DeepNeuralNetwork.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and Defines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "    Imports all modules and submodules that were necessary for this study.\n",
    "    Special mention to timeseries-cv (tsxv) which was created with the purpose to be used in this thesis (https://github.com/DidierRLopes/timeseries-cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xlsxwriter\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as dr\n",
    "import seaborn as sns\n",
    "\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout\n",
    "from tensorflow.keras.layers import LSTM, SimpleRNN\n",
    "from tensorflow.keras.models import Sequential\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# pip install timeseries-cv\n",
    "from tsxv import splitTrain, splitTrainVal, splitTrainValTest\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Functions\n",
    "    Defines helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute MAE over an array of different horizons\n",
    "def horizon_mae(y_true, y_pred, horizonSteps):\n",
    "    ae  = abs(y_pred - y_true.T);\n",
    "    list_MAE = list()\n",
    "    for i in horizonSteps:\n",
    "        list_MAE.append(round(np.mean(ae[0:i]), 2))\n",
    "    return list_MAE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define Mean Absolute Percentage Error\n",
    "def absolute_percentage_error(y_true, y_pred):\n",
    "    if (len(y_true[y_true == 0])):\n",
    "        print(\"Division by zero!\")\n",
    "        return None;\n",
    "    else:\n",
    "        return 100*(abs((y_pred - y_true.T) / y_true.T));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute MAPE over an array of different horizons\n",
    "def horizon_mape(y_true, y_pred, horizonSteps):\n",
    "    ape = absolute_percentage_error(y_true, y_pred);\n",
    "    if ape is not None:\n",
    "        list_MAPE = list()\n",
    "        for i in horizonSteps:\n",
    "            list_MAPE.append(round(np.mean(ape[0:i]), 2))\n",
    "        return list_MAPE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Revert diffLogSeries values to real scale values using initialValue (Diff(Log(x)) -> x)\n",
    "def revert_logDiff(initialValue, diffLogSeries):\n",
    "    original = initialValue\n",
    "    #for i in np.arange(len(diffLogSeries)):\n",
    "    #    original = np.append(original, original[-1]*(np.e**diffLogSeries[i]))\n",
    "    for diffLogVal in diffLogSeries:\n",
    "        original = np.append(original, original[-1]*(np.e**diffLogVal))\n",
    "    return original[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Univariate Time-Series\n",
    "    df: Dataframe with an index and a value column\n",
    "    title: Title of data being used\n",
    "    serie: Serie 'Name'\n",
    "    xlab: Label of df.index\n",
    "    ylab: Label of df.values\n",
    "    \n",
    "    stepsToForecast: Steps to forecast out-of-sample (and in-sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stepsToForecast = [1, 3, 12];\n",
    "#df = dr.DataReader('CPIAUCSL', \"fred\", start='1947-01-01', end='2019-09-01')\n",
    "#df_train = df[['CPIAUCSL']][:-stepsToForecast[-1]].rename(columns={'CPIAUCSL': 'train'})\n",
    "#df_test = df[['CPIAUCSL']][-stepsToForecast[-1]:].rename(columns={'CPIAUCSL': 'test'})\n",
    "#title = \"CPIAUCSL: All Items in U.S. City Average\"\n",
    "#serie = \"CPIAUCSL\"\n",
    "#xlab = \"Months/Year\"; \n",
    "#ylab = \"Consumer Price Index\";\n",
    "\n",
    "#stepsToForecast = [1, 3, 12];\n",
    "#df = dr.DataReader(\"TRFVOLUSM227NFWA\", \"fred\", start=\"1947-01-01\", end=\"2019-09-01\")\n",
    "#df_train = df[['TRFVOLUSM227NFWA']][:-stepsToForecast[-1]].rename(columns={'TRFVOLUSM227NFWA': 'train'})\n",
    "#df_test = df[['TRFVOLUSM227NFWA']][-stepsToForecast[-1]:].rename(columns={'TRFVOLUSM227NFWA': 'test'})\n",
    "#title = \"Vehicle Miles Traveled\";\n",
    "#serie = \"VMT\"\n",
    "#xlab = \"Months/Year\";\n",
    "#ylab = \"Miles Traveled (Millions)\";\n",
    "\n",
    "#stepsToForecast = [1, 5, 21];\n",
    "#df = pd.read_csv(\"PSI_20_Data_1992_Stooq.csv\") \n",
    "#df = pd.DataFrame(df, columns= ['Date','Close'])\n",
    "#df.set_index('Date', drop=True, inplace=True)\n",
    "#df.index = pd.DatetimeIndex(df.index)\n",
    "#df = df.truncate(after='2019-09-27')\n",
    "#df_train = df[['Close']][:-stepsToForecast[-1]].rename(columns={'Close': 'train'})\n",
