Ensemble_Ukdale.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "Ensemble_Ukdale.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RachitBansal/AppliancePower_TimeSeries/blob/master/Ensemble_Ukdale.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rU0SxZHFwyah",
        "colab_type": "code",
        "outputId": "854a15c0-84a3-4939-e18d-0be1101e7807",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "####code to  upload the data and resample the data (day wise)\n",
        "from sklearn.externals import joblib\n",
        "from keras.layers import Dense ,LSTM\n",
        "from keras.models import Sequential \n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from math import sqrt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import pyplot\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import RepeatVector\n",
        "from keras.layers import TimeDistributed\n",
        "from keras.layers.convolutional import Conv1D\n",
        "from keras.layers.convolutional import MaxPooling1D\n",
        "from keras.layers import Flatten\n",
        "from sklearn.metrics import r2_score as r2\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "# import r2\n",
        "# import mane\n",
        "\n",
        "import keras.backend as K\n",
        "\n",
        "import os"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n",
            "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 soon switch to TensorFlow 2.x.<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": "vwHoyoEboN9L",
        "colab_type": "code",
        "outputId": "c2c67169-72b7-481d-c45d-6ce864bb5984",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": 2,
      "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 /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJUZ2WAuwyat",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras.backend as K\n",
        "\n",
        "def r2_score(y_true, y_pred):\n",
        "    SS_res =  K.sum(K.square(y_true-y_pred))\n",
        "    SS_tot = K.sum(K.square(y_true - K.mean(y_true)))\n",
        "    return (1 - SS_res/(SS_tot + K.epsilon()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "WfbXbhRfwybJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def return_data(final_data, i=0, steps=128, training_fraction=0.6):\n",
        "\n",
        "    # training_fraction = float(input(\"Enter the fraction of data to be sent to training :\"))\n",
        "    length = len(final_data)\n",
        "    train_size = int(training_fraction*length)\n",
        "    \n",
        "    # copy = final_data[:,i].reshape(-1,1)\n",
        "    # sc = MinMaxScaler()\n",
        "    # copy = sc.fit_transform(copy)\n",
        "    \n",
        "    #now we are going to divide into training and test set\n",
        "    x_train = final_data[:train_size, i]\n",
        "    y_train = final_data[:train_size, i]\n",
        "    #x_train = x_train.reshape((-1,1,1))\n",
        "    train_x = []\n",
        "    train_y = []\n",
        "    # print(\"enter n steps : \")\n",
        "    n_steps = steps\n",
        "    list_i = 0\n",
        "    print(\"Entering loop\")\n",
        "    print(x_train.shape)\n",
        "    print(y_train.shape)\n",
        "    k=0\n",
        "    while k<x_train.shape[0]-n_steps-1:\n",
        "        train = x_train[k:k+n_steps]\n",
        "        train_x.append([float(x) for x in train])\n",
        "        train_y.append(y_train[k+n_steps])\n",
        "        k= k+1\n",
        "    \n",
        "    train_x = np.asarray(train_x)\n",
        "    train_y = np.asarray(train_y)    \n",
        "    train_x = train_x.reshape((-1,n_steps,1))\n",
        "    train_y = train_y.reshape((-1,1,1))\n",
        "    \n",
        "    x_test = final_data[train_size:, i]\n",
        "    y_test = final_data[train_size:, i]\n",
        "    #x_test = x_test.reshape((-1,1,1))\n",
        "    test_x = []\n",
        "    test_y = []\n",
        "    k=0\n",
        "    while k < x_test.shape[0]-n_steps-1:\n",
        "        test= x_test[k:k+n_steps]\n",
        "        test_x.append([x for x in test])\n",
        "        test_y.append(y_test[k+n_steps])\n",
        "        k = k+1\n",
        "    test_x = np.asarray(test_x)\n",
        "    test_y = np.asarray(test_y)\n",
        "    \n",
        "    test_x = test_x.reshape((-1,n_steps,1))\n",
        "    test_y = test_y.reshape((-1,1,1))\n",
        "    \n",
        "    '''\n",
        "    regressor = Sequential()\n",
        "    regressor.add(LSTM(units = 10,activation = \"relu\",input_shape= (None,1)))\n",
        "    regressor.add(Dense(units = 1))\n",
        "    regressor.compile(optimizer = \"adam\",loss = \"mean_squared_error\",metrics = [\"accuracy\"])\n",
        "    regressor.fit(train_x,train_y,epochs = 100 ,batch_size = 7,validation_split = 0.1)\n",
        "    '''\n",
        "    # print(\"Enter 1 if you want to process next equipment and 0 if not :\")\n",
        "    # decision = int(input())\n",
        "    # if decision==1:\n",
        "    #     pass\n",
        "    # else:\n",
        "    #     break\n",
        "\n",
        "    return train_x, test_x, train_y, test_y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_cr0a9KjX57",
        "colab_type": "code",
        "outputId": "9b97667a-5180-4411-cef1-86c3225d65b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "# equipment = int(input(\"Equipment: \"))\n",
        "# file_path = './drive/My Drive/ukdale_equip_'+str(equipment)+'_house1.pkl'\n",
        "# data = joblib.load(file_path)\n",
        "# data = np.array(data)\n",
        "# final_data = data\n",
        "# train_x, test_x, train_y, test_y = return_data(final_data)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Equipment: 6\n",
            "Entering loop\n",
            "(281457,)\n",
            "(281457,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMFHjoOUb0bj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.save(file='./drive/My Drive/ukdale_5.npy', arr=test_x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAd7k-9KOSyK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "0f9f926f-36dc-4f87-d2aa-f440c38e517c"
