Long Short Term Memory Networks for Energy Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Long Short Term Memory Networks for Energy Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [
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        "qRP_zUpe08FU"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CDAC-lab/ETFA-Workshop/blob/main/Long%20Short%20Term%20Memory%20Networks%20for%20Energy%20Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKMa10tYg9k-"
      },
      "source": [
        "# Long Short Term Memory Networks for Sequential Data Analysis - Energy Consumption Forecasting\n",
        "\n",
        "In this tutorial we will conduct energy consumption forecasting using a Recurrent Neural Network.\n",
        "\n",
        "**Dataset:**\n",
        "The  dataset is from the La Trobe University Energy Analytics Platform (LEAP), the Net Zero initiative at La Trobe. The dataset contains energy consumption\n",
        "readings (MeterReading column) of a teaching building at 1-hour time intervals."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTvsvXrShEE4"
      },
      "source": [
        "## Load Libraries\n",
        "\n",
        "Load required libraries for modeling and data processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyuH_oEpST02"
      },
      "source": [
        "# %tensorflow_version 1.x\n",
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, GRU\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from matplotlib.dates import DateFormatter\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGpIqf46hM7Z"
      },
      "source": [
        "## Load data from google drive\n",
        "\n",
        "Libraries to load data from google drive shared location."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjtQi3t6_kBh"
      },
      "source": [
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVGQ7TRGA4Sm"
      },
      "source": [
        "Authenticate and create the python document (PyDrive) client."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6Kbe6Zu_kD8"
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ArRh-xt_3FF"
      },
      "source": [
        "When prompted, click on the link to get authentication to allow Google to access your Drive. You should see a screen with “Google Cloud SDK wants to access your Google Account” at the top. After you allow permission, copy the given verification code and paste it in the box in Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-MgKXCw_kGd"
      },
      "source": [
        "dataset_file_id = '1X42C-CLgn06m5c4NksutyKe87jZseFNk'"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "El-El5_EW6V5"
      },
      "source": [
        "Download the dataset file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySvIiqpN_peM"
      },
      "source": [
        "downloaded = drive.CreateFile({'id':dataset_file_id}) \n",
        "downloaded.GetContentFile('building_type_teaching_ts.csv')  "
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHxxf06tW76f"
      },
      "source": [
        "Load the dataset file to a dataframe using Pandas library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iy3xbn2JQES9"
      },
      "source": [
        "df = pd.read_csv('building_type_teaching_ts.csv', index_col=0)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxXDZgayXDDa"
      },
      "source": [
        "Explore the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZEx4GZG_pgw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "28e8df31-6593-44cb-87b6-274a5fe1a896"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 8,
      "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",
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              "    }\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>MeterReading</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timestamp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01 00:00:00</th>\n",
              "      <td>21.532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 01:00:00</th>\n",
              "      <td>28.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 02:00:00</th>\n",
              "      <td>28.937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 03:00:00</th>\n",
              "      <td>28.688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 04:00:00</th>\n",
              "      <td>29.531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 05:00:00</th>\n",
              "      <td>28.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 06:00:00</th>\n",
              "      <td>27.562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 07:00:00</th>\n",
              "      <td>50.437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 08:00:00</th>\n",
              "      <td>57.656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 09:00:00</th>\n",
