AML Project_V0.5_Autoencoders_April End_Train_VG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ec8a2606",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from datetime import date\n",
    "import datetime as dt\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.layers import Input,Dropout,Dense,LSTM,TimeDistributed,RepeatVector\n",
    "from keras.models import Model\n",
    "from keras import regularizers\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71598afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataPreprocessor:\n",
    "    def __init__(self, data_path):\n",
    "        self.data_path = data_path\n",
    "        self.df = pd.read_csv(self.data_path)\n",
    "        self.df = self.df.drop(self.df.columns[0], axis=1)\n",
    "        self.df['timestamp'] = pd.to_datetime(self.df['timestamp'])\n",
    "        self.train_data = None\n",
    "        self.test_data = None\n",
    "        self.analog_train = None\n",
    "        self.analog_test = None\n",
    "        self.digital_train = None\n",
    "        self.digital_test = None\n",
    "\n",
    "    def preprocessing_df(self):\n",
    "        # create a new column where 1 day before the timeframe as well as the timeframe indicated in the paper is labeled as unhealthy data i.e. 1\n",
    "        # healthy data is set as 0\n",
    "        self.df['is_anomaly'] = np.where(\n",
    "            ((self.df['timestamp'] >= \"2020-04-11 11:50:00\") & (self.df['timestamp'] <= \"2020-04-12 23:30:00\")) |\n",
    "            ((self.df['timestamp'] >= \"2020-04-17 00:00:00\") & (self.df['timestamp'] <= \"2020-04-19 01:30:00\")) |\n",
    "            ((self.df['timestamp'] >= \"2020-04-28 03:20:00\") & (self.df['timestamp'] <= \"2020-04-29 22:20:00\")) |\n",
    "            \n",
    "            \n",
    "            ((self.df['timestamp'] >= \"2020-05-12 14:00:00\") & (self.df['timestamp'] <= \"2020-05-13 23:59:00\")) |\n",
    "            ((self.df['timestamp'] >= \"2020-05-17 05:00:00\") & (self.df['timestamp'] <= \"2020-05-20 20:00:00\")) |\n",
    "            \n",
    "            \n",
    "            ((self.df['timestamp'] >= \"2020-05-28 23:30:00\") & (self.df['timestamp'] <= \"2020-05-30 06:00:00\")) |\n",
    "            \n",
    "            ((self.df['timestamp'] >= \"2020-05-31 15:00:00\") & (self.df['timestamp'] <= \"2020-06-01 15:40:00\")) |\n",
    "            ((self.df['timestamp'] >= \"2020-06-02 10:00:00\") & (self.df['timestamp'] <= \"2020-06-03 11:00:00\")) |\n",
    "            ((self.df['timestamp'] >= \"2020-06-04 10:00:00\") & (self.df['timestamp'] <= \"2020-06-07 14:30:00\")) |\n",
    "            \n",
    "            ((self.df['timestamp'] >= \"2020-07-07 17:30:00\") & (self.df['timestamp'] <= \"2020-07-08 19:00:00\")) |\n",
    "            ((self.df['timestamp'] >= \"2020-07-14 14:30:00\") & (self.df['timestamp'] <= \"2020-07-15 19:00:00\")) |\n",
    "            ((self.df['timestamp'] >= \"2020-07-16 04:30:00\") & (self.df['timestamp'] <= \"2020-07-17 05:30:00\"))\n",
    "            ,\n",
    "            1, 0\n",
    "        )\n",
    "        self.data_path = \"dataset_train_processed.csv\" # change this path accordingly when you want to change the file location\n",
    "        self.df.to_csv(self.data_path)\n",
    "        self.df = pd.read_csv(self.data_path)\n",
    "\n",
    "    def preprocessing_autoencoder(self):\n",
    "        # self.train_data = self.df[\n",
    "        #     (self.df['timestamp'] >= \"2022-01-01 06:00:00\") & (self.df['timestamp'] <= \"2022-02-28 02:00:00\")]\n",
    "        # self.test_data = self.df[\n",
    "        #     (self.df['timestamp'] >= \"2022-02-28 06:00:00\")]\n",
    "        self.train_data = self.df[\n",
    "            (self.df['timestamp'] >= \"2020-02-01 00:00:00\") & (self.df['timestamp'] < \"2020-04-30 11:59:59\") & \n",
    "            (self.df['is_anomaly'] == 0)]\n",
    "        self.test_data = self.df[\n",
    "            (self.df['timestamp'] >= \"2020-05-01 00:00:00\") & (self.df['timestamp'] < \"2020-07-31 11:59:59\")]\n",
    "        self.train_data.drop(self.train_data.columns[0], axis=1,\n",
    "                             inplace=True)  # there is an additional unnecessary column created at index 0 from preprocessing.df that needs to be removed if preprocessing_df is called\n",
    "        self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n",
    "        scaler = StandardScaler() # standardising only the analog data, leaving the digital data as is\n",
    "        self.analog_train = pd.DataFrame(scaler.fit_transform(self.train_data.iloc[:, 1:8]))\n",
    "        self.digital_train = self.train_data.iloc[:, 8:16]\n",
    "        self.analog_test = pd.DataFrame(scaler.transform(self.test_data.iloc[:, 1:8]))\n",
    "        self.digital_test = self.test_data.iloc[:, 8:16]\n",
    "\n",
    "    def preprocessing_arima(self):\n",
    "        df = self.df\n",
    "        columns = [\"timestamp\", \"TP2\"]\n",
    "        df = df[columns]\n",