    "#df_test = df[['Close']][-stepsToForecast[-1]:].rename(columns={'Close': 'test'})\n",
    "#title = 'PSI20: Historical Closing Prices'\n",
    "#serie = \"PSI20\"\n",
    "#xlab = 'Business Days/Year'; \n",
    "#ylab = 'Closing Price (Euro)';\n",
    "\n",
    "stepsToForecast = [1, 5, 21];\n",
    "df = dr.data.get_data_yahoo('SPY', start= '1993-01-01', end='2019-09-27')\n",
    "df_train = df[['Close']][:-stepsToForecast[-1]].rename(columns={'Close': 'train'})\n",
    "df_test = df[['Close']][-stepsToForecast[-1]:].rename(columns={'Close': 'test'})\n",
    "title = 'SPY: Historical Closing Prices'\n",
    "serie = \"SPY\"\n",
    "xlab = 'Business Days/Year'; \n",
    "ylab = 'Closing Price (Dollar)';\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Deep Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing\n",
    "    The options here include:\n",
    "        - Slicing the data\n",
    "        - Standardization or Normalization\n",
    "        - Smoothing techniques\n",
    "        - Stationarity techniques\n",
    "        - Working with residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Allows training data to be sliced\n",
    "series_input = df_train.values[-20*12:].copy()\n",
    "\n",
    "# Smoothing\n",
    "# gamma = 0.3\n",
    "# ExpMA = 0.0 \n",
    "# for i in range(series_input.size):\n",
    "#    ExpMA = gamma*series_input[i] + (1-gamma)*ExpMA\n",
    "#    series_input[i] = ExpMA\n",
    "\n",
    "# Select between 'None', Normalization', 'Standardization', 'DiffLog'\n",
    "PreProcessing_str = 'None'\n",
    "\n",
    "if (PreProcessing_str == 'Normalization'):\n",
    "    scaler = MinMaxScaler()\n",
    "    series_input = scaler.fit_transform(series_input)\n",
    "elif (PreProcessing_str == 'Standardization'):\n",
    "    scaler = StandardScaler()\n",
    "    series_input = scaler.fit_transform(series_input)\n",
    "elif (PreProcessing_str == 'DiffLog'):\n",
    "    series_input_bd = series_input # before diff\n",
    "    series_input = np.log(pd.DataFrame(series_input)).diff().dropna().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation of Data Pre-Processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/DidierRodriguesLopes/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  \n",
      "/Users/DidierRodriguesLopes/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(df.index, df.values, LineWidth=2)\n",
    "plt.title(serie + ' (Time Series)')\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "plt.xlim([df.index[0], df.index[-1]])\n",
    "plt.grid()\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(series_input, LineWidth=2)\n",
    "plt.title('Processed ' + serie + ' (Time Series)')\n",
    "plt.grid()\n",
    "plt.xlim(0, len(series_input)-1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection (tune hyper-parameters)\n",
    "    In this work, the models to be used will be:\n",
    "        - Multilayer Perceptron (MLP)\n",
    "        - Recurrent Neural Network (RNN)\n",
    "        - Long Short-Term Memory (LSTM)\n",
    "        \n",
    "    Where the tunable hyper-parameters are:\n",
    "        Explicit:\n",
    "            - numInputs: Number of samples to be fed to the ANN\n",
    "            - numJumps: Number of samples to jump in training (speed up technique)\n",
    "            - train_epochs: Number of epochs for the model to train\n",
    "        Implicit:\n",
    "            - Number of layers\n",
    "            - Number of neurons of EACH layer\n",
    "            - Activation function of EACH layer\n",
    "            - Dropout\n",
    "            - Optimization algorithm and loss function used in training\n",
    "        \n",
    "    Note: numOutputs is chosen a-priori."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Select between 'MLP', 'RNN', or 'LSTM'\n",
    "NN_str = 'MLP'\n",
    "\n",
    "numInputs = 12\n",
    "numJumps = 1\n",
    "\n",
    "train_epochs = 10\n",
    "\n",
    "model = Sequential()\n",
    "if (NN_str == 'MLP'):\n",
    "    model.add(Dense(50, activation='relu', input_dim=numInputs))\n",
    "    model.add(Dense(100, activation='relu'))\n",
    "    model.add(Dense(80, activation='relu'))\n",
    "    model.add(Dense(30, activation='relu'))\n",
    "    model.add(Dense(stepsToForecast[-1], activation='linear'))\n",
    "elif (NN_str == 'RNN'):\n",
    "    model.add(SimpleRNN(100, input_shape=(numInputs, 1), activation='linear', return_sequences=True))    \n",
    "    model.add(SimpleRNN(50, activation='linear', return_sequences=True)) \n",