              "      <td>57.594</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     MeterReading\n",
              "Timestamp                        \n",
              "2018-01-01 00:00:00        21.532\n",
              "2018-01-01 01:00:00        28.625\n",
              "2018-01-01 02:00:00        28.937\n",
              "2018-01-01 03:00:00        28.688\n",
              "2018-01-01 04:00:00        29.531\n",
              "2018-01-01 05:00:00        28.875\n",
              "2018-01-01 06:00:00        27.562\n",
              "2018-01-01 07:00:00        50.437\n",
              "2018-01-01 08:00:00        57.656\n",
              "2018-01-01 09:00:00        57.594"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Myd8uBxEMPtd"
      },
      "source": [
        "Visualize the data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "azDTAmX0Lxjb",
        "outputId": "9e5de5f2-0a0c-4baf-e877-463c8614afaf"
      },
      "source": [
        "df[:500].plot(figsize=(20,8))\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-100.,    0.,  100.,  200.,  300.,  400.,  500.,  600.]),\n",
              " <a list of 8 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3G6tfATXFCX"
      },
      "source": [
        "Dataset contains 2 column:  \n",
        "\n",
        "\n",
        "1.   Timestamp\n",
        "2.   MeterReading (Our predictor)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CddET7ihu2V"
      },
      "source": [
        "## Preprocessing\n",
        "\n",
        "We will conduct 2 pre-processing steps for this dataset,  \n",
        "1. Normalize the dataset  \n",
        "2. Format the dataset to adapt recurrent learning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLAfWQdih7Ud"
      },
      "source": [
        "### Standardization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qT-qZudgaYpm"
      },
      "source": [
        "Copy the index (i.e., actual time frame) for evaluation and visualization purposes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjzVMMIRaUo5"
      },
      "source": [
        "date_times = df.index.values"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvdvpowEahZW"
      },
      "source": [
        "Load features from the dataframe, and normalize between 0-1.  \n",
        "This scaler *object* is the min max scaler, which can be used to inverse the scale after prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2SJt65uTrOr"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H062Wyq9LRLc"
      },
      "source": [
        "Scale the dataset using the min-max scaler object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUPDHcKbUhYj"
      },
      "source": [
        "values = df.values.astype('float32')\n",
        "scaled_values = scaler.fit_transform(values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LePVf772iBRl"
      },
      "source": [
        "### Format to adapt recurrent learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75xoRb-iauJl"
      },
      "source": [
        "Define how many time intervals is required/used to predict the energy consumption.\n",
        "* For this experiment, we take 6 hours of previous energy consumption (i.e., six 1-hour intervals) - used as *n_1hour_interval*  \n",
        "* Define the number of feature columns in order to construct data records combining sequence of *n_1hour_interval* s "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGMreYfBWPsj"
      },
      "source": [
        "n_1hour_interval = 6\n",
        "n_features = df.shape[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2P3eKTQqbmCc"
      },
      "source": [
        "The following function will enable to construct input records capturing more than 1 hour of input time steps.  \n",
        "This is one of the most important pre-processing steps to use Backpropagation through time by recurrent neural networks when learning sequence predictions problems."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DcSLhQdWZbB"
      },
      "source": [
        "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
        "    n_vars = 1 if type(data) is list else data.shape[1]\n",
        "    df = pd.DataFrame(data)\n",
        "    cols, names = list(), list()\n",
        "    # input sequence (t-n, ... t-1)\n",
        "    for i in range(n_in, 0, -1):\n",
        "        cols.append(df.shift(i))\n",
        "        names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]\n",
        "    # forecast sequence (t, t+1, ... t+n)\n",
        "    for i in range(0, n_out):\n",
        "        cols.append(df.shift(-i))\n",
        "        if i == 0:\n",
        "            names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]\n",
        "        else:\n",
        "            names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]\n",