    "        df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "        df = df.resample(\"5T\", on=\"timestamp\").mean() # data can also be resampled to other timeframes: refer to pandas documentation for more info\n",
    "        df = df.fillna(method=\"ffill\")\n",
    "        self.train_data = df[(df.index >= pd.to_datetime(\"2020-02-01 00:00:00\")) & (df.index <= pd.to_datetime(\"2020-04-30 11:59:59\"))]\n",
    "        self.test_data = df[(df.index >= pd.to_datetime(\"2020-05-01 00:00:00\")) & (df.index <= pd.to_datetime(\"2020-05-04 11:59:59\"))] # change accordingly depending on your forecast timeframe\n",
    "\n",
    "    def preprocessing_ma(self):\n",
    "        columns = [\"timestamp\", \"TP2\"]\n",
    "        self.df = self.df[columns]\n",
    "        self.train_data = self.df[\n",
    "            (self.df['timestamp'] >= \"2020-02-01 00:00:00\") & (self.df['timestamp'] <= \"2020-04-30 11:59:59\")]\n",
    "        self.test_data = self.df[\n",
    "            (self.df['timestamp'] >= \"2020-05-01 00:00:00\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7cbdd869",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"MetroPT3(AirCompressor).CSV\") # change this to the file path of the train dataset\n",
    "    preprocessor.preprocessing_df()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a521880",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import keras.backend as K\n",
    "from tensorflow import keras\n",
    "from keras.models import Model, save_model\n",
    "from keras.layers import Conv1D, Conv1DTranspose, Lambda, Reshape, GlobalAveragePooling1D\n",
    "from keras.callbacks import EarlyStopping\n",
    "from tensorflow.python.framework.ops import disable_eager_execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3e57bc67",
   "metadata": {},
   "outputs": [],
   "source": [
    "disable_eager_execution() # variational autoencoder will not work without this\n",
    "# an alternative is to replace all instances of keras with tf.keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ae0c884",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AutoencoderModels:\n",
    "\n",
    "    def __init__(self, train_data, test_data):\n",
    "        self.train_data = train_data\n",
    "        self.test_data = test_data\n",
    "        self.model = None\n",
    "        self.hist = None\n",
    "\n",
    "    def leaky_relu(self, x):  # to be used as the activation function of the output layer for the analog models; greatly reduces the loss value\n",
    "        return tf.nn.leaky_relu(x, alpha=0.01)\n",
    "\n",
    "    def sae_analog(self):  # both analog and digital sensors have 8 features/columns\n",
    "        # Encoder portion\n",
    "        input_layer = keras.Input(shape=(1, self.train_data.shape[1])) # shape of input is (no. of timesteps = 1, no. of features = 8) - same for all models\n",
    "        hidden_1 = Conv1D(filters=32, padding=\"same\", kernel_size=3, activation='relu')(input_layer)\n",
    "        hidden_2 = Conv1D(filters=16, padding=\"same\", kernel_size=3, activation='relu')(hidden_1)\n",
    "        bottleneck = Conv1D(filters=6, padding=\"same\", kernel_size=3, activation=\"relu\", activity_regularizer=keras.regularizers.l1_l2(l1=0.005, l2=0.1))(hidden_2)\n",
    "        #  activity_regularizer=keras.regularizers.l1_l2(l1=0.005, l2=0.1)\n",
    "        # Decoder portion\n",
    "        hidden_3 = Conv1DTranspose(filters=16, padding=\"same\", kernel_size=3, activation='relu')(bottleneck)\n",
    "        hidden_4 = Conv1DTranspose(filters=32, padding=\"same\", kernel_size=3, activation='relu')(hidden_3)\n",
    "        output_layer = Conv1DTranspose(filters=self.train_data.shape[1], padding=\"same\", kernel_size=3,\n",
    "                              activation=self.leaky_relu)(\n",
    "            hidden_4)\n",
    "        model = Model(inputs=input_layer, outputs=output_layer)\n",
    "        model.compile(optimizer='adam', loss='mse') # loss function can also be set to RMSE, MAE etc. for parameter tuning\n",
    "        callback = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n",
    "        hist = model.fit(np.expand_dims(self.train_data, 1), np.expand_dims(self.train_data, 1), validation_split=0.2,\n",
    "                         epochs=50, callbacks=[callback], batch_size=60)\n",
    "        save_model(model, \"sae_analog.h5\")\n",
    "        self.model = model\n",
    "        self.hist = hist\n",
    "\n",
    "    def sae_digital(self):\n",
    "        # Encoder portion\n",
    "        input_layer = keras.Input(shape=(1, self.train_data.shape[1]))\n",
    "        hidden_1 = Conv1D(filters=32, padding=\"same\", kernel_size=3, activation='relu')(input_layer)\n",
    "        hidden_2 = Conv1D(filters=16, padding=\"same\", kernel_size=3, activation='relu')(hidden_1)\n",
    "        bottleneck = Conv1D(filters=6, padding=\"same\", kernel_size=3, activation=\"relu\")(hidden_2)\n",
    "\n",
    "        # Decoder Portion\n",
    "        hidden_3 = Conv1DTranspose(filters=16, padding=\"same\", kernel_size=3, activation='relu')(bottleneck)\n",