    "    model.add(SimpleRNN(25, activation='linear', return_sequences=False))\n",
    "    model.add(Dense(stepsToForecast[-1], activation='linear'))\n",
    "elif (NN_str == 'LSTM'):\n",
    "    model.add(LSTM(25,input_shape=(numInputs, 1), activation='linear', return_sequences=True))  \n",
    "    model.add(LSTM(25, activation='linear', return_sequences=True))\n",
    "    model.add(LSTM(50, activation='linear', return_sequences=True))\n",
    "    model.add(LSTM(30, activation='linear', return_sequences=True))\n",
    "    model.add(LSTM(20, activation='linear', return_sequences=True))\n",
    "    model.add(LSTM(15, activation='linear', return_sequences=False))\n",
    "    model.add(Dense(stepsToForecast[-1], activation='linear'))\n",
    "else:\n",
    "    print(\"This model is not recognized.\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mae')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Validation\n",
    "    Select one of the following cross-validation methods:\n",
    "        - Forward Chaining\n",
    "    .split_train_val_test_forwardChaining(series_input, numInputs, stepsToForecast[-1], numJumps) \n",
    "        - K-Fold\n",
    "    .split_train_val_test_kFold(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "        - Group K-Fold\n",
    "    .split_train_val_test_groupKFold(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "        \n",
    "    Note: More info at https://github.com/DidierRLopes/timeseries-cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Cross-Validation Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "# splitValTest = True considers [Train, Val, Test]\n",
    "# splitValTest = False considers [Train, Val&Test]\n",
    "splitValTest = True\n",
    "# Select between 'ForwardChaining', 'KFold', or 'GroupKFold'\n",
    "CV_str = 'ForwardChaining'\n",
    "\n",
    "if (splitValTest):\n",
    "    if (CV_str == 'ForwardChaining'):\n",
    "        X_train, y_train, X_cv, y_cv, X_test, y_test = splitTrainValTest \\\n",
    "            .split_train_val_test_forwardChaining(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "    elif (CV_str == 'KFold'):\n",
    "        X_train, y_train, X_cv, y_cv, X_test, y_test = splitTrainValTest \\\n",
    "            .split_train_val_test_kFold(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "    elif (CV_str == 'GroupKFold'):\n",
    "        X_train, y_train, X_cv, y_cv, X_test, y_test = splitTrainValTest \\\n",
    "            .split_train_val_test_groupKFold(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "    else:\n",
    "        print(\"This model Cross-Validation technique is not recognized.\")\n",
    "\n",
    "    if (PreProcessing_str == 'DiffLog'):\n",
    "        if (CV_str == 'ForwardChaining'):\n",
    "            X_train_bd, y_train_bd, X_cv_bd, y_cv_bd, X_test_bd, y_test_bd = splitTrainValTest \\\n",
    "                .split_train_val_test_forwardChaining(series_input_bd, numInputs, stepsToForecast[-1], numJumps)\n",
    "        elif (CV_str == 'KFold'):\n",
    "            X_train_bd, y_train_bd, X_cv_bd, y_cv_bd, X_test_bd, y_test_bd = splitTrainValTest \\\n",
    "                .split_train_val_test_kFold(series_input_bd, numInputs, stepsToForecast[-1], numJumps)\n",
    "        elif (CV_str == 'GroupKFold'):\n",
    "            X_train_bd, y_train_bd, X_cv_bd, y_cv_bd, X_test_bd, y_test_bd = splitTrainValTest \\\n",
    "                .split_train_val_test_groupKFold(series_input_bd, numInputs, stepsToForecast[-1], numJumps)\n",
    "        else:\n",
    "            print(\"This model Cross-Validation technique is not recognized.\")\n",
    "else:\n",
    "    if (CV_str == 'ForwardChaining'):\n",
    "        X_train, y_train, X_cv, y_cv = splitTrainVal \\\n",
    "            .split_train_val_forwardChaining(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "    elif (CV_str == 'KFold'):\n",
    "        X_train, y_train, X_cv, y_cv = splitTrainVal \\\n",
    "            .split_train_val_kFold(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "    elif (CV_str == 'GroupKFold'):\n",
    "        X_train, y_train, X_cv, y_cv = splitTrainVal \\\n",
    "            .split_train_val_groupKFold(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "    else:\n",
    "        print(\"This model Cross-Validation technique is not recognized.\")\n",
    "\n",
    "\n",
    "    if (PreProcessing_str == 'DiffLog'):\n",
    "        if (CV_str == 'ForwardChaining'):\n",