        "    # put it all together\n",
        "    agg = pd.concat(cols, axis=1)\n",
        "    agg.columns = names\n",
        "    # drop rows with NaN values\n",
        "    if dropnan:\n",
        "        agg.dropna(inplace=True)\n",
        "    return agg"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-j0bQnoWieB"
      },
      "source": [
        "df_reframed = series_to_supervised(scaled_values, n_1hour_interval, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ik5qUUSgWlr5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "20032a96-cf0f-452b-9aba-9ee66a1ff813"
      },
      "source": [
        "df_reframed.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>var1(t-6)</th>\n",
              "      <th>var1(t-5)</th>\n",
              "      <th>var1(t-4)</th>\n",
              "      <th>var1(t-3)</th>\n",
              "      <th>var1(t-2)</th>\n",
              "      <th>var1(t-1)</th>\n",
              "      <th>var1(t)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.206663</td>\n",
              "      <td>0.274741</td>\n",
              "      <td>0.277736</td>\n",
              "      <td>0.275346</td>\n",
              "      <td>0.283437</td>\n",
              "      <td>0.277141</td>\n",
              "      <td>0.264538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.274741</td>\n",
              "      <td>0.277736</td>\n",
              "      <td>0.275346</td>\n",
              "      <td>0.283437</td>\n",
              "      <td>0.277141</td>\n",
              "      <td>0.264538</td>\n",
              "      <td>0.484091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.277736</td>\n",
              "      <td>0.275346</td>\n",
              "      <td>0.283437</td>\n",
              "      <td>0.277141</td>\n",
              "      <td>0.264538</td>\n",
              "      <td>0.484091</td>\n",
              "      <td>0.553379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.275346</td>\n",
              "      <td>0.283437</td>\n",
              "      <td>0.277141</td>\n",
              "      <td>0.264538</td>\n",
              "      <td>0.484091</td>\n",
              "      <td>0.553379</td>\n",
              "      <td>0.552784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.283437</td>\n",
              "      <td>0.277141</td>\n",
              "      <td>0.264538</td>\n",
              "      <td>0.484091</td>\n",
              "      <td>0.553379</td>\n",
              "      <td>0.552784</td>\n",
              "      <td>0.554886</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    var1(t-6)  var1(t-5)  var1(t-4)  var1(t-3)  var1(t-2)  var1(t-1)   var1(t)\n",
              "6    0.206663   0.274741   0.277736   0.275346   0.283437   0.277141  0.264538\n",
              "7    0.274741   0.277736   0.275346   0.283437   0.277141   0.264538  0.484091\n",
              "8    0.277736   0.275346   0.283437   0.277141   0.264538   0.484091  0.553379\n",
              "9    0.275346   0.283437   0.277141   0.264538   0.484091   0.553379  0.552784\n",
              "10   0.283437   0.277141   0.264538   0.484091   0.553379   0.552784  0.554886"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcpNhxmfiMgW"
      },
      "source": [
        "### Train-Test split\n",
        "\n",
        "Split the dataset into train set and test set.  \n",
        "First *n_obs* parametarized number of feature columns in the dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CX5D4dbDMknY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d5a0c91-9d88-46da-a91d-57f5811fac31"
      },
      "source": [
        "n_obs = n_1hour_interval * n_features\n",
        "n_obs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvULONDdfxSr"
      },
      "source": [
        "For this experiment we use 70/30 train/test split."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvhhVeeFfoLN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bcb9c77-07e6-4e72-c5f3-1891f3747ed6"
      },
      "source": [
        "n_train_records = int(len(df_reframed.values) * .7) \n",
        "n_train_records"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6127"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jl9nb6yUS3bQ"
      },
      "source": [
        "Split the dataset for training and testing.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KN8rJI3SfcYW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c9280c1-86e3-416f-c7d5-c7da93e7c02a"
      },
      "source": [
        "train = df_reframed.values[:n_train_records, :]\n",
        "test = df_reframed.values[n_train_records:, :]\n",
        "test_date_times = date_times[n_train_records:]\n",
        "print('Train shape:', train.shape)\n",
        "print('Test shape:', test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train shape: (6127, 7)\n",
