_features)\n",
        "wf5_nwp1d2_features=hour_day(wf5_nwp1d2_features)\n",
        "wf6_nwp1d2_features=hour_day(wf6_nwp1d2_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzidYFnpgKYc"
      },
      "source": [
        "wf1_nwp4d1_features=hour_day(wf1_nwp4d1_features)\n",
        "wf2_nwp4d1_features=hour_day(wf2_nwp4d1_features)\n",
        "wf3_nwp4d1_features=hour_day(wf3_nwp4d1_features)\n",
        "wf4_nwp4d1_features=hour_day(wf4_nwp4d1_features)\n",
        "wf5_nwp4d1_features=hour_day(wf5_nwp4d1_features)\n",
        "wf6_nwp4d1_features=hour_day(wf6_nwp4d1_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1F9CMQexmTT"
      },
      "source": [
        "wf1_nwp1d1_test_features=hour_day(wf1_nwp1d1_test_features)\n",
        "wf2_nwp1d1_test_features=hour_day(wf2_nwp1d1_test_features)\n",
        "wf3_nwp1d1_test_features=hour_day(wf3_nwp1d1_test_features)\n",
        "wf4_nwp1d1_test_features=hour_day(wf4_nwp1d1_test_features)\n",
        "wf5_nwp1d1_test_features=hour_day(wf5_nwp1d1_test_features)\n",
        "wf6_nwp1d1_test_features=hour_day(wf6_nwp1d1_test_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1X8i-2LCPGOQ"
      },
      "source": [
        "wf6_nwp1d1_test_features.head();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLDu1ev36HLd"
      },
      "source": [
        "wf1_nwp1d2_test_features=hour_day(wf1_nwp1d2_test_features)\n",
        "wf2_nwp1d2_test_features=hour_day(wf2_nwp1d2_test_features)\n",
        "wf3_nwp1d2_test_features=hour_day(wf3_nwp1d2_test_features)\n",
        "wf4_nwp1d2_test_features=hour_day(wf4_nwp1d2_test_features)\n",
        "wf5_nwp1d2_test_features=hour_day(wf5_nwp1d2_test_features)\n",
        "wf6_nwp1d2_test_features=hour_day(wf6_nwp1d2_test_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDAppq53grte"
      },
      "source": [
        "wf1_nwp4d1_test_features=hour_day(wf1_nwp4d1_test_features)\n",
        "wf2_nwp4d1_test_features=hour_day(wf2_nwp4d1_test_features)\n",
        "wf3_nwp4d1_test_features=hour_day(wf3_nwp4d1_test_features)\n",
        "wf4_nwp4d1_test_features=hour_day(wf4_nwp4d1_test_features)\n",
        "wf5_nwp4d1_test_features=hour_day(wf5_nwp4d1_test_features)\n",
        "wf6_nwp4d1_test_features=hour_day(wf6_nwp4d1_test_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vG9ZkYMm7_yl"
      },
      "source": [
        "wf6_nwp4d1_test_features.head();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiRZRrDW0BPQ"
      },
      "source": [
        "#merging Y_train with X_train\n",
        "y_1yr['ID']=y_1yr['ID'].astype('float')\n",
        "#wf1_nwpd1_features['ID']=wf1_nwpd1_features['ID'].astype('int')\n",
        "wf1_train=wf1_nwp1d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf2_train=wf2_nwp1d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf3_train=wf3_nwp1d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf4_train=wf4_nwp1d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf5_train=wf5_nwp1d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf6_train=wf6_nwp1d1_features.merge(y_1yr,on='ID',how='left')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b45QUmd7Hg7c"
      },
      "source": [
        "wf1_train_d2=wf1_nwp1d2_features.merge(y_1yr,on='ID',how='left')\n",
        "wf2_train_d2=wf2_nwp1d2_features.merge(y_1yr,on='ID',how='left')\n",
        "wf3_train_d2=wf3_nwp1d2_features.merge(y_1yr,on='ID',how='left')\n",
        "wf4_train_d2=wf4_nwp1d2_features.merge(y_1yr,on='ID',how='left')\n",
        "wf5_train_d2=wf5_nwp1d2_features.merge(y_1yr,on='ID',how='left')\n",
        "wf6_train_d2=wf6_nwp1d2_features.merge(y_1yr,on='ID',how='left')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tmZeoXsZhA8h"
      },
      "source": [
        "wf1_train_4=wf1_nwp4d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf2_train_4=wf2_nwp4d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf3_train_4=wf3_nwp4d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf4_train_4=wf4_nwp4d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf5_train_4=wf5_nwp4d1_features.merge(y_1yr,on='ID',how='left')\n",
        "wf6_train_4=wf6_nwp4d1_features.merge(y_1yr,on='ID',how='left')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBNrPXLy5JaD"
      },
      "source": [
        "y_test['ID']=y_test['ID'].astype('float')\n",
        "wf1_test=wf1_nwp1d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf2_test=wf2_nwp1d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf3_test=wf3_nwp1d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf4_test=wf4_nwp1d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf5_test=wf5_nwp1d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf6_test=wf6_nwp1d1_test_features.merge(y_test,on='ID',how='left')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlK1SU7KHgN8"
      },
      "source": [
        "wf1_test_d2=wf1_nwp1d2_test_features.merge(y_test,on='ID',how='left')\n",
        "wf2_test_d2=wf2_nwp1d2_test_features.merge(y_test,on='ID',how='left')\n",
        "wf3_test_d2=wf3_nwp1d2_test_features.merge(y_test,on='ID',how='left')\n",
        "wf4_test_d2=wf4_nwp1d2_test_features.merge(y_test,on='ID',how='left')\n",
        "wf5_test_d2=wf5_nwp1d2_test_features.merge(y_test,on='ID',how='left')\n",
        "wf6_test_d2=wf6_nwp1d2_test_features.merge(y_test,on='ID',how='left')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HF5zC0iKhSKY"
      },
      "source": [
        "wf1_test_4=wf1_nwp4d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf2_test_4=wf2_nwp4d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf3_test_4=wf3_nwp4d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf4_test_4=wf4_nwp4d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf5_test_4=wf5_nwp4d1_test_features.merge(y_test,on='ID',how='left')\n",
        "wf6_test_4=wf6_nwp4d1_test_features.merge(y_test,on='ID',how='left')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEo30Rf1wIk5"
      },
      "source": [
        "#wf1_train=wf1_train.set_index('Time')\n",
        "#wf2_train=wf2_train.set_index('Time')\n",
        "#wf3_train=wf3_train.set_index('Time')\n",
        "#wf4_train=wf4_train.set_index('Time')\n",
        "#wf5_train=wf5_train.set_index('Time')\n",
        "#wf6_train=wf6_train.set_index('Time')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9mAVGPTnFNC"
      },
      "source": [
        "#creating dataframe with speed, direction, Temperature\n",
        "def time_model(time,wf_train):\n",
        "  if time=='00h':\n",
        "    var = ['hour','day','month','week','NWP1_00h_D-1_speed','NWP1_00h_D-1_dir','NWP1_00h_D-1_T','Production']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  elif time == '06h':\n",
        "    var = ['hour','day','month','week','NWP1_06h_D-1_speed','NWP1_06h_D-1_dir','NWP1_06h_D-1_T','Production']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  elif time == '12h':\n",
        "    var = ['hour','day','month','week','NWP1_12h_D-1_speed','NWP1_12h_D-1_dir','NWP1_12h_D-1_T','Production']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  elif time =='18h':\n",
        "    var = ['hour','day','month','week','NWP1_18h_D-1_speed','NWP1_18h_D-1_dir','NWP1_18h_D-1_T','Production']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  return wf_train_h\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkflie70yF8x"
      },
      "source": [
        "#creating dataframe with speed, direction, Temperature\n",
        "def time_model_d2(time,wf_train):\n",
        "  if time=='00h':\n",
        "    var = ['hour','day','month','week','NWP1_00h_D-1_speed','NWP1_00h_D-1_dir','NWP1_00h_D-2_T']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  elif time == '06h':\n",
        "    var = ['hour','day','month','week','NWP1_06h_D-1_speed','NWP1_06h_D-1_dir','NWP1_06h_D-2_T']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  elif time == '12h':\n",
        "    var = ['hour','day','month','week','NWP1_12h_D-1_speed','NWP1_12h_D-1_dir','NWP1_12h_D-2_T']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  elif time =='18h':\n",
