ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from itertools import product\n",
    "import matplotlib.pyplot as plt\n",
    "from pmdarima.arima.utils import ndiffs\n",
    "from typing import List, Union, Any, Optional, Dict\n",
    "\n",
    "\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller, kpss\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAXResults\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tools.sm_exceptions import ConvergenceWarning, InterpolationWarning\n",
    "\n",
    "\n",
    "from sklearn import set_config\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "\n",
    "set_config(\n",
    "    display='diagram',\n",
    "    transform_output=\"pandas\"\n",
    ")\n",
    "\n",
    "warnings.simplefilter('ignore', ConvergenceWarning)\n",
    "warnings.simplefilter('ignore', InterpolationWarning)\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # set to 1 to avoid tensorflow warnings\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24210, 9) (3459, 9) (6918, 9)\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv('./Data/train_df.csv')\n",
    "test_df = pd.read_csv('./Data/test_df.csv')\n",
    "val_df = pd.read_csv('./Data/val_df.csv')\n",
    "print(train_df.shape, val_df.shape, test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning for Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataHandler:\n",
    "    def __init__(self, label_columns: Optional[List[str]] = None):\n",
    "        \"\"\"\n",
    "        Initialize the DataHandler.\n",
    "\n",
    "        label_columns: list of column name(s) to be used as labels\n",
    "        \"\"\"\n",
    "        self.label_columns = label_columns\n",
    "        self.column_indices = None\n",
    "        self.label_columns_indices = None\n",
    "\n",
    "    def set_data(self, data: Dict[str, np.ndarray]):\n",
    "        \"\"\"\n",
    "        Set data for training, validation, and testing. Also create indices for the columns and label columns.\n",
    "\n",
    "        data: a dictionary with keys 'train', 'val', and 'test' and their corresponding dataframes as values\n",
    "        \"\"\"\n",
    "        self.train_df = data['train']\n",
    "        self.val_df = data['val']\n",
    "        self.test_df = data['test']\n",
    "\n",
    "        self.column_indices = {name: i for i, name in enumerate(self.train_df.columns)}\n",
    "        if self.label_columns is not None:\n",
    "            self.label_columns_indices = {name: i for i, name in enumerate(self.label_columns)}\n",
    "\n",
    "class WindowManager:\n",
    "    def __init__(self, input_width: int, label_width: int, shift: int, data_handler: DataHandler):\n",
    "        \"\"\"\n",
    "        Initialize the WindowManager.\n",
    "\n",
    "        input_width: number of steps (hours) of the input\n",
    "        label_width: number of steps (hours) of the output label\n",
    "        shift: number of steps to shift the window for the next prediction\n",
    "        data_handler: a DataHandler instance to handle data\n",
    "        \"\"\"\n",
    "        self.input_width = input_width\n",
    "        self.label_width = label_width\n",
    "        self.shift = shift\n",
    "        self.data_handler = data_handler\n",
    "\n",
    "        self.total_window_size = input_width + shift\n",
    "        self.input_indices = np.arange(self.total_window_size)[:input_width]\n",
    "        self.label_indices = np.arange(self.total_window_size)[-label_width:]\n",
    "\n",
    "    def split_to_inputs_labels(self, features):\n",
    "        \"\"\"\n",
    "        Split the features into inputs and labels based on the input and label widths.\n",
    "        \"\"\"\n",
    "        inputs = features[:, :self.input_width, :]\n",
    "        labels = features[:, -self.label_width:, :]\n",
    "\n",
    "        if self.data_handler.label_columns is not None:\n",
    "            labels = tf.stack([labels[:, :, self.data_handler.column_indices[name]] for name in self.data_handler.label_columns], axis=-1)\n",
    "\n",
    "        inputs.set_shape([None, self.input_width, None])\n",
    "        labels.set_shape([None, self.label_width, None])\n",
    "        return inputs, labels\n",