      },
      "source": [
        "# def load_data_all_eq(equipment):\n",
        "#   if(equipment!=4 and equipment!=5):\n",
        "#     file_path = './drive/My Drive/UkDale/ukdale_equip_'+str(equipment)+'_house1.pkl'\n",
        "#     data = joblib.load(file_path)\n",
        "#     data = np.array(data)\n",
        "#     final_data = data\n",
        "#     train_x, test_x, train_y, test_y = return_data(final_data)\n",
        "#     np.save(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_x.npy', arr=train_x)\n",
        "#     np.save(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_y.npy', arr=train_y)\n",
        "#     np.save(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_tx.npy', arr=test_x)\n",
        "#     np.save(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_ty.npy', arr=test_y)\n",
        "#     print(equipment, \" done\")\n",
        "\n",
        "# for i in range(14):\n",
        "#   load_data_all_eq(i+1)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entering loop\n",
            "(281456,)\n",
            "(281456,)\n",
            "1  done\n",
            "Entering loop\n",
            "(281456,)\n",
            "(281456,)\n",
            "2  done\n",
            "Entering loop\n",
            "(281456,)\n",
            "(281456,)\n",
            "3  done\n",
            "Entering loop\n",
            "(281457,)\n",
            "(281457,)\n",
            "6  done\n",
            "Entering loop\n",
            "(281457,)\n",
            "(281457,)\n",
            "7  done\n",
            "Entering loop\n",
            "(280968,)\n",
            "(280968,)\n",
            "8  done\n",
            "Entering loop\n",
            "(275409,)\n",
            "(275409,)\n",
            "9  done\n",
            "Entering loop\n",
            "(281456,)\n",
            "(281456,)\n",
            "10  done\n",
            "Entering loop\n",
            "(275409,)\n",
            "(275409,)\n",
            "11  done\n",
            "Entering loop\n",
            "(275409,)\n",
            "(275409,)\n",
            "12  done\n",
            "Entering loop\n",
            "(275409,)\n",
            "(275409,)\n",
            "13  done\n",
            "Entering loop\n",
            "(275409,)\n",
            "(275409,)\n",
            "14  done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Xnpue9CP1Nc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bc6b6496-a5be-4868-aa9a-3ca66bdef79e"
      },
      "source": [
        "equipment = input(\"equipment: \")\n",
        "train_x = np.load(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_x.npy')\n",
        "train_y = np.load(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_y.npy')\n",
        "test_x = np.load(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_tx.npy')\n",
        "test_y = np.load(file='./drive/My Drive/UkDale/ukdale_'+str(equipment)+'_ty.npy')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "equipment: 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWCAMPYdwybO",
        "colab_type": "code",
        "outputId": "7d8dc67c-60e0-4632-e9fc-f95c75f996ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(281328, 128, 1) (281328, 1, 1) (187509, 128, 1) (187509, 1, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3LEcdkykrwW",
        "colab_type": "code",
        "outputId": "7352d863-8191-4aff-94dc-7e6e60a3f489",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(np.mean(train_x.reshape(-1)), np.mean(train_y.reshape(-1)))\n",
        "print(np.mean(test_x.reshape(-1)), np.mean(test_y.reshape(-1)))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.6438399292263006 2.6419917913135795\n",
            "5.671209476111833 5.671206039887863\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXa7rGl4wybT",
        "colab_type": "text"
      },
      "source": [
        "## Ensemble:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evhtKS4dwybU",
        "colab_type": "text"
      },
      "source": [
        "### Sub-Models:\n",
        "- Using 3 CNN_LSTMs "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3hySRFgwybW",
        "colab_type": "code",
        "outputId": "cef71ad1-7b2a-4c82-8460-aac4c5c7b8e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "def fit_cnn_lstm(x_train, y_train):\n",
        "    \n",
        "    n_timesteps, n_features = x_train.shape[1], x_train.shape[2]\n",
        "    \n",
        "    model = Sequential()\n",
        "    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,1)))\n",
        "    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
        "    model.add(MaxPooling1D(pool_size=2))\n",
        "    model.add(Flatten())\n",
        "    model.add(RepeatVector(1))\n",
        "    model.add(LSTM(200, activation='relu', return_sequences=True))\n",
        "    model.add(TimeDistributed(Dense(100, activation='relu')))\n",
        "    model.add(TimeDistributed(Dense(1)))\n",
        "    model.summary()\n",
        "    model.compile(loss='mse', optimizer='adam',metrics = [\"accuracy\"])\n",
        "\n",
        "    model.fit(x_train, y_train, epochs=10, batch_size=128, verbose = 1)\n",
        "    \n",
        "    return model\n",
        "\n",
        "# os.makedirs('ensemble')\n",
        "\n",
        "# fit and save models\n",
        "n_members = 3\n",
        "for i in range(n_members):\n",
        "    model = fit_cnn_lstm(train_x, train_y)\n",
        "    filename = './drive/My Drive/ukdale5_model_' + str(i + 1) + '.h5'\n",
        "    model.save(filename)\n",
        "    print('>>> Saved %s' % filename)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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: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",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_1 (Conv1D)            (None, 126, 64)           256       \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 124, 64)           12352     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 62, 64)            0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 3968)              0         \n",