    "        hidden_4 = Conv1DTranspose(filters=32, padding=\"same\", kernel_size=3, activation='relu')(hidden_3)\n",
    "        output_layer = Conv1DTranspose(filters=self.train_data.shape[1], padding=\"same\", kernel_size=3, activation=\"sigmoid\")(\n",
    "            hidden_4)\n",
    "        model = Model(inputs=input_layer, outputs=output_layer)\n",
    "        model.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "        callback = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n",
    "        hist = model.fit(np.expand_dims(self.train_data, 1), np.expand_dims(self.train_data, 1), validation_split=0.2,\n",
    "                         epochs=50, callbacks=[callback], batch_size=60)\n",
    "        save_model(model, \"sae_digital.h5\")\n",
    "        self.model = model\n",
    "        self.hist = hist\n",
    "\n",
    "    def vae_analog(self):\n",
    "\n",
    "        def sampling(args):  # reparameterization trick used in VAE to sample data points from the latent space\n",
    "            z_mean, z_log_var = args  # log variance instead of variance is better for data that is not assumed to have a normal distribution\n",
    "            batch_size = K.shape(z_mean)[0]\n",
    "            dim = K.int_shape(z_mean)[1]\n",
    "            epsilon = K.random_normal(shape=(batch_size, dim)) # is a normal distribution with mean 0, variance 1\n",
    "            return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
    "\n",
    "        def vae_loss(x_actual, x_decoded):\n",
    "            reconstruction_loss = K.mean(K.square(x_actual - x_decoded)) # measures the ability of the VAE to reconstruct the input data accurately\n",
    "            kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1) # measures the difference between the learned latent distribution and the distribution of the data\n",
    "            return reconstruction_loss + kl_loss\n",
    "\n",
    "        # Encoder portion\n",
    "        input_layer = keras.Input(shape=(1, self.train_data.shape[1]))\n",
    "        hidden_1 = Conv1D(filters=32, padding=\"same\", kernel_size=3, activation='relu')(input_layer)\n",
    "        hidden_2 = Conv1D(filters=16, padding=\"same\", kernel_size=3, activation='relu')(hidden_1)\n",
    "        z_mean = Conv1D(filters=6, padding=\"same\", kernel_size=3, activation=\"linear\")(hidden_2)\n",
    "        z_log_var = Conv1D(filters=6, padding=\"same\", kernel_size=3, activation=\"linear\")(hidden_2)\n",
    "        z = Lambda(sampling)([z_mean, z_log_var])  # this layer is the lower-dimensional representation of the input data learned from the sampling method i.e. the latent space\n",
    "        # the combination of z_mean z_log_var and the sampling layer can be thought of as similar to the bottleneck layer in a traditional autoencoder\n",
    "\n",
    "        # Decoder portion\n",
    "        hidden_3 = Conv1DTranspose(filters=16, padding=\"same\", kernel_size=3, activation='relu')(z)\n",
    "        hidden_4 = Conv1DTranspose(filters=32, padding=\"same\", kernel_size=3, activation='relu')(hidden_3)\n",
    "        output_layer = Conv1DTranspose(filters=self.train_data.shape[1], padding=\"same\", kernel_size=3,\n",
    "                              activation=self.leaky_relu)(hidden_4)\n",
    "        output_layer = GlobalAveragePooling1D()(output_layer)\n",
    "        output_layer = Reshape((1, self.train_data.shape[1]))(output_layer)\n",
    "        model = Model(inputs=input_layer, outputs=output_layer)\n",
    "        model.compile(optimizer='adam', loss=vae_loss)\n",
    "        callback = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n",
    "        hist = model.fit(np.expand_dims(self.train_data, 1), np.expand_dims(self.train_data, 1), validation_split=0.2,\n",
    "                         epochs=50, callbacks=[callback], batch_size=60)\n",
    "        save_model(model,\"vae_analog.h5\")\n",
    "        self.model = model\n",
    "        self.hist = hist\n",
    "\n",
    "    def vae_digital(self): # exact same architecture as vae_analog\n",
    "\n",
    "        def sampling(args):\n",
    "            z_mean, z_log_var = args\n",
    "            batch_size = K.shape(z_mean)[0]\n",
    "            dim = K.int_shape(z_mean)[1]\n",
    "            epsilon = K.random_normal(shape=(batch_size, dim))\n",
    "            return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
    "\n",
    "        def vae_loss(x_actual, x_decoded):\n",
    "            reconstruction_loss = K.mean(K.square(x_actual - x_decoded)) # measures the ability of the VAE to reconstruct the input data accurately\n",
    "            kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1) # measures the difference between the learned latent distribution and the distribution of the data\n",
    "            return reconstruction_loss + kl_loss\n",
    "\n",
    "        # Encoder portion\n",
    "        input_layer = keras.Input(shape=(1, self.train_data.shape[1]))\n",
    "        hidden_1 = Conv1D(filters=32, padding=\"same\", kernel_size=3, activation='relu')(input_layer)\n",