    "\n",
    "    def make_dataset(self, data):\n",
    "        \"\"\"\n",
    "        Create a time series dataset from the data.\n",
    "        \"\"\"\n",
    "        data = np.array(data, dtype=np.float32)\n",
    "        ds = keras.preprocessing.timeseries_dataset_from_array(\n",
    "            data=data, targets=None, sequence_length=self.total_window_size,\n",
    "            sequence_stride=1, shuffle=False, batch_size=32,\n",
    "        )\n",
    "        ds = ds.map(self.split_to_inputs_labels)\n",
    "        return ds\n",
    "\n",
    "    @property\n",
    "    def train(self):\n",
    "        \"\"\"\n",
    "        Get the training dataset.\n",
    "        \"\"\"\n",
    "        return self.make_dataset(self.data_handler.train_df)\n",
    "\n",
    "    @property\n",
    "    def val(self):\n",
    "        \"\"\"\n",
    "        Get the validation dataset.\n",
    "        \"\"\"\n",
    "        return self.make_dataset(self.data_handler.val_df)\n",
    "\n",
    "    @property\n",
    "    def test(self):\n",
    "        \"\"\"\n",
    "        Get the testing dataset.\n",
    "        \"\"\"\n",
    "        return self.make_dataset(self.data_handler.test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data(input_width):\n",
    "    \"\"\"\n",
    "    Create data for training, validation, and testing.\n",
    "    \"\"\"\n",
    "    data_handler = DataHandler(label_columns=['Global_active_power'])\n",
    "    data_handler.set_data({\n",
    "        'train': train_df,\n",
    "        'val': val_df,\n",
    "        'test': test_df\n",
    "    })\n",
    "\n",
    "    window = WindowManager(input_width=input_width, label_width=24, shift=24, data_handler=data_handler)\n",
    "\n",
    "    # create normalized data\n",
    "    normalizer = layers.Normalization(axis=-1)\n",
    "\n",
    "    # Adapt it to the training data\n",
    "    normalizer.adapt(window.train.map(lambda x, y: x))\n",
    "\n",
    "    normalized_train_ds = window.train.map(lambda x, y: (normalizer(x), y), num_parallel_calls=tf.data.AUTOTUNE)\n",
    "    normalized_val_ds = window.val.map(lambda x, y: (normalizer(x), y), num_parallel_calls=tf.data.AUTOTUNE)\n",
    "    normalized_test_ds = window.test.map(lambda x, y: (normalizer(x), y), num_parallel_calls=tf.data.AUTOTUNE)\n",
    "\n",
    "\n",
    "    return {\n",
    "        'train': normalized_train_ds,\n",
    "        'val': normalized_val_ds,\n",
    "        'test': normalized_test_ds\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_predictions(model, df):\n",
    "\n",
    "    # Create empty lists to hold labels and predictions\n",
    "    test_labels = []\n",
    "    test_predictions = []\n",
    "\n",
    "    # Loop over all batches in the test dataset\n",
    "    for test_inputs, test_batch_labels in df['test']:\n",
    "        # Make predictions for this batch\n",
    "        test_batch_predictions = model.predict(test_inputs, verbose=0)\n",
    "\n",
    "        # Append the labels and predictions to our lists\n",
    "        test_labels.append(test_batch_labels.numpy())\n",
    "        test_predictions.append(test_batch_predictions)\n",
    "\n",
    "    # Concatenate the labels and predictions from all batches\n",
    "    test_labels = np.concatenate(test_labels)\n",
    "    test_predictions = np.concatenate(test_predictions)\n",
    "\n",
    "    return {\n",
    "        'labels': test_labels,\n",
    "        'predictions': test_predictions\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed(seed):\n",
    "    keras.utils.set_random_seed(seed)\n",
    "    tf.config.experimental.enable_op_determinism()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compile_and_fit(model, df, init_lr=0.001, max_epochs=50):\n",
    "\n",
    "\n",
    "    lr_reduce_callback = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor='val_loss', factor=0.1, patience=3, verbose=0,\n",
    "        mode='auto', min_delta=0.0001, min_lr=0.000001\n",
    "    )\n",
    "\n",
    "    # early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "    #                                                patience=patience, mode='min')\n",
    "\n",
    "    model.compile(loss=\"mse\",\n",