            "_________________________________________________________________\n",
            "repeat_vector_1 (RepeatVecto (None, 1, 3968)           0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 1, 200)            3335200   \n",
            "_________________________________________________________________\n",
            "time_distributed_1 (TimeDist (None, 1, 100)            20100     \n",
            "_________________________________________________________________\n",
            "time_distributed_2 (TimeDist (None, 1, 1)              101       \n",
            "=================================================================\n",
            "Total params: 3,368,009\n",
            "Trainable params: 3,368,009\n",
            "Non-trainable params: 0\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 /usr/local/lib/python3.6/dist-packages/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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Epoch 1/10\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: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",
            "281328/281328 [==============================] - 46s 163us/step - loss: 117.5093 - acc: 0.0941\n",
            "Epoch 2/10\n",
            "175872/281328 [=================>............] - ETA: 11s - loss: 60.1183 - acc: 0.1159"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fb-kn9OWwybc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "def load_all_models(n_models):\n",
        "    all_models = list()\n",
        "    for i in range(n_models):\n",
        "        # define filename for this ensemble\n",
        "        filename = './drive/My Drive/ukdale5_model_' + str(i + 1) + '.h5'\n",
        "        # load model from file\n",
        "        model = load_model(filename)\n",
        "        # add to list of members\n",
        "        all_models.append(model)\n",
        "        print('>>> loaded %s' % filename)\n",
        "    return all_models\n",
        "\n",
        "n_members = 3\n",
        "members = load_all_models(n_members)\n",
        "print('Loaded %d models' % len(members))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMxJxeZ6wybh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#### Evaluating the 3 CNN_LSTMS Individually"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0J5Ij0Twybn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for model in members:\n",
        "    # testy_enc = to_categorical(testy)\n",
        "    _, acc = model.evaluate(test_x, test_y, verbose=0)\n",
        "    print('Model Accuracy: %.3f' % acc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2F48COSorqVg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = []\n",
        "for j in range(n_members):\n",
        "  model_preds = []\n",
        "  for i in range(1000):\n",
        "      model_preds.append(members[j].predict(test_x[i].reshape(1,-1,1))[0][0][0])\n",
        "  preds.append(model_preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7CVOG3crwon",
        "colab_type": "code",
        "outputId": "bfb9809b-3690-4491-b6da-924606ea0ccf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(test_y[:300].reshape(-1), c = 'orange')\n",
        "list_colrs = ['red', 'blue', 'green']\n",
        "for j in range(n_members):\n",
        "  plt.plot(preds[j], c = list_colrs[j])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DS8Dza4mwybq",
        "colab_type": "text"
      },
      "source": [
        "### Creating Dataset to go into XGBoost\n",
        "- Outputs from the 3 sub models flow into XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvlHse1vwybs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def stacked_dataset(members, inputX):\n",
        "    stackX = None\n",
        "    for model in members:\n",
        "        # make prediction\n",
        "        yhat = model.predict(inputX, verbose=0)\n",
        "        # stack predictions into [rows, members, probabilities]\n",
        "        if stackX is None:\n",
        "            stackX = yhat\n",
        "        else:\n",
        "            stackX = np.dstack((stackX, yhat))\n",
        "\n",
        "    # flatten [rows, members x probabilities]\n",
        "    stackX = stackX.reshape((stackX.shape[0], stackX.shape[1]*stackX.shape[2]))\n",
        "    return stackX"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9vulhTywybw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "import xgboost"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfAGnlgSwybz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit_stacked_model(members, inputX, inputy, n_est=45, alpha = 10):\n",
        "    # create dataset using ensemble\n",
        "    stackedX = stacked_dataset(members, inputX)\n",
        "    # fit standalone model\n",
        "    model = xgboost.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1, max_depth = 5, alpha = alpha, n_estimators = n_est)\n",
        "    model.fit(stackedX, inputy)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwOXaNTUwyb4",
        "colab_type": "code",
        "outputId": "953f8e13-1901-4e11-af84-2b9cf50ac721",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model = fit_stacked_model(members, test_x, test_y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[15:21:18] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xab7e8jVwyb7",
        "colab_type": "text"
      },
      "source": [
        "### Compiling Ensemble Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMVRTOGjwyb9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def stacked_prediction(members, model, inputX):\n",
        "    # create dataset using ensemble\n",
        "    stackedX = stacked_dataset(members, inputX)\n",
        "    # make a prediction\n",
        "    yhat = model.predict(stackedX)\n",