    "        hidden_2 = Conv1D(filters=16, padding=\"same\", kernel_size=3, activation='relu')(hidden_1)\n",
    "        z_mean = Conv1D(filters=6, padding=\"same\", kernel_size=3, activation=\"linear\")(hidden_2)\n",
    "        z_log_var = Conv1D(filters=6, padding=\"same\", kernel_size=3, activation=\"linear\")(hidden_2)\n",
    "        z = Lambda(sampling)([z_mean, z_log_var])\n",
    "\n",
    "        # Decoder portion\n",
    "        hidden_3 = Conv1DTranspose(filters=16, padding=\"same\", kernel_size=3, activation='relu')(z)\n",
    "        hidden_4 = Conv1DTranspose(filters=32, padding=\"same\", kernel_size=3, activation='relu')(hidden_3)\n",
    "        output_layer = Conv1D(filters=self.train_data.shape[1], padding=\"same\", kernel_size=3,\n",
    "                              activation=\"sigmoid\")(hidden_4)\n",
    "        output_layer = GlobalAveragePooling1D()(output_layer)\n",
    "        output_layer = Reshape((1, self.train_data.shape[1]))(output_layer)\n",
    "        model = Model(inputs=input_layer, outputs=output_layer)\n",
    "        model.compile(optimizer='adam', loss=vae_loss)\n",
    "        callback = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n",
    "        hist = model.fit(np.expand_dims(self.train_data, 1), np.expand_dims(self.train_data, 1), validation_split=0.2,\n",
    "                         epochs=50, callbacks=[callback], batch_size=60)\n",
    "        save_model(model, \"vae_digital.h5\")\n",
    "        self.model = model\n",
    "        self.hist = hist\n",
    "\n",
    "    def autoencoder_predict_digital(self):\n",
    "        train_pred = self.model.predict(np.expand_dims(self.train_data, 1))\n",
    "        print(\"Shape of train_pred before reshaping:\", train_pred.shape)\n",
    "        \n",
    "        train_pred = np.reshape(train_pred, (len(train_pred), 8))\n",
    "    \n",
    "        test_pred = self.model.predict(np.expand_dims(self.test_data, 1))\n",
    "        print(\"Shape of test_pred before reshaping:\", test_pred.shape)\n",
    "        test_pred = np.reshape(test_pred, (len(test_pred), 8))\n",
    "        return train_pred, test_pred\n",
    "    \n",
    "    def autoencoder_predict_analog(self):\n",
    "        train_pred = self.model.predict(np.expand_dims(self.train_data, 1))\n",
    "        print(\"Shape of train_pred before reshaping:\", train_pred.shape)\n",
    "        \n",
    "        train_pred = np.reshape(train_pred, (len(train_pred), 7))\n",
    "    \n",
    "        test_pred = self.model.predict(np.expand_dims(self.test_data, 1))\n",
    "        print(\"Shape of test_pred before reshaping:\", test_pred.shape)\n",
    "        test_pred = np.reshape(test_pred, (len(test_pred), 7))\n",
    "        return train_pred, test_pred\n",
    "\n",
    "    def plot_losses(self):\n",
    "        plt.plot(self.hist.history[\"loss\"])\n",
    "        plt.plot(self.hist.history[\"val_loss\"])\n",
    "        plt.title(\"Training/Validation Loss\")\n",
    "        plt.xlabel(\"epoch\")\n",
    "        plt.ylabel(\"loss\")\n",
    "        plt.legend([\"training_loss\", \"validation_loss\"])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e0da8437",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 482639 samples, validate on 120660 samples\n",
      "Epoch 1/50\n",
      "482639/482639 [==============================] - ETA: 0s - loss: 0.2028"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2335: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "482639/482639 [==============================] - 89s 185us/sample - loss: 0.2028 - val_loss: 0.0448\n",
      "Epoch 2/50\n",
      "482639/482639 [==============================] - 76s 157us/sample - loss: 0.0221 - val_loss: 0.0216\n",
      "Epoch 3/50\n",
      "482639/482639 [==============================] - 94s 195us/sample - loss: 0.0119 - val_loss: 0.0195\n",
      "Epoch 4/50\n",
      "482639/482639 [==============================] - 74s 153us/sample - loss: 0.0094 - val_loss: 0.0174\n",
      "Epoch 5/50\n",
      "482639/482639 [==============================] - 74s 154us/sample - loss: 0.0088 - val_loss: 0.0167\n",
      "Epoch 6/50\n",
      "482639/482639 [==============================] - 107s 222us/sample - loss: 0.0080 - val_loss: 0.0144\n",
      "Epoch 7/50\n",
      "482639/482639 [==============================] - 83s 172us/sample - loss: 0.0078 - val_loss: 0.0140\n",
      "Epoch 8/50\n",
      "482639/482639 [==============================] - 76s 157us/sample - loss: 0.0073 - val_loss: 0.0139\n",
      "Epoch 9/50\n",
      "482639/482639 [==============================] - 83s 173us/sample - loss: 0.0076 - val_loss: 0.0148\n",
      "Epoch 10/50\n",
      "482639/482639 [==============================] - 81s 167us/sample - loss: 0.0070 - val_loss: 0.0142\n",
      "Epoch 11/50\n",
      "482639/482639 [==============================] - 71s 148us/sample - loss: 0.0063 - val_loss: 0.0179\n",
      "Epoch 12/50\n",
      "482639/482639 [==============================] - 72s 149us/sample - loss: 0.0048 - val_loss: 0.0124\n",