    "                  optimizer=keras.optimizers.Adam(learning_rate=init_lr),\n",
    "                  metrics=[keras.metrics.MeanAbsoluteError()])\n",
    "    \n",
    "    history = model.fit(df['train'],\n",
    "                       epochs=max_epochs,\n",
    "                       validation_data=df['val'],\n",
    "                       callbacks=[lr_reduce_callback]\n",
    "                       )\n",
    "    \n",
    "    return history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN (LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "756/756 [==============================] - 8s 9ms/step - loss: 6035.8730 - mean_absolute_error: 54.0024 - val_loss: 6210.6934 - val_mean_absolute_error: 57.6142 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 4280.2812 - mean_absolute_error: 44.6366 - val_loss: 4650.0767 - val_mean_absolute_error: 50.8638 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 3526.9646 - mean_absolute_error: 43.3482 - val_loss: 3883.9141 - val_mean_absolute_error: 47.8489 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 3225.3279 - mean_absolute_error: 43.3567 - val_loss: 3783.2261 - val_mean_absolute_error: 47.2330 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2944.8347 - mean_absolute_error: 40.5365 - val_loss: 3210.7869 - val_mean_absolute_error: 40.9881 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2705.6687 - mean_absolute_error: 38.0956 - val_loss: 2914.3684 - val_mean_absolute_error: 38.7820 - lr: 0.0010\n",
      "Epoch 7/50\n",
      "756/756 [==============================] - 6s 8ms/step - loss: 2582.6904 - mean_absolute_error: 37.2374 - val_loss: 2736.9170 - val_mean_absolute_error: 37.5507 - lr: 0.0010\n",
      "Epoch 8/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2503.5735 - mean_absolute_error: 36.7109 - val_loss: 2628.9221 - val_mean_absolute_error: 36.8941 - lr: 0.0010\n",
      "Epoch 9/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2439.7163 - mean_absolute_error: 36.2946 - val_loss: 2553.1443 - val_mean_absolute_error: 36.4554 - lr: 0.0010\n",
      "Epoch 10/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2385.4927 - mean_absolute_error: 35.9219 - val_loss: 2488.3760 - val_mean_absolute_error: 36.0486 - lr: 0.0010\n",
      "Epoch 11/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2336.6116 - mean_absolute_error: 35.5799 - val_loss: 2410.1191 - val_mean_absolute_error: 35.5065 - lr: 0.0010\n",
      "Epoch 12/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2291.4023 - mean_absolute_error: 35.2225 - val_loss: 2352.1191 - val_mean_absolute_error: 35.0866 - lr: 0.0010\n",
      "Epoch 13/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2249.0037 - mean_absolute_error: 34.8382 - val_loss: 2316.4309 - val_mean_absolute_error: 34.7977 - lr: 0.0010\n",
      "Epoch 14/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2214.0977 - mean_absolute_error: 34.4949 - val_loss: 2282.2974 - val_mean_absolute_error: 34.5072 - lr: 0.0010\n",
      "Epoch 15/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2187.3591 - mean_absolute_error: 34.2131 - val_loss: 2259.6484 - val_mean_absolute_error: 34.3233 - lr: 0.0010\n",
      "Epoch 16/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2165.8420 - mean_absolute_error: 34.0074 - val_loss: 2239.8452 - val_mean_absolute_error: 34.1687 - lr: 0.0010\n",
      "Epoch 17/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2146.4146 - mean_absolute_error: 33.8244 - val_loss: 2230.1350 - val_mean_absolute_error: 34.0614 - lr: 0.0010\n",
      "Epoch 18/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2129.2024 - mean_absolute_error: 33.6573 - val_loss: 2216.9224 - val_mean_absolute_error: 33.9656 - lr: 0.0010\n",
      "Epoch 19/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2114.9409 - mean_absolute_error: 33.5177 - val_loss: 2206.9868 - val_mean_absolute_error: 33.8904 - lr: 0.0010\n",
      "Epoch 20/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2101.0542 - mean_absolute_error: 33.3646 - val_loss: 2188.7893 - val_mean_absolute_error: 33.7567 - lr: 0.0010\n",
      "Epoch 21/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2086.7737 - mean_absolute_error: 33.2245 - val_loss: 2172.1062 - val_mean_absolute_error: 33.6320 - lr: 0.0010\n",