        "    return yhat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3nIDeYFwycC",
        "colab_type": "text"
      },
      "source": [
        "### Evaluating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzbHSGRewycD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# yhat = stacked_prediction(members, model, test_x)\n",
        "# acc = r2_score(testy, yhat)\n",
        "# print('--> Stacked Test Accuracy: %.3f' % acc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X15ccjv-v5FC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# for i in range(1000):\n",
        "#   print(test_x[i].reshape(1,-1,1).shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS2wOMaPtoZd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_e_list = [15,20,25,30,35]           # number of estimators going into xgboost\n",
        "for n_e in n_e_list:\n",
        "  model = fit_stacked_model(members, test_x, test_y, n_e)  \n",
        "  preds = stacked_prediction(members, model, test_x[:1000])\n",
        "  plt.plot(preds)\n",
        "  plt.plot(test_y[:1000].reshape(-1))\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKhzi7M1yxiU",
        "colab_type": "code",
        "outputId": "3d329795-816c-4129-c224-1a9a390807ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "model = fit_stacked_model(members, test_x, test_y, 40)  \n",
        "preds = stacked_prediction(members, model, test_x[:1000])\n",
        "plt.plot(preds)\n",
        "plt.plot(test_y[:1000].reshape(-1))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20:14:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJUqUKFFAlMK9RIkSJQqI/w+2mKj3P923OwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwYCG-h8zNIQ",
        "colab_type": "code",
        "outputId": "a6590fe3-4b98-4cc7-f9c8-99585a2eb633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "model = fit_stacked_model(members, test_x, test_y, 50)  \n",
        "preds = stacked_prediction(members, model, test_x[:1000])\n",
        "plt.plot(preds)\n",
        "plt.plot(test_y[:1000].reshape(-1))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20:15:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sZgTQ-41BhK",
        "colab_type": "code",
        "outputId": "6c1a3097-ce99-478a-8f7d-0b0bc540e1b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "model = fit_stacked_model(members, test_x, test_y, 60)  \n",
        "preds = stacked_prediction(members, model, test_x[:1000])\n",
        "plt.plot(preds)\n",
        "plt.plot(test_y[:1000].reshape(-1))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20:23:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKCBK4V6iRIkSBUQp3EuUKFGigPj/AUP/mR8l6dkxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHaanLpB1g94",
        "colab_type": "code",
        "outputId": "b467a27e-1472-4497-8f97-65e27f35427a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "model = fit_stacked_model(members, test_x, test_y, 60)  \n",
        "preds = stacked_prediction(members, model, test_x[:200])\n",
        "plt.plot(preds)\n",
        "plt.plot(test_y[:200].reshape(-1))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[21:46:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5m4YhpFAzNwNwzASyP8BiFsfyUrm8VUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkffNoBE1w-7",
        "colab_type": "code",
        "outputId": "05081cae-a374-4b45-c3c9-344d08dae0a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# model = fit_stacked_model(members, test_x, test_y, 60)  \n",
        "preds = stacked_prediction(members, model, test_x[:200])\n",
        "plt.plot(preds)\n",
        "plt.plot(test_y[:200].reshape(-1))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7um1/wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EXz4NzJ3K7E",
        "colab_type": "code",
        "outputId": "d74d1052-19fb-46f4-9e6d-ca5c105d4e12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model = fit_stacked_model(members, test_x, test_y, 100, 20)  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20:34:26] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSe3j1Xn7zwo",
        "colab_type": "code",
        "outputId": "32ef8ec0-e1c9-488e-a6f1-82b163061bee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "t1 = time.time()\n",
        "e_preds = stacked_prediction(members, model, test_x[150:200])\n",
        "t2 = time.time()\n",
        "for i in range(50):\n",
        "  members[0].predict(test_x[150+i].reshape(1,-1,1))[0][0][0]\n",
        "t3 = time.time()\n",
        "print(\"Time taken by Ensemble to test 50 samples: \", t2-t1, \" seconds\")\n",
        "print(\"Time taken by CNN-LSTM to test 50 samples: \", t3-t2, \" seconds\")\n",
        "print(\"Time taken by ARIMA to test 50 samples: \", 0.10671567916870117, \" seconds\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time taken by Ensemble to test 50 samples:  0.03192448616027832  seconds\n",
            "Time taken by CNN-LSTM to test 50 samples:  0.13576292991638184  seconds\n",
            "Time taken by ARIMA to test 50 samples:  0.10671567916870117  seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2T9phmu15Uf",
        "colab_type": "code",
        "outputId": "4a20ff40-e475-4b6d-b085-b0e3cd3790e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "preds = []\n",
        "for j in range(n_members):\n",
        "  model_preds = []\n",
        "  for i in range(100):\n",
        "      model_preds.append(members[j].predict(test_x[800+i].reshape(1,-1,1))[0][0][0])\n",
        "  preds.append(model_preds)\n",
        "e_preds = stacked_prediction(members, model, test_x[800:900])\n",
        "preds.append(e_preds)\n",
        "\n",
        "plt.plot(test_y[800:900].reshape(-1), c = 'orange')\n",
        "list_colrs = ['red', 'blue', 'green', 'black']\n",
        "labels = ['cnn-lstm 1', 'cnn-lstm 2', 'cnn-lstm 3', 'xgboost ensemble']\n",
        "for j in range(n_members+1):\n",
        "  plt.plot(preds[j], c = list_colrs[j], label=labels[j])\n",
