Bringing_GEFCom2014_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKZguk6OiiQq"
      },
      "source": [
        "# Retrieving the  GEFCom2014 (E, V2) Data\n",
        "\n",
        "The data in this project is taken from the GEFCom2014 forecasting competition. It consists of hourly electricity load (\"eload\") and temperature (\"tempf\") values for a USA city. \n",
        "The data will be brought into the Colab session from an internet repository, aand the extracted Ecel file will be used to create a comma-seprated-value (csv) text file, similar to the one in Chapter 10 of te textbook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6QNEjWp4iiQu",
        "jupyter": {
          "outputs_hidden": true
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "outputExpanded": false
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "import os\n",
        "import shutil\n",
        "import matplotlib.pyplot as plt\n",
        "# from common.utils import load_data, extract_data, download_file\n",
        "%matplotlib inline\n",
        "\n",
        "# !wget https://mlftsfwp.blob.core.windows.net/mlftsfwp/GEFCom2014.zip\n",
        "!wget https://www.dropbox.com/s/pqenrr2mcvl0hk9/GEFCom2014.zip\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGs_D6GelaHB",
        "outputId": "9901b1d1-a6c0-4f03-c8a7-db835d841c8d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  GEFCom2014.zip\n",
            "replace GEFCom2014 Data/GEFCom2014-S_V2.zip? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
            " extracting: GEFCom2014 Data/GEFCom2014-S_V2.zip  \n",
            " extracting: GEFCom2014 Data/GEFCom2014-W_V2.zip  \n",
            "  inflating: GEFCom2014 Data/READ ME_V2.txt  \n",
            "  inflating: GEFCom2014 Data/Provisional_Leaderboard_V2.xlsx  \n",
            " extracting: GEFCom2014 Data/GEFCom2014-L_V2.zip  \n",
            " extracting: GEFCom2014 Data/GEFCom2014-E_V2.zip  \n",
            " extracting: GEFCom2014 Data/GEFCom2014-P_V2.zip  \n"
          ]
        }
      ],
      "source": [
        "# !ls -l \n",
        "!unzip GEFCom2014.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23EATWkn2RSR",
        "outputId": "65e6314e-3f3e-4f2e-ad6c-6b71aaffa9ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 376488\n",
            "-rw-r--r-- 1 root root     37912 Apr 19 06:21  ELOAD_lstm.keras\n",
            "-rw-r--r-- 1 root root   1573348 Apr 19 06:38  GEF14.csv\n",
            "drwxrwxr-x 2 root root      4096 Apr 19 06:41 'GEFCom2014 Data'\n",
            "-rw-rw-r-- 1 root root   2348089 Feb 11  2016  GEFCom2014-E_V2.zip\n",
            "-rw-r--r-- 1 root root   2452214 Jan 25  2016  GEFCom2014-E.xlsx\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip.1\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip.2\n",
            "drwxr-xr-x 1 root root      4096 Apr  8 13:32  sample_data\n"
          ]
        }
      ],
      "source": [
        "!ls -l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDOKGKL91-FQ",
        "outputId": "f64fbb31-577a-467c-c9c3-5e4285ce5c2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 123420\n",
            "-rw-rw-r-- 1 root root  2348089 Feb 11  2016  GEFCom2014-E_V2.zip\n",
            "-rw-rw-r-- 1 root root  2599214 Feb 11  2016  GEFCom2014-L_V2.zip\n",
            "-rw-rw-r-- 1 root root  3338992 Feb 11  2016  GEFCom2014-P_V2.zip\n",
            "-rw-rw-r-- 1 root root 36734790 Feb 11  2016  GEFCom2014-S_V2.zip\n",
            "-rw-rw-r-- 1 root root 81149634 Feb 11  2016  GEFCom2014-W_V2.zip\n",
            "-rw-rw-r-- 1 root root   195932 Feb 11  2016  Provisional_Leaderboard_V2.xlsx\n",
            "-rw-rw-r-- 1 root root      389 Feb 11  2016 'READ ME_V2.txt'\n"
          ]
        }
      ],
      "source": [
        "!ls -l 'GEFCom2014 Data'/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWY9eUUQSu-K"
      },
      "source": [
        "# We are interested in GEFCom2014-E_V2.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BuAtuOTA21N4"
      },
      "outputs": [],
      "source": [
        "# Let's bring it to the top level before unzipping it\n",
        "!mv  'GEFCom2014 Data'/GEFCom2014-E_V2.zip  ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vdas6cAJ1094",
        "outputId": "e7e6fa58-a5bc-4281-ed7a-1c9cdff327dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  GEFCom2014-E_V2.zip\n",
            "replace GEFCom2014-E.xlsx? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
            "  inflating: GEFCom2014-E.xlsx       \n"
          ]
        }
      ],
      "source": [
        "!unzip GEFCom2014-E_V2.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEhJVlpe10lB",