            "Test shape: (2627, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijgZHhOQTCfI"
      },
      "source": [
        "Construct X set as the features and Y set as the predictor/ or the output variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsYQtfr0hGi5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07e45ff2-0092-46ee-b697-eebd88ce2e34"
      },
      "source": [
        "X_train, y_train = train[:, :n_obs], train[:, -1]  \n",
        "X_test, y_test = test[:, :n_obs], test[:, -1]\n",
        "print('X_train shape:', X_train.shape)\n",
        "print('y_train shape:', y_train.shape)\n",
        "print('\\nX_test shape:', X_test.shape)\n",
        "print('y_test shape:', y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (6127, 6)\n",
            "y_train shape: (6127,)\n",
            "\n",
            "X_test shape: (2627, 6)\n",
            "y_test shape: (2627,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xO2zRgPbTWsJ"
      },
      "source": [
        "The inputs (X) are reshaped into the 3D format expected by RNN, namely (samples, timesteps, features)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILo91M7TNHTK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b3da7be-af81-4ac3-8eff-3ac1bddfee6e"
      },
      "source": [
        "X_train = X_train.reshape((X_train.shape[0], n_1hour_interval, n_features))\n",
        "X_test = X_test.reshape((X_test.shape[0], n_1hour_interval, n_features))\n",
        "print('X_train shape:', X_train.shape)\n",
        "print('X_test shape:', X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (6127, 6, 1)\n",
            "X_test shape: (2627, 6, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FnBRAdt4iRNV"
      },
      "source": [
        "## Modelling\n",
        "\n",
        "We design a RNN based on hierarchically stacked three layered LSTM network architecture that consists of 32, 24 and 12 LSTM Networks respectively, accommodating deeper abstraction of temporal hierarchy to be trained by the forecasting model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZO0VjqTV_rT"
      },
      "source": [
        "Model design:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWsaWoW8eldP"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(32, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(LSTM(24, return_sequences=True))\n",
        "model.add(LSTM(12))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZWv6RvBWh9v"
      },
      "source": [
        "Compile the model with [Mean Squarred Error](https://keras.io/losses/) loss functions and [ADAM optimizer](https://keras.io/optimizers/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNwwQOCSeyxa"
      },
      "source": [
        "model.compile(loss='mse', optimizer='adam')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HikHUXlcW1Mj"
      },
      "source": [
        "Train the model with parameters;  \n",
        "*   Train epochs: 50\n",
        "*   Batch size: 32\n",
        "*   Use 0.3 from the training dataset as model validation data.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZExRa8Oe0l-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bab6d15-25b3-4c76-8435-68c833d719fa"
      },
      "source": [
        "train_epochs = 50\n",
        "batch_size = 32\n",
        "validation_split = 0.3\n",
        "history = model.fit(X_train, y_train, epochs=train_epochs, batch_size=batch_size, validation_split=validation_split, verbose=1, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "134/134 [==============================] - 11s 18ms/step - loss: 0.0577 - val_loss: 0.0369\n",
            "Epoch 2/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0345 - val_loss: 0.0299\n",
            "Epoch 3/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0204 - val_loss: 0.0097\n",
            "Epoch 4/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0103 - val_loss: 0.0085\n",
            "Epoch 5/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0095 - val_loss: 0.0077\n",
            "Epoch 6/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0088 - val_loss: 0.0071\n",
            "Epoch 7/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0081 - val_loss: 0.0065\n",
            "Epoch 8/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0073 - val_loss: 0.0058\n",
            "Epoch 9/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0063 - val_loss: 0.0047\n",
            "Epoch 10/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0053 - val_loss: 0.0040\n",
            "Epoch 11/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0045 - val_loss: 0.0036\n",
            "Epoch 12/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0042 - val_loss: 0.0034\n",
            "Epoch 13/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0040 - val_loss: 0.0034\n",