      "Epoch 13/50\n",
      "482639/482639 [==============================] - 69s 143us/sample - loss: 0.0046 - val_loss: 0.0134\n",
      "Epoch 14/50\n",
      "482639/482639 [==============================] - 74s 154us/sample - loss: 0.0043 - val_loss: 0.0128\n",
      "Epoch 15/50\n",
      "482639/482639 [==============================] - 76s 158us/sample - loss: 0.0039 - val_loss: 0.0126\n",
      "Epoch 16/50\n",
      "482639/482639 [==============================] - 74s 154us/sample - loss: 0.0040 - val_loss: 0.0161\n",
      "Epoch 17/50\n",
      "482639/482639 [==============================] - 74s 153us/sample - loss: 0.0039 - val_loss: 0.0118\n",
      "Epoch 18/50\n",
      "482639/482639 [==============================] - 79s 164us/sample - loss: 0.0036 - val_loss: 0.0127\n",
      "Epoch 19/50\n",
      "482639/482639 [==============================] - 76s 158us/sample - loss: 0.0036 - val_loss: 0.0116\n",
      "Epoch 20/50\n",
      "482639/482639 [==============================] - 80s 166us/sample - loss: 0.0034 - val_loss: 0.0140\n",
      "Epoch 21/50\n",
      "482639/482639 [==============================] - 108s 225us/sample - loss: 0.0036 - val_loss: 0.0122\n",
      "Epoch 22/50\n",
      "482639/482639 [==============================] - 82s 171us/sample - loss: 0.0034 - val_loss: 0.0167\n",
      "Epoch 23/50\n",
      "482639/482639 [==============================] - 96s 198us/sample - loss: 0.0035 - val_loss: 0.0126\n",
      "Epoch 24/50\n",
      "482639/482639 [==============================] - 94s 194us/sample - loss: 0.0033 - val_loss: 0.0114\n",
      "Epoch 25/50\n",
      "482639/482639 [==============================] - 100s 206us/sample - loss: 0.0031 - val_loss: 0.0120\n",
      "Epoch 26/50\n",
      "482639/482639 [==============================] - 112s 232us/sample - loss: 0.0033 - val_loss: 0.0148\n",
      "Epoch 27/50\n",
      "482639/482639 [==============================] - 106s 220us/sample - loss: 0.0033 - val_loss: 0.0112\n",
      "Epoch 28/50\n",
      "482639/482639 [==============================] - 99s 205us/sample - loss: 0.0032 - val_loss: 0.0111\n",
      "Epoch 29/50\n",
      "482639/482639 [==============================] - 90s 186us/sample - loss: 0.0032 - val_loss: 0.0137\n",
      "Epoch 30/50\n",
      "482639/482639 [==============================] - 89s 185us/sample - loss: 0.0036 - val_loss: 0.0116\n",
      "Epoch 31/50\n",
      "482639/482639 [==============================] - 91s 188us/sample - loss: 0.0031 - val_loss: 0.0150\n",
      "Epoch 32/50\n",
      "482639/482639 [==============================] - 97s 200us/sample - loss: 0.0033 - val_loss: 0.0125\n",
      "Epoch 33/50\n",
      "482639/482639 [==============================] - 105s 218us/sample - loss: 0.0034 - val_loss: 0.0137\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1723690628.py:90: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  save_model(model,\"vae_analog.h5\")\n",
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train_pred before reshaping: (603299, 1, 7)\n",
      "Shape of test_pred before reshaping: (647594, 1, 7)\n",
      "Model: \"model_4\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_5 (InputLayer)        [(None, 1, 7)]               0         []                            \n",
      "                                                                                                  \n",
      " conv1d_15 (Conv1D)          (None, 1, 32)                704       ['input_5[0][0]']             \n",
      "                                                                                                  \n",
      " conv1d_16 (Conv1D)          (None, 1, 16)                1552      ['conv1d_15[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_17 (Conv1D)          (None, 1, 6)                 294       ['conv1d_16[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_18 (Conv1D)          (None, 1, 6)                 294       ['conv1d_16[0][0]']           \n",
      "                                                                                                  \n",
      " lambda_2 (Lambda)           (None, None, 6)              0         ['conv1d_17[0][0]',           \n",
      "                                                                     'conv1d_18[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_transpose_11 (Conv1  (None, None, 16)             304       ['lambda_2[0][0]']            \n",
      " DTranspose)                                                                                      \n",
      "                                                                                                  \n",
      " conv1d_transpose_12 (Conv1  (None, None, 32)             1568      ['conv1d_transpose_11[0][0]'] \n",
      " DTranspose)                                                                                      \n",
      "                                                                                                  \n",
      " conv1d_transpose_13 (Conv1  (None, None, 7)              679       ['conv1d_transpose_12[0][0]'] \n",