    "            X_train_bd, y_train_bd, X_cv_bd, y_cv_bd = splitTrainVal \\\n",
    "                .split_train_val_forwardChaining(series_input_bd, numInputs, stepsToForecast[-1], numJumps)\n",
    "        elif (CV_str == 'KFold'):\n",
    "            X_train_bd, y_train_bd, X_cv_bd, y_cv_bd = splitTrainVal \\\n",
    "                .split_train_val_kFold(series_input_bd, numInputs, stepsToForecast[-1], numJumps)\n",
    "        elif (CV_str == 'GroupKFold'):\n",
    "            X_train_bd, y_train_bd, X_cv_bd, y_cv_bd = splitTrainVal \\\n",
    "                .split_train_val_groupKFold(series_input_bd, numInputs, stepsToForecast[-1], numJumps)\n",
    "        else:\n",
    "            print(\"This model Cross-Validation technique is not recognized.\")\n",
    "    \n",
    "dict_mae = {} \n",
    "for ix in np.arange(len(X_train)):\n",
    "    \n",
    "    print(\"%d/%d\" % (ix+1, len(X_train)));\n",
    "    \n",
    "    if (NN_str == 'MLP'):\n",
    "        X_train[ix] = np.squeeze(X_train[ix], axis=2)\n",
    "        X_cv[ix] = np.squeeze(X_cv[ix], axis=2)\n",
    "        if (splitValTest):\n",
    "            X_test[ix] = np.squeeze(X_test[ix], axis=2)\n",
    "        if (PreProcessing_str == 'DiffLog'):\n",
    "            X_train_bd[ix] = np.squeeze(X_train_bd[ix], axis=2)\n",
    "            X_cv_bd[ix] = np.squeeze(X_cv_bd[ix], axis=2)\n",
    "            if (splitValTest):\n",
    "                X_test_bd[ix] = np.squeeze(X_test_bd[ix], axis=2)\n",
    "    y_train[ix] = np.squeeze(y_train[ix], axis=2)\n",
    "    y_cv[ix] = np.squeeze(y_cv[ix], axis=2)\n",
    "    if (splitValTest):\n",
    "        y_test[ix] = np.squeeze(y_test[ix], axis=2)\n",
    "    if (PreProcessing_str == 'DiffLog'):\n",
    "            y_train_bd[ix] = np.squeeze(y_train_bd[ix], axis=2)\n",
    "            y_cv_bd[ix] = np.squeeze(y_cv_bd[ix], axis=2)\n",
    "            if (splitValTest):\n",
    "                y_test_bd[ix] = np.squeeze(y_test_bd[ix], axis=2)\n",
    "    \n",
    "    ## ------- TRAINING MODEL -------\n",
    "    model.fit(X_train[ix], y_train[ix], epochs = train_epochs, verbose = 0);\n",
    "    \n",
    "    ## ------- TRAINING ERROR -------\n",
    "    if (PreProcessing_str in ['Normalization', 'Standardization']): \n",
    "        y_pred_train_t = scaler.inverse_transform(model.predict(X_train[ix]))\n",
    "        y_train_t = scaler.inverse_transform(y_train[ix])\n",
    "    elif (PreProcessing_str == 'DiffLog'):\n",
    "        y_pred_train_t = list()\n",
    "        y_train_t = list()\n",
    "        for i in np.arange(len(X_train[ix])):\n",
    "            if (NN_str == 'LSTM'):\n",
    "                y_pred_train_t.append(revert_logDiff(np.array([X_train_bd[ix][i][-1]]), \\\n",
    "                    np.array(model.predict(X_train[ix][i].reshape(1, numInputs, 1))).tolist()[0]))\n",
    "            else:\n",
    "                y_pred_train_t.append(revert_logDiff(np.array([X_train_bd[ix][i][-1]]), \\\n",
    "                        np.array(model.predict(X_train[ix][i].reshape(1, numInputs))).tolist()[0]))\n",
    "            y_train_t.append(revert_logDiff(np.array([y_train_bd[ix][i][0]]), y_train[ix][i]))\n",
    "    else:\n",
    "        y_pred_train_t = model.predict(X_train[ix])\n",
    "        y_train_t = y_train[ix]\n",
    "        \n",
    "    list_mae_train = list()\n",
    "    for i in np.arange(len(y_train_t)):\n",
    "        if (PreProcessing_str == 'DiffLog'):\n",
    "            mae_train = mean_absolute_error(y_train_t[i], y_pred_train_t[i])\n",
    "        else:\n",
    "            mae_train = mean_absolute_error(y_train_t[i,:], y_pred_train_t[i,:])\n",
    "        list_mae_train.append(mae_train)\n",
    "        \n",
    "    ## ------- CROSS VALIDATION ERROR -------\n",
    "    if (PreProcessing_str in ['Normalization', 'Standardization']): \n",
    "        y_pred_cv_t = scaler.inverse_transform(model.predict(X_cv[ix]))\n",
    "        y_cv_t = scaler.inverse_transform(y_cv[ix])\n",
    "    elif (PreProcessing_str == 'DiffLog'):\n",
    "        y_pred_cv_t = list()\n",
    "        y_cv_t = list()\n",
    "        for i in np.arange(len(X_cv[ix])):\n",
    "            if (NN_str == 'LSTM'):\n",
    "                y_pred_cv_t.append(revert_logDiff(np.array([X_cv_bd[ix][i][-1]]), \\\n",
    "                        np.array(model.predict(X_cv[ix][i].reshape(1, numInputs, 1))).tolist()[0]))\n",
    "            else:\n",