      "Epoch 22/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2073.8196 - mean_absolute_error: 33.0924 - val_loss: 2165.1055 - val_mean_absolute_error: 33.5719 - lr: 0.0010\n",
      "Epoch 23/50\n",
      "756/756 [==============================] - 7s 9ms/step - loss: 2062.7051 - mean_absolute_error: 32.9749 - val_loss: 2154.8105 - val_mean_absolute_error: 33.4850 - lr: 0.0010\n",
      "Epoch 24/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2053.7043 - mean_absolute_error: 32.8757 - val_loss: 2145.3987 - val_mean_absolute_error: 33.4175 - lr: 0.0010\n",
      "Epoch 25/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2044.7986 - mean_absolute_error: 32.7903 - val_loss: 2139.5129 - val_mean_absolute_error: 33.3669 - lr: 0.0010\n",
      "Epoch 26/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2037.0195 - mean_absolute_error: 32.7130 - val_loss: 2132.5190 - val_mean_absolute_error: 33.3006 - lr: 0.0010\n",
      "Epoch 27/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2029.9402 - mean_absolute_error: 32.6307 - val_loss: 2128.4238 - val_mean_absolute_error: 33.2583 - lr: 0.0010\n",
      "Epoch 28/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2023.6550 - mean_absolute_error: 32.5664 - val_loss: 2123.5532 - val_mean_absolute_error: 33.2100 - lr: 0.0010\n",
      "Epoch 29/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2018.2333 - mean_absolute_error: 32.5122 - val_loss: 2120.9004 - val_mean_absolute_error: 33.1838 - lr: 0.0010\n",
      "Epoch 30/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2013.5487 - mean_absolute_error: 32.4632 - val_loss: 2120.7100 - val_mean_absolute_error: 33.1695 - lr: 0.0010\n",
      "Epoch 31/50\n",
      "756/756 [==============================] - 7s 9ms/step - loss: 2009.2087 - mean_absolute_error: 32.4180 - val_loss: 2116.5469 - val_mean_absolute_error: 33.1304 - lr: 0.0010\n",
      "Epoch 32/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2005.0792 - mean_absolute_error: 32.3738 - val_loss: 2113.6990 - val_mean_absolute_error: 33.1040 - lr: 0.0010\n",
      "Epoch 33/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 2001.5759 - mean_absolute_error: 32.3345 - val_loss: 2112.2998 - val_mean_absolute_error: 33.0848 - lr: 0.0010\n",
      "Epoch 34/50\n",
      "756/756 [==============================] - 7s 9ms/step - loss: 1997.9734 - mean_absolute_error: 32.3095 - val_loss: 2108.7551 - val_mean_absolute_error: 33.0659 - lr: 0.0010\n",
      "Epoch 35/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1993.9612 - mean_absolute_error: 32.2642 - val_loss: 2104.8115 - val_mean_absolute_error: 33.0517 - lr: 0.0010\n",
      "Epoch 36/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1990.5686 - mean_absolute_error: 32.2293 - val_loss: 2101.4001 - val_mean_absolute_error: 33.0283 - lr: 0.0010\n",
      "Epoch 37/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1987.2966 - mean_absolute_error: 32.1986 - val_loss: 2096.6702 - val_mean_absolute_error: 32.9961 - lr: 0.0010\n",
      "Epoch 38/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1984.1063 - mean_absolute_error: 32.1643 - val_loss: 2094.3982 - val_mean_absolute_error: 32.9822 - lr: 0.0010\n",
      "Epoch 39/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1981.2543 - mean_absolute_error: 32.1345 - val_loss: 2092.6362 - val_mean_absolute_error: 32.9746 - lr: 0.0010\n",
      "Epoch 40/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1977.9711 - mean_absolute_error: 32.0975 - val_loss: 2089.9766 - val_mean_absolute_error: 32.9618 - lr: 0.0010\n",
      "Epoch 41/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1974.8303 - mean_absolute_error: 32.0687 - val_loss: 2087.4207 - val_mean_absolute_error: 32.9345 - lr: 0.0010\n",
      "Epoch 42/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1972.6104 - mean_absolute_error: 32.0495 - val_loss: 2107.0916 - val_mean_absolute_error: 33.0701 - lr: 0.0010\n",
      "Epoch 43/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1970.0526 - mean_absolute_error: 32.0184 - val_loss: 2116.9785 - val_mean_absolute_error: 33.1150 - lr: 0.0010\n",