        "outputId": "affa5873-675b-47db-f09e-5b9acde9d9a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 376488\n",
            "-rw-r--r-- 1 root root     37912 Apr 19 06:21  ELOAD_lstm.keras\n",
            "-rw-r--r-- 1 root root   1573348 Apr 19 06:38  GEF14.csv\n",
            "drwxrwxr-x 2 root root      4096 Apr 19 06:41 'GEFCom2014 Data'\n",
            "-rw-rw-r-- 1 root root   2348089 Feb 11  2016  GEFCom2014-E_V2.zip\n",
            "-rw-r--r-- 1 root root   2452214 Jan 25  2016  GEFCom2014-E.xlsx\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip.1\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip.2\n",
            "drwxr-xr-x 1 root root      4096 Apr  8 13:32  sample_data\n"
          ]
        }
      ],
      "source": [
        "# We can now verify that we hot the file GEFCom2014-E.xlsx\n",
        "!ls -l"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9XUk2iATKLS"
      },
      "source": [
        "***We have GEFCom2014-E.xlsx***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fn2AuDsr8f_u"
      },
      "source": [
        "This script will \"translate\" the .xlsx file to a .cvs file, so that the initial situation is the same as for the Jena temperature example from Chapter 10 of textbook - IN THIS PROCESS WE WILL ALSO REMOVE THE MLTIPLE INITIAL LINES THAT DO NOT HAVE LOAD VALUES (this is done through the \"skip_rows\" parameter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tbmtWFA25-PK"
      },
      "outputs": [],
      "source": [
        "# CONVERT GEFCom2014-E.xlsx to a PANDAS DATAFRAME called GEFDF\n",
        "# import pandas as pd\n",
        "\n",
        "GEFDF = pd.read_excel('GEFCom2014-E.xlsx', skiprows=range(1, 17545), dtype = {'A':np.int32  ,'B':np.int8   ,'C':np.int32  ,'D':np.float64}, index_col = None )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YJoZMmsUkER",
        "outputId": "64be70f4-f773-4e6a-93e1-273c3209d5fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            Date  Hour    load          T\n",
            "0     2006-01-01     1  3010.0  22.666667\n",
            "1     2006-01-01     2  2853.0  20.666667\n",
            "2     2006-01-01     3  2758.0  21.333333\n",
            "3     2006-01-01     4  2705.0  19.000000\n",
            "4     2006-01-01     5  2709.0  19.333333\n",
            "...          ...   ...     ...        ...\n",
            "78883 2014-12-31    20  4012.0  18.000000\n",
            "78884 2014-12-31    21  3856.0  16.666667\n",
            "78885 2014-12-31    22  3671.0  17.000000\n",
            "78886 2014-12-31    23  3499.0  15.333333\n",
            "78887 2014-12-31    24  3345.0  15.333333\n",
            "\n",
            "[78888 rows x 4 columns]\n"
          ]
        }
      ],
      "source": [
        "# We can \"see\" the Pandas DataFrame (called GEFDF) that has been obtained\n",
        "print(GEFDF)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y6YK-qjnrZ37"
      },
      "outputs": [],
      "source": [
        "# Now writing out the GEFDF dataframe to a text (csv) file\n",
        "GEFDF.to_csv('GEF14.csv',  encoding='utf-8', index=False, header=True, columns=['Hour','load','T'],line_terminator='\\n' )\n",
        "with open('GEF14.csv') as f:\n",
        "    lines = f.readlines()\n",
        "    last = len(lines) - 1\n",
        "    lines[last] = lines[last].replace('\\r','').replace('\\n','')\n",
        "with open('GEF14.csv', 'w') as wr:\n",
        "    wr.writelines(lines)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIdtesaR8Kh-",
        "outputId": "ede6e7a5-0977-47c1-eeb3-331265dcd9fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 376488\n",
            "-rw-r--r-- 1 root root     37912 Apr 19 06:21  ELOAD_lstm.keras\n",
            "-rw-r--r-- 1 root root   1573348 Apr 19 06:41  GEF14.csv\n",
            "drwxrwxr-x 2 root root      4096 Apr 19 06:41 'GEFCom2014 Data'\n",
            "-rw-rw-r-- 1 root root   2348089 Feb 11  2016  GEFCom2014-E_V2.zip\n",
            "-rw-r--r-- 1 root root   2452214 Jan 25  2016  GEFCom2014-E.xlsx\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip.1\n",
            "-rw-r--r-- 1 root root 126360077 Aug 11  2020  GEFCom2014.zip.2\n",
            "drwxr-xr-x 1 root root      4096 Apr  8 13:32  sample_data\n"
          ]
        }
      ],
      "source": [
        "# verifying we have created the csv file GEF14.csv\n",
        "!ls -l ./"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "us1hUlHQ9bWu"
      },
      "source": [
        "From this point onwards, the preparation is similar as for the Jena temperature prediction case from the book but the columns in this file are just:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWJo3EwS9o2o"
      },
      "source": [