        "    var = ['hour','day','month','week','NWP1_18h_D-1_speed','NWP1_18h_D-1_dir','NWP1_18h_D-2_T']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  return wf_train_h\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fn7y1fBIhn52"
      },
      "source": [
        "def time_model_4(time,wf_train):\n",
        "  if time=='00h':\n",
        "    var = ['hour','day','month','week','NWP4_00h_D-1_speed','NWP4_00h_D-1_dir','NWP4_00h_D-1_CLCT']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  elif time =='18h':\n",
        "    var = ['hour','day','month','week','NWP4_18h_D-1_speed','NWP4_18h_D-1_dir','NWP4_18h_D-1_CLCT']\n",
        "    wf_train_h=wf_train.loc[:,var]\n",
        "  return wf_train_h\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3exFWtsLsZND"
      },
      "source": [
        "wf1_train_00h=time_model('00h',wf1_train)\n",
        "wf2_train_00h=time_model('00h',wf2_train)\n",
        "wf3_train_00h=time_model('00h',wf3_train)\n",
        "wf4_train_00h=time_model('00h',wf4_train)\n",
        "wf5_train_00h=time_model('00h',wf5_train)\n",
        "wf6_train_00h=time_model('00h',wf6_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7s266Qm7y6-"
      },
      "source": [
        "wf1_nwp1d2_features.head();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TstJKyU7Iez"
      },
      "source": [
        "wf1_train_12h=time_model_d2('12h',wf1_nwp1d2_features)\n",
        "wf2_train_12h=time_model_d2('12h',wf2_nwp1d2_features)\n",
        "wf3_train_12h=time_model_d2('12h',wf3_nwp1d2_features)\n",
        "wf4_train_12h=time_model_d2('12h',wf4_nwp1d2_features)\n",
        "wf5_train_12h=time_model_d2('12h',wf5_nwp1d2_features)\n",
        "wf6_train_12h=time_model_d2('12h',wf6_nwp1d2_features)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6b3GNlTJ9Uqa"
      },
      "source": [
        "wf1_train_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf2_train_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf3_train_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf4_train_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf5_train_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf6_train_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7feMvWJFieSs"
      },
      "source": [
        "wf1_train_400h=time_model_4('00h',wf1_nwp4d1_features)\n",
        "wf2_train_400h=time_model_4('00h',wf2_nwp4d1_features)\n",
        "wf3_train_400h=time_model_4('00h',wf3_nwp4d1_features)\n",
        "wf4_train_400h=time_model_4('00h',wf4_nwp4d1_features)\n",
        "wf5_train_400h=time_model_4('00h',wf5_nwp4d1_features)\n",
        "wf6_train_400h=time_model_4('00h',wf6_nwp4d1_features)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ei6lLEXkyVtC"
      },
      "source": [
        "wf1_test_00h=time_model('00h',wf1_test)\n",
        "wf2_test_00h=time_model('00h',wf2_test)\n",
        "wf3_test_00h=time_model('00h',wf3_test)\n",
        "wf4_test_00h=time_model('00h',wf4_test)\n",
        "wf5_test_00h=time_model('00h',wf5_test)\n",
        "wf6_test_00h=time_model('00h',wf6_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkTDLQba8-2Q"
      },
      "source": [
        "wf1_test_12h=time_model_d2('12h',wf1_nwp1d2_test_features)\n",
        "wf2_test_12h=time_model_d2('12h',wf2_nwp1d2_test_features)\n",
        "wf3_test_12h=time_model_d2('12h',wf3_nwp1d2_test_features)\n",
        "wf4_test_12h=time_model_d2('12h',wf4_nwp1d2_test_features)\n",
        "wf5_test_12h=time_model_d2('12h',wf5_nwp1d2_test_features)\n",
        "wf6_test_12h=time_model_d2('12h',wf6_nwp1d2_test_features)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YFKOf6ojKha"
      },
      "source": [
        "wf1_test_400h=time_model_4('00h',wf1_nwp4d1_test_features)\n",
        "wf2_test_400h=time_model_4('00h',wf2_nwp4d1_test_features)\n",
        "wf3_test_400h=time_model_4('00h',wf3_nwp4d1_test_features)\n",
        "wf4_test_400h=time_model_4('00h',wf4_nwp4d1_test_features)\n",
        "wf5_test_400h=time_model_4('00h',wf5_nwp4d1_test_features)\n",
        "wf6_test_400h=time_model_4('00h',wf6_nwp4d1_test_features)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nJexFfn9TGO"
      },
      "source": [
        "wf1_test_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf2_test_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf3_test_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf4_test_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf5_test_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n",
        "wf6_test_12h.columns=['hour', 'day', 'month', 'week', 'NWP1_12h_D-2_speed','NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4f8z1inXSydf"
      },
      "source": [
        "import math\n",
        "#direction conversion from radians to  degrees\n",
        "def radians_degrees(wf_train_h,dir):\n",
        "  wf_train_h[dir]=wf_train_h[dir].apply(math.degrees)\n",
        "  wf_train_h[dir] = wf_train_h[dir].apply(lambda x: x+360  if x <  0 else x) \n",
        "  return wf_train_h"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6_9bSO2uqju"
      },
      "source": [
        "wf1_train_00h=radians_degrees(wf1_train_00h,'NWP1_00h_D-1_dir')\n",
        "wf2_train_00h=radians_degrees(wf2_train_00h,'NWP1_00h_D-1_dir')\n",
        "wf3_train_00h=radians_degrees(wf3_train_00h,'NWP1_00h_D-1_dir')\n",
        "wf4_train_00h=radians_degrees(wf4_train_00h,'NWP1_00h_D-1_dir')\n",
        "wf5_train_00h=radians_degrees(wf5_train_00h,'NWP1_00h_D-1_dir')\n",
        "wf6_train_00h=radians_degrees(wf6_train_00h,'NWP1_00h_D-1_dir')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KaIwuk4D_K5e"
      },
      "source": [
        "wf1_train_12h=radians_degrees(wf1_train_12h,'NWP1_12h_D-2_dir')\n",
        "wf2_train_12h=radians_degrees(wf2_train_12h,'NWP1_12h_D-2_dir')\n",
        "wf3_train_12h=radians_degrees(wf3_train_12h,'NWP1_12h_D-2_dir')\n",
        "wf4_train_12h=radians_degrees(wf4_train_12h,'NWP1_12h_D-2_dir')\n",
        "wf5_train_12h=radians_degrees(wf5_train_12h,'NWP1_12h_D-2_dir')\n",
        "wf6_train_12h=radians_degrees(wf6_train_12h,'NWP1_12h_D-2_dir')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goJpB53Qjo77"
      },
      "source": [
        "wf1_train_400h=radians_degrees(wf1_train_400h,'NWP4_00h_D-1_dir')\n",
        "wf2_train_400h=radians_degrees(wf2_train_400h,'NWP4_00h_D-1_dir')\n",
        "wf3_train_400h=radians_degrees(wf3_train_400h,'NWP4_00h_D-1_dir')\n",
        "wf4_train_400h=radians_degrees(wf4_train_400h,'NWP4_00h_D-1_dir')\n",
        "wf5_train_400h=radians_degrees(wf5_train_400h,'NWP4_00h_D-1_dir')\n",
        "wf6_train_400h=radians_degrees(wf6_train_400h,'NWP4_00h_D-1_dir')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KDxqtf5zWej"
      },
      "source": [
        "wf1_test_00h=radians_degrees(wf1_test_00h,'NWP1_00h_D-1_dir')\n",
        "wf2_test_00h=radians_degrees(wf2_test_00h,'NWP1_00h_D-1_dir')\n",
        "wf3_test_00h=radians_degrees(wf3_test_00h,'NWP1_00h_D-1_dir')\n",
        "wf4_test_00h=radians_degrees(wf4_test_00h,'NWP1_00h_D-1_dir')\n",
        "wf5_test_00h=radians_degrees(wf5_test_00h,'NWP1_00h_D-1_dir')\n",
        "wf6_test_00h=radians_degrees(wf6_test_00h,'NWP1_00h_D-1_dir')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6sokQbDbIZhP"
      },
      "source": [
        "wf1_test_12h=radians_degrees(wf1_test_12h,'NWP1_12h_D-2_dir')\n",
        "wf2_test_12h=radians_degrees(wf2_test_12h,'NWP1_12h_D-2_dir')\n",
        "wf3_test_12h=radians_degrees(wf3_test_12h,'NWP1_12h_D-2_dir')\n",
        "wf4_test_12h=radians_degrees(wf4_test_12h,'NWP1_12h_D-2_dir')\n",
        "wf5_test_12h=radians_degrees(wf5_test_12h,'NWP1_12h_D-2_dir')\n",
        "wf6_test_12h=radians_degrees(wf6_test_12h,'NWP1_12h_D-2_dir')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1e2o7iyOl99S"
      },
      "source": [
        "wf1_test_400h=radians_degrees(wf1_test_400h,'NWP4_00h_D-1_dir')\n",
        "wf2_test_400h=radians_degrees(wf2_test_400h,'NWP4_00h_D-1_dir')\n",
        "wf3_test_400h=radians_degrees(wf3_test_400h,'NWP4_00h_D-1_dir')\n",
        "wf4_test_400h=radians_degrees(wf4_test_400h,'NWP4_00h_D-1_dir')\n",