      " DTranspose)                                                                                      \n",
      "                                                                                                  \n",
      " global_average_pooling1d_2  (None, 7)                    0         ['conv1d_transpose_13[0][0]'] \n",
      "  (GlobalAveragePooling1D)                                                                        \n",
      "                                                                                                  \n",
      " reshape_2 (Reshape)         (None, 1, 7)                 0         ['global_average_pooling1d_2[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 5395 (21.07 KB)\n",
      "Trainable params: 5395 (21.07 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\") # change according to where your data is stored\n",
    "    \n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    # autoencoder = AutoencoderModels(preprocessor.digital_train, preprocessor.digital_test) # comment out depending on whether digital/analog data is used\n",
    "    autoencoder = AutoencoderModels(preprocessor.analog_train, preprocessor.analog_test)\n",
    "    # autoencoder.sae_analog() # comment out according to which method you call from the AutoencoderModels class\n",
    "    # autoencoder.vae_digital()\n",
    "    autoencoder.vae_analog()\n",
    "    # autoencoder.vae_digital()\n",
    "    train_pred, test_pred = autoencoder.autoencoder_predict_analog()\n",
    "    with open(\"vae_analog_train_pred\", \"wb\") as file: # name of file path can change depending on where you want to save it\n",
    "        pickle.dump(train_pred, file)\n",
    "    with open(\"vae_analog_test_pred\", \"wb\") as file: # similarly, this file path can be changed\n",
    "        pickle.dump(test_pred, file)\n",
    "    print(autoencoder.model.summary())\n",
    "    autoencoder.plot_losses()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "feb1a7d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 482639 samples, validate on 120660 samples\n",
      "Epoch 1/50\n",
      "482580/482639 [============================>.] - ETA: 0s - loss: 0.0170"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2335: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "482639/482639 [==============================] - 161s 334us/sample - loss: 0.0170 - val_loss: 0.0206\n",
      "Epoch 2/50\n",
      "482639/482639 [==============================] - 157s 324us/sample - loss: 0.0125 - val_loss: 0.0188\n",
      "Epoch 3/50\n",
      "482639/482639 [==============================] - 157s 324us/sample - loss: 0.0142 - val_loss: 0.0202\n",
      "Epoch 4/50\n",
      "482639/482639 [==============================] - 143s 295us/sample - loss: 0.0125 - val_loss: 0.0187\n",
      "Epoch 5/50\n",
      "482639/482639 [==============================] - 133s 275us/sample - loss: 0.0124 - val_loss: 0.0175\n",
      "Epoch 6/50\n",
      "482639/482639 [==============================] - 136s 283us/sample - loss: 0.0129 - val_loss: 0.0202\n",
      "Epoch 7/50\n",
      "482639/482639 [==============================] - 129s 266us/sample - loss: 0.0124 - val_loss: 0.0188\n",
      "Epoch 8/50\n",
      "482639/482639 [==============================] - 131s 271us/sample - loss: 0.0126 - val_loss: 0.0201\n",
      "Epoch 9/50\n",
      "482639/482639 [==============================] - 137s 284us/sample - loss: 0.0126 - val_loss: 0.0188\n",
      "Epoch 10/50\n",
      "482639/482639 [==============================] - 100s 208us/sample - loss: 0.0130 - val_loss: 0.0202\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1723690628.py:128: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  save_model(model, \"vae_digital.h5\")\n",
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train_pred before reshaping: (603299, 1, 8)\n",
      "Shape of test_pred before reshaping: (647594, 1, 8)\n",
      "Model: \"model_5\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_6 (InputLayer)        [(None, 1, 8)]               0         []                            \n",
      "                                                                                                  \n",
      " conv1d_19 (Conv1D)          (None, 1, 32)                800       ['input_6[0][0]']             \n",
      "                                                                                                  \n",
      " conv1d_20 (Conv1D)          (None, 1, 16)                1552      ['conv1d_19[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_21 (Conv1D)          (None, 1, 6)                 294       ['conv1d_20[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_22 (Conv1D)          (None, 1, 6)                 294       ['conv1d_20[0][0]']           \n",
      "                                                                                                  \n",
      " lambda_3 (Lambda)           (None, None, 6)              0         ['conv1d_21[0][0]',           \n",