      "Epoch 44/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1966.9984 - mean_absolute_error: 32.0038 - val_loss: 2106.4641 - val_mean_absolute_error: 33.0603 - lr: 0.0010\n",
      "Epoch 45/50\n",
      "756/756 [==============================] - 7s 9ms/step - loss: 1998.7435 - mean_absolute_error: 31.7423 - val_loss: 1962.3917 - val_mean_absolute_error: 32.3963 - lr: 1.0000e-04\n",
      "Epoch 46/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1968.5437 - mean_absolute_error: 31.8099 - val_loss: 1944.4539 - val_mean_absolute_error: 32.3279 - lr: 1.0000e-04\n",
      "Epoch 47/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1963.4600 - mean_absolute_error: 31.8281 - val_loss: 1938.0557 - val_mean_absolute_error: 32.3175 - lr: 1.0000e-04\n",
      "Epoch 48/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1960.9734 - mean_absolute_error: 31.8321 - val_loss: 1934.9115 - val_mean_absolute_error: 32.3171 - lr: 1.0000e-04\n",
      "Epoch 49/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1959.2378 - mean_absolute_error: 31.8308 - val_loss: 1933.2865 - val_mean_absolute_error: 32.3200 - lr: 1.0000e-04\n",
      "Epoch 50/50\n",
      "756/756 [==============================] - 6s 9ms/step - loss: 1957.8409 - mean_absolute_error: 31.8279 - val_loss: 1932.1908 - val_mean_absolute_error: 32.3237 - lr: 1.0000e-04\n"
     ]
    }
   ],
   "source": [
    "lstm_df=create_data(input_width=24)\n",
    "set_seed(42)\n",
    "\n",
    "lstm_model = keras.Sequential([\n",
    "    layers.LSTM(32, return_sequences=True),\n",
    "    layers.Dense(1, kernel_initializer=tf.initializers.zeros),\n",
    "])\n",
    "\n",
    "history = compile_and_fit(lstm_model, lstm_df, max_epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_2 (LSTM)               (None, 24, 32)            5248      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 24, 1)             33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,281\n",
      "Trainable params: 5,281\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "lstm_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_img_file = 'results/lstm_model.png'\n",
    "keras.utils.plot_model(lstm_model, to_file=dot_img_file, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_df = pd.DataFrame(history.history)\n",
    "\n",
    "plt.figure(figsize=(18, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history_df['loss'], label='Training Loss')\n",
    "plt.plot(history_df['val_loss'], label='Validation Loss')\n",
    "plt.legend()\n",
    "plt.title('Losses')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss (MSE)')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history_df['mean_absolute_error'], label='Training MAE')\n",
    "plt.plot(history_df['val_mean_absolute_error'], label='Validation MAE')\n",
    "plt.legend()\n",
    "plt.title('Mean Absolute Errors')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MAE')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RNN-LSTM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>36.139565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MBE</th>\n",
       "      <td>-0.937574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>26.935583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RNN-LSTM\n",
       "RMSE  36.139565\n",
       "MBE   -0.937574\n",
       "MAE   26.935583"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_preds=make_predictions(lstm_model, lstm_df)\n",
    "\n",
    "true_vals_lstm=lstm_preds['labels'].ravel()\n",
    "lstm_preds=lstm_preds['predictions'].ravel()\n",
    "evaluate_model(true_vals_lstm, lstm_preds, 'RNN-LSTM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "756/756 [==============================] - 6s 7ms/step - loss: 7108.0688 - mean_absolute_error: 63.3297 - val_loss: 8233.2812 - val_mean_absolute_error: 72.1307 - lr: 1.0000e-04\n",
      "Epoch 2/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 5064.7373 - mean_absolute_error: 48.1903 - val_loss: 4565.4185 - val_mean_absolute_error: 49.1891 - lr: 1.0000e-04\n",
      "Epoch 3/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 3084.7637 - mean_absolute_error: 39.6694 - val_loss: 3157.6323 - val_mean_absolute_error: 41.8488 - lr: 1.0000e-04\n",
      "Epoch 4/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2675.9087 - mean_absolute_error: 38.6567 - val_loss: 2799.5583 - val_mean_absolute_error: 39.6297 - lr: 1.0000e-04\n",