        "wf5_test_400h=radians_degrees(wf5_test_400h,'NWP4_00h_D-1_dir')\n",
        "wf6_test_400h=radians_degrees(wf6_test_400h,'NWP4_00h_D-1_dir')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqYvyHZXImbg"
      },
      "source": [
        "wf1_train_2d=wf1_train_00h.merge(wf1_train_12h,on=['hour','week','day','month'],how='left')\n",
        "wf2_train_2d=wf2_train_00h.merge(wf2_train_12h,on=['hour','week','day','month'],how='left')\n",
        "wf3_train_2d=wf3_train_00h.merge(wf3_train_12h,on=['hour','week','day','month'],how='left')\n",
        "wf4_train_2d=wf4_train_00h.merge(wf4_train_12h,on=['hour','week','day','month'],how='left')\n",
        "wf5_train_2d=wf5_train_00h.merge(wf5_train_12h,on=['hour','week','day','month'],how='left')\n",
        "wf6_train_2d=wf6_train_00h.merge(wf6_train_12h,on=['hour','week','day','month'],how='left')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4a42126mQan"
      },
      "source": [
        "wf1_train_2d=wf1_train_2d.merge(wf1_train_400h,on=['hour','week','day','month'],how='left')\n",
        "wf2_train_2d=wf2_train_2d.merge(wf2_train_400h,on=['hour','week','day','month'],how='left')\n",
        "wf3_train_2d=wf3_train_2d.merge(wf3_train_400h,on=['hour','week','day','month'],how='left')\n",
        "wf4_train_2d=wf4_train_2d.merge(wf4_train_400h,on=['hour','week','day','month'],how='left')\n",
        "wf5_train_2d=wf5_train_2d.merge(wf5_train_400h,on=['hour','week','day','month'],how='left')\n",
        "wf6_train_2d=wf6_train_2d.merge(wf6_train_400h,on=['hour','week','day','month'],how='left')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxpVnyaIMzVK"
      },
      "source": [
        "wf1_test_2d=wf1_test_00h.merge(wf1_test_12h,on=['hour','week','day','month'],how='left')\n",
        "wf2_test_2d=wf2_test_00h.merge(wf2_test_12h,on=['hour','week','day','month'],how='left')\n",
        "wf3_test_2d=wf3_test_00h.merge(wf3_test_12h,on=['hour','week','day','month'],how='left')\n",
        "wf4_test_2d=wf4_test_00h.merge(wf4_test_12h,on=['hour','week','day','month'],how='left')\n",
        "wf5_test_2d=wf5_test_00h.merge(wf5_test_12h,on=['hour','week','day','month'],how='left')\n",
        "wf6_test_2d=wf6_test_00h.merge(wf6_test_12h,on=['hour','week','day','month'],how='left')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHB0ewK9myla"
      },
      "source": [
        "wf1_test_2d=wf1_test_2d.merge(wf1_test_400h,on=['hour','week','day','month'],how='left')\n",
        "wf2_test_2d=wf2_test_2d.merge(wf2_test_400h,on=['hour','week','day','month'],how='left')\n",
        "wf3_test_2d=wf3_test_2d.merge(wf3_test_400h,on=['hour','week','day','month'],how='left')\n",
        "wf4_test_2d=wf4_test_2d.merge(wf4_test_400h,on=['hour','week','day','month'],how='left')\n",
        "wf5_test_2d=wf5_test_2d.merge(wf5_test_400h,on=['hour','week','day','month'],how='left')\n",
        "wf6_test_2d=wf6_test_2d.merge(wf6_test_400h,on=['hour','week','day','month'],how='left')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_L32yhi4IKH"
      },
      "source": [
        "def CAPE_CNR_function(dataframe_y_true, dataframe_y_pred):\n",
        "    # CAPE function\n",
        "    cape_cnr = 100*np.sum(np.abs(dataframe_y_pred-dataframe_y_true))/np.sum(dataframe_y_true)\n",
        "    return cape_cnr\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6S8YKhSoOWlC"
      },
      "source": [
        "wf1_test_2d.to_csv('/content/drive/My Drive/cnr/wf1_test.csv')\n",
        "wf2_test_2d.to_csv('/content/drive/My Drive/cnr/wf2_test.csv')\n",
        "wf3_test_2d.to_csv('/content/drive/My Drive/cnr/wf3_test.csv')\n",
        "wf4_test_2d.to_csv('/content/drive/My Drive/cnr/wf4_test.csv')\n",
        "wf5_test_2d.to_csv('/content/drive/My Drive/cnr/wf5_test.csv')\n",
        "wf6_test_2d.to_csv('/content/drive/My Drive/cnr/wf6_test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zx1F3_OCpQOV"
      },
      "source": [
        "wf1_train_2d.to_csv('/content/drive/My Drive/cnr/wf1_train.csv')\n",
        "wf2_train_2d.to_csv('/content/drive/My Drive/cnr/wf2_train.csv')\n",
        "wf3_train_2d.to_csv('/content/drive/My Drive/cnr/wf3_train.csv')\n",
        "wf4_train_2d.to_csv('/content/drive/My Drive/cnr/wf4_train.csv')\n",
        "wf5_train_2d.to_csv('/content/drive/My Drive/cnr/wf5_train.csv')\n",
        "wf6_train_2d.to_csv('/content/drive/My Drive/cnr/wf6_train.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbiMQDcSnor7"
      },
      "source": [
        "#Y_test_nextday=y_aug_test.iloc[:,1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhY7X47HvB_z"
      },
      "source": [
        "test_aug.reset_index(inplace=True)\n",
        "y_aug_test.reset_index(inplace=True)\n",
        "y_truess=pd.DataFrame(y_aug,columns=['y_trues'])\n",
        "y_truess.reset_index(inplace=True)\n",
        "y_true=pd.concat([test_aug['Time'],test_aug['WF'],y_aug_test['Production']],axis=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOv7uK-GlYtN"
      },
      "source": [
        "## ML Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96yngMIHlXS0"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.neural_network  import MLPRegressor\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn import svm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2BNZhtMlXw2"
      },
      "source": [
        "def modelrf(df,test,Regressor):\n",
        "  data= df\n",
        "  X= data.loc[:,['hour', 'day', 'month', 'week', 'NWP1_00h_D-1_speed',\n",
        "       'NWP1_00h_D-1_dir', 'NWP1_00h_D-1_T',\n",
        "       'NWP1_12h_D-2_speed', 'NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T','NWP4_00h_D-1_speed', 'NWP4_00h_D-1_dir', 'NWP4_00h_D-1_CLCT']]\n",
        "  y=data['Production']\n",
        "  regr = Regressor()\n",
        "  regr.fit(X, y)\n",
        "  preds=regr.predict(test.iloc[:,:-1])\n",
        "  #featureimport=regr.feature_importances_\n",
        "  #print(featureimport)\n",
        "  return preds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAAtcxagGOC6"
      },
      "source": [
        "#votreg=VotingRegressor(estimators=[('gb', reg1), ('rf', reg2), ('lr', reg3)])\n",
        "regressors=[RandomForestRegressor,GradientBoostingRegressor,MLPRegressor,AdaBoostRegressor,svm.SVR]\n",
        "regls=['RandomForestRegressor','GradientBoostingRegressor','MLPRegressor','AdaBoostRegressor','SVR']\n",
        "wf_train_00h=[wf1_train_2d,wf2_train_2d,wf3_train_2d,wf4_train_2d,wf5_train_2d,wf6_train_2d]\n",
        "wf_test_00h=[wf1_test_2d,wf2_test_2d,wf3_test_2d,wf4_test_2d,wf5_test_2d,wf6_test_2d]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyploKiK7wIJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b3122019-f4b8-4824-c46c-6e05c5cc7d69"
      },
      "source": [
        "wf2_test_00h.head()"
      ],
      "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>hour</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>week</th>\n",
              "      <th>NWP1_00h_D-1_speed</th>\n",
              "      <th>NWP1_00h_D-1_dir</th>\n",
              "      <th>NWP1_00h_D-1_T</th>\n",
              "      <th>Production</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.745148</td>\n",
              "      <td>124.571730</td>\n",
              "      <td>274.22</td>\n",
              "      <td>7.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3.801696</td>\n",
              "      <td>117.508020</td>\n",
              "      <td>274.08</td>\n",
              "      <td>11.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3.962684</td>\n",
              "      <td>111.446727</td>\n",
              "      <td>273.97</td>\n",
              "      <td>4.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3.695665</td>\n",
              "      <td>103.812138</td>\n",
              "      <td>273.87</td>\n",
              "      <td>6.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3.560107</td>\n",
              "      <td>96.754121</td>\n",
              "      <td>273.69</td>\n",
              "      <td>1.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   hour  day  month  ...  NWP1_00h_D-1_dir  NWP1_00h_D-1_T  Production\n",