    "                y_pred_cv_t.append(revert_logDiff(np.array([X_cv_bd[ix][i][-1]]), \\\n",
    "                        np.array(model.predict(X_cv[ix][i].reshape(1, numInputs))).tolist()[0]))\n",
    "            y_cv_t.append(revert_logDiff(np.array([y_cv_bd[ix][i][0]]), y_cv[ix][i]))\n",
    "    else:\n",
    "        y_pred_cv_t = model.predict(X_cv[ix])\n",
    "        y_cv_t = y_cv[ix]\n",
    "\n",
    "    list_mae_cv = list()\n",
    "    for i in np.arange(len(y_cv_t)):\n",
    "        if (PreProcessing_str == 'DiffLog'):\n",
    "            mae_cv = mean_absolute_error(y_cv_t[i], y_pred_cv_t[i])\n",
    "        else:\n",
    "            mae_cv = mean_absolute_error(y_cv_t[i,:], y_pred_cv_t[i,:])\n",
    "        list_mae_cv.append(mae_cv)\n",
    "\n",
    "    if (splitValTest):\n",
    "        ## ------- TEST ERROR -------\n",
    "        if (PreProcessing_str in ['Normalization', 'Standardization']): \n",
    "            y_pred_test_t = scaler.inverse_transform(model.predict(X_test[ix]))\n",
    "            y_test_t = scaler.inverse_transform(y_test[ix])\n",
    "        elif (PreProcessing_str == 'DiffLog'):\n",
    "            y_pred_test_t = list()\n",
    "            y_test_t = list()\n",
    "            for i in np.arange(len(X_test[ix])):\n",
    "                if (NN_str == 'LSTM'):\n",
    "                    y_pred_test_t.append(revert_logDiff(np.array([X_test_bd[ix][i][-1]]), \\\n",
    "                            np.array(model.predict(X_test[ix][i].reshape(1, numInputs, 1))).tolist()[0]))\n",
    "                else:\n",
    "                    y_pred_test_t.append(revert_logDiff(np.array([X_test_bd[ix][i][-1]]), \\\n",
    "                            np.array(model.predict(X_test[ix][i].reshape(1, numInputs))).tolist()[0]))\n",
    "                y_test_t.append(revert_logDiff(np.array([y_test_bd[ix][i][0]]), y_test[ix][i]))\n",
    "        else:\n",
    "            y_pred_test_t = model.predict(X_test[ix])\n",
    "            y_test_t = y_test[ix]\n",
    "\n",
    "        list_mae_test = list()\n",
    "        for i in np.arange(len(y_test_t)):   \n",
    "            if (PreProcessing_str == 'DiffLog'):\n",
    "                mae_test = mean_absolute_error(y_test_t[i], y_pred_test_t[i])\n",
    "            else:\n",
    "                mae_test = mean_absolute_error(y_test_t[i,:], y_pred_test_t[i,:])\n",
    "            list_mae_test.append(mae_test)\n",
    "    \n",
    "        dict_mae[str(ix)] = {'train': list_mae_train, 'cv': list_mae_cv, 'test': list_mae_test}\n",
    "    else:\n",
    "        dict_mae[str(ix)] = {'train': list_mae_train, 'cv': list_mae_cv}\n",
    "    \n",
    "trainMae = np.mean([np.mean(dict_mae[str(runIx)]['train']) for runIx in np.arange(len(X_train))]);\n",
    "cvMae = np.mean([np.mean(dict_mae[str(runIx)]['cv']) for runIx in np.arange(len(X_train))]);\n",
    "if (splitValTest):\n",
    "    testMae = np.mean([np.mean(dict_mae[str(runIx)]['test']) for runIx in np.arange(len(X_train))]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation Performance\n",
    "    Assess Training / Cross-Validation / Test\n",
    "    DEBUG_LEVEL = 0 - Only shows STATS of ALL runs\n",
    "                  1 - Shows the average MAE through runs \n",
    "                  2 - Shows the MAE at each run at each train/cv/test set    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STATS of ALL runs\n",
      "Train avg MAE avg = 11.9394\n",
      "Cross-Validation avg MAE avg = 13.2245\n",
      "Test avg MAE avg = 13.6253\n"
     ]
    }
   ],
   "source": [
    "DEBUG_LEVEL = 0\n",
    "\n",
    "print(\"STATS of ALL runs\")    \n",
    "print(\"Train avg MAE avg = %.4f\" % trainMae) \n",
    "print(\"Cross-Validation avg MAE avg = %.4f\" % cvMae)\n",
    "if (splitValTest):\n",
    "    print(\"Test avg MAE avg = %.4f\" % testMae)\n",
    "\n",
    "list_train_mae = list()\n",
    "list_cv_mae = list()\n",
    "if (splitValTest):\n",
    "    list_test_mae = list()\n",
    "for runIx in np.arange(len(X_train)):\n",
    "    if (DEBUG_LEVEL > 0):\n",
    "        print(\"\")\n",
    "        print(\"----- RUN NUMBER %d -----\" % (runIx+1))\n",
    "\n",
    "    list_train_mae.append(np.mean(dict_mae[str(runIx)]['train']))\n",
    "    if (DEBUG_LEVEL > 0):\n",
    "        print (\"Train MAE avg = %.3f\" % np.mean(dict_mae[str(runIx)]['train'])) \n",
    "        if (DEBUG_LEVEL > 1):\n",
    "            if (len(dict_mae[str(runIx)]['train'])>1):\n",