        "  plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP+LznDf1A4oyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQRCNwZwF47h",
        "colab_type": "code",
        "outputId": "8dfe972e-fcd3-4fcf-ceee-a3722b0d856d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "arima_preds = np.load('./drive/My Drive/arima_preds_200.npy')\n",
        "\n",
        "preds = []\n",
        "lstm_preds = []\n",
        "for i in range(200):\n",
        "  lstm_preds.append(members[0].predict(test_x[i].reshape(1,-1,1))[0][0][0])\n",
        "e_preds = stacked_prediction(members, model, test_x[:200])\n",
        "preds.append(lstm_preds)\n",
        "preds.append(e_preds)\n",
        "preds.append(arima_preds)\n",
        "print(len(preds))\n",
        "\n",
        "plt.plot(test_y[:200].reshape(-1), c = 'orange', label='actual')\n",
        "list_colrs = ['red', 'black', 'blue']\n",
        "labels = ['cnn-lstm', 'xgboost ensemble', 'ARIMA']\n",
        "for j in range(3):\n",
        "  plt.plot(preds[j], c = list_colrs[j], label=labels[j])\n",
        "  plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CCwsljI6vsO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJWz1zM56yGq",
        "colab_type": "code",
        "outputId": "1843400f-3287-44e0-d84b-bf1b19e160a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "time.time()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1576855756.3322115"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHado0yIKPwv",
        "colab_type": "code",
        "outputId": "608a0895-a30f-4e58-d228-71dfe8980bf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "arima_preds = np.load('./drive/My Drive/arima_preds_200.npy')\n",
        "\n",
        "preds = []\n",
        "lstm_preds = []\n",
        "for i in range(50):\n",
        "  lstm_preds.append(members[0].predict(test_x[i].reshape(1,-1,1))[0][0][0])\n",
        "e_preds = stacked_prediction(members, model, test_x[:50])\n",
        "preds.append(lstm_preds)\n",
        "preds.append(e_preds)\n",
        "preds.append(arima_preds[:50])\n",
        "print(len(preds))\n",
        "\n",
        "plt.plot(test_y[:50].reshape(-1), c = 'orange', label='actual')\n",
        "list_colrs = ['red', 'black', 'blue']\n",
        "labels = ['cnn-lstm', 'xgboost ensemble', 'ARIMA']\n",
        "for j in range(3):\n",
        "  plt.plot(preds[j], c = list_colrs[j], label=labels[j])\n",
        "  plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCXchhDAgCXchhDAgCXchhDCg/wdLF8keNdEGqAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dsixwtjIJBQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "arima_preds = np.load('./drive/My Drive/arima_preds_1000_eq3.npy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4TJVurFHZSP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_comparison_graph(n_test, idx=0):\n",
        "    preds = []\n",
        "    lstm_preds = []\n",
        "    for i in range(n_test):\n",
        "      lstm_preds.append(members[0].predict(test_x[i].reshape(1,-1,1))[0][0][0])\n",
        "    e_preds = stacked_prediction(members, model, test_x[idx:idx+n_test])\n",
        "    preds.append(lstm_preds)\n",
        "    preds.append(e_preds)\n",
        "    preds.append(arima_preds[idx:idx+n_test])\n",
        "    print(len(preds))\n",
        "\n",
        "    plt.plot(test_y[idx:idx+n_test].reshape(-1), c = 'orange', label='actual')\n",
        "    list_colrs = ['red', 'black', 'blue']\n",
        "    labels = ['cnn-lstm', 'xgboost ensemble', 'ARIMA']\n",
        "    for j in range(3):\n",
        "      plt.plot(preds[j], c = list_colrs[j], label=labels[j])\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AF-O4MN1O-r",
        "colab_type": "code",
        "outputId": "cf1803be-3aba-41aa-fb2e-40179dc52f98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "make_comparison_graph(1000, idx=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-e3868a42f75f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmake_comparison_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-61-d251c0fc3194>\u001b[0m in \u001b[0;36mmake_comparison_graph\u001b[0;34m(n_test, idx)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_preds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me_preds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marima_preds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mn_test\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'arima_preds' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wo4-VxhrI9ku",
        "colab_type": "code",
        "outputId": "c76af942-3b36-4d1f-b26a-819a209f8d11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(0, 100, 10):\n",
        "  make_comparison_graph(10, 300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3deVxU9f7H8ddXwBB3Ra/mclHUilzI\nkEXKm7jgvi/kVlpqpaV578+t3WtmZbfFbpJe0zQ1y5UyxQzIDUXMfUlFScENNxQT2b6/P44gJCLI\nDAdmPs/HYx7DzJw55zMDvOfM93zP96u01gghhLBNpcwuQAghhPVIyAshhA2TkBdCCBsmIS+EEDZM\nQl4IIWyYo9kFZOfq6qrd3NzMLkMIIUqUnTt3XtBaV8vtsWIV8m5ubkRHR5tdhhBClChKqT/u9pg0\n1wghhA2TkBdCCBsmIS+EEDZMQl4IIWyYhLwQQtgwCXkhhLBhEvJCCGHDJOSFEHZr3bp1bNq0yewy\nrEpCXghhd7TWTJ8+nY4dO9KmTRvWrFljdklWIyEvhLAraWlpjBo1ikmTJtG/f3+aNm1Kr169CA0N\nNbs0q5CQF0LYjevXr9OrVy9mzZrFhAkTWLx4MevXr8fDw4MePXrwyy+/mF2ixUnICyHswvnz5wkI\nCODHH3/k888/Z/r06ZQqVYoqVarw888/07BhQ7p27UpERITZpVqUhLwQwuYdO3aMli1bsnfvXlas\nWMGoUaNyPO7q6sqGDRtwc3OjS5cubN682aRKLU9CXghh07Zv346fnx9XrlwhLCyMHj165Lpc9erV\n+eWXX6hVqxYdO3YkMjKyiCu1Dgl5IYTNCgkJoXXr1lSoUIGtW7fi5+eX5/I1a9YkLCyMv/3tb3To\n0IEdO3YUUaXWIyEvhLBJs2bNomfPnjRu3JjIyEgaNWqUr+fVqlWL8PBwqlatSvv27fntt9+sXKl1\nScgLIWxKRkYGkyZN4qWXXqJTp06Eh4dTvXr1Aq2jTp06hIWFUaFCBdq1a8eePXusVK31ScgLIWxG\nSkoKQ4YMYfr06YwcOZKVK1dStmzZ+1qXm5sb4eHhuLi40LZtW/bv32/haouGhLwQwiYkJibSsWNH\nFi1axLRp05g1axaOjoWb4bR+/fqEhYXh5OREmzZtOHTokIWqLToS8kKIEi8uLo4nn3ySjRs3smDB\nAiZNmoRSyiLrbtiwIeHh4SilCAgI4MiRIxZZb1GRkBdClGj79u3D19eX2NhY1q5dy+DBgy2+jYce\neoiwsDDS09MJCAggJibG4tuwFgl5IUSJFRYWxhNPPIHWmk2bNtG2bVurbcvDw4MNGzZw48YNWrdu\nzYkTJ6y2LUuSkBdClEiLFy+mQ4cO1KlTh23bttGsWTOrb7Np06Zs2LCBa9euERAQwMmTJ62+zcKS\nkBdClChaa95//30GDhyIv78/mzdvpk6dOkW2/ccee4yff/