              "0     1   16      1  ...        124.571730          274.22        7.37\n",
              "1     2   16      1  ...        117.508020          274.08       11.72\n",
              "2     3   16      1  ...        111.446727          273.97        4.18\n",
              "3     4   16      1  ...        103.812138          273.87        6.82\n",
              "4     5   16      1  ...         96.754121          273.69        1.11\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4T28q0wlXQJ"
      },
      "source": [
        "def models(wf_train_00h,wf_test_00h):\n",
        "  i=1\n",
        "  for wf_train_h,wf_test_h in zip(wf_train_00h,wf_test_00h):\n",
        "    y_true=wf_test_h['Production']\n",
        "    print('wf'+str(i))\n",
        "\n",
        "    for name,reg in zip(regls,regressors):\n",
        "      wf_forecast=modelrf(wf_train_h,wf_test_h,reg)\n",
        "    \n",
        "    #wf_all_preds_prophet=np.concatenate([wf1_forecast,wf2_forecast,wf3_forecast,wf4_forecast,wf5_forecast,wf6_forecast])\n",
        "      cape=CAPE_CNR_function(y_true, wf_forecast)\n",
        "      print(name,cape)\n",
        "    i=i+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbYaJdZoRmCu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "outputId": "75904ff9-3b80-4d68-a054-c644860e1f68"
      },
      "source": [
        "models(wf_train_00h,wf_test_00h)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "wf1\n",
            "RandomForestRegressor 55.002437457760955\n",
            "GradientBoostingRegressor 65.88090792620339\n",
            "MLPRegressor 983.5073804797099\n",
            "AdaBoostRegressor 54.90768724397837\n",
            "SVR 64.60926899635977\n",
            "wf2\n",
            "RandomForestRegressor 56.4646528315467\n",
            "GradientBoostingRegressor 49.560761095506756\n",
            "MLPRegressor 4009.158026084101\n",
            "AdaBoostRegressor 62.695001566701336\n",
            "SVR 67.71189155122082\n",
            "wf3\n",
            "RandomForestRegressor 60.58144802348055\n",
            "GradientBoostingRegressor 82.29157089032593\n",
            "MLPRegressor 245.1750204199419\n",
            "AdaBoostRegressor 64.8159093968641\n",
            "SVR 67.96545503180415\n",
            "wf4\n",
            "RandomForestRegressor 60.05501916217076\n",
            "GradientBoostingRegressor 57.129713063367696\n",
            "MLPRegressor 1555.9552337581997\n",
            "AdaBoostRegressor 64.00292087138125\n",
            "SVR 87.82600157671236\n",
            "wf5\n",
            "RandomForestRegressor 52.707046485467785\n",
            "GradientBoostingRegressor 61.49442351319677\n",
            "MLPRegressor 1767.497029844495\n",
            "AdaBoostRegressor 50.03422243517666\n",
            "SVR 73.48040535707851\n",
            "wf6\n",
            "RandomForestRegressor 58.77039799641102\n",
            "GradientBoostingRegressor 59.83225739796431\n",
            "MLPRegressor 957.6544230496866\n",
            "AdaBoostRegressor 59.832653666498274\n",
            "SVR 218.97181127023794\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMydba6km9Oz"
      },
      "source": [
        "regressors=[RandomForestRegressor]\n",
        "regls=['RandomForestRegressor']\n",
        "#wf_train_00h=[wf1_train_00h,wf2_train_00h,wf3_train_00h,wf4_train_00h,wf5_train_00h,wf6_train_00h]\n",
        "#wf_test_00h=[wf1_test_00h,wf2_test_00h,wf3_test_00h,wf4_test_00h,wf5_test_00h,wf6_test_00h]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ8hDsrnaSMm"
      },
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit, GridSearchCV\n",
        "\n",
        "def modelrf_cv(df,test,Regressor):\n",
        "  data= df\n",
        "  X= data.loc[:,['hour', 'day', 'month', 'week', 'NWP1_00h_D-1_speed',\n",
        "       'NWP1_00h_D-1_dir', 'NWP1_00h_D-1_T',\n",
        "       'NWP1_12h_D-2_speed', 'NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T','NWP4_00h_D-1_speed', 'NWP4_00h_D-1_dir', 'NWP4_00h_D-1_CLCT']]\n",
        "  y=data['Production']\n",
        "  regr = Regressor()\n",
        "  param_search = {'n_estimators':[100,150,250,300,400],\n",
        "                  'max_depth' : [3, 5],\n",
        "                  'max_features': [\"auto\", \"sqrt\"]}\n",
        "\n",
        "  tscv = TimeSeriesSplit(n_splits=2)\n",
        "  gsearch = GridSearchCV(estimator=regr, cv=tscv,\n",
        "                          param_grid=param_search)\n",
        "  gsearch.fit(X, y)\n",
        "  print(gsearch.best_score_)\n",
        "  preds=gsearch.predict(test.iloc[:,:-1])\n",
        "  #featureimport=regr.feature_importances_\n",
        "  #print(featureimport)\n",
        "  return preds\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0ktD2Z2pvqn"
      },
      "source": [
        "def models_cv(wf_train_00h,wf_test_00h):\n",
        "  i=1\n",
        "  all_forecast=[]\n",
        "  for wf_train_h,wf_test_h in zip(wf_train_00h,wf_test_00h):\n",
        "    y_true=wf_test_h['Production']\n",
        "    print('wf'+str(i))\n",
        "\n",
        "    for name,reg in zip(regls,regressors):\n",
        "      wf_forecast=modelrf_cv(wf_train_h,wf_test_h,reg)\n",
        "      all_forecast.append(wf_forecast)\n",
        "    \n",
        "    #wf_all_preds_prophet=np.concatenate([wf1_forecast,wf2_forecast,wf3_forecast,wf4_forecast,wf5_forecast,wf6_forecast])\n",
        "      cape=CAPE_CNR_function(y_true, wf_forecast)\n",
        "      print(name,cape)\n",
        "    i=i+1\n",
        "\n",
        "  return np.concatenate(all_forecast)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iowWwcvtpW0A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "5e0a2810-9b26-48c1-baf5-a39406214e0e"
      },
      "source": [
        "forecast_preds=models_cv(wf_train_00h,wf_test_00h)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "wf1\n",
            "-0.20729402229168947\n",
            "RandomForestRegressor 55.36096264437146\n",
            "wf2\n",
            "-0.17593176829968796\n",
            "RandomForestRegressor 56.77820719207288\n",
            "wf3\n",
            "-0.08871179220757303\n",
            "RandomForestRegressor 54.5231873963377\n",
            "wf4\n",
            "-0.0796037037201976\n",
            "RandomForestRegressor 57.58395739274467\n",
            "wf5\n",
            "-0.22250260743135908\n",
            "RandomForestRegressor 58.10321582722437\n",
            "wf6\n",
            "-0.049564698704676724\n",
            "RandomForestRegressor 55.49171916786734\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Idj2Z-9FEIGI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "de2ec005-11f8-4517-958e-37791a35aa62"
      },
      "source": [
        "CAPE_CNR_function(y_test['Production'], forecast_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56.29289400708638"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ot3b7Vzi8yLg"
      },
      "source": [
        "all_preds_prop=pd.DataFrame(wf_all_preds_prophet,columns=['y_preds'])\n",
        "all_preds_prop.reset_index(inplace=True)\n",
        "all_preds_prop=all_preds_prop.drop('index',axis=1)\n",
        "y_preds=pd.concat([test_aug['Time'],test_aug['WF'],all_preds_prop],axis=1)\n",
        "y_preds=y_preds.set_index(y_preds['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdnWcgEN84da"
      },
      "source": [
        "y_true=y_true.set_index(y_true['Time'])\n",
        "y_preds=y_preds.set_index(y_preds['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntk4lnP18--K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "outputId": "9edf0804-9af0-4dd1-a50e-bda83c21e7be"
      },
      "source": [
        "wf_ls =['WF1','WF2','WF3','WF4','WF5','WF6']\n",
        "i=1\n",
        "fig, axes = plt.subplots(nrows=3, ncols=2, sharex=True, sharey=True, figsize=(15,10))\n",
        "axes_list = [item for sublist in axes for item in sublist] \n",
        "for wf in wf_ls:\n",
        "  ax = axes_list.pop(0)\n",
        "  y_true_wf1=y_true[y_true['WF']==wf]\n",
        "  ax1=y_true_wf1.plot('Time','Production',ax=ax)\n",