      "Epoch 5/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2522.9050 - mean_absolute_error: 37.8055 - val_loss: 2610.8320 - val_mean_absolute_error: 38.1869 - lr: 1.0000e-04\n",
      "Epoch 6/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2429.6611 - mean_absolute_error: 37.1305 - val_loss: 2493.9631 - val_mean_absolute_error: 37.2044 - lr: 1.0000e-04\n",
      "Epoch 7/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2369.5320 - mean_absolute_error: 36.6375 - val_loss: 2417.1494 - val_mean_absolute_error: 36.5327 - lr: 1.0000e-04\n",
      "Epoch 8/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2328.7905 - mean_absolute_error: 36.2659 - val_loss: 2363.9248 - val_mean_absolute_error: 36.0465 - lr: 1.0000e-04\n",
      "Epoch 9/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2299.9292 - mean_absolute_error: 35.9801 - val_loss: 2324.8479 - val_mean_absolute_error: 35.6835 - lr: 1.0000e-04\n",
      "Epoch 10/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2278.5591 - mean_absolute_error: 35.7563 - val_loss: 2295.1926 - val_mean_absolute_error: 35.3939 - lr: 1.0000e-04\n",
      "Epoch 11/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2262.2266 - mean_absolute_error: 35.5802 - val_loss: 2272.3533 - val_mean_absolute_error: 35.1640 - lr: 1.0000e-04\n",
      "Epoch 12/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2249.6472 - mean_absolute_error: 35.4414 - val_loss: 2254.3494 - val_mean_absolute_error: 34.9793 - lr: 1.0000e-04\n",
      "Epoch 13/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2239.6064 - mean_absolute_error: 35.3273 - val_loss: 2239.9128 - val_mean_absolute_error: 34.8278 - lr: 1.0000e-04\n",
      "Epoch 14/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2231.5205 - mean_absolute_error: 35.2365 - val_loss: 2228.1401 - val_mean_absolute_error: 34.7042 - lr: 1.0000e-04\n",
      "Epoch 15/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2224.8083 - mean_absolute_error: 35.1595 - val_loss: 2218.4731 - val_mean_absolute_error: 34.6018 - lr: 1.0000e-04\n",
      "Epoch 16/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2219.1880 - mean_absolute_error: 35.0955 - val_loss: 2210.4077 - val_mean_absolute_error: 34.5151 - lr: 1.0000e-04\n",
      "Epoch 17/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2214.3931 - mean_absolute_error: 35.0398 - val_loss: 2203.4407 - val_mean_absolute_error: 34.4405 - lr: 1.0000e-04\n",
      "Epoch 18/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2210.2668 - mean_absolute_error: 34.9923 - val_loss: 2197.3643 - val_mean_absolute_error: 34.3761 - lr: 1.0000e-04\n",
      "Epoch 19/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2206.6702 - mean_absolute_error: 34.9490 - val_loss: 2192.0630 - val_mean_absolute_error: 34.3176 - lr: 1.0000e-04\n",
      "Epoch 20/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2203.4648 - mean_absolute_error: 34.9098 - val_loss: 2187.3958 - val_mean_absolute_error: 34.2654 - lr: 1.0000e-04\n",
      "Epoch 21/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2200.5825 - mean_absolute_error: 34.8743 - val_loss: 2183.2046 - val_mean_absolute_error: 34.2197 - lr: 1.0000e-04\n",
      "Epoch 22/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2197.9795 - mean_absolute_error: 34.8425 - val_loss: 2179.4390 - val_mean_absolute_error: 34.1783 - lr: 1.0000e-04\n",
      "Epoch 23/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2195.6023 - mean_absolute_error: 34.8132 - val_loss: 2176.1365 - val_mean_absolute_error: 34.1422 - lr: 1.0000e-04\n",
      "Epoch 24/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2193.4746 - mean_absolute_error: 34.7859 - val_loss: 2173.1350 - val_mean_absolute_error: 34.1079 - lr: 1.0000e-04\n",
      "Epoch 25/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2191.5476 - mean_absolute_error: 34.7608 - val_loss: 2170.4546 - val_mean_absolute_error: 34.0769 - lr: 1.0000e-04\n",
      "Epoch 26/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2189.7866 - mean_absolute_error: 34.7386 - val_loss: 2168.0344 - val_mean_absolute_error: 34.0498 - lr: 1.0000e-04\n",