6Zy5cvExAQQHx8fJFt+35IyAshSoz0\n9HRGjx7NxIkTCQoKYt26dVSqVKnI6/Dy8iI0NJTz58/TunVrzpw5U+Q15JeEvBCiRPjzzz/p3bs3\nX3zxBePHj2fRokU88MADptXj4+PD2rVrOX36NAEBAZw7d860WvIiIS+EKPYSEhIICAggJCSEmTNn\n8v7771OqlPnx5e/vz08//cTJkydp06YNCQkJZpd0B/PfJSGEyENMTAwtW7Zkz549LF++nNGjR5td\nUg6tWrXihx9+ICYmhrZt23Lx4kWzS8pBQl4IUWxFRUXh5+fH5cuXCQsLo2fPnmaXlKuAgABWr17N\n77//Trt27bh8+bLZJWWRkBdCFEshISE89dRTlC9fPl/DBJutffv2rFixggMHDhAYGEhiYqLZJQES\n8kKIYig4OPi+hgk2W6dOnVi2bBm7du2iQ4cOXL161eySJOSFEMWH1prJkyfz4osv3vcwwWbr2rUr\nS5cuZceOHXTu3JmkpCRT65GQF0IUC5nDBL/33nuMGDGiUMMEm61Xr14sXryYrVu30rVrV/7880/T\napGQF0KYLjExkU6dOvHNN9/w7rvvEhwcXOhhgs3Wr18/Fi5cyMaNG+nWrRs3btwwpQ6rv4tKqVjg\nGpAOpGmtvay9TSFEyREXF0enTp04dOgQX3/9NUOGDDG7JIsZMGAAaWlpPPvss/Ts2ZNVq1bh7Oxc\npDUU1Udla631hSLalhCihNi/fz8dO3YkMTGRn376iXbt2pldksUNGTKEtLQ0nnvuOfr06cPy5cuL\n9Exdaa4RQpgiPDycJ554goyMDDZt2mSTAZ9p2LBhBAcHs2bNGvr3709qamqRbbso9uQ1sF4ppYEv\ntdazi2CbQgggNTWV2NhYYmJisi6nT59Ga21qXenp6YSEhNCwYUPWrl1L3bp1Ta2nKIwcOZLU1FRe\nfvllnn76aZYsWYKTk5PVt1sUIf+E1jpeKVUd+FkpdVhrvTHzQaXUCGAEYBe/aCEs7fr16zlCPCYm\nhmPHjhETE8PJkydJT0/PWrZMmTLUrl0bBwcHEys2dOvWjTlz5lC5cmWzSykyo0ePJjU1lXHjxjF4\n8GC++eYbqx9gtnrIa63jb12fV0qtBLyBjdkenw3MBvDy8jJ390KIYkhrzaVLl7KCO3uIx8TEcPbs\n2RzLV6lSBXd3d3x9fRk4cCDu7u40aNAAd3d3atSoYbG5T8X9efXVV0lNTWXChAk4Ojry9ddfW/VD\n16ohr5QqC5TSWl+79XN7YIo1tylESZSRkUF8fHyue+MxMTF3nCJfq1Yt3N3d6dSpE+7u7jku9rRn\nXFKNHz+e1NRUXn/9dZycnJg7d67VRtW09p7834CVt/YcHIHFWut1Vt6mEMVSSkrKHe3jmUF+/Phx\nbt68mbWso6Mjbm5uWXvkmXvi7u7u1K9fnzJlypj4SoQlvPbaa6SmpvLOO+/g5OREcHCwVYLeqiGv\ntT4OWH/iRSGyOX36NNu2bcu6REdHm3YiSl5cXFxwd3fnoYceolOnTjmCvG7duiX+ZCBxb2+99Rap\nqalMmzaNKlWqMH36dItvQ/6KRImWnJzMrl27coR65uTKpUuXpnnz5gwfPrxYNGE4ODhQt27drCCX\n9nGhlGLq1KmUKVOG7t27W2cbZnelys7Ly0tHR0ebXYYoprTWxMbG5gj0Xbt2ZfU5/vvf/46fnx++\nvr74+vri6elp6vRwQhQVpdTOu40mIHvyothKSkoiOjo6R6hnzqPp4uJCixYtGDduHL6+vvj4+FCz\nZk2TKxai+JGQF8VCRkYGR44cyRHo+/btIyMjA4BGjRoRGBiIr68vfn5+NG7cWNqshcgH+S8Rprh8\n+TJRUVFZgb59+/asKdMqVqyIj48P3bt3x9fXF29vb6pWrWpyxUKUTBLywurS09PZv39/jr30w4cP\nA8aBp8aNG9OnT5+stvSHH37Yan2GhbA3EvI25Nq1a1l7x/Hx8WaXk9UEExUVxfXr1wFwdXXF19eX\nQYMG4efnh5eXFxUqVDC5UiFsl4R8CaW15siRI0RGRhIZGcm2bdvYv39/Vhu2q6trseieV7duXYYO\nHZq1l16/fv1iUZcQ9kJCvoS4evUqUVFROUI9exu2r68vvXr1yuppUqlSJZMrFkIUBxLyxVBmM0dm\noEdGRnLgwAG01iil8PDwoHfv3lk9TaQNWwhxNxLyxUBiYmKOvfTsPU0qVaqEj48Pffr0wc/PDx8f\nHypWrGhyxUKIkkJCvohlZGTw+++/59hLP3jw4B176X5+fvj5+fHQQw/JXroQ4r5JyFtZYmIi27dv\nz7GXfuXKFcDYS/f19aVfv374+fnh7e0te+lCCIuSkLegjIwMDh8+nGMv/dChQ1l76Y8++ih9+/bN\n2ktv1KiR7KULIaxKQr6Qzp8/z/r16wkNDeXnn3/OGlulcuXK+Pr6EhQUlLWXLv3BhRBFTUK+gFJS\nUoiMjGTdunWEhoaya9cuwOiX3q5dO9q2bYu/vz8NGzaUvXQhhOkk5PMhJiaG0NBQQkNDCQsLIykp\nCUdHR/z8/Jg6dSqBgYE0b95cQl0IUexIyOfi2rVrhIeHZwV7TEwMAPXq1WPQoEEEBgYSEBAgzS9C\niGJPQh7jgOnu3buzQn3r1q2kpqZStmxZWrduzdixYwkMDKRBgwZySr4QokSx25A/d+5cjgOm58+f\nB8DT05Nx48YRGBhIy5YtZWYhIUSJZjchn5KSwpYtW7L21nfv3g1AtWrVaN++PYGBgbRr144aNWqY\nXKkQQliOTYf8sWPHsnrBhIeHc/36dRwdHfH392fatGkEBgbi6ekpB0yFEDbLpkL+6tWrOQ6YHj9+\nHID69eszZMgQAgMDad26tRwwFULYDZsI+V9//ZU333yTrVu3kpaWRtmyZQkICMhqW2/QoIHZJQoh\nhClsIuQd0tK4dukS/3rhBQKfeoqWXl6Uzn7A9OxZ+GuvmLxuF2TZ3G7nl9bWWTb78ne7Lopliovi\nVk9e8vO3dK9lCruOuz12P88pzGNFtWx2uf1dF/bn/C5XsSJYYS5jmwj5Jy5d4rf9+2H/fvj8c7PL\nEUKIgpswAaZPt/hqbSLkad4cgoNv387rU/Retwvz3MzbxWXvJHP5u10XxTLFRXGrJzf5+cZxr2UK\nu467PXY/zynMY0W5bH6/1Rfm5/ws17hx/mouINsIeXd34yKEECIH6TsohBA2TEJeCCFsmIS8EELY\nMAl5IYSwYRLyQghhwyTkhRDChknICyGEDZOQF0IIGyYhL4QQNkxCXgghbJiEvBBC2DCrh7xSqoNS\n6nel1DGl1ERrb08IIcRtVg15pZQD8F+gI+ABPK2U8rDmNoUQQtxm7VEovYFjWuvjAEqpb4HuwEGL\nbmXnWLi826KrFIV37nJlyjonU67MDbNLEaL4q+wJj39i8dVau7mmFnAq2+24W/dlUUqNUEpFK6Wi\nExISrFyOKCqpaQ48/tJsPJ6bz/ZDj5hdjhB2y/Tx5LXWs4HZAF5eXvc3T5sVPv1E4awNgfgLUKkS\nPPnPWXz0EYweXTLm7hDCllg75OOBOtlu1751n7Bx8+bB3/4Ge/fCc8/BK6/A1q0wezaUL292dULk\nn9aQlgY3b0JKyt2v83osP8t06gT9+lm+fmuH/A6goVKqHka4BwEDrLxNYbLz5+HHH2HMGKheHVav\nhvffh9dfh927YdkyePRRs6sUlqQ1pKdDamr+L2lpuV/yesxSz8m+fGrqvUPZ0pycoHRpeOCB29eP\nWKlV06ohr7VOU0qNBkIBB+ArrfUBa25TmG/xYuOfZ+hQ43apUjBpEvj6QlAQeHvDnDkwQD7ui1xU\nFCxcCDduFDyQ77WMGRwcwNHx9sXJKefte10yQzZ74P41fHO7zs8yd1vWycn4nygqShdk0lsr8/Ly\n0tHR0WaXIQpBa/D0NP6Yo6LufPz0aejfHzZvhpdegv/8x1hWWNe1a/Daa/D55+DiYhwrcXK6fckM\nyMJcCrqO/IRyXo87OMgxnkxKqZ1aa6/cHjP9wKuwLbt2Ge3w//1v7o8/+CCEhcHkyTBjBuzYAd9/\nD3//e9HWaU9CQmDUKIiPN67ffRcqVDC7KlFUZFgDYVHz5xt75k8/ffdlnJzgww9hxQr4/Xd47DH4\n6aciK9FunDkDffpA9+7GnvvWrTBzpgS8vZGQFxZz8yYsWgQ9ekDlyvdevmdP2LkT6taFzp2NA7Pp\n6dav09ZlZEBwsHEg78cfYdo0+O0345iIsD8S8sJifvgBLl2CZ5/N/3MaNIDISOMg7bvvQmCg0TtH\n3J+DB6FVK3jxRXj8cdi3zzjo7eRkdmXCLBLywmLmz4dataBdu4I9r0wZ+OormDsXtmyB5s2Na5F/\nycnw5pvGQe9Dh4zfxYYN0LCh2ZUJs0nIC4s4cwbWroUhQ4xeD/dj2DBjr97ZGZ56Cj7+2OitI/L2\n669GuP/730bPpcOH4ZlnpOeJMEjIC4tYuNBoCy5IU01uPD0hOhq6dIFx46BvX7h61SIl2pzLl2H4\ncOMDMSUFQkON30O1amZXJooTCXlRaFobzQMtW0KjRoVfX6VKRs+bDz+EVavAy8toWxYGrWHpUnj4\nYWP4iPHjYf9+aN/e7MpEcSQhLwotKspoB848w9USlIJ//cvoU3/tGvj4wNdfW279JdUffxjfcoKC\njF5J0dHGkBEuLmZXJoorCXlRaPPmGQdPrTG4UqtWxglWPj5GU9Dw4cZBRnuTlmYco/DwMNrgP/kE\ntm0zmreEyIuEvCiUGzfg22+hd2/rnWRTowb8/LPRFfB//zOahY4ft862iqNdu4w+7uPGQevWRjfJ\nMWPu/wC3sC8S8qJQVq2CxETLNtXkxtHROKknJAROnDD6gIeEWHebZrt+Hf7v/6BFC4iLM9rhf/jB\naKYRIr8k5EWhzJtnjDvz1FNFs72uXY2zN+vXN07XnzjRaMqwNaGh0LixMb7PsGHGMY9+/aRbpCg4\nCXlx306dMk64eeaZoh06tV4942SpESOMg45t28LZs0W3fWs6fx4GDoQOHYwxgH791ZhoJT/DRAiR\nGwl5cd8WLDC68z3zTNFv29kZvvzS6HETFWUMcvbrr0Vfh6VobXwrevhhY1TOt96CPXuMA89CFIaE\nvLgvmX3j//EPo+nELEOGwPbtxpSCbdrABx+UvLNkjx41ah82zJgxa88eePttGWdfWIaEvLgvW7bA\nsWPWP+CaH02aGP3Fe/aECROM6ytXzK7q3lJSjEHZmjQxjjN8+aXxbcRa08AJ+yQhL+7LvHlQrpwx\nXnlxUKECfPed0Zd8zRqj982uXWZXdXeRkUaNr78O3boZB1ZHjCjaYxvCPsiflCiw69eNQO3bF8qW\nNbua25SCsWONveGbN8HPzxjZsjg13yQmGrMz+fsb3zZCQoz3smZNsysTtkqm/xMFtnw5JCUVj6aa\n3LRsaTR/DBwIzz9vzCcbFGQ8ptTtS1HfPnzYOKHpzBl4+WWYOtU4liCENclE3qLAWrc2uk8ePVq8\n+22np8M77xhD8BYXTZvCnDng7W12JcKWyETewmKOH4eICCM4i3PAg3Ha/5QpRg+chASj2SbzApa7\nnd9lnZ2NPv0yS5MoShLyokAWLDDCfcgQsyvJvwYNjIsQ9kgOvIp8y8gw+sa3aSPjpwhRUkjIi3yL\niDDGMy+uB1yFEHeSkBf5Nn++0R+9Z0+zKxFC5JeEvMiXq1dh2TKjK2KZMmZXI4TILwl5kS/ffWdM\nECJNNUKULBLyIl/mzzdGSPTxMbsSIURBSMiLezpyxBiQ7Nlni3/feCFEThLy4p7mzzcGzho82OxK\nhBAFJSEv8pSebpwA1aEDPPig2dUIIQpKQl7kacMGiI83mmqEECWPhLzI0/z5UKWKMea5EKLkkZAX\nd3X5MqxcCQMGyFR0QpRUEvLirr791