        "  y_preds_wf1=y_preds[y_preds['WF']==wf]\n",
        "  y_preds_wf1.plot('Time','y_preds',ax=ax1)\n",
        "  ax.set_title(wf)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADdSxKsUL8LZ"
      },
      "source": [
        "#FB Prophet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHImyK0MLwUb"
      },
      "source": [
        "from fbprophet import Prophet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHfxqU96Bjq1"
      },
      "source": [
        "def prophet_convert(wf_train_h):\n",
        "  wf_train_h['ds']=wf_train_h.index\n",
        "  wf_train_h['y']=wf_train_h['Production']\n",
        "  return wf_train_h"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pNIUTS8CZka"
      },
      "source": [
        "wf1_train_00h= prophet_convert(wf1_train_00h)\n",
        "wf2_train_00h= prophet_convert(wf2_train_00h)\n",
        "wf3_train_00h= prophet_convert(wf3_train_00h)\n",
        "wf4_train_00h= prophet_convert(wf4_train_00h)\n",
        "wf5_train_00h= prophet_convert(wf5_train_00h)\n",
        "wf6_train_00h= prophet_convert(wf6_train_00h)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKOL-cqS5_fe"
      },
      "source": [
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x94ZBK7j9luB"
      },
      "source": [
        "def rmse(df,forecast):\n",
        "  rmse = np.mean((df['y'].values - forecast['yhat'].tail(24).values)**2)\n",
        "  return rmse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2qFOWg8C00L"
      },
      "source": [
        "def prophet_model(df):\n",
        "  param_grid = {  \n",
        "    'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],\n",
        "    'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0],\n",
        "  }\n",
        "\n",
        "  # Generate all combinations of parameters\n",
        "  all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]\n",
        "  newape = []\n",
        "  k=0  # Store the RMSEs for each params here\n",
        "  for params in all_params:\n",
        "    m = Prophet(**params,yearly_seasonality=False)\n",
        "    m.add_regressor('NWP1_00h_D-1_speed')\n",
        "    m.add_regressor('NWP1_00h_D-1_dir')\n",
        "    m.add_regressor('NWP1_00h_D-1_T')\n",
        "    m.fit(df[df['ds']<pd.Timestamp(2019,5,1)])\n",
        "    future = m.make_future_dataframe(periods=24,freq='H')\n",
        "    future['NWP1_00h_D-1_speed']=df['NWP1_00h_D-1_speed'].values\n",
        "    future['NWP1_00h_D-1_dir']=df['NWP1_00h_D-1_dir'].values\n",
        "    future['NWP1_00h_D-1_T']=df['NWP1_00h_D-1_T'].values\n",
        "    forecast = m.predict(future)\n",
        "    \n",
        "    out=df[df['ds']>=pd.Timestamp(2019,5,1)]\n",
        "    ape=rmse(out,forecast)\n",
        "    print(ape)\n",
        "    if len(newape)==0:\n",
        "      wf_forecast = forecast\n",
        "      newparams = params      \n",
        "      newape.append(ape)\n",
        "      k=ape\n",
        "    elif ape<k:\n",
        "      wf_forecast = forecast\n",
        "      newparams = params      \n",
        "      k=ape\n",
        "  #fig2 = m.plot_components(forecast)\n",
        "  return wf_forecast,newparams"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XveIDx8E48a"
      },
      "source": [
        "wf1_forecast,wf1params = prophet_model(wf1_train_00h)\n",
        "wf2_forecast,wf2params = prophet_model(wf2_train_00h)\n",
        "wf3_forecast,wf3params = prophet_model(wf3_train_00h)\n",
        "wf4_forecast,wf4params = prophet_model(wf4_train_00h)\n",
        "wf5_forecast,wf5params = prophet_model(wf5_train_00h)\n",
        "wf6_forecast,wf6params = prophet_model(wf6_train_00h)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OO7QYfqjG-Oc"
      },
      "source": [
        "wf_all_preds_prophet=np.concatenate([wf1_forecast['yhat'].tail(24).values,wf2_forecast['yhat'].tail(24).values,wf3_forecast['yhat'].tail(24).values,wf4_forecast['yhat'].tail(24).values,wf5_forecast['yhat'].tail(24).values,wf6_forecast['yhat'].tail(24).values])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEhYGdAZUZdm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5f41f86f-94ac-4fcb-f443-3f54054f0a57"
      },
      "source": [
        "cape=CAPE_CNR_function(Y_test_nextday, wf_all_preds_prophet)\n",
        "cape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53.702086016964685"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PylBT4CZU2TO"
      },
      "source": [
        "all_preds_prop=pd.DataFrame(wf_all_preds_prophet,columns=['y_preds'])\n",
        "all_preds_prop.reset_index(inplace=True)\n",
        "all_preds_prop=all_preds_prop.drop('index',axis=1)\n",
        "y_preds=pd.concat([test_aug['Time'],test_aug['WF'],all_preds_prop],axis=1)\n",
        "y_preds=y_preds.set_index(y_preds['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjiYEw0tfW38"
      },
      "source": [
        "y_true=y_true.set_index(y_true['Time'])\n",
        "y_preds=y_preds.set_index(y_preds['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9qBBQeRVLqY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "outputId": "f5cc57d1-05ec-4c47-8fef-02786da204cf"
      },
      "source": [
        "wf_ls =['WF1','WF2','WF3','WF4','WF5','WF6']\n",
        "i=1\n",
        "fig, axes = plt.subplots(nrows=3, ncols=2, sharex=True, sharey=True, figsize=(15,10))\n",
        "axes_list = [item for sublist in axes for item in sublist] \n",
        "for wf in wf_ls:\n",
        "  ax = axes_list.pop(0)\n",
        "  y_true_wf1=y_true[y_true['WF']==wf]\n",
        "  ax1=y_true_wf1.plot('Time','Production',ax=ax)\n",
        "  y_preds_wf1=y_preds[y_preds['WF']==wf]\n",
        "  y_preds_wf1.plot('Time','y_preds',ax=ax1)\n",
        "  ax.set_title(wf)\n",
        "plt.show()\n",
        "#y_true"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hgOMv9kmSVs"
      },
      "source": [
        "wf1_forecast=wf1_forecast.set_index(wf1_forecast['ds'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uH2pp-pXmm1Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "4b99107c-c4b6-4ca6-b4a7-49aa5938db1c"
      },
      "source": [
        "plt.subplots(figsize=(25,10))\n",
        "wf1_forecast['yhat'][8000:].plot(color='r')\n",
        "wf1_train_00h['y'][8000:].plot(color='lightgreen')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd5fed40f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQpzUq8iWUGQ"
      },
      "source": [
        "## Neural Nets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxkpsyP1WSxf"
      },
      "source": [
        "import tensorflow as tf\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_JCiwLuhCNH"
      },
      "source": [
        "def train_test(train,test):\n",
        "  train_X= train.loc[:,['hour', 'day', 'month', 'week', 'NWP1_00h_D-1_speed',\n",
        "        'NWP1_00h_D-1_dir', 'NWP1_00h_D-1_T',\n",
        "        'NWP1_12h_D-2_speed', 'NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']]\n",
        "  train_y=train['Production']\n",
        "  test_X = test.loc[:,['hour', 'day', 'month', 'week', 'NWP1_00h_D-1_speed',\n",
        "        'NWP1_00h_D-1_dir', 'NWP1_00h_D-1_T',\n",
        "        'NWP1_12h_D-2_speed', 'NWP1_12h_D-2_dir', 'NWP1_12h_D-2_T']]\n",
        "  test_y=test['Production']\n",
        "  return train_X.values,train_y.values,test_X.values,test_y.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puaKhdPoiE6N"
      },
      "source": [
        "train_X,train_y,test_X,test_y=train_test(wf1_train_2d,wf1_test_2d)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFSS_amEr7-B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "228df307-6f54-4bf6-8811-3b49c0685ce7"
      },
      "source": [
        "train_X.shape[0],train_X.shape[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8759, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1QHV51TgvQ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a66a0b87-80a2-4478-b931-e84a53e319bf"
      },
      "source": [
        "\n",
        "# reshape input to be 3D [samples, timesteps, features]\n",