      "                                                                     'conv1d_22[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_transpose_14 (Conv1  (None, None, 16)             304       ['lambda_3[0][0]']            \n",
      " DTranspose)                                                                                      \n",
      "                                                                                                  \n",
      " conv1d_transpose_15 (Conv1  (None, None, 32)             1568      ['conv1d_transpose_14[0][0]'] \n",
      " DTranspose)                                                                                      \n",
      "                                                                                                  \n",
      " conv1d_23 (Conv1D)          (None, None, 8)              776       ['conv1d_transpose_15[0][0]'] \n",
      "                                                                                                  \n",
      " global_average_pooling1d_3  (None, 8)                    0         ['conv1d_23[0][0]']           \n",
      "  (GlobalAveragePooling1D)                                                                        \n",
      "                                                                                                  \n",
      " reshape_3 (Reshape)         (None, 1, 8)                 0         ['global_average_pooling1d_3[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 5588 (21.83 KB)\n",
      "Trainable params: 5588 (21.83 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\") # change according to where your data is stored\n",
    "    \n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    autoencoder = AutoencoderModels(preprocessor.digital_train, preprocessor.digital_test) # comment out depending on whether digital/analog data is used\n",
    "    # autoencoder = AutoencoderModels(preprocessor.analog_train, preprocessor.analog_test)\n",
    "    # autoencoder.sae_analog() # comment out according to which method you call from the AutoencoderModels class\n",
    "    autoencoder.vae_digital()\n",
    "    # autoencoder.vae_analog()\n",
    "    # autoencoder.vae_digital()\n",
    "    train_pred, test_pred = autoencoder.autoencoder_predict_digital()\n",
    "    with open(\"vae_digital_train_pred\", \"wb\") as file: # name of file path can change depending on where you want to save it\n",
    "        pickle.dump(train_pred, file)\n",
    "    with open(\"vae_digital_test_pred\", \"wb\") as file: # similarly, this file path can be changed\n",
    "        pickle.dump(test_pred, file)\n",
    "    print(autoencoder.model.summary())\n",
    "    autoencoder.plot_losses()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9fe5c3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 482639 samples, validate on 120660 samples\n",
      "Epoch 1/50\n",
      "482280/482639 [============================>.] - ETA: 0s - loss: 0.4078"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2335: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "482639/482639 [==============================] - 87s 181us/sample - loss: 0.4076 - val_loss: 0.1360\n",
      "Epoch 2/50\n",
      "482639/482639 [==============================] - 107s 221us/sample - loss: 0.1031 - val_loss: 0.0930\n",
      "Epoch 3/50\n",
      "482639/482639 [==============================] - 90s 186us/sample - loss: 0.0742 - val_loss: 0.0689\n",
      "Epoch 4/50\n",
      "482639/482639 [==============================] - 96s 199us/sample - loss: 0.0381 - val_loss: 0.0334\n",
      "Epoch 5/50\n",
      "482639/482639 [==============================] - 72s 148us/sample - loss: 0.0185 - val_loss: 0.0275\n",
      "Epoch 6/50\n",
      "482639/482639 [==============================] - 88s 182us/sample - loss: 0.0131 - val_loss: 0.0245\n",
      "Epoch 7/50\n",
      "482639/482639 [==============================] - 90s 186us/sample - loss: 0.0112 - val_loss: 0.0257\n",
      "Epoch 8/50\n",
      "482639/482639 [==============================] - 79s 164us/sample - loss: 0.0098 - val_loss: 0.0207\n",
      "Epoch 9/50\n",
      "482639/482639 [==============================] - 87s 181us/sample - loss: 0.0089 - val_loss: 0.0240\n",
      "Epoch 10/50\n",
      "482639/482639 [==============================] - 80s 166us/sample - loss: 0.0083 - val_loss: 0.0183\n",
      "Epoch 11/50\n",
      "482639/482639 [==============================] - 82s 170us/sample - loss: 0.0080 - val_loss: 0.0195\n",
      "Epoch 12/50\n",
      "482639/482639 [==============================] - 78s 162us/sample - loss: 0.0080 - val_loss: 0.0202\n",
      "Epoch 13/50\n",
      "482639/482639 [==============================] - 79s 164us/sample - loss: 0.0078 - val_loss: 0.0229\n",
      "Epoch 14/50\n",
      "482639/482639 [==============================] - 69s 144us/sample - loss: 0.0071 - val_loss: 0.0203\n",
      "Epoch 15/50\n",
      "482639/482639 [==============================] - 63s 131us/sample - loss: 0.0056 - val_loss: 0.0168\n",
      "Epoch 16/50\n",