            "Epoch 14/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0039 - val_loss: 0.0034\n",
            "Epoch 15/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0039 - val_loss: 0.0034\n",
            "Epoch 16/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0038 - val_loss: 0.0034\n",
            "Epoch 17/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0038 - val_loss: 0.0034\n",
            "Epoch 18/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0038 - val_loss: 0.0034\n",
            "Epoch 19/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0037 - val_loss: 0.0034\n",
            "Epoch 20/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0037 - val_loss: 0.0034\n",
            "Epoch 21/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0037 - val_loss: 0.0034\n",
            "Epoch 22/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0036 - val_loss: 0.0033\n",
            "Epoch 23/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0036 - val_loss: 0.0033\n",
            "Epoch 24/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0035 - val_loss: 0.0033\n",
            "Epoch 25/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0034 - val_loss: 0.0032\n",
            "Epoch 26/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0033 - val_loss: 0.0032\n",
            "Epoch 27/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0033 - val_loss: 0.0031\n",
            "Epoch 28/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0032 - val_loss: 0.0031\n",
            "Epoch 29/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0032 - val_loss: 0.0030\n",
            "Epoch 30/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0031 - val_loss: 0.0030\n",
            "Epoch 31/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0031 - val_loss: 0.0030\n",
            "Epoch 32/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0031 - val_loss: 0.0030\n",
            "Epoch 33/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0031 - val_loss: 0.0029\n",
            "Epoch 34/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0030 - val_loss: 0.0028\n",
            "Epoch 35/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0030 - val_loss: 0.0028\n",
            "Epoch 36/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0029 - val_loss: 0.0028\n",
            "Epoch 37/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0029 - val_loss: 0.0028\n",
            "Epoch 38/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0029 - val_loss: 0.0027\n",
            "Epoch 39/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0028 - val_loss: 0.0027\n",
            "Epoch 40/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0028 - val_loss: 0.0027\n",
            "Epoch 41/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0028 - val_loss: 0.0027\n",
            "Epoch 42/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0028 - val_loss: 0.0027\n",
            "Epoch 43/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0028 - val_loss: 0.0027\n",
            "Epoch 44/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0028 - val_loss: 0.0027\n",
            "Epoch 45/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0027 - val_loss: 0.0027\n",
            "Epoch 46/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0027 - val_loss: 0.0027\n",
            "Epoch 47/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0027 - val_loss: 0.0027\n",
            "Epoch 48/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0027 - val_loss: 0.0027\n",
            "Epoch 49/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0027 - val_loss: 0.0027\n",
            "Epoch 50/50\n",
            "134/134 [==============================] - 1s 9ms/step - loss: 0.0027 - val_loss: 0.0027\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TCkGsCDYGvJ"
      },
      "source": [
        "Visualize the learning curve."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3eHE8CkfGKO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "16694d25-49e4-4b2d-9757-a710c93ecb37"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7f68345650>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vQ107vGiWPw"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cv_lYkSnYOgh"
      },
      "source": [
        "Make predictions using the trained RNN model for the X_test (test dataset.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOmBrkpcfz01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02804dfd-b92e-480d-ad54-5840f77d52ef"
      },
      "source": [
        "yhat = model.predict(X_test, batch_size=batch_size)\n",
        "yhat.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2627, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Jvj1m4eYkX0"
      },
      "source": [