        "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
        "test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n",
        "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8759, 1, 10) (8759,) (6190, 1, 10) (6190,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_CCVjTIWSVM"
      },
      "source": [
        "def model_lstm(train_X,train_y,test_X,test_y): \n",
        "  lstm_model = tf.keras.models.Sequential([\n",
        "      # Shape [batch, time, features] => [batch, time, lstm_units]\n",
        "      tf.keras.layers.LSTM(128, return_sequences=True),\n",
        "      # Shape => [batch, time, features]\n",
        "      tf.keras.layers.Dense(units=1)\n",
        "  ])\n",
        "\n",
        "  lstm_model.compile(loss='mae', optimizer='adam')\n",
        "\n",
        "  history = lstm_model.fit(train_X, train_y, epochs=100, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n",
        "\n",
        "  return history,lstm_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-P6Xx1nnGlW"
      },
      "source": [
        "history,modellstm=model_lstm(train_X,train_y,test_X,test_y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QoO2ZMbqms4"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features)))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(50, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_qHrkL2p2e5"
      },
      "source": [
        "def model_cnn(train_X,train_y,test_X,test_y): \n",
        "  cnn_model = tf.keras.models.Sequential([\n",
        "      tf.keras.layers.Conv2D(filters=32, kernel_size=3,\n",
        "                      activation=\"relu\",batch_size=32,\n",
        "                      input_shape=[train_X.shape[0], train_X.shape[1]]),\n",
        "      # Shape [batch, time, features] => [batch, time, lstm_units]\n",
        "      #tf.keras.layers.LSTM(128, return_sequences=True),\n",
        "      # Shape => [batch, time, features]\n",
        "      tf.keras.layers.MaxPooling1D(pool_size=2),\n",
        "      tf.keras.layers.Flatten(),\n",
        "      tf.keras.layers.Dense(50, activation='relu'),\n",
        "      tf.keras.layers.Dense(1)\n",
        "  ])\n",
        "  print(cnn_model.summary())\n",
        "  cnn_model.compile(loss='mae', optimizer='adam')\n",
        "\n",
        "  history = cnn_model.fit(train_X, train_y, epochs=100, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n",
        "\n",
        "  return history,cnn_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUGAcse8rZB5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "dd603fc4-5cc0-4eef-db55-8b75d820e662"
      },
      "source": [
        "history,modelcnn=model_cnn(train_X,train_y,test_X,test_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-227-7a001918c877>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodelcnn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_cnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_X\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_X\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-226-7e436be9dc27>\u001b[0m in \u001b[0;36mmodel_cnn\u001b[0;34m(train_X, train_y, test_X, test_y)\u001b[0m\n\u001b[1;32m     10\u001b[0m       \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFlatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m       \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m       \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m   ])\n\u001b[1;32m     14\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcnn_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, layers, name)\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0mlayers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m    204\u001b[0m           \u001b[0;31m# and create the node connecting the current layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m           \u001b[0;31m# to the input layer we just created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m           \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m           \u001b[0mset_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    924\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m       return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0;32m--> 926\u001b[0;31m                                                 input_list)\n\u001b[0m\u001b[1;32m    927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m     \u001b[0;31m# Maintains info about the `Layer.call` stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m   1090\u001b[0m       \u001b[0;31m# TODO(reedwm): We should assert input compatibility after the inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m       \u001b[0;31m# are casted, not before.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1092\u001b[0;31m       \u001b[0minput_spec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_input_compatibility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_spec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1093\u001b[0m       \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1094\u001b[0m       \u001b[0;31m# Use `self._name_scope()` to avoid auto-incrementing the name.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    194\u001b[0m                          \u001b[0;34m', found ndim='\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m                          \u001b[0;34m'. Full shape received: '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                          str(x.shape.as_list()))\n\u001b[0m\u001b[1;32m    197\u001b[0m     \u001b[0;31m# Check dtype.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [32, 8759, 10]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iv5bDnQkHJ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "ee03e1c7-1862-4aa1-af1d-30a2d067affd"
      },
      "source": [
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9BjMoKfazyp9j308w+2Yz02Ox5ANwbJ05GLh4wJV/hAn/gZPbYPvX9f8+hLC2wnyzCHxhQcVtRUWV77/kBTh7qOz9x9bDb3+FPQ2zTnCtErrWulBr3QcIAQYopXpcyotprWdoraO01lEBAZeRjDOSQDmYFX9E1cr3dDm13SzW4VPqQqqLh1kWb8+PkH2+7OML8syapg7OZsHswyvN/VtngYMT9L2rZN+et0FINCx7ueopfkXTkXXO2hHYlq2z4H8j4c1Is3Tk7h/MdNXvD4F/BMHJuLL7H/gV1k83k+kV5Jr7Cgtg8TQzNcnwpxskzDqdG2utU4EVwLhym04AoQBKKSfAGzhbHwFWKjPZJPOqlm4TRvmeLqe2m3JL+Vp5/3uhIMdMIVBazCdw7jDcMtOUVxY9bZb02/4VdJlQtuTl4ADjXjflsDWyBmqTdmw9/Ks9bJ9j7UhsR8JGk3PCh5lOBN/eC5s+Mp0vHJzM/0pp278GZ3c4sxN++5u5L2amWfVs7D8abE2F2vRyCVBK+Vh+bgFcBZTrOsEC4F7Lz7cAyxusfg6WtUSl3FKj0j1dCvNNDb1Nn4r7telt7t/6WcmAo+zzZv3UDiPMBc/xb8LZg/DFTWZb//sqPk9If7PW6ob3K55qiqbjkGXI+YInTBlBGFrDttmVL/d4MhbCBsNtn8G0g3D/Enj2KNy7ELrfBDu/g1zLHC1Z50wLvf9ks9zkhvfMbKgrXrH8P13XYG+hNr1c2gCfKaUcMQeAuVrrn5RSfwNitNYLgE+AL5RSB4FzwMQGixgkoddFYBfY9Z2poxfmlr0gWlr/e02dfPatZhqBxE1mXdarXzEHhk5joNsNsOcHMzq1/ZWVP8/oF0355v1BprTj2x563GJ61IimIXGjGXxXkGcuZE9Z2Tw6GKx5y5QN3X3Nmat/pFla0ifUXJdb8DjsX2z27XefaX2DacCcO1yyMLyrl+kkUKz/vRD3pRmo1+8ey4C9fPM379cJjqyB7x4wLflr/tWg3alr08tlB1Bh8Uut9Yulfs4Bbq3f0KqRkQRt+zfay9m0gC6mTBK/xNyurIUO0OcuSDthTsPnP2ju63uXmey/2LhXzVS9gx6tuieLV2u4+wfYtxDOHTFnBd8/DC18oHPFuS2atZwLJjk05niJwgI4vhX63gl974ZProa598A9C8DJpebHJ2yCHx41B/0Rz15eLHmZpqEREnV5z1MbmWdh5atmwr4LJyH7HGSdhV+eheB+kHbc9Ajrezds+8Jc9O9maUkX18fb9qv8uUOizf9Z7OcmoW+fY6btbt3LfLY3fwwzx8Kg30NAZIO+TdscKZqZ3DxaFPWhuKfLjm/MCFHfDpXv5+QCo1+AkX82LbjDq2DAlLL7tAyGZ+JrvnYRGm2+APKzzR/zdw/CQ8vBv9OlvY/8bDOAySsIfDs2/rqutVFUVPsum3mZ8E4vCBtiTuMdnRs2tmJndkJ+JoQNMt3mrpsO8x+CXfOgz6SqH1dUBOvfgd/+DroQNv4Xhj5xeUsyLnsZNs8wZwi1WTD9cuz4xiwMM2kOBHU39509ZM4m9y4wZ5IT/mNa1Lu+MzOYXkzolrJUVTEqZRL5r3+CvT/B8c0w5q8lB+rgPvD0PnDzadj3iC3O5ZKXZeb6lpJL7RT3dEk5YP6Ba0o4Dg7QbgiMfL7klLM0R6e6tSidW8Dts03CmjPJtEqzU+HYBtj/i2mh5WWZr8MrYfkr8MPvzWlrXpZJJNvnwLtRpvvkh8Pgn8HwXnTFUbANQWtTc945r+p9cjPgp6fglQDT62HRM7BvcfUToB2PMafy+xeZ0/HKusM1hATLeMDQQeZ7z1vN7KS7v6/6MVrD3LtNAu46ASZ+Zc76LqfrXeZZiP3C/Fx80bAmKQfhre7mOs7WWWaCvvKS9sLCJyF+Wdn4Yz+DtlElyRzAryMMf8ocUB5YYrY5uUDoQJPQi52INQ2h6qbi7jURHF3M3y7K/F5La9GqUc7EbK+Fnmn5ECWh105xT5fs81XXzxuaTyjc+hl8fj281c2MLi1POZqWn3IwZYi42eDsYUo45w6ZUtH4f5lWVvIBM5jpt7/CHQ3U770g1/T62fg+JO0x92kNvcr9ox5dZwZmnT9mVozKTIa4r2DL/8x77n5D5c+fsBFQMOI5UwpwfARu/Kj2Pbc2fghJu+F30+uWKBI2mm5z3m3NbaVMjBvfN38jlSWto2vNeIQRz8OVljKLXwTEfGrWur0UWz6GguySEseR1dD+iqr31xoW/Z85kJw7DAunmms+bXqbnifB/cz1nT0LAG0ONn/YYkaTH99iprj4XS0XTw8fBsv/bg46Hn5mfEXY4Oof4+Fnen7tnm+uLxX/fhuZDSZ0y6AiKbnUTnFPl4T11kvoAO2Hw62fwv6fwb+zaQ21aGUW8z53xPxzhw02rSMXD5NEdn9vWlwjnjfL6pU+u1AOptfAidjKa5s5FywXwPzM9ZbixT/OHTHP3bpH9af53z9sXj+wO1z/X9NVbeFU8zsM6GzOHFa9br5ahZu1XtsNMY8tzIfp/cxo2ioT+gYI6mESuqOLOThlnYPfvVN2xa3KpB034wMKc806sl1/V/3+xbQ2F7vLT7Xc/UbTZ3rforJjC4pt/sh8VkOnlhw8+t9n+mEn7a37NNV5WeY5O4+D8W/AoeWw7K/w4LKqD047vzVJ/9o3IeoB0/1v3yJz36aPzIHexcu0uDuOgs9vgF+eh5v/Z1rnLp7mb6g2woeb78fWmb/HCyeqrp+X1v8+k9CLL55agQ0mdJnHpc4Cu1gSehUXRBtLt+vNV2lVTXHc4UrzVZWBD5s67spX4c5vzX1am/rnjrlweIX5JweTMIP7mikQzh819zk4meRQWffLI6tNMr9imrmmoJRJEh8OMxcQ710Ii54ytdfek+Daf5uDUDFHZ+h3N6z4h3m9VuFln7+wwLQae99hbg9/CtxawpIXTe+g0S9B9INVl8dWvgZoUwZY8oJJ6k6uVf+uiqUmQPopUz8vLbiv6ZG0+/uKCT010STOIY+XrZf3nmRKJVtnwTWv1/zapW3/ylyQHGKpwY94rqSHSZdrK+6ffd7Up9v2N10BlTIX61v3NI/Nz4bTO831meIzjOFPw6rXIPIaM59Rz1vA1bN28QX3NX3Ij64tubYRXIuE3uFKeGStOVBbie3V0Fu0Mqc2La1zSmOTukwwc0dc6gXJpsitpWkxxi+BxM2mRPL9I6YenbQXoh+C+xbDHXNM8tfanKlc8y+Yssr0B144FX5+tmz9urAAfn6uZEWo4hZjy2DTWyF5H7zT25Qgxv4Tbni/bDIv1udOcxZRXCcu7cwucx2odGKNfhB+v8G0CH+eZlq/lUk+YMpRUQ+Y1u35I7D5f7X7nRXXz8sndKVMK/3wyoojSGNmlsRXmoef6U+9/WuTUGurqBDWv2uSc/EZTe9J5mLkb38rGVVZ2m9/NweACf+pvCTl3MI0DEqXi4Y/Zbolfvcg5GeZaSpqq3Qd/USs+RxrO+9K655WneXV9lroYYMq/kGK6kWMNl/2JvohWP8eLH3J1N8TN5kW9RXTyv5TRV5T8bGT5pqyxYb34PQuk5hbtTNlkqTdcNvnFXtwdBxlegJteB9u/9wcJKvi3das6xo325SMHEv9q11MrOXqsq3awV3fmYPSti/Ma5WPYcUrpvU4/GmzwEvEGFj1L9PaL38Ru6gQ0hJLzhASN5p5ewK7VYy3+42w7m3TGu93t7kvP8eUKyLHl6zTW1rUZNM75sc/mJHGp7ab5OkRaEqi/p3NUojhw8HJDU7vMBe4zx+Fq/5W8hk5Opnbc+4w4yAmfmVa01qbWnvMTNNVti4lQydXuO5d08MqsHvduzkX19GdXE1PscoO2k2Q7bXQhSjm6gnD/s+Uk07tgFtnmYnCatNCcnA0Q7Bv/Mgkog+GmAuNK/5hElBVo/mGP21GClaXzIv1u8eUOA4uK3t/wgbTs6SyC2dKmdZk7gWTXEs7EWsu9g1+rGS1rqtfMReZV75a8bkWPW3OJpa+ZM48EjaZPt+VtXLb9DYDxkr3dtk937SMBzxU+ftrN9SUF3bNM2cuoQPN782vo0nscbPhq9vg9XD4d4SZO3/TB6ZE1GVC2efqMh5u+ACOrjEXz88fg3n3m0XQI8aYA3VdhQ00n++Et+reai6uo5+MrV25pYmwvRa6EKVFP2CSZo+ba3fhqrzeE82p/w+/N4NMlIOpCVeXAGqbHDqPNQuZx34GkZbpj7Q2LfTqenSEDzc9UbZ/bWq/xY9b9hK08IXBfyjZN7CrKb9s+Z8Z4FI8Inf39+ZsI7C7aXknbja9dcpfwyj9nrrfCOveMX2pc1JNaSSgS9WjgpWCB5aas6PSUysXK8g1Fxbjl5quqh2uNGc5VXVo6DPJnEHMmwzT+wDKXE8Y+uSlT8l8qSOUi+vo+VnQtoH7yNcjSejCtjm3MC3ty+ETZkZKbvvcdJ8s3Vf5cjg6myS1broZndgy2NS8M05XXzZ0cDAjMde+BRdOQcs2JT06rvmXuX5Q2th/QMp+033S3ddcK1kw1fS7vv8X04f+p/8DdNkh6+X1uMm85jeWXhrK0Vw3qO4AVt0ALydXk8A7jqp6n/K6ToA755kJ3kY8X1Jnb2zFdfTDK2yqha6stYxTVFSUjomJscprC9Fozh2B/w4wNdy7vzct5x8ehUc3QFAltexiKQfhvf6mtjzgYbOwt1ML04vCsZJ2WM4FmHWtmUCtVbjp2vjImpL6+Znd5qAw7P+qH5WaYFnqwCvInF1czkhQW7f5f7D6DXhyZ+16ETUSpdRWrXWl8yVIC12IhuTbHm6aAd9OhnkPmDlt3LxLpmSoin8EhAyAuK/NKlPnj5o5cipL5mBa7Xd9Z+ZmSdpjrieU7i4Z1L12Zx7VteCbm+gHTTfJqn7nTZDtRCqErep+oxmm/vM0c7vT2NrVhPvcYUolK1+DyGuh48jq9/cMhMk/m94kMhHa5VPKppI5SC8XIRrHwCklq9S0q2EYebHuN4Gj5VT/6r/X7jEt20gyb8Zs6/AjhC0b9YLpPVFdD5fSWviY+eVdPExXQCFqIAldiMaiVO3nXSk25A817yOEhZRchBDCTkhCF0IIOyEJXQgh7ESNCV0pFaqUWqGU2qOU2q2UmlrJPt5KqYVKqe2WfSY3TLhCCCGqUpuLogXA01rrWKWUF7BVKbVUa72n1D6PAXu01r9TSgUA+5VSs7XWeQ0RtBBCiIpqbKFrrU9prWMtP6cDe4Hy08RpwEsppQBP4BzmQCCEEKKR1KnbolIqHOgLbCq36T1gAXAS8AJu11oX1UN8QgghaqnWF0WVUp7Ad8CTWusL5TaPBeKAYKAP8J5SqtyUcKCUmqKUilFKxSQnV7JitxBCiEtWq4SulHLGJPPZWuv5lewyGZivjYPAEaDC7ENa6xla6yitdVRAQMDlxC2EEKKc2vRyUcAnwF6t9VtV7JYAjLbsHwREAofrK0ghhBA1q00NfShwN7BTKRVnue9PQBiA1vpD4O/ALKXUTkABz2qtUxogXiGEEFWoMaFrrddiknR1+5wErq6voIQQQtSdjBQVQgg7IQldCCHshCR0IYSwE5LQhRDCTkhCF0IIOyEJXQgh7IQkdCGEsBOS0IUQwk5IQhdCCDshCV0IIeyETSd0rTUbD59Fa23tUIQQwupsOqFvOnKOiTM2smxvkrVDEUIIq7PphH7ifDYAP+86ZeVIhBDC+mw6oSdn5ALw294k8gtlxTshRPNm2wk93ST0tOx8Nh85Z+VohBDCumw+oQe1dMXN2YFfd5+2djhCCGFVNp/QQ1u5c0WnAJbsPkNRkfR2EUI0X7ad0DNyCfByZWz31py+kMOOE2nWDkkIIazGthN6uknoo7sG4uigpOwihGjWbDah5xYUkpadT4CnKz7uLgzq4CsJXQjRrNWY0JVSoUqpFUqpPUqp3UqpqVXsN0IpFWfZZ1X9h1pWSkYeAAFergCM7d6aw8mZHExKb+iXFkKIJqk2LfQC4GmtdTdgEPCYUqpb6R2UUj7A+8B1WuvuwK31Hmk5xV0WixP6mK5BADJqVAjRbNWY0LXWp7TWsZaf04G9QNtyu00C5mutE49Ol2MAACAASURBVCz7NXhWLZ/Qg31a0KW1Fyv2lX3pgsIips7Zxtr4lIYOSQghrKpONXSlVDjQF9hUblNnoJVSaqVSaqtS6p76Ca9q5RM6wMgugcQcO8+FnPyL960/dJYf407y1Nw40rLzKzyPEELYi1ondKWUJ/Ad8KTW+kK5zU5Af+BaYCzwglKqcyXPMUUpFaOUiklOTr6MsCHFMuzfz6MkoY/qEkhhkWbNgZLW+I9xJ2nh7MjZzDxeXbz3sl5TCCGasloldKWUMyaZz9Zaz69kl+PAr1rrTK11CrAa6F1+J631DK11lNY6KiAg4HLiJjk9l1buzrg4lbyFvqE+eLdwZsV+U3bJyS/k192nmdCrDQ8N78CcLYmsOyilFyGEfapNLxcFfALs1Vq/VcVuPwLDlFJOSil3YCCm1t5givugl+bk6MAVnQNYuT+JoiLN8n1JZOQWcEPftjw5phPt/T14fv5OsvIKGjI0IYSwitq00IcCdwOjLN0S45RS45VSjyilHgHQWu8FfgF2AJuBj7XWuxosakpGiZY3qksAKRl57DqZxo9xJwjwcmVQBz/cnB157aaeJJzL4oUfdsuiGEIIu+NU0w5a67WAqsV+bwBv1EdQtZGcnku/MJ8K91/RKQCl4IdtJ1mxL5m7BrXD0cGEP7CDH1NHd+Kd3+Jp4+3GM2MjGytcIYRocDUm9KZIa11pyQXAz9OVPqE+fLbhKIVFmuv7BJfZ/uSYTiSl5/DeioMEtnTlnsHhjRO0EEI0MJsc+p+ZV0h2fmGlCR1gZKTp7dLe34NeId5ltiml+Pv1PRjTNYiXFuzml10yXYAQwj7YZEKvrA96aaO6BAJwXe9gzDXdspwcHXj3jr70CvFh2rfbSTyXVWb7zuNpbE9MreeohRCiYdl2Qvd0q3R7j7befHR3f6Zc0aHK52jh4sh7d/QF4Ik52y4uYbdiXxI3f7Ce2z7aQGzC+XqOXAghGo5tJ/QqWuhgJuvycK3+EkGorzv/vKkn2xJSeWdZPMv3neHhL7bSKciToJZuPPRZDMfOZtZr7EII0VBsNKHnANUn9Nr6Xe9gbosK4b8rD/LwF1uJbO3F7AcHMmtyNIVaM/nTLaRm5VV4XFpWPrtkQQ0hRBNimwk9IxcnB4VPC+d6eb6Xr+tOZJAX3YO9+fKBgfi4u9AhwJP/3RPF8dRs7v10C0mWgwjAydRsbnh/Hdf/dx1HUqQFL4RoGmwzoafn4u/pioNDjd3ja8XdxYmFjw9j/qND8HYvOUhEh/vy3h192X/6Ate9u44dx1M5djaTWz/cQIrloPL+ioP1EoMQQlwum03o9VFuKc3Z0aHSA8TV3Vvz3aNDcHRQ3PrhBm7+YANZeQV8/dAgJg0MY/62ExV6yRRLSs/hbwv3kJkrUw0IIRqebSb0Kob9N5Tuwd4s+MNQ+ob54OgAc6YMpkdbbx65siOODor3V1beSn918T5mrjvCsr1nGi1WIUTzZZsJPT2XAM/GS+hgRqB+/dAg1j47isjWXgAEtXRjYnQo38Yc5/j5sq30bQnn+X7bCQBZXEMI0ShsLqEXFWlSMvIatYVeTCmFs2PZX9kjV3ZEKfhg5aGL92mt+dtPewjwcuXKzgGsPZgik4EJIRqczSX081l5FBZpqyT0ygT7tODWqFDmbEnk9V/2kZVXwILtJ9mWkMq0sZGM7d6aU2k5HErOsHaoQgg7Z3OTcyVn1DyoqLE9d00X8gqK+GDlIRbEnaSgqIjuwS25pV8IJ1KzAVgTn0JEoJeVIxVC2DOba6HXZpRoY2vp5sy/b+3Nt48MxsvNiaT0XF6c0A0HB0Worzvt/Nylji6EaHA210JPzynAyUE1+kXR2ogO9+Wnx4dxMjWHMD/3i/cP7+TP97EnyCsoKrNkXnlaaz5Ze4ThnQIuXngVQojasrkW+viebTjwyjW0K5UwmxInR4cyyRxgWEQAmXmFbCs12VdlF0ljjp3nlUV7+dcv+xo8TiGE/bG5hA7g4KAqnRa3qRrc0Q8HBWstC1T/sus0/f6+lJ93niqz30erDgOwYn8SZy7kVHgeIYSojk0mdFvj3cKZ3qE+rIlP4fMNR3l09lbSsvN5acFuMiyjSA8mZbBs7xmu7xNMkYZ5W49bN2ghhM2RhN5IhncKIC4xlRd/3M3oLkHMfnAQSem5TP8tHoCP1xzG1cmBFyd0Y0B7X76NSZS+60KIOqkxoSulQpVSK5RSe5RSu5VSU6vZN1opVaCUuqV+w7R9Y7oGohRMGhjGh3f1Y3BHP26PCmXm2iNsOHSW+bEnuKV/CH6erkyMDuXo2Sw2HTln7bCFEDakNi30AuBprXU3YBDwmFKqW/mdlFKOwOvAkvoN0T70CvEh5s9j+McNPXCyjDb947hI3F0cue/TzeQXFfHgcLPC0jU92uDl6sTcLYnWDFkIYWNqTOha61Na61jLz+nAXqBtJbs+DnwHJNVrhHbEz9O1zMVcP09XnhkbSW5BEVd3C6K9vwdglse7rk8wi3aeIi0731rhCiFsTJ1q6EqpcKAvsKnc/W2BG4EPanj8FKVUjFIqJjk5uW6R2qlJA8J4YlQEz13Ttcz9t0eHkltQxH9XHCSvoMhK0QkhbEmtE7pSyhPTAn9Sa32h3Oa3gWe11tVmHq31DK11lNY6KiAgoO7R2iEnRweeujryYuu8WM+23oztHsSM1YcZ9eZK5sYkUlBYfWLPLSgkK0/mXheiuVK16UmhlHIGfgJ+1Vq/Vcn2I0BxLcEfyAKmaK1/qOo5o6KidExMzCUF3VxorVl1IJk3lxxg54k0JvRqw3uT+pXZp6hIs/noOX7YdoJFO0/h7+nKsqeuxLGeVnMSQjQtSqmtWuuoyrbVOPRfmaLvJ8DeypI5gNa6fan9ZwE/VZfMRe0opRgRGciVnQN4a+kB3l1+kJv6nWFUlyAACos0D30ew/J9SXi4ONIrxIcNh8+yJj6ZEZGBVo5eCNHYalNyGQrcDYxSSsVZvsYrpR5RSj3SwPEJTGJ/fFQnIgI9efHH3WTnFQLwzrIDLN+XxB/HRRLzl6v47P4B+Hq4MDdGescI0RzV2ELXWq+lpJxSI631fZcTkKici5MDr9zQg4kzNvLeinj6t2vF9OUHuS0qhN+PiLi430192/LZhqOkZJiFtIUQzYeMFLUhgzr4cVO/tsxYfZipc+LoHtySv13fo8w+t0eHkl+o+T72RKXPcTApnWfn7ZCeM0LYIUnoNuZP47vi7uKEAj64sz9uzo5ltncK8qJfmA/fVDF1wFebEvkmJpEdx1MbKWIhRGORhG5j/D1d+ebhQcz//ZAK0/QWmxgdxsGkDGJLTddbbJ1lxsetxypuE0LYNknoNqhL65bVLmd3ba82eLg4Mmdz2YujSRdy2H8mHZCELoQ9koRuhzxcnfhd72B+2nHq4vS8AOsOmdZ5l9ZexCakymyOQtgZSeh26rboULLzC1m04+TF+9bEp9DK3Zk7B7UjJSOXxHPZVoxQCFHfJKHbqb6hPkQEejI3xiyUobVm3cEUhkT4Ex3eCoCtCTI9rxD2RBK6nVJKcVtUCFuPnedgUgYHkzI4cyGXYRH+dAr0wtPVSeroQtgZSeh27Ma+ITg5KL6NSby4numwCH8cHRR9w3yIPSZdF4WwJ5LQ7ViAlyujugTyXewJVu5Ppp2fO6G+pqtjv7BW7Dt9ocxFUyGEbZOEbuduiwolJSOXVQeSGRrhf/H+/u1aUaRhe6K00oWwF5LQ7dyIyAACvMycLsNLJfQ+YT4oJf3RhbAnktDtnJOjA7dFheDi5MDgjn4X72/p5kznQC9J6ELYkRpnWxS2b+roztzaPxQfd5cy9/dr14pFO05SVKRxkAUxhLB50kJvBlycHAgvt8QdmDr6hZwC4mSiLiHsgiT0Zmxs9yC8Wzjz/opDFbYVFWk2HDrLs/N2MOifv7H6gCzqLURTJyWXZszLzZkHh7XnzaUH2HUijR5tvQE4kpLJPTM3kXguGw8XR4o0fLv1OFd0loW9hWjKpIXezN07NBwvNyfeXR4PQHZeIY9+uZX0nALemdiHmL9cxXW9g1m5P6nCohj//nX/xel4hRDWJwm9mWvp5sz9Q9vz6+4z7D11gT9/v5P9Z9J5+/Y+XN+nLS1cHBnTLYj0nAI2HymZ+2XXiTTeW3GQp+bGyeAkIZoISeiC+4e2x8vViQc/i2H+thNMHd2JEZGBF7cPi/DHzdmBZXvPXLxvzpYEnB0VZy7kMv23eGuELYQop8aErpQKVUqtUErtUUrtVkpNrWSfO5VSO5RSO5VS65VSvRsmXNEQvN2duW9