        "hour( 1 to 24) ; LOAD (e.g., 4012.0 ) ; TEMP (in oF ,  e.g., 86.5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdqxITW013z4"
      },
      "source": [
        "**Inspecting the data of the GEFC14.csv FILE**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_ruVaOML0nP"
      },
      "source": [
        "This will separate the heade from the rest, whicj will be kept in \"lines\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrhuNXiv13z4",
        "outputId": "2b3204d0-fa84-4831-ae8d-8e33ddaaa0d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Hour', 'load', 'T']\n",
            "78888\n"
          ]
        }
      ],
      "source": [
        "# THIS CODE CELL IS ESSENTIALLY THE SAME AS IN THE EXAMPLE FROM CH. 10 IN BOOK\n",
        "import os\n",
        "fname = os.path.join(\"GEF14.csv\")\n",
        "\n",
        "with open(fname) as f:\n",
        "    data = f.read()\n",
        "\n",
        "lines = data.split(\"\\n\")\n",
        "header = lines[0].split(\",\")\n",
        "lines = lines[1:]\n",
        "print(header)\n",
        "print(len(lines))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIuGahi713z5"
      },
      "source": [
        "**Parsing the data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0q6uLMwMA--"
      },
      "source": [
        "We will separate temperatures in tempf, loads in eload and both in raw_data (Hour will be ignored) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4i3MFge813z6",
        "outputId": "70939dc1-ffcd-48fb-8f3c-147653554904"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "78888\n"
          ]
        }
      ],
      "source": [
        "# VERY SIMILAR TO THE CORRESPONDING C0DE CELL FROM CHAPTER 10 IN BOOK\n",
        "# eload (electric load) is the timeseries we will predict\n",
        "# tempf (temperature in Fahrenheit) is the temperature at the same time\n",
        "# import numpy as np\n",
        "eload = np.zeros((len(lines),))\n",
        "tempf = np.zeros((len(lines),))\n",
        "raw_data = np.zeros((len(lines), len(header)-2))   #chgd )-1  to )-2 to also\n",
        "# remove the HOUR column, in addition to the DATE column\n",
        "print(len(lines))\n",
        "\n",
        "for m in range(78888):\n",
        "    thisline  = lines[m]\n",
        "    values = [float(x) for x in thisline.split(\",\")[1:]]\n",
        "    eload[m] = values[0]         #Captures JUST E LOAD\n",
        "    tempf[m] = values[1]            #Captures JUST TEMPF\n",
        "    raw_data[m] = values[0]         #Like this, raw_data Captures JUST E LOAD\n",
        "    # raw_data[m, :] = values[:]   # Like this, raw_data CAPTURES BOTH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-5-eLAr13z6"
      },
      "source": [
        "**Plotting the eload timeseries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "HghihQjm13z6",
        "outputId": "e2173604-51a5-4b02-8113-0949a8644867"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe936763950>]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(len(eload)), eload)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "6N_QbQ5v6vfD",
        "outputId": "2e31a2c8-e863-4518-fc3d-ee274e29dad3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe9367e5250>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(range(len(tempf)), tempf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQ759qZa_rrD"
      },
      "source": [
        "**THIS IS THE BEGINNING OF THE ACTUAL SOLUTION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CXgz-HWg13z7"
      },
      "source": [
        "**Computing the number of samples we'll use for each data split**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kt1MTQWNAJIa",
        "outputId": "e8b392d7-6384-4091-afdb-4204a0a73d16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "num_train_samples: 39444\n",
            "num_val_samples: 19722\n",
            "num_test_samples: 19722\n"
          ]
        }
      ],
      "source": [
        "num_train_samples = int(0.5 * len(raw_data))\n",
        "num_val_samples = int(0.25 * len(raw_data))\n",
        "num_test_samples = len(raw_data) - num_train_samples - num_val_samples\n",
        "print(\"num_train_samples:\", num_train_samples)\n",
        "print(\"num_val_samples:\", num_val_samples)\n",
        "print(\"num_test_samples:\", num_test_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XJzh0onC13z8"
      },
      "source": [
        "### Preparing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "jmeBA4Vd6b1P",
        "outputId": "522ec381-6983-4068-c9c1-983b8f885b18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe936601190>]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display the ELOAD for the first 10 days\n",