        "Reshape the test dataset for calculations. (From 3D to 2D)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ihCqKwf2xQJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1364f440-6610-4621-bd6d-bf4a68ad1112"
      },
      "source": [
        "X_test = X_test.reshape((X_test.shape[0], n_1hour_interval * n_features))\n",
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2627, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5qkg2BlZBPm"
      },
      "source": [
        "Evaluate the accuracy using root mean square error (RMSE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tC8izqihgfme",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "251defc4-e61a-4d80-bd93-1849bf17a37a"
      },
      "source": [
        "mse = mean_squared_error(y_test, yhat)\n",
        "rmse = np.sqrt(mse)\n",
        "rmse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05748086"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "osm4Nv5HV4gi",
        "outputId": "38b7dc11-8928-4805-acf5-2882691650d9"
      },
      "source": [
        " X_test[:, -7:].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2627, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GB_ze2XoZ_qE"
      },
      "source": [
        "Rescale the predictions using the min-max scaler object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5Pwxo8cggaO"
      },
      "source": [
        "inv_yhat = np.concatenate((yhat, X_test[:, -7:]), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:, 0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fVilMyGaG70"
      },
      "source": [
        "Rescale the test data (y_test) using the min-max scaler object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcMnQPgm3i9w"
      },
      "source": [
        "y_test = y_test.reshape((len(y_test), 1))\n",
        "inv_y = np.concatenate((y_test, X_test[:, -7:]), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:, 0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhtXTdumY5fS"
      },
      "source": [
        "Get the rmse for actual prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0e25V9aYLt-",
        "outputId": "55205c59-6a2e-45be-d326-3cf907e1c427"
      },
      "source": [
        "mseActual = mean_squared_error(inv_yhat, inv_y)\n",
        "rmseActual = np.sqrt(mseActual)\n",
        "nrmseActual = rmseActual/np.mean(inv_y)\n",
        "print(\"RMSE :\", rmseActual)\n",
        "print(\"NRMSE: \",nrmseActual)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE : 5.9888735\n",
            "NRMSE:  0.11231839\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Vf87rARbJW-"
      },
      "source": [
        "Setup plot axes for prediction plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCXqyz9S558o"
      },
      "source": [
        "x_axis_labels = list(map(lambda x: pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S'), test_date_times[n_1hour_interval:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGhi93SVbOVc"
      },
      "source": [
        "Plot the forecast with respect to actual energy consumption."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCaHbVTH4rza",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "aa8de952-34d7-4c0a-aeb1-3897460981f4"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(40,7))\n",
        "ax.plot(x_axis_labels, inv_y, label='actual')\n",
        "ax.plot(x_axis_labels, inv_yhat, label='predicted')\n",
        "date_formatter = DateFormatter('%y-%m-%d %H:%M')\n",
        "ax.xaxis.set_major_formatter(date_formatter)\n",
        "plt.gcf().autofmt_xdate()\n",
        "plt.legend()\n",
        "plt.ylabel('Energy Consumption (per 1 hour)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2880x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzMyHXO27QpU"
      },
      "source": [
        "Save the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIXfjWaXZAtV",
        "outputId": "afcdf33f-12ae-4038-c244-6f8a026a268d"
      },
      "source": [
        "model.save(\"LSTM_model\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses while saving (showing 5 of 15). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: LSTM_model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: LSTM_model/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRP_zUpe08FU"
      },
      "source": [
        "## Deployment\n",
        "\n",
        "We will deploy the trained model as a service using FastAPI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWh6_E1J7ZSB"
      },
      "source": [
        "\n",
        "Install required packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xaDefTdG1BX1",
        "outputId": "3aa76d15-ad9a-4226-df53-007fdade7833"
      },
      "source": [
        "!pip install colabcode\n",
        "!pip install fastapi\n",
        "!pip install  python-multipart\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting colabcode\n",