    "                print(\"Individual trains: \" + str([round(x,4) for x in dict_mae[str(runIx)]['train']]))\n",
    "    \n",
    "    list_cv_mae.append(np.mean(dict_mae[str(runIx)]['cv']))\n",
    "    if (DEBUG_LEVEL > 0):\n",
    "        print (\"Cross-Validation MAE avg = %.3f\" % np.mean(dict_mae[str(runIx)]['cv'])) \n",
    "        if (DEBUG_LEVEL > 1):\n",
    "            if (len(dict_mae[str(runIx)]['cv'])>1):\n",
    "                print(\"Individual cross-validations: \" + str([round(x,4) for x in dict_mae[str(runIx)]['cv']]))\n",
    "    \n",
    "    if (splitValTest):\n",
    "        list_test_mae.append(np.mean(dict_mae[str(runIx)]['test']))\n",
    "        if (DEBUG_LEVEL > 0):\n",
    "            print (\"Test MAE avg = %.3f\" % np.mean(dict_mae[str(runIx)]['test'])) \n",
    "            if (DEBUG_LEVEL > 1):\n",
    "                if (len(dict_mae[str(runIx)]['test'])>1):\n",
    "                    print(\"Individual tests: \" + str([round(x,4) for x in dict_mae[str(runIx)]['test']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Mean Absolute Errors of each run\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(np.arange(len(X_train))+1, list_train_mae, 'bo', markersize=10, label = 'Train')\n",
    "plt.plot(np.arange(len(X_train))+1, list_cv_mae, 'ro', markersize=10, label = 'Cross-Validation')\n",
    "if (splitValTest):\n",
    "    plt.plot(np.arange(len(X_train))+1, list_test_mae, 'go', markersize=10, label = 'Test')\n",
    "plt.legend()\n",
    "plt.xlabel('# RUNS')\n",
    "plt.ylabel('Average Mean Absolute Error')\n",
    "plt.title('Average Mean Absolute Error at each run')\n",
    "if (len(X_train) > 500):\n",
    "    plt.xticks(np.arange(0, len(X_train), 100))\n",
    "if (len(X_train) > 250):\n",
    "    plt.xticks(np.arange(0, len(X_train), 50))\n",
    "elif (len(X_train) > 50):\n",
    "    plt.xticks(np.arange(0, len(X_train), 10))\n",
    "else:\n",
    "    plt.xticks(np.arange(1,len(X_train)+1))\n",
    "plt.xlim([0, len(X_train)+1])\n",
    "plt.grid()\n",
    "\n",
    "if (splitValTest):\n",
    "    # Plot Mean Absolute Errors of each run\n",
    "    x_bp = np.concatenate((np.repeat('Train', len(list_train_mae)), \\\n",
    "                           np.repeat('Cross-validation', len(list_cv_mae)), \n",
    "                           np.repeat('Test', len(list_test_mae))), axis = 0)\n",
    "\n",
    "    y_bp = np.concatenate((list_train_mae, list_cv_mae, list_test_mae), axis = 0)\n",
    "else:\n",
    "    # Plot Mean Absolute Errors of each run\n",
    "    x_bp = np.concatenate((np.repeat('Train', len(list_train_mae)), \\\n",
    "                           np.repeat('Cross-validation', len(list_cv_mae))), axis = 0)\n",
    "    y_bp = np.concatenate((list_train_mae, list_cv_mae), axis = 0)\n",
    "\n",
    "    \n",
    "df_bp = pd.DataFrame(data={'Data Type': x_bp, 'Mean Average Error': y_bp})\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "bplot=sns.boxplot(y='Mean Average Error', x='Data Type', data=df_bp, width=0.5)\n",
    "bplot.artists[0].set_facecolor('blue')\n",
    "bplot.artists[1].set_facecolor('red')\n",
    "plt.title('Boxplot with average Training, Cross-Validation and Testing error');\n",
    "plt.plot([-1,0], [trainMae, trainMae],'b--', lw=2, alpha=0.2)\n",
    "plt.plot([-1,1], [cvMae, cvMae],'r--', lw=2, alpha=0.2)\n",
    "if (splitValTest):\n",
    "    bplot.artists[2].set_facecolor('green')\n",
    "    plt.plot([-1,2], [testMae, testMae],'g--', lw=2, alpha=0.2)\n",
    "    plt.xlim([-1, 2.5])\n",
    "    plt.text(1.30, 1*testMae, 'Average: ' + str(round(testMae,4)), fontsize=14, color='g', fontweight='bold')\n",
    "else:\n",
    "    plt.xlim([-1, 1.5])\n",
    "plt.text(-0.70, 1*trainMae, 'Average: ' + str(round(trainMae,4)), fontsize=14, color='b', fontweight='bold')\n",
    "plt.text(0.30, 1*cvMae, 'Average: ' + str(round(cvMae,4)), fontsize=14, color='r', fontweight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform statistics on predictions\n",
    "    This aims to run several predictions in order to have a stronger argument on predictions performance\n",
    "    MAX_ITER corresponds to the amount of training & prediction used for statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/4\n",
      "2/4\n",
      "3/4\n",
      "4/4\n"
     ]
    }
   ],
   "source": [
    "MAX_ITER = 4\n",
    "\n",
    "# This uses the entire training set to train\n",