oOCdSs7mycwBPjOpUZnsLF0eGRQSwdM8ZtNZk5RXw47aTTOgVzO1Rocxce4R4y8IZQgjrqc1F0QLgaa11rFLKC9iqlFqqtd5Tap8jwJVa6/NKqWuAGcDABohXNJCHr+yIu4sTdwwIrbRP+lXdAlm29wz7Tqez80Qa6bkFTIwOJSLQk593neLFH3fz1UMDUUr6swthLTW20LXWp7TWsZaf04G9QNty+6zXWhcPOdwIhNR3oKJhebo68eiIjhUGHxUb1SUIpWDpnjPM2ZxAhwAPBrT3xc/TlWljI9lw+CwLd5xq5KiFEKXVqYaulAoH+gKbqtntAeDnKh4/RSkVo5SKSU6Wfs22JMDLlb6hPszedIzYhFTuiA672BqfNLAdPdt68/KC3SSl55R5XF5BEbkFhdYIWYhmp9YJXSnlCXwHPKm1vlDFPiMxCf3ZyrZrrWdoraO01lEBAdKn2daM6RbEmQu5ODsqbupXcpLm6KD4z+29ycwt4I/zdlxcq/R0Wg7XvLOaq95aTcLZLGuFLUSzUauErpRyxiTz2Vrr+VXs0wv4GLhea322/kIUTcXV3YLM9+6t8fN0LbMtItCLP43vysr9yXy58RiJ57K47aMNnE7L4UJOPrd8uJ79p+XCqRANSdW08rsy59WfAee01k9WsU8YsBy4R2u9vjYvHBUVpWNiYuoYrrAmrTWfbzjGiMgA2vlVnBtGa829n25h85GztHJ3ITO3gM8fGIiHiyN3fbKJnPwiPp0cTb+wVlaIXgj7oJTaqrWOqnRbLRL6MGANsBMoHir4JyAMQGv9oVLqY+Bm4Jhle0FVL1hMErp9OnMhh3Fvr0YpxRcPDKB7sJlOIPFcFnd9sonUrHx+ffIKWnu7WTlSIWzTZSX0hiIJ3X4lnsvC1cmBwJZlk/bh5Ayunb6WqPBWfDZ5gEzZK8QlqC6hy0hRUe9Cfd0rJHOADgGe/GVCV9bEpzBr/dGL9xcUFpGek9+IEQphn2S2RdGoJg0IY/neJF77ZR8RgZ5sPXaeOVsSyC0oYv1zo3B3kT9JIS6VtNBFo1JK8fotvWjp5sQ9MzczfXk8rb1bkJqVz4p9MjZBiMshzSHR6Pw9Xfno7v6sjT/LTf3aEuzTgoH/XMbinae4tlcba4cnhM2ShC6son87X/q38714e2z31syPPUF2XiEtXBytGJkQtktKLqJJuLZnG7LzC1m5P8naoQhhsyShiyZhQHtf/DxcWLRTJvgS4lJJQhdNgpOjA2N7tGb5viRy8mUyLyEuhSR00WSM79GGrLxCVu6X3i5CXAq5KCqajEEdfPH1cGHB9hO4OTvw6+4zHErK4I1be1U6d4wQoixJ6KLJcHJ0YGz3IL7enMjinafxcHHEQSnu+mQT8x4ZQlAlo0+FECUkoYsm5eErzFJ4QyP8GNLRnwNn0rljxkbu+ngTcx8eTCuPyldUEkJIDV00MeH+HrwwoRujugTh5uxIrxAfPr43mmPnsrjv081k58kFUyGqIgldNHmDO/rx7h192X48rcykXkKIsiShC5swtntrRkYG8OGqQ1yQmRmFqJQkdGEznr46krTsfD5Zc8TaoQjRJElCFzajR1tvrunRmk/WHuF8Zp61wxGiyZGELmzK/13Vmcy8Aj5afdjaoQjR5EhCFzalc5AX1/cOZtb6IySl51g7HCGaFEnowuZMHdOZ3IIivtxwrOadhWhGakzoSqlQpdQKpdQepdRupdTUSvZRSqnpSqmDSqkdSql+DROuENDe34PRXQKZvSmB3IKy/dI3Hj5LZm6BlSITwrpq00IvAJ7WWncDBgGPKaW6ldvnGqCT5WsK8EG9RilEOfcNac/ZzDx+2l4y3e6qA8lMnLGRt5YesGJkQlhPjQlda31Kax1r+Tkd2Au0Lbfb9cDn2tgI+CilZC0x0WCGRvgREejJrPVH0VqTnVfIX37YCcC3MYkyolQ0S3WqoSulwoG+wKZym9oCiaVuH6di0kcpNUUpFaOUiklOlilSxaVTSnHfkHB2nkgjNuE87y6PJ/FcNk9d1ZkLOQX8GHfC2iEK0ehqndCVUp7Ad8CTWusLl/JiWusZWusorXVUQEDApTyFEBfd1K8tLd2ceGXRXmasPswt/UN4fFQEXVp78fmGY2itrR2iEI2qVgldKeWMSeaztdbzK9nlBBBa6naI5T4hGoy7ixMTB4SxLSEVLzcn/jS+K0op7hkczp5TF9h67Ly1QxSiUdWml4sCPgH2aq3fqmK3BcA9lt4ug4A0rbUsDika3N2D2tHSzYmXr+uOr2Vq3Rv6BuPl5sTnpbo15hcWsfHwWf65eC9X/2cVf12421ohC9FgajMf+lDgbmCnUirOct+fgDAArfWHwGJgPHAQyAIm13+oQlQU6uvO9peuxrQ7DHcXJ27pH8KXG4/xw7ZA1sSnsGzvGdKy83F2VLT39+DTdUfp2dabm/qFWDF6IeqXsladMSoqSsfExFjltYX9O5ycwag3VwHQ0s2JMV2DuKpbEMM7B+Dm5MCdH29ix/E0Fj4+lIhALwC2JZzn6NlMOgZ40jHAEw9XWf9FND1Kqa1a66hKt0lCF/Zqye7TuDk7MqiDHy5OZauLZy7kMP6dNfh5uvCPG3vy3vKDrDpQtudV3zAf3rm9L2F+7o0ZthDVkoQuRCXWxCdzz8zNaA0+7s48cmVHRnUJ5HByJgfOpPPxmsMopXh7Yh9GRgZaO1whAEnoQlRp7pZEkjNyuWdwO7zcnMtsSzibxcNfbmXf6Qs8c3Ukj42MsFKUQpSoLqFLkVA0a7dFh1a5LczPnfmPDuGP3+3gjV/30z24JSOkpS6aMJltUYhqtHBx5N+39iIi0JM/f7+LjHITf525kMOsdUe47cMNdHvxF6Z9u50DZ9KtFK1o7iShC1EDVydHXr+5FyfTsnn9530A5OQX8reFexj06m+8vHAPadn5jO3emoU7TnL1f1Zz/6wtnM3ItXLkormRkosQtdC/XSsmD2nPzHVH6B7cklnrj7LvdDp3Dgxj8tDwi10fX5zQjS83HuPdFQeZNm8Hn9wbVaaPfGGR5lRaNkdTsjiRmsXILoEEerlZ620JOyMJXYhaemZsZ5buPc1z83fi5+HCzPuiGNUlqMw+rTxceHx0J1q2cOalBbuZtf4ok4e2B2BtfApPzNnGuVLroUa1a8U3Dw/G0UFRk7XxKSgFQyP86/eNCbshCV2IWnJ3ceLdO/oxb2siT4zuVG3L+p7B7VgTn8yri/cxoL0v2xPTeOHHXUQEeDJtbCThfh4cTM7ghR928eGqQ9X2oIk/k84ri/Ze7Cf/8BUdeGZsJM6Ol1YxTU7PZea6I9wWFUp7f49Leg7RNEm3RSEayLnMPMa9vZqc/EIu5BQwIjKAd+/oe7F7pNaax7/exi+7TvP974fSM8SbvIIiFu88xf4z6ZzPzCMpPZdVB5Jxd3HkiVGdOHYuky83JhAd3or3JvUjqGXdyjU5+YVMnLGRuMRUXJwc+MPICB6+sgOuTo4N8SsQDUD6oQthJesPpnDfp1u4Y0AoL0zohlO5VnVaVj5j316Nu6sjE6NDmbn2KKcv5ODsqPBxd8HX3YWBHXyZOroTfp6uAPwYd4Ln5+/Ew9WJT++Lpkdb71rForXmiTlxLNx+kldv6sn6Q2dZuP0kHQM8+OjuKCICPcvsX1SkUYoy1wBq/b4PpZCVW8iYbkE172wjMnMLmsR0EJLQhbCinPxC3JyrbgGvO5jCnR+bNWMGd/DjkREduaKTf7WJdP/pdCZ/upm07Hw+uKs/V3QO4FRaNp+uO8rWY+cJadWCcD8Pwv3dCfP1IMzXndmbjvH2snj+OC6S348wJZ6V+5N45tvtODoo5j48mHZ+Hhfvf2ruds5l5uHq5ICbsyNXdA7g9yM60rVNyyrjSs3K4+8/7eW72OOAKQ/9cVwXHB0UOfmFfLL2CGvjUwj2aUGYrzv927ViWKemf03gg5WHeHd5PAsfH0bHAM+aH9CAJKEL0cSt2J+Er7sLvUN9av2YMxdyuO/TLcSfSWdEZCAr9yehgT6hPpxOy+FkWjbl/71v7hfCv2/tVeZgsf90OhNnbMDdxYlvHh7Eoh2neP2XfXQO8uLqbkHkFhRxPiuPRTtOkZlXyJiugTw5pnOZMwOtNYt2nuLlBbtJzcrnkSs7kpqdx5cbExjdJZBbo0J49ed9HDubRbc2LTmflceptBwAvnxgYKVJPSe/kJij59l76gIDO/jSs633JZ0tXK7Ec1mMeWsVuQVFTOjVhvcm9avT43MLColLSKVjoCf+lrOsyyEJXQg7lZ6Tz+9nxxJz9DwTB4Ry/9D2hPqaycRy8gtJPJdF4vksEs5mUajhrkFhldbLd51IY9L/NlJQpMnKK+Tanm1449ZeuLuUlBjSsvL5bMNRPl13hNTsfG7pF8K0cZGkZuXz0o+72XD4LD3bevP6zb3oFmxa8V9sOMrLC/dQWKTpEODB367rcTF5Z+QWMGH6GjTw65NXXDyLOZScwcsLdrPpyDnyCoouvn57fw+u7xPMg8M74NmIpY8HZm1hw+GzTOjVhrkxx1n0xDC6B1df5tJas3xfEgu2n2T53iTScwtwclCM7hrIbVGhXNk5oEL5rbYkoQthx7TW5BYUVVvWqY24xFQe/zqW26NCeWxkRJWt4Qs5+fx3+UFmrjuCs6MDuQVFeLo68czYSCYNCKvQBXPzkXPsP32B26PDKsx6WVxu+sPICJ4ZG0nC2Sxu/Wg9eQVF3NQvhGER/nRp48XqA8n8GHeSDYfP0i+sFZ/dP6DSpK61ZueJNOISU9l/Op0DZ9JxdnQgItCTiEBPhncKqFPPnmV7zvDg5zH8aXwXbo8O44p/raB/u1bMvC+6yscUFBbx0oLdzN6UQCt3Z67qFsTIyEC2JaYyP/Y4KRl53DO4HX+7vket4yhNEroQot4dTclk+vJ4Wro588ToThdXjKqrp+bGsSDuJB/fG8VffjDTK8yZMogurSvW6n/eeYo/fL2NfmE+zJo8AA9XJ7TWHErOZMH2k/wYd4JjZ7MA8HJzIjLIi/wizaGkDDIsreR7BoczdUwnvFs4V3h+rTWpWfmAWeXqpg/W4+7iyKInhuPs6MAHKw/x+i/7mPfIYKLCfSs8Pj0nnz98tY1VB5J55MqOPHN15zIt8fzCIn7bm0SYr/vFs5i6koQuhGiyzmXmMfrNlZzPysfL1YmvHhpEz5CqSxo/7TjJ1Dlx9A9rRWRrL1YdSCbhXBZKwZCOflzfpy3DIvxp4+128SxDa83x89m8v/IQc7Yk0MrdhfuHhhMd7kuvEB/Sc/OZt/U432xJvHhAKDZnyiAGdfADICuvgCvfWEm4nzv/vrU3bX1a4OigOHo2izXxyXyx4RiHUzL5xw09mDggrEF+X5LQhRBN2sLtJ/nHor28N6lvpS3f8hZsP8mTc7bh6uTIkI5+jIgM4KpurWntXXO//F0n0nhl0R42Hj4HgJODQmOmZRjY3pcxXYNwdjQHgo6WMk1pszcd48/f7wLA2VHh3cKFFMu8PeF+7vz9hh4VHlOfJKELIZo8rXWderGkZuXh5ux4ydcOzmXmsS3hPLEJ5wG4qV9IrbskxiWmcuB0OodTMklKz6FvqA9XdA642O2zIUlCF0IIO1FdQq+x34xSaqZSKkkptauK7d5KqYVKqe1Kqd1KqcmXG7AQQoi6q01HyFnAuGq2Pwbs0Vr3BkYAbyqlLu1ytxBCiEtWY0LXWq8GzlW3C+ClTPHL07JvQTX7CyGEaAD1sWLRe0BX4CSwE5iqtS6qbEel1BSlVIxSKiY5ObkeXloIIUSx+kjoY4E4IBjoA7ynlKq0x7zWeobWOkprHRUQ0HDdeoQQojmqj4Q+GZivjYPAEaBLPTyvEEKIOqiPhJ4AjAZQSgUBkcDhenheIYQQdVDjlGVKqa8xvVf8lVLHgZcAZwCt9YfA34FZSqmdgAKe1VqnNFjEQgghKmW1gUVKqWTg2CU+3B9ojgeN5vi+m+N7hub5vpvje4a6v+92WutKL0JaLaFfDqVUTFUjpexZc3zfzfE9Q/N8383xPUP9vu/6qKELIYRoAiShCyGEnbDVhD7D2gFYSXN8383xPUPzfN/N8T1DPb5vm6yhCyGEqMhWW+hCCCHKkYQuhBB2wuYSulJqnFJqv1LqoFLqOWvH0xCUUqFKqRVKqT2WOeanWu73VUotVUrFW763snasDUEp5aiU2qaU+slyu71SapPlM//G3qZnVkr5KKXmKaX2KaX2KqUGN4fPWin1f5a/711Kqa+VUm72+FlXtqZEVZ+vMqZb3v8OpVS/uryWTSV0pZQj8F/gGqAbcIdSqpt1o2oQBcDTWutuwCDgMcv7fA74TWvdCfjNctseTQX2lrr9OvAfrXUEcB54wCpRNZx3gF+01l2A3pj3bteftVKqLfAEEKW17gE4AhOxz896FhXXlKjq870G6GT5mgJ8UJcXsqmEDgwADmqtD2ut84A5wPVWjqneaa1Paa1jLT+nY/7B22Le62eW3T4DbrBOhA1HKRUCXAt8bLmtgFHAPMsudvW+lVLewBXAJwBa6zytdSrN4LPGTD3SQinlBLgDp7DDz7qKNSWq+nyvBz63THa4EfBRZPoS3gAAAihJREFUSrWp7WvZWkJvCySWun3ccp/dUkqFA32BTUCQ1vqUZdNpIMhKYTWkt4E/AsVz6vsBqVrr4kVT7O0zbw8kA59aykwfK6U8sPPPWmt9Avg3ZnK/U0AasBX7/qxLq+rzvawcZ2sJvVlRSnkC3wFPaq0vlN6mTX9Tu+pzqpSaACRprbdaO5ZG5AT0Az7QWvcFMilXXrHTz7oVpjXaHrOWggfVL3Vpt+rz87W1hH4CCC11O8Ryn91RSjljkvlsrfV8y91nik+/LN+TrBVfAxkKXKeUOoopp43C1Jd9LKflYH+f+XHguNZ6k+X2PEyCt/fPegxwRGudrLXOB+ZjPn97/qxLq+rzvawcZ2sJfQvQyXIl3AVzEWWBlWOqd5a68SfAXq31W6U2LQDutfx8L/BjY8fWkLTWz2utQ7TW4ZjPdrnW+k5gBXCLZTe7et9a69NAolIq0nLXaGAPdv5ZY0otg5RS7pa/9+L3bbefdTlVfb4LgHssvV0GAWmlSjM101rb1BcwHjgAHAL+bO14Gug9DsOcgu3ALO8XZ3nffpgr4vHAMsDX2rE24O9gBPCT5ecOwGbgIPAt4Grt+Or5vfYBYiyf9w9Aq+bwWQN/BfYBu4AvAFd7/KyBrzHXCfIxZ2QPVPX5YtaU+K8lv+3E9AKq9WvJ0H8hhLATtlZyEUIIUQVJ6EIIYSckoQshhJ2QhC6EEHZCEroQQtgJSehCCGEnJKELIYSd+H8rqa11qDjlMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0FscRQMkgyz"
      },
      "source": [
        "yhat = lstm_model.predict(test_X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAKK600pmYIV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1ec22b4d-f033-4e4f-bdaa-616b1700b794"
      },
      "source": [
        "test_y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.34, 7.28, 5.74, ..., 7.71, 2.74, 0.78])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYHwXEnbkk38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "dd28b3c0-a755-448d-deea-041c2c2812db"
      },
      "source": [
        "CAPE_CNR_function(test_y,yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "360465.5031389815"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    }
  ]
}