            "  Downloading colabcode-0.3.0-py3-none-any.whl (5.0 kB)\n",
            "Collecting nest-asyncio==1.4.3\n",
            "  Downloading nest_asyncio-1.4.3-py3-none-any.whl (5.3 kB)\n",
            "Collecting uvicorn==0.13.1\n",
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            "\u001b[?25hCollecting tornado>=6.1.0\n",
            "  Downloading tornado-6.1-cp37-cp37m-manylinux2010_x86_64.whl (428 kB)\n",
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            "  Downloading jupyter_server-1.10.2-py3-none-any.whl (392 kB)\n",
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            "  Downloading nbclassic-0.3.1-py3-none-any.whl (18 kB)\n",
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            "\u001b[K     |████████████████████████████████| 122 kB 53.0 MB/s \n",
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            "\u001b[?25h  Downloading jupyter_client-6.1.12-py3-none-any.whl (112 kB)\n",
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            "\u001b[?25hCollecting jsonschema>=3.0.1\n",
            "  Downloading jsonschema-3.2.0-py2.py3-none-any.whl (56 kB)\n",
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            "Collecting json5\n",
            "  Downloading json5-0.9.6-py2.py3-none-any.whl (18 kB)\n",
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            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.2->jupyterlab==3.0.7->colabcode) (1.4.3)\n",
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            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.2->jupyterlab==3.0.7->colabcode) (0.8.4)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.2->jupyterlab==3.0.7->colabcode) (0.5.0)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->jupyter-server~=1.2->jupyterlab==3.0.7->colabcode) (0.5.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->jupyterlab==3.0.7->colabcode) (2.4.7)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->jupyterlab-server~=2.0->jupyterlab==3.0.7->colabcode) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->jupyterlab-server~=2.0->jupyterlab==3.0.7->colabcode) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->jupyterlab-server~=2.0->jupyterlab==3.0.7->colabcode) (2021.5.30)\n",
            "Building wheels for collected packages: pyngrok\n",
            "  Building wheel for pyngrok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyngrok: filename=pyngrok-5.1.0-py3-none-any.whl size=19006 sha256=2c5934f7bfd3bef860a4fbf993e5bdfa76fda63822e7ad4ad61db5d42d911f90\n",
            "  Stored in directory: /root/.cache/pip/wheels/bf/e6/af/ccf6598ecefecd44104069371795cb9b3afbcd16987f6ccfb3\n",
            "Successfully built pyngrok\n",
            "Installing collected packages: tornado, jsonschema, sniffio, jupyter-client, websocket-client, requests-unixsocket, anyio, jupyter-server, json5, nbclassic, jupyterlab-server, h11, uvicorn, pyngrok, nest-asyncio, jupyterlab, colabcode\n",
            "  Attempting uninstall: tornado\n",
            "    Found existing installation: tornado 5.1.1\n",
            "    Uninstalling tornado-5.1.1:\n",
            "      Successfully uninstalled tornado-5.1.1\n",
            "  Attempting uninstall: jsonschema\n",
            "    Found existing installation: jsonschema 2.6.0\n",
            "    Uninstalling jsonschema-2.6.0:\n",
            "      Successfully uninstalled jsonschema-2.6.0\n",
            "  Attempting uninstall: jupyter-client\n",
            "    Found existing installation: jupyter-client 5.3.5\n",
            "    Uninstalling jupyter-client-5.3.5:\n",
            "      Successfully uninstalled jupyter-client-5.3.5\n",
            "  Attempting uninstall: nest-asyncio\n",
            "    Found existing installation: nest-asyncio 1.5.1\n",
            "    Uninstalling nest-asyncio-1.5.1:\n",
            "      Successfully uninstalled nest-asyncio-1.5.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\u001b[0m\n",
            "Successfully installed anyio-3.3.0 colabcode-0.3.0 h11-0.12.0 json5-0.9.6 jsonschema-3.2.0 jupyter-client-6.1.12 jupyter-server-1.10.2 jupyterlab-3.0.7 jupyterlab-server-2.7.2 nbclassic-0.3.1 nest-asyncio-1.4.3 pyngrok-5.1.0 requests-unixsocket-0.2.0 sniffio-1.2.0 tornado-6.1 uvicorn-0.13.1 websocket-client-1.2.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "jupyter_client",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting fastapi\n",
            "  Downloading fastapi-0.68.1-py3-none-any.whl (52 kB)\n",
            "\u001b[K     |████████████████████████████████| 52 kB 785 kB/s \n",
            "\u001b[?25hCollecting pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2\n",
            "  Downloading pydantic-1.8.2-cp37-cp37m-manylinux2014_x86_64.whl (10.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.1 MB 9.9 MB/s \n",
