ALL_MODEL_COMPARISON.ipynb

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  "metadata": {
    "colab": {
      "name": "ALL_MODEL_COMPARISON.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNEkK44fMdrUbGq6mafes2K",
      "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/RachitBansal/Power-Forecasting/blob/master/ALL_MODEL_COMPARISON.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOz6h0i5KdZB",
        "colab_type": "code",
        "outputId": "49a07248-f841-4845-e646-62df73b16cf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /gdrive\n",
            "/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-u7FIDILKe1h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "08222254-394e-4202-a0ab-c3da24df33a3"
      },
      "source": [
        "import os\n",
        "from keras.models import load_model,Sequential \n",
        "from keras.layers import Dense\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pr4gTBPEN1i1",
        "colab_type": "code",
        "outputId": "bc6f3094-96aa-4039-ddfe-540960748d57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "\n",
        "print(\"Enter no of equipments : \")\n",
        "num_points  = int(input())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter no of equipments : \n",
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2L6SbiqiQXl0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cnn_lstm_path = \"My Drive/Greend\"\n",
        "lstm_path = \"My Drive/Greend/Saved_weights/LSTM_weights\"\n",
        "ann_path = \"My Drive/Greend/Saved_weights/ANN_weights\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSDonyyeRWnn",
        "colab_type": "code",
        "outputId": "a4b9b0ec-3143-44dc-f172-02672b50ec06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "os.listdir(\"My Drive/Greend/Saved_weights\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['LSTM_weights', 'ANN_weights', 'CNN_LSTM_weights']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2QR_WnaRX0B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cnnlstm = os.listdir(cnn_lstm_path)\n",
        "lstm = os.listdir(lstm_path)\n",
        "ann = os.listdir(ann_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4Zd0kavS8os",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "0c2e9895-3225-49ab-9bb2-c73ee3a9ef7d"
      },
      "source": [
        "Cnn = []\n",
        "Lstm  =[]\n",
        "Ann = []\n",
        "for i in range(9):\n",
        "    model1=load_model(cnn_lstm_path+'/weights{}_model_1.h5'.format(i))\n",
        "    model2=load_model(lstm_path+'/{}_model_1.h5'.format(i))\n",
        "    model3=load_model(ann_path+'/{}_model_1.h5'.format(i))\n",
        "    Cnn.append(model1)\n",
        "    Lstm.append(model2)\n",
        "    Ann.append(model3)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OC3R0RlHGPrn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_data(path):\n",
        "    x_test = []\n",
        "    y_test = []\n",
        "    for i in range(num_points):\n",
        "        filenamex = path+\"/xt_greend_eq{}.npy\".format(i)\n",
        "        filenamey = path+\"/yt_greend_eq{}.npy\".format(i)\n",
        "        x_temp = np.load(filenamex)/300\n",
        "        y_temp = np.load(filenamey)/300\n",
        "        lent = x_temp.shape[0]\n",
        "        lent = int(0.8*lent)\n",
        "        x_temp = x_temp[:lent]\n",
        "        y_temp = y_temp[:lent]\n",
        "        x_test.append(x_temp)\n",
        "        y_test.append(y_temp)\n",
        "    return x_test,y_test    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUAb5MsoHMfd",
        "colab_type": "code",
        "outputId": "e1f5b687-0fae-4412-cc92-2bc699f01ce7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(\"Enter the number of points for Predictions and graphs : \")\n",
        "points = int(input())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the number of points for Predictions and graphs : \n",
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmb6nomQUFPB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test,y_test = load_data(cnn_lstm_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0-AicYPUG68",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def all_model_graphs(eq):\n",
        "    i = eq\n",
        "    cnnpred = Cnn[i].predict(x_test[i][:points].reshape(points,128,1)).reshape(-1,)\n",
        "    lstmpred = Lstm[i].predict(x_test[i][:points].reshape(points,128,1)).reshape(-1,)\n",