    "Xtest, ytest = splitTrain.split_train(series_input, numInputs, stepsToForecast[-1], numJumps)\n",
    "\n",
    "if (NN_str == 'MLP'):\n",
    "    Xtest = np.squeeze(Xtest, axis=2)\n",
    "    ytest = np.squeeze(ytest, axis=2)\n",
    "\n",
    "real_array = np.array(df_test.values).flatten()\n",
    "\n",
    "if (NN_str in ['RNN', 'LSTM'] ):\n",
    "    Xtest = np.array(Xtest)\n",
    "    Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], 1))\n",
    "    ytest = np.array(ytest)\n",
    "    ytest = np.reshape(ytest, (ytest.shape[0], ytest.shape[1], 1))\n",
    "\n",
    "i = 0\n",
    "list_pred = list()\n",
    "while (i < MAX_ITER):\n",
    "    \n",
    "    print(\"%d/%d\" % (i+1, MAX_ITER));\n",
    "    \n",
    "    ## ------- TRAIN MODEL WITH ALL DATA-------\n",
    "    model.fit(Xtest, ytest, epochs = train_epochs, verbose = 0);\n",
    "\n",
    "    if (NN_str == 'MLP'):\n",
    "        yhat = model.predict(series_input[-numInputs:].T, verbose=0)\n",
    "        yhat_inSample = model.predict(series_input[-numInputs-stepsToForecast[-1]:-stepsToForecast[-1]].T, verbose=0)\n",
    "    else:\n",
    "        yhat = model.predict(series_input[-numInputs:].reshape(1, numInputs, 1), verbose=0)\n",
    "        yhat_inSample = model.predict(series_input[-numInputs-stepsToForecast[-1]:-stepsToForecast[-1]].reshape(1, numInputs, 1), verbose=0)\n",
    "\n",
    "    y_pred_test = yhat.tolist()\n",
    "    y_pred_inSample = yhat_inSample.tolist()\n",
    "\n",
    "    if (PreProcessing_str in ['Normalization', 'Standardization']): \n",
    "        y_pred_test_t = scaler.inverse_transform(y_pred_test)\n",
    "        y_pred_inSample_t = scaler.inverse_transform(y_pred_inSample)\n",
    "    elif (PreProcessing_str == 'DiffLog'):\n",
    "        y_pred_test_t = revert_logDiff(df_train.values[-1], y_pred_test[0])\n",
    "        y_pred_inSample_t = revert_logDiff(df_train.values[-stepsToForecast[-1]-1], y_pred_inSample[0])\n",
    "    else:\n",
    "        y_pred_test_t = y_pred_test\n",
    "        y_pred_inSample_t = y_pred_inSample\n",
    "\n",
    "    predictions_array = np.array(y_pred_test_t).flatten()\n",
    "    predictions_inSample_array = np.array(y_pred_inSample_t).flatten()\n",
    "    \n",
    "    i = i + 1\n",
    "    mae = horizon_mae(real_array, predictions_array, stepsToForecast)\n",
    "    mape = horizon_mape(real_array, predictions_array, stepsToForecast)\n",
    "    list_pred.append((mae, mape, predictions_array, predictions_inSample_array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Statistics cleaning\n",
    "    This aims to extract the performance of our NN. For that we need to select PRED_OUTL and the forecast horizon that we are predicting for stepsToForecast(PRED_IX)\n",
    "    PRED_OUTL corresponds to the number of predictions that are considered outliers\n",
    "    PRED_IX corresponds to the number steps to forecast you want to organize your data\n",
    "\n",
    "    Note that:\n",
    "        - PRED_OUTL < MAX_ITER, with both being even numbers\n",
    "        - The percentage considered as outlier is given by 100*(PRED_OUTL/MAX_ITER)\n",
    "        - 1 < PRED_IX < len(stepsToForecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MAE(MAPE) with a horizon of 21 samples for 50.0% (2\\4) of runs is: \t4.3 (1.44%) and 4.82 (1.61%)\n"
     ]
    }
   ],
   "source": [
    "PRED_IX = 3\n",
    "PRED_OUTL = 2\n",
    "\n",
    "# Sorts elements based on longer term horizon\n",
    "list_pred.sort(key=lambda elem: elem[0][PRED_IX-1])\n",
    "\n",
    "if (PRED_OUTL % 2) != 0:\n",
    "    PRED_OUTL = PRED_OUTL + 1\n",
    "    \n",
    "if (PRED_OUTL >= len(list_pred)):\n",
    "    print(\"You cannot consider all the runs as outliers. No outliers will be considered\")\n",
    "    PRED_OUTL = 0\n",
    "\n",
    "if (PRED_OUTL == 0):\n",
    "    list_pred_outlRemoved = list_pred\n",
    "else:\n",
    "    list_pred_outlRemoved = list_pred[int(PRED_OUTL/2):-int(PRED_OUTL/2)]\n",
    "\n",
    "print('The MAE(MAPE) with a horizon of ' + str(stepsToForecast[PRED_IX-1]) + ' samples for ' \\\n",
    "      + str(round(100-100*(PRED_OUTL/MAX_ITER),2)) + '% (' \\\n",
    "      + str(MAX_ITER-PRED_OUTL) + '\\\\' + str(MAX_ITER) + ') of runs is: \\t'\n",
    "      + str(list_pred_outlRemoved[0][0][PRED_IX-1]) + ' (' \\\n",
    "      + str(list_pred_outlRemoved[0][1][PRED_IX-1]) + '%) and ' \\\n",
    "      + str(list_pred_outlRemoved[-1][0][PRED_IX-1]) + ' (' \\\n",