            "\u001b[?25hCollecting starlette==0.14.2\n",
            "  Downloading starlette-0.14.2-py3-none-any.whl (60 kB)\n",
            "\u001b[K     |████████████████████████████████| 60 kB 6.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2->fastapi) (3.7.4.3)\n",
            "Installing collected packages: starlette, pydantic, fastapi\n",
            "Successfully installed fastapi-0.68.1 pydantic-1.8.2 starlette-0.14.2\n",
            "Collecting python-multipart\n",
            "  Downloading python-multipart-0.0.5.tar.gz (32 kB)\n",
            "Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from python-multipart) (1.15.0)\n",
            "Building wheels for collected packages: python-multipart\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-multipart: filename=python_multipart-0.0.5-py3-none-any.whl size=31678 sha256=09d0798634b5afbb0738231c9d2ea1742084b95a505b13c1fc10bec349c2bd38\n",
            "  Stored in directory: /root/.cache/pip/wheels/2c/41/7c/bfd1c180534ffdcc0972f78c5758f89881602175d48a8bcd2c\n",
            "Successfully built python-multipart\n",
            "Installing collected packages: python-multipart\n",
            "Successfully installed python-multipart-0.0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzcUsVdlkaRz"
      },
      "source": [
        "\n",
        "# import required packages\n",
        "from typing import Optional\n",
        "\n",
        "from fastapi import FastAPI, File, UploadFile\n",
        "from fastapi.middleware.cors import CORSMiddleware\n",
        "from io import BytesIO\n",
        "import numpy as np\n",
        "import cv2\n",
        "from tensorflow.keras.models import load_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKohLtVk7M3P"
      },
      "source": [
        "Crete Fast API app instance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpnJJ2CMkeJm"
      },
      "source": [
        "app = FastAPI()\n",
        "app.add_middleware(\n",
        "    CORSMiddleware,\n",
        "    allow_origins=['*'],\n",
        "    allow_credentials=True,\n",
        "    allow_methods=['*'],\n",
        "    allow_headers=['*'],\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JCPeMqKk34n",
        "outputId": "fdd78dd6-66b7-4a88-aff0-b0085cd72188"
      },
      "source": [
        "%%writefile models.py\n",
        "from pydantic import BaseModel, conlist\n",
        "from typing import List\n",
        "\n",
        "\n",
        "class LSTM(BaseModel):\n",
        "    data: List[conlist(float, min_items=6, max_items=6)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing models.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLUCL-RdkvbZ"
      },
      "source": [
        "from models import LSTM\n",
        "@app.on_event(\"startup\")\n",
        "def load_pretrained_model():\n",
        "    global model\n",
        "    model = load_model('LSTM_model')\n",
        "\n",
        "@app.post(\"/predict/\")\n",
        "async def get_predictions(LSTM: LSTM):\n",
        "    data = dict(LSTM)['data']\n",
        "    data=np.array(data)\n",
        "    data=data.reshape(n_1hour_interval,-1)\n",
        "    scaledData =scaler.transform(data)\n",
        "    input = scaledData.flatten().reshape((1, n_1hour_interval, n_features))\n",
        "    output=model.predict(input)\n",
        "    output=scaler.inverse_transform(output)\n",
        "    finalOut=str(output[0][0])\n",
        "    return {\"status\":1,\"Prediction\": finalOut}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1poBFoWo7FEV"
      },
      "source": [
        "\n",
        "We will use ColabCode library to depoly the FastAPI app inside the colab Notebook"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5qHGebmll0q"
      },
      "source": [
        "from colabcode import ColabCode\n",
        "server = ColabCode(port=10000, code=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gh9i7AFM7JAS"
      },
      "source": [
        "\n",
        "Run the server\n",
        "\n",
        "Visit the UI using the Public URL generated.\n",
        "\n",
        "e.g. https://PublicURL/docs/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zR8eIqMpl_nk",
        "outputId": "ae4cc874-35df-4e1d-a04a-921c51178545"
      },
      "source": [
        "server.run_app(app=app)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Public URL: NgrokTunnel: \"https://03a3-34-67-92-199.ngrok.io\" -> \"http://localhost:10000\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouIp8mqJ65u7"
      },
      "source": [
        "Sample data to try out ;\n",
        "\n",
        "*   Input  : 65.187, 72.062, 86.312, 89.437, 91.063, 96.561\n",
        "*   Intended Output :  95.062\n",
        "\n",
        "\n"
      ]
    }
  ]
}