ph8Q5pqhCi5JOTFXc2fb8w/2ry52ZUIIe6XhLzI1cGDEBVl\nnOEqfeOFKLkk5EWu5s0DR0djCj0hRMlltZBXSr2tlIpXSu2+delkrW0Jy0pLg4ULoXNnqF7d7GqE\nEIVh7ZmhPtZaz7DyNoSFrVsH587JYGRC2AJprhF3mDcPqlWDTvLdS4gSz9ohP1optVcp9ZVSqnJu\nCyilRiilopVS0QkJCVYuR9zLhQvwww8waJBMOC2ELShUyCulNiil9udy6Q7MAtwBT+AM8FFu69Ba\nz9Zae2mtvapVq1aYcoQFLF4MqanSN14IW1GoNnmtddv8LKeUmgP8WJhtiaIxb57RL75pU7MrEUJY\ngjV719TMdrMnsN9a2xKWsXu3cZEDrkLYDmv2rvlAKeUJaCAWGGnFbQkLmD8fSpeGp582uxIhhKVY\nLeS11jLFRAmSkgKLFhmjTVatanY1QghLkS6UAoA1a4yeNdJUI4RtkZAXgHHAtWZNaN/e7EqEEJYk\nIS84exZ++smYw9XR2udACyGKlIS8YNEiYy5XaaoRwvZIyNs5rY2mGl9fePhhs6sRQliahLydi46G\nAwfkDFchbJWEvJ2bPx+cnSEoyOxKhBDWICFvx5KTjbFqevWCihXNrkYIYQ0S8nZs9Wq4ckWaaoSw\nZdJhzo7Nnw916kBAgNmViIJKTU0lLi6O5ORks0sRRcjZ2ZnatWvjVIBxwCXk7VR8PKxfD5Mng4OD\n2dWIgoqLi6N8+fK4ubmhZKZ1u6C15uLFi8TFxVGvXr18P0+aa+zUggWQkQHPPGN2JeJ+JCcnU7Vq\nVQl4O6KUomrVqgX+9iYhb4e0NppqnnwSGjQwuxpxvyTg7c/9/M4l5O1QZCQcOSJnuAphDyTk7dC8\neeDiAn36mF2JsAcRERFs3bq1UOsoV66chaqxPxLydubPP2HpUujbF8qXN7saYQ8sEfLi/knvGjuz\nYgVcuyZ9423KzrFwebdl11nZEx7/JM9FevTowalTp0hOTmbMmDGMGDGCdevWMXnyZNLT03F1dWXu\n3LkEBwfj4ODAN998w8yZM5k7dy5dunShz62vkuXKlSMpKYmkpCS6d+/O5cuXSU1NZerUqXTv3t2y\nr8sOScjbmXnzoF49aNXK7EpESffVV19RpUoVbty4QYsWLejevTvDhw9n48aN1KtXj0uXLlGlShVe\neOEFypUrx7/+9S8A5s6dm+v6nJ2dWblyJRUqVODChQv4+vrSrVs3OcBcSBLyduSPPyAsDN55B0pJ\nQ53tuMcet7V89tlnrFy5EoBTp04xe/ZsWrVqldWHu0qVKgVan9aayZMns3HjRkqVKkV8fDznzp2j\nRo0aFq/dnkjI25Gvvzauhwwxtw5R8kVERLBhwwYiIyNxcXHhqaeewtPTk8OHD9/zuY6OjmRkZACQ\nkZFBSkoKAIsWLSIhIYGdO3fi5OSEm5ubnNFrAbI/ZycyMoy+8QEB4OZmdjWipEtMTKRy5cq4uLhw\n+PBhtm3bRnJyMhs3buTEiRMAXLp0CYDy5ctz7dq1rOe6ubmxc+dOAEJCQkhNTc1aZ/Xq1XFyciI8\nPJw//vijiF+VbZKQtxObNsGJE9I3XlhGhw4dSEtL45FHHmHixIn4+vpSrVo1Zs+eTa9evWjWrBn9\n+/cHoGvXrqxcuRJPT082bdrE8OHD+fXXX2nWrBmRkZGULVsWgIEDBxIdHU2TJk1YsGABD8ssNhah\ntNZm15DFy8tLR0dHm12GTXr2WaNnzdmzRh95UbIdOnSIRx55xOwyhAly+90rpXZqrb1yW1725O3A\ntWvw/ffQv78EvBD2RkLeDixbZpwEJU01QtgfCXk7MG8eNGoEfn5mVyKEKGoS8jbu2DHjoOuzz4Kc\nUyKE/ZGQt3Fff22c+CR944WwTxLyNiw93Qj5du2gVi2zqxFCmEFC3oaFhcGpU3LAVZQMsbGxNG7c\n+K6PX7lyhS+++KIIK7INEvI2bP58qFQJZCA/YQsk5O+PjF1jQdeuwdatUL061K4Nrq7mHey8csU4\n+WnoUHB2NqcGUUTGjoXdFh5q2NMTPsl74LMFCxYwY8YMlFI0bdoUBwcHKlSoQHR0NGfPnuWDDz6g\nT58+RERE8Pbbb+Pq6sr+/ft5/PHH+eabb/IcXfLAgQMMHTqUlJQUMjIyWL58OW+88QYxMTF4enrS\nrl07OnfuzFtvvUWlSpXYt28f/fr1o0mTJnz66afcuHGDVatW4e7ubtn3pQSSkLeQixeNcWH27r19\nX+nSRlt4rVpG6NeuffvnzOuaNcHRCr+FpUshOVmaaoR1HDhwgKlTp7J161ZcXV25dOkS48aN48yZ\nM2zevJnDhw/TrVu3rDHjd+3axYEDB3jwwQfx9/dny5YtPPHEE3ddf3BwMGPGjGHgwIGkpKSQnp7O\n9OnT2b9/P7tvfaBFRESwZ88eDh06RJUqVahfvz7PP/88UVFRfPrpp8ycOZNP7vFBZQ8k5C3g8mXj\n4OaRI7BwIZQtC3FxEB9/+zo6GlatMoI3O6WgRo3cPwAyf65Vq+Bnqs6fD48+Cl65nugsbIoJQRYW\nFkbfvn1xdXUFbg8r3KNHD0qVKoWHhwfnzp3LWt7b25vatWsD4OnpSWxsbJ4h7+fnx7vvvktcXBy9\nevWiYcOGuS7XokULatasCYC7uzvt27cHoEmTJoSHhxf+hdoACflCunIF2reHAwcgJAQCA+++rNbG\nB0L2D4DsPx89ChERxjr/qnLl3D8Asl9XqmR8aBw6BNu2wYcfSt94UbQeeOCBrJ+zj4uV/X4HBwfS\n0tLYvn07I0eOBGDKlCk0bdo0a5kBAwbg4+PDmjVr6NSpE19++SX169fPc3ulSpXKul2qVCnS0tIs\n98JKMAn5Qrh6FTp0gD17YOXKvAMejMCtUsW4ZPt7vsP167l/CGT+vGsXnD9vfGhk5+JiBH5GBjg4\nwKBBhX+NQuQmICCAnj17Mm7cOKpWrZo1rHBB+Pj4ZDW9gNG7JtPx48epX78+r7zyCidPnmTv3r00\na9Ysx5DFIn8k5O9TUhJ06gQ7dxpjw3TubLl1ly1rDEPQqNHdl0lJgTNn7mwWyvww6NvXaAYSwhoe\nffRRXnvtNf7xj3/g4ODAY489ZtH1f/fddyxcuBAnJydq1KjB5MmTqVKlCv7+/jRu3JiOHTvS2ZL/\ndDZMhhq+D9evGwG/ZYtxgLN3b7MrEvZGhhq2XwUdalj25Avozz+hWzfYvBkWL5aAF0IUb4U6GUop\n1VcpdUAplaGU8vrLY5OUUseUUr8rpe7RWl0yJCdDjx4QHm4MF3Br4hshhCi2Crsnvx/oBXyZ/U6l\nlAcQBDwKPAhsUEo10lqnF3J7prl5E3r1gg0b4Kuv5KCmEKJkKNSevNb6kNb691we6g58q7W+qbU+\nARwDvAuzLTOlpBgHMteuhdmzjWF7hRCiJLDW2DW1gFPZbsfduu8OSqkRSqlopVR0QkKClcq5f6mp\nEBQEP/wAX3wBzz9vdkVCCJF/92yuUUptAHLrjPea1np1YQvQWs8GZoPRu6aw67OktDQYONDoA//Z\nZ/Dii2ZXJIQQBXPPPXmtdVutdeNcLnkFfDxQJ9vt2rfuKzHS0+GZZ4wJsD/6CF5+2eyKhCjZ3Nzc\nuHDhglXWvXv3bn766SerrNtS7vb63377bWbMmGG17VqruSYECFJKPaCUqgc0BKKstC2LS0+HYcOM\nLpLvvQfjxpldkRAiLyUh5GoLNukAAAyKSURBVM1SqN41SqmewEygGrBGKbVbax2otT6glPoOOAik\nAaNKSs+ajAwYMQIWLIApU2DiRLMrEiJvY8eOzTE8gCV4enrmOYLjjh07eO6554iKiiI9PR1vb2+W\nLl2Kh4cHo0ePJiwsjDp16uDk5MSwYcOyRqP84IMPWLt2LWXKlGHx4sU0aNCA2NhYhg0bxoULF6hW\nrRrz5s2jbt26d73/+++/55133sHBwYGKFSuyYcMG3nzzTW7cuMHmzZuZNGkS/bP1b05PT2fixIlE\nRERw8+ZNRo0axciRI/McAnnixImEhITg6OhI+/btmTFjBgkJCbzwwgucPHkSgE8++QR/f3/efvtt\nTpw4wfHjxzl58iQff/wx27ZtY+3atdSqVYsffvgBJyenu77+7GJiYhg1ahQJCQm4uLgwZ84cHn74\n4UL9Lgvbu2al1rq21voBrfXftNaB2R57V2vtrrV+SGu9tlBVFhGt4aWXjC6Sb7xhXIQQd2rRogXd\nunXj9ddfZ/z48QwaNIjGjRuzYsUKYmNjOXjwIAsXLiQyMjLH8ypWrMi+ffsYPXo0Y8eOBeDll1/m\nmWeeYe/evQwcOJBXXnklz/unTJlCaGgoe/bsISQkhNKlSzNlyhT69+/P7t27cwQ8wNy5c6lYsSI7\nduxgx44dzJkzhxMnTgDGEMiffPIJBw8e5Pjx42zZsoWLFy+ycuVKDhw4wN69e3n99dcBGDNmDK++\n+io7duxg+fLlPJ+tF0ZMTAxhYWGEhIQwaNAgWrduzb59+yhTpgxr1qzJ8/VnN2LECGbOnMnOnTuZ\nMWMGL730UmF/VXLGayatjXb3L7809t7fecfsioTIH7PGTH/zzTdp0aIFzs7OfPbZZwBs3ryZvn37\nUqpUKWrUqEHr1q1zPOfpp5/Oun711VcBiIyMZMWKFQAMHjyY8ePH53m/v78/zz77LP369aNXr173\nrHP9+vXs3buXZcuWAZCYmMjRo0cpXbp0rkMg+/r64uzszHPPPUeXLl3o0qULABs2bODgwYNZ6716\n9SpJSUkAdOzYEScnJ5o0aUJ6ejodOnQAjCGPsw+8ltvrz5SUlMTWrVvp27dv1n03b9685+u7Fwl5\njIAfNw7++1/45z9h2jQZoleIe7l48SJJSUmkpqaSnJxM2bJl7/mc7LNB5TUzVF6Cg4PZvn07a9as\n4fHHH2fnzp15Lq+1ZubMmQT+ZZjYiIiIXIdAdnR0JCoqil9++YVly5bx+eefExYWRkZGBtu2bcM5\nl6nWsg9x7OTklPXa/jrkcV6vPyMjg0qVKlm86c3u53jVGiZMMOZdGDNGxmAXIr9GjhzJv//9bwYO\nHMiECRMAYy97+fLlZGRkcO7cOSIiInI8Z+nSpVnXfn5+ALRs2ZJvv/0WgEWLFvHkk0/meX9MTAw+\nPj5MmTKFatWqcerUKcqXL3/XYYgDAwOZNWsWqampABw5coTr16/f9XUlJSWRmJhIp06d+Pjjj9mz\nZw8A7du3Z+bMmVnL3U8Y5/b6M1WoUIF69erx/fffA8aHU+a2C8Ou9+S1htdfN4L9pZfg448l4IXI\njwULFuDk5MSAAQNIT0+nZcuWhIWF0bt3b3755Rc8PDyoU6cOzZs3p2LFilnPu3z5Mk2bNuWBBx5g\nyZIlAMycOZOhQ4fy4YcfZh1gzev+//u//+Po0aNorWnTpg3NmjWjbt26TJ8+HU9PzzsOvD7//PPE\nxsbSvHlztNZUq1aNVatW3fW1Xbt2je7du5OcnIzWmv/85z8AfPbZZ4waNYqmTZuSlpZGq1atCA4O\nLtD7ltvrz27RokW8+OKLTJ06ldTUVIKCgmjWrFmBtvFXdj3U8NtvG23vw4dDcDCUsvvvNaKkKM5D\nDSclJVGuXDkuXryIt7c3W7ZsoYZMbmAxMtRwPr37rhHwQ4dKwAthSV26dOHKlSukpKTwxhtvSMCb\nzC5D/oMPjGaawYNhzhwJeCEs6a/t8MJcdhdvH39sHGgNCoJ584y5UIUQwlbZVcjPnGl0lezTBxYu\nlIAXQtg+uwn54GB45RVjZqfFi8HRLhuqhBD2xi5C/n//M4YJ7tLFmHj71jASQghh82w+5L/+2hhw\nrEMHWLYMSpc2uyIhbMeqVatQSnH48GEAYmNjKVOmDJ6ennh4eDBkyJCsk5AiIiKyhgiYP38+Sik2\nbNhwx7oyhx8AuHDhAk5OTgXujy5us+mQX7TI6CLZpg2sWAHZzmAWQljAkiVLeOKJJ3Kc2OPu7s7u\n3bvZt28fcXFxfPfdd7k+t0mTJllntGau668n/nz//ff4+vrmeuKQyB+bbZleuhSGDIGnnoLVq6FM\nGbMrEsI6xo4FCw93gqenMdRHXpKSkti8eTPh4eF07dqVd/4yqp+DgwPe3t7Ex+c+X9CTTz7Jpk2b\nSE1N5ebNmxw7dgxPT88cyyxZsoSPPvqIAQMGEBcXlzWYmMg/m9yTX77cmLbP39+Ym9XFxeyKhLA9\nq1evpkOHDjRq1IiqVaveMVBYcnIy27dvzxqR8a+UUrRt25bQ0FBWr15Nt27dcjx+6tQpzpw5g7e3\nN/369csa90UUjM3tya9ebfSB9/GBNWsgHwPjCVGimTTSMEuWLGHMmDEABAUFsWTJEkaPHk1MTAye\nnp6cOHGCzp0707Rp07uuIygoiM8++4zExEQ++ugjpk2blvXY0qVL6devX9Zyw4YN45///Kd1X5QN\nsqmQX7MG+vaF5s1h7VooX97sioSwTZcuXSIsLIx9+/ahlCI9PR2lFKNGjcpqk79w4QL+/v6EhITc\nsZeeydvbm3379uHi4kKjRo1yPLZkyRLOnj3LokWLADh9+jRHjx6lYcOGVn99tsRmmmtCQ6FXL2ja\n1Pi5QgWzKxLCdi1btozBgwfzxx9/EBsby6lTp6hXrx6nTp3KWsbV1ZXp06fz3nvv5bmu6dOn59iD\nB2M44KSkJOLj44mNjSU2NpZJkybJAdj7YBMhHxZmnOTk4QHr10OlSmZXJIRtW7JkCT179sxxX+/e\nve8I9B49evDnn3+yadOmu66rY8eOd8wgdbf1S8gXnE0MNXzokNHDYNEicHW1QmFCFDPFeahhYV12\nOdTwI48YTTRCCCFysonmGiGEELmTkBeihCpOTa2iaNzP71xCXogSyNnZmYsXL0rQ2xGtNRcvXsTZ\n2blAz7OJNnkh7E3t2rWJi4sjISHB7FJEEXJ2di7w0A4S8kKUQE5OTtSrV8/sMkQJIM01QghhwyTk\nhRDChknICyGEDStWZ7wqpRKAP+7z6a7ABQuWU9LJ+5GTvB+3yXuRky28H3/XWlfL7YFiFfKFoZSK\nvttpvfZI3o+c5P24Td6LnGz9/ZDmGiGEsGES8kIIYcNsKeRnm11AMSPvR07yftwm70VONv1+2Eyb\nvBBCiDvZ0p68EEKIv5CQF0IIG2YTIa+U6qCU+l0pdUwpNdHsesyklKqjlApXSh1USh1QSo0xuyaz\nKaUclFK7lFI/ml2L2ZRSlZRSy5RSh5VSh5RSfmbXZBal1Ku3/kf2K6WWKKUKNrxjCVHiQ14p5QD8\nF+gIeABPK6U8zK3KVGnAP7XWHoAvMMrO3w+AMcAhs4soJj4F1mmtHwaaYafvi1KqFvAK4KW1bgw4\nAEHmVmUdJT7kAW/gmNb6uNY6BfgW6G5yTabRWp/RWv926+drGP/EtcytyjxKqdpAZ+B/ZtdiNqVU\nRaAVMBdAa52itb5iblWmcgTKKKUcARfgtMn1WIUthHwt4FS223HYcahlp5RyAx4Dtptbiak+AcYD\nGWYXUgzUAxKAebear/6nlCprdlFm0FrHAzOAk8AZIFFrvd7cqqzDFkJe5EIpVQ5YDozVWl81ux4z\nKKW6AOe11jvNrqWYcASaA7O01o8B1wG7PIallKqM8Y2/HvAgUFYpNcjcqqzDFkI+HqiT7XbtW/fZ\nLaWUE0bAL9JarzC7HhP5A92UUrEYzXgBSqlvzC3JVHFAnNY685vdMozQt0dtgRNa6wStdSqwAmhp\nck1WYQshvwNoqJSqp5QqjXHwJMTkmkyjlFIYba6HtNb/MbseM2mtJ2mta2ut3TD+LsK01ja5t5Yf\nWuuzwCml1EO37moDHDSxJDOdBHyVUi63/mfaYKMHoUv89H9a6zSl1GggFOMI+Vda6wMml2Umf2Aw\nsE8ptfvWfZO11j+ZWJMoPl4GFt3aIToODDW5HlNorbcrpZYBv2H0SNuFjQ5vIMMaCCGEDbOF5hoh\nhBB3ISEvhBA2TEJeCCFsmIS8EELYMAl5IYSwYRLyQghhwyTkhRDChv0/7tKz3wr0mH4AAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdTtyf3nKphT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "arima_preds = np.load('./drive/My Drive/arima_preds_1000.npy')\n",
        "\n",
        "preds = []\n",
        "lstm_preds = []\n",
        "for i in range(1000):\n",
        "  lstm_preds.append(members[0].predict(test_x[i].reshape(1,-1,1))[0][0][0])\n",
        "e_preds = stacked_prediction(members, model, test_x[:1000])\n",
        "preds.append(lstm_preds)\n",
        "preds.append(e_preds)\n",
        "preds.append(arima_preds)\n",
        "print(len(preds))\n",
        "\n",
        "plt.plot(test_y[:1000].reshape(-1), c = 'orange', label='actual')\n",
        "list_colrs = ['red', 'black', 'blue']\n",
        "labels = ['cnn-lstm', 'xgboost ensemble', 'ARIMA']\n",
        "for j in range(3):\n",
        "  plt.plot(preds[j], c = list_colrs[j], label=labels[j])\n",
        "  plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWpkpPsN6kxF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "pickle.dump(model, open(\"ensemble_weights.dat\", \"wb\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rM6ahGKGwycI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model = Sequential()\n",
        "# model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(28,28)))\n",
        "# model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
        "# model.add(MaxPooling1D(pool_size=2))\n",
        "# model.add(Flatten())\n",
        "# model.add(RepeatVector(1))\n",
        "# model.add(LSTM(200, activation='relu', return_sequences=True))\n",
        "# model.add(TimeDistributed(Dense(100, activation='relu')))\n",
        "# model.add(TimeDistributed(Dense(1)))\n",
        "# model.compile(loss= mane_loss, optimizer='adam',metrics = [\"accuracy\", r2])\n",
        "# # fit network\n",
        "# history = model.fit(x_train, y_train, epochs=30, batch_size=5, validation_split = 0.1)\n",
        "\n",
        "# pyplot.plot(history.history['loss'])\n",
        "# pyplot.plot(history.history['val_loss'])\n",
        "# pyplot.title('model train vs validation loss')\n",
        "# pyplot.ylabel('loss')\n",
        "# pyplot.xlabel('epoch')\n",
        "# pyplot.legend(['train', 'validation'], loc='upper right')\n",
        "# pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-K24VZ1wycM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "    y_pred = model.predict(test_x)\n",
        "    y_pred = y_pred.reshape((-1,1))\n",
        "    test_y  = test_y.reshape((-1,1))\n",
        "    mse = mean_squared_error(test_y,y_pred)\n",
        "    rms = sqrt(mse)\n",
        "    mae = mean_absolute_error(test_y,y_pred)\n",
        "    print(\"mean_squared_error : \",mse)\n",
        "    print(\"root_mean_squared_error : \",rms)\n",
        "    print(\"mean_absolute_error : \",mae)\n",
        "    #errors_saver[\"errors_equipment{}\".format(i+1)] = {\"mean_squared_error\":mse,\"mean_absolute_error\":mae,\"root_mean_squared_error\":rms}\n",
        "    y_pred = sc.inverse_transform(y_pred)\n",
        "    test_y = sc.inverse_transform(test_y)\n",
        "\n",
        "\n",
        "    l=0\n",
        "    while l<len(test_y)-7:\n",
        "        plt.scatter([1,2,3,4,5,6,7],y_pred[l:l+7,:],color  = \"cyan\",label = \"predicted_day\")\n",
        "        #plt.savefig(\"equipment2_day(prediction){}\".format(i//288))\n",
        "        #plt.legend()\n",
        "        #plt.show()\n",
        "        plt.scatter([1,2,3,4,5,6,7],test_y[l:l+7,:],color = \"blue\",label = \"real_values\")\n",
        "        plt.legend()\n",
        "        plt.xlabel(\"day\")\n",
        "        plt.ylabel(\"power_consumption\")\n",
        "        #plt.savefig(\"equipment9_day{}.png\".format(i//24))\n",
        "        plt.show()\n",
        "        l = l+7"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}