      "Epoch 27/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2188.1577 - mean_absolute_error: 34.7186 - val_loss: 2165.8296 - val_mean_absolute_error: 34.0247 - lr: 1.0000e-04\n",
      "Epoch 28/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2186.6550 - mean_absolute_error: 34.6997 - val_loss: 2163.7854 - val_mean_absolute_error: 34.0011 - lr: 1.0000e-04\n",
      "Epoch 29/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2185.2673 - mean_absolute_error: 34.6815 - val_loss: 2161.9514 - val_mean_absolute_error: 33.9795 - lr: 1.0000e-04\n",
      "Epoch 30/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2183.9163 - mean_absolute_error: 34.6633 - val_loss: 2160.2439 - val_mean_absolute_error: 33.9583 - lr: 1.0000e-04\n",
      "Epoch 31/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2182.6614 - mean_absolute_error: 34.6465 - val_loss: 2158.6436 - val_mean_absolute_error: 33.9392 - lr: 1.0000e-04\n",
      "Epoch 32/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2181.4829 - mean_absolute_error: 34.6307 - val_loss: 2157.1467 - val_mean_absolute_error: 33.9225 - lr: 1.0000e-04\n",
      "Epoch 33/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2180.3738 - mean_absolute_error: 34.6165 - val_loss: 2155.7285 - val_mean_absolute_error: 33.9074 - lr: 1.0000e-04\n",
      "Epoch 34/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2179.3127 - mean_absolute_error: 34.6029 - val_loss: 2154.4419 - val_mean_absolute_error: 33.8933 - lr: 1.0000e-04\n",
      "Epoch 35/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2178.3035 - mean_absolute_error: 34.5902 - val_loss: 2153.2651 - val_mean_absolute_error: 33.8807 - lr: 1.0000e-04\n",
      "Epoch 36/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2177.3704 - mean_absolute_error: 34.5786 - val_loss: 2152.1230 - val_mean_absolute_error: 33.8685 - lr: 1.0000e-04\n",
      "Epoch 37/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2176.5010 - mean_absolute_error: 34.5679 - val_loss: 2151.0840 - val_mean_absolute_error: 33.8573 - lr: 1.0000e-04\n",
      "Epoch 38/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2175.6677 - mean_absolute_error: 34.5579 - val_loss: 2150.1484 - val_mean_absolute_error: 33.8471 - lr: 1.0000e-04\n",
      "Epoch 39/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2174.8757 - mean_absolute_error: 34.5481 - val_loss: 2149.2568 - val_mean_absolute_error: 33.8363 - lr: 1.0000e-04\n",
      "Epoch 40/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2174.1260 - mean_absolute_error: 34.5381 - val_loss: 2148.4089 - val_mean_absolute_error: 33.8266 - lr: 1.0000e-04\n",
      "Epoch 41/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2173.4094 - mean_absolute_error: 34.5287 - val_loss: 2147.5698 - val_mean_absolute_error: 33.8168 - lr: 1.0000e-04\n",
      "Epoch 42/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2172.7012 - mean_absolute_error: 34.5197 - val_loss: 2146.7996 - val_mean_absolute_error: 33.8080 - lr: 1.0000e-04\n",
      "Epoch 43/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2172.0215 - mean_absolute_error: 34.5115 - val_loss: 2146.0857 - val_mean_absolute_error: 33.8008 - lr: 1.0000e-04\n",
      "Epoch 44/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2171.3584 - mean_absolute_error: 34.5040 - val_loss: 2145.4170 - val_mean_absolute_error: 33.7936 - lr: 1.0000e-04\n",
      "Epoch 45/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2170.7148 - mean_absolute_error: 34.4962 - val_loss: 2144.7695 - val_mean_absolute_error: 33.7868 - lr: 1.0000e-04\n",
      "Epoch 46/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2170.0935 - mean_absolute_error: 34.4887 - val_loss: 2144.1750 - val_mean_absolute_error: 33.7806 - lr: 1.0000e-04\n",
      "Epoch 47/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2169.4883 - mean_absolute_error: 34.4818 - val_loss: 2143.5640 - val_mean_absolute_error: 33.7739 - lr: 1.0000e-04\n",
      "Epoch 48/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2168.8972 - mean_absolute_error: 34.4743 - val_loss: 2142.9680 - val_mean_absolute_error: 33.7672 - lr: 1.0000e-04\n",
      "Epoch 49/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2168.3311 - mean_absolute_error: 34.4674 - val_loss: 2142.3904 - val_mean_absolute_error: 33.7606 - lr: 1.0000e-04\n",