        "    annpred = Ann[i].predict(x_test[i][:points].reshape(-1,128)).reshape(-1,)\n",
        "    ensemblepred = np.load(cnn_lstm_path+\"/results_ensemble{}_model_500.npy\".format(i))\n",
        "    ensemblepred = ensemblepred[:points].reshape(-1)\n",
        "    realpoints = y_test[i][:points].reshape(-1,)\n",
        "    try:\n",
        "        arimapred = np.load(cnn_lstm_path+\"/ARIMA_RESULT_eq{}.npy\".format(i))\n",
        "        arimapred = arimapred[:points]\n",
        "    except:\n",
        "        print(\"ARIMA reading not present!!!!!!!\")\n",
        "    print(\"Graph of equipment {} : \\n\".format(i+1))\n",
        "    try:\n",
        "        plt.plot(arimapred,label  = \"arima\")\n",
        "    except:\n",
        "        None\n",
        "    plt.plot(cnnpred,label = \"cnn-lstm\")\n",
        "    plt.plot(lstmpred,label = \"lstm\")\n",
        "    plt.plot(annpred,label = \"ann\")\n",
        "    plt.plot(ensemblepred,label = \"ensemble\")\n",
        "    plt.plot(realpoints,label = \"Actual Values\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"Metrics of equipment {} : \\n\\n\".format(i+1))\n",
        "    print(\"CNN_LSTM: \\n\")\n",
        "    error = mean_squared_error(realpoints, cnnpred)\n",
        "    print('Test MSE: %.3f' % error)\n",
        "    print(\"RMSE : %.3f\"%(np.sqrt(error)))\n",
        "    print(\"MAE : %.3f\"%(mean_absolute_error(realpoints,cnnpred)))\n",
        "        \n",
        "    print(\"\\n\\nLSTM: \\n\")\n",
        "    error = mean_squared_error(realpoints, lstmpred)\n",
        "    print('Test MSE: %.3f' % error)\n",
        "    print(\"RMSE : %.3f\"%(np.sqrt(error)))\n",
        "    print(\"MAE : %.3f\"%(mean_absolute_error(realpoints,lstmpred)))\n",
        "\n",
        "    print(\"\\n\\nANN: \\n\")\n",
        "    error = mean_squared_error(realpoints, annpred)\n",
        "    print('Test MSE: %.3f' % error)\n",
        "    print(\"RMSE : %.3f\"%(np.sqrt(error)))\n",
        "    print(\"MAE : %.3f\"%(mean_absolute_error(realpoints,annpred)))\n",
        "\n",
        "    print(\"\\n\\nENSEMBLE : \\n\")\n",
        "    error = mean_squared_error(realpoints, ensemblepred)\n",
        "    print('Test MSE: %.3f' % error)\n",
        "    print(\"RMSE : %.3f\"%(np.sqrt(error)))\n",
        "    print(\"MAE : %.3f\"%(mean_absolute_error(realpoints,ensemblepred)))\n",
        "    try:\n",
        "        print(\"\\n\\nARIMA : \\n\")\n",
        "        error = mean_squared_error(realpoints,arimapred)\n",
        "        print('Test MSE: %.3f' % error)\n",
        "        print(\"RMSE : %.3f\"%(np.sqrt(error)))\n",
        "        print(\"MAE : %.3f\"%(mean_absolute_error(realpoints,arimapred)))\n",
        "    except:\n",
        "        None\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZ1wDs-LLwWD",
        "colab_type": "code",
        "outputId": "a4ef8fbf-7a44-4761-ed10-dfff2a1fbb06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        }
      },
      "source": [
        "all_model_graphs(0)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Graph of equipment 1 : \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tXcsKx5nxIIJ3FYHu5Vm9qDI5VerL6mQiL3gdP5wCFzfhjhBF/0lztGlnR8Pc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\nCkS1smr5igAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Metrics of equipment 1 : \n",
            "\n",
            "\n",
            "CNN_LSTM: \n",
            "\n",
            "Test MSE: 1615.965\n",
            "RMSE : 40.199\n",
            "MAE : 25.321\n",
            "\n",
            "\n",
            "LSTM: \n",
            "\n",
            "Test MSE: 39946.565\n",
            "RMSE : 199.866\n",
            "MAE : 138.547\n",
            "\n",
            "\n",
            "ANN: \n",
            "\n",
            "Test MSE: 1268.352\n",
            "RMSE : 35.614\n",
            "MAE : 18.593\n",
            "\n",
            "\n",
            "ENSEMBLE : \n",
            "\n",
            "Test MSE: 1281.254\n",
            "RMSE : 35.795\n",
            "MAE : 21.351\n",
            "\n",
            "\n",
            "ARIMA : \n",
            "\n",
            "Test MSE: 53374.278\n",
            "RMSE : 231.029\n",
            "MAE : 195.696\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScR3hAUwLyrT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}