        "plt.plot(range(240),eload[:240])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "nvziEJlO6-qP",
        "outputId": "a14e6bf4-e201-4480-82f3-4a3edb1961b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe937bed950>]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display the tempf for the first 10 days\n",
        "plt.plot(range(240),tempf[:240])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVa4k2Sl13z8"
      },
      "source": [
        "**Normalizing the data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oPd0nXLcAceU"
      },
      "outputs": [],
      "source": [
        "mean = raw_data[:num_train_samples].mean(axis=0)\n",
        "raw_data -= mean\n",
        "std = raw_data[:num_train_samples].std(axis=0)\n",
        "raw_data /= std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7VBCq2qh2uZ",
        "outputId": "f6fc2cf4-b1bb-4b9c-8401-a57cba9f239a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([3310.41855542])"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "896UeHyeh73B",
        "outputId": "54bcf827-6575-4310-f3cd-4146b4f38a88"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([585.83380329])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "std"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3U1oGI313z9"
      },
      "source": [
        "**Instantiating datasets for training, validation, and testing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B5jOv2CDBgbe"
      },
      "outputs": [],
      "source": [
        "# LETS JUST USE ELOAD TO FORECAST ELOAD\n",
        "# THIS TIME, ( 1-input case)\n",
        "from tensorflow import keras\n",
        "\n",
        "horizon = 6       # Num. of hours ahead for forecast\n",
        "sampling_rate = 1\n",
        "sequence_length = 36\n",
        "delay = sampling_rate * (sequence_length + horizon - 1) \n",
        "batch_size = 128\n",
        "\n",
        "train_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    raw_data[:-delay],    \n",
        "    targets=raw_data[delay:], # This would used \"Normalized Targets\"\n",
        "    # targets=eload[delay:], # This would used \"Not-normalized eload targets\"\n",
        "    sampling_rate=sampling_rate,\n",
        "    sequence_length=sequence_length,\n",
        "    shuffle=True,                  #changed to false JUST FOR VERIF\n",
        "    batch_size= num_train_samples,\n",
        "    start_index=0,\n",
        "    end_index=num_train_samples)\n",
        "\n",
        "val_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    raw_data[:-delay],    # changed from raw_data to just eload not really\n",
        "    targets=raw_data[delay:],  # This would used \"Normalized Targets\"\n",
        "    # targets=eload[delay:], # This would used \"Not-normalized eload targets\"\n",
        "    sampling_rate=sampling_rate,\n",
        "    sequence_length=sequence_length,\n",
        "    shuffle=True,\n",
        "    batch_size=num_val_samples,\n",
        "    start_index=num_train_samples,\n",
        "    end_index=num_train_samples + num_val_samples)\n",
        "\n",
        "test_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    raw_data[:-delay],     # changed from raw_data to just eload\n",
        "    targets=raw_data[delay:],  # This would used \"Normalized Targets\"\n",
        "    # targets=eload[delay:], # This would used \"Not-normalized eload targets\"\n",
        "    sampling_rate=sampling_rate,\n",
        "    sequence_length=sequence_length,\n",
        "    shuffle=False,\n",
        "    batch_size=num_test_samples,\n",
        "    start_index=num_train_samples + num_val_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQHASx7E13z9"
      },
      "source": [
        "**Inspecting the output of one of our datasets**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18SMXrO3EVwS",
        "outputId": "289765a4-9946-46b1-da35-72fa66f8cf52"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "samples shape: (39409, 36, 1)\n",
            "targets shape: (39409, 1)\n"
          ]
        }
      ],
      "source": [
        "for samples, targets in train_dataset:\n",
        "    print(\"samples shape:\", samples.shape)\n",
        "    print(\"targets shape:\", targets.shape)\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VMGr_Bddrpwe"
      },
      "outputs": [],
      "source": [
        "# Import\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFmOkO13lFYG"
      },
      "source": [
        "**End of \" Bringing GEFCom14 DATA\"**"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernel_info": {
      "name": "python3"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
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