      "Epoch 50/50\n",
      "756/756 [==============================] - 5s 7ms/step - loss: 2167.7795 - mean_absolute_error: 34.4606 - val_loss: 2141.8274 - val_mean_absolute_error: 33.7544 - lr: 1.0000e-04\n"
     ]
    }
   ],
   "source": [
    "cnn_df=create_data(input_width=26)\n",
    "set_seed(42)\n",
    "\n",
    "cnn_model = keras.Sequential([\n",
    "    layers.Conv1D(32, activation='relu', kernel_size=(3)),\n",
    "    layers.Dense(units=32, activation='relu'),\n",
    "    layers.Dense(1, kernel_initializer=tf.initializers.zeros),\n",
    "])\n",
    "\n",
    "history_cnn = compile_and_fit(cnn_model, cnn_df, init_lr=0.0001, max_epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_4 (Conv1D)           (None, 24, 32)            800       \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 24, 32)            1056      \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 24, 1)             33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,889\n",
      "Trainable params: 1,889\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_img_file = 'results/cnn_model.png'\n",
    "keras.utils.plot_model(cnn_model, to_file=dot_img_file, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_df = pd.DataFrame(history_cnn.history)\n",
    "\n",
    "plt.figure(figsize=(18, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history_df['loss'], label='Training Loss')\n",
    "plt.plot(history_df['val_loss'], label='Validation Loss')\n",
    "plt.legend()\n",
    "plt.title('Losses')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss (MSE)')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history_df['mean_absolute_error'], label='Training MAE')\n",
    "plt.plot(history_df['val_mean_absolute_error'], label='Validation MAE')\n",
    "plt.legend()\n",
    "plt.title('Mean Absolute Errors')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MAE')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>37.180767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MBE</th>\n",
       "      <td>-0.380837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>28.162018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CNN\n",
       "RMSE  37.180767\n",
       "MBE   -0.380837\n",
       "MAE   28.162018"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_preds=make_predictions(cnn_model, cnn_df)\n",
    "\n",
    "true_vals=cnn_preds['labels'].ravel()\n",
    "cnn_preds=cnn_preds['predictions'].ravel()\n",
    "evaluate_model(true_vals, cnn_preds, 'CNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting  predictions and true values\n",
    "\n",
    "plt.plot(true_vals[:24], label='True Values')\n",
    "plt.plot(cnn_preds[:24], label='CNN Predictions')\n",
    "plt.plot(np.concatenate((lstm_preds[2:24],lstm_preds[70:72] ), axis=0), label='LSTM Predictions')\n",
    "plt.plot(test_pred.values[2:26], label='SARIMAX Predictions')\n",
    "plt.legend()\n",
    "plt.title('True Values vs Predictions for the first 24 hours', fontsize=10)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Global Active Power')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNN</th>\n",
       "      <th>RNN-LSTM</th>\n",
       "      <th>SARIMAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>37.180767</td>\n",
       "      <td>36.139565</td>\n",
       "      <td>49.773119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MBE</th>\n",
       "      <td>-0.380837</td>\n",
       "      <td>-0.937574</td>\n",
       "      <td>-29.125007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>28.162018</td>\n",
       "      <td>26.935583</td>\n",
       "      <td>43.074521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CNN   RNN-LSTM    SARIMAX\n",
       "RMSE  37.180767  36.139565  49.773119\n",
       "MBE   -0.380837  -0.937574 -29.125007\n",
       "MAE   28.162018  26.935583  43.074521"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([evaluate_model(true_vals, cnn_preds, 'CNN'), evaluate_model(true_vals_lstm, lstm_preds, 'RNN-LSTM'), sarimax_metric], axis=1)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "autoskln",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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