    "      + str(list_pred_outlRemoved[-1][1][PRED_IX-1]) + '%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Prediction\n",
    "    This will show the plot of, within the specified confidence interval, that perform the best prediction based on the horizon samples chosen (using PRED_IX above)\n",
    "    This allows to understand what happens throughout the horizon forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction for 1 Business Days/Year: MAE = 0.24 (0.08%)\n",
      "Prediction for 5 Business Days/Year: MAE = 1.74 (0.59%)\n",
      "Prediction for 21 Business Days/Year: MAE = 4.3 (1.44%)\n"
     ]
    }
   ],
   "source": [
    "cmap = plt.get_cmap(\"tab10\")\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(211)\n",
    "plt.title(serie + ': Real versus Predicted ') \n",
    "plt.plot(range(0,len(real_array)+1), np.concatenate((df_train.values[-1], real_array), axis=0), 'k-o', linewidth=2)\n",
    "plt.plot(range(1,len(predictions_array)+1), list_pred_outlRemoved[0][2], color = cmap(0), linewidth=2)\n",
    "plt.plot([0,1], [df_train.values[-1][0], list_pred_outlRemoved[0][2][0]], '--', color = cmap(0), linewidth=2)\n",
    "\n",
    "plt.xlim([0,len(predictions_array)])\n",
    "plt.xticks(np.arange(0, len(predictions_array)+1, step=1))\n",
    "plt.grid(color='k', linestyle='--', linewidth=.5)\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "plt.legend(['Real value', 'Predicted value'])\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.axhline(y=0, color='k', linestyle='--', linewidth=2)\n",
    "plt.plot(range(0,len(real_array-predictions_array)+1), np.concatenate((np.array([0]), real_array-list_pred_outlRemoved[0][2]), axis=0), 'g-*', linewidth=2)\n",
    "plt.xticks(np.arange(0, len(predictions_array)+1, step=1))\n",
    "plt.xlim([0,len(predictions_array)])\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel('Prediction error')\n",
    "plt.grid(color='k', linestyle='--', linewidth=.5)\n",
    "plt.show()\n",
    "\n",
    "for i in np.arange(len(stepsToForecast)):\n",
    "    print('Prediction for ' + str(stepsToForecast[i]) + ' ' + xlab + ': MAE = '\\\n",
    "          + str(list_pred_outlRemoved[0][0][i]) + ' (' + str(list_pred_outlRemoved[0][1][i]) + '%)' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Forecast in-sample vs out-sample\n",
    "    This will show the plot of an in-sample vs an out-sample prediction, to understand how the NN is capable   to adapt to the seen data vs to generalize, respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.title(serie + ': In-sample and Out-of-sample Forecast Comparison') \n",
    "plt.plot(1+np.arange(stepsToForecast[-1])-stepsToForecast[-1], df_train.values[-stepsToForecast[-1]:], 'k', linewidth = 4)\n",
    "plt.plot(1+np.arange(stepsToForecast[-1])-stepsToForecast[-1], list_pred_outlRemoved[0][3], color=cmap(1), linewidth = 2 )\n",
    "plt.plot(range(1,len(predictions_array)+1), list_pred_outlRemoved[0][2], color=cmap(0), linewidth=2)\n",
    "plt.legend(['Real value', 'In-Sample Forecast', 'Out-Sample Forecast'])\n",
    "plt.plot(1+np.arange(stepsToForecast[-1]), df_test.values, 'k-o' , linewidth = 3)\n",
    "plt.plot(1+np.arange(stepsToForecast[-1]+numInputs)-stepsToForecast[-1]-numInputs, df_train.values[-numInputs-stepsToForecast[-1]:], 'k', linewidth = 4)\n",
    "\n",
    "plt.xlim([1-stepsToForecast[-1]-numInputs,+stepsToForecast[-1]])\n",
    "plt.xticks([0], '0')\n",
    "\n",
    "plt.xlabel('Past and future observations')\n",
    "plt.ylabel(ylab)\n",
    "\n",
    "plt.axvspan(-stepsToForecast[-1]-numInputs, -stepsToForecast[-1], facecolor=cmap(1), alpha=0.2)\n",
    "xmin, xmax, ymin, ymax = plt.axis()\n",
    "plt.hlines(ymin, -numInputs, 0, colors=cmap(0), linewidth=10)\n",
    "plt.vlines(-numInputs, ymin, ymax, colors=cmap(0), linewidth=2, linestyle='--')\n",
    "plt.vlines(0, ymin, ymax, colors=cmap(0), linewidth=2, linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([292.82110596, 292.94393921, 292.24252319, 291.97662354,\n",
       "       293.42797852, 293.79840088, 290.68667603, 292.78170776,\n",
       "       293.94799805, 294.13656616, 294.37350464, 294.56634521,\n",
       "       293.24020386, 294.1517334 , 293.71780396, 295.21102905,\n",
       "       293.98025513, 294.23284912, 293.37399292, 294.80410767,\n",
       "       294.14260864])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_pred_outlRemoved[0][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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