      "482639/482639 [==============================] - 69s 143us/sample - loss: 0.0050 - val_loss: 0.0168\n",
      "Epoch 17/50\n",
      "482639/482639 [==============================] - 68s 142us/sample - loss: 0.0043 - val_loss: 0.0161\n",
      "Epoch 18/50\n",
      "482639/482639 [==============================] - 70s 144us/sample - loss: 0.0042 - val_loss: 0.0173\n",
      "Epoch 19/50\n",
      "482639/482639 [==============================] - 64s 132us/sample - loss: 0.0039 - val_loss: 0.0165\n",
      "Epoch 20/50\n",
      "482639/482639 [==============================] - 66s 137us/sample - loss: 0.0035 - val_loss: 0.0142\n",
      "Epoch 21/50\n",
      "482639/482639 [==============================] - 68s 141us/sample - loss: 0.0035 - val_loss: 0.0151\n",
      "Epoch 22/50\n",
      "482639/482639 [==============================] - 70s 145us/sample - loss: 0.0033 - val_loss: 0.0143\n",
      "Epoch 23/50\n",
      "482639/482639 [==============================] - 65s 135us/sample - loss: 0.0033 - val_loss: 0.0134\n",
      "Epoch 24/50\n",
      "482639/482639 [==============================] - 59s 122us/sample - loss: 0.0032 - val_loss: 0.0149\n",
      "Epoch 25/50\n",
      "482639/482639 [==============================] - 78s 161us/sample - loss: 0.0032 - val_loss: 0.0159\n",
      "Epoch 26/50\n",
      "482639/482639 [==============================] - 63s 130us/sample - loss: 0.0030 - val_loss: 0.0264\n",
      "Epoch 27/50\n",
      "482639/482639 [==============================] - 58s 121us/sample - loss: 0.0032 - val_loss: 0.0142\n",
      "Epoch 28/50\n",
      "482639/482639 [==============================] - 61s 127us/sample - loss: 0.0032 - val_loss: 0.0182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1723690628.py:30: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  save_model(model, \"sae_analog.h5\")\n",
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train_pred before reshaping: (603299, 1, 7)\n",
      "Shape of test_pred before reshaping: (647594, 1, 7)\n",
      "Model: \"model_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_7 (InputLayer)        [(None, 1, 7)]            0         \n",
      "                                                                 \n",
      " conv1d_24 (Conv1D)          (None, 1, 32)             704       \n",
      "                                                                 \n",
      " conv1d_25 (Conv1D)          (None, 1, 16)             1552      \n",
      "                                                                 \n",
      " conv1d_26 (Conv1D)          (None, 1, 6)              294       \n",
      "                                                                 \n",
      " conv1d_transpose_16 (Conv1  (None, 1, 16)             304       \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      " conv1d_transpose_17 (Conv1  (None, 1, 32)             1568      \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      " conv1d_transpose_18 (Conv1  (None, 1, 7)              679       \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5101 (19.93 KB)\n",
      "Trainable params: 5101 (19.93 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\") # change according to where your data is stored\n",
    "    \n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    # autoencoder = AutoencoderModels(preprocessor.digital_train, preprocessor.digital_test) # comment out depending on whether digital/analog data is used\n",
    "    autoencoder = AutoencoderModels(preprocessor.analog_train, preprocessor.analog_test)\n",
    "    # autoencoder.sae_analog() # comment out according to which method you call from the AutoencoderModels class\n",
    "    # autoencoder.vae_digital()\n",
    "    autoencoder.sae_analog()\n",
    "    # autoencoder.vae_digital()\n",
    "    train_pred, test_pred = autoencoder.autoencoder_predict_analog()\n",
    "    with open(\"sae_analog_train_pred\", \"wb\") as file: # name of file path can change depending on where you want to save it\n",
    "        pickle.dump(train_pred, file)\n",
    "    with open(\"sae_analog_test_pred\", \"wb\") as file: # similarly, this file path can be changed\n",
    "        pickle.dump(test_pred, file)\n",
    "    print(autoencoder.model.summary())\n",
    "    autoencoder.plot_losses()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8dcb2d3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 482639 samples, validate on 120660 samples\n",
      "Epoch 1/50\n",
      "482520/482639 [============================>.] - ETA: 0s - loss: 0.0138"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2335: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "482639/482639 [==============================] - 76s 158us/sample - loss: 0.0138 - val_loss: 0.0149\n",
      "Epoch 2/50\n",
      "482639/482639 [==============================] - 88s 182us/sample - loss: 3.3785e-04 - val_loss: 0.0210\n",
      "Epoch 3/50\n",
      "482639/482639 [==============================] - 90s 186us/sample - loss: 2.3491e-04 - val_loss: 0.0025\n",
      "Epoch 4/50\n",
      "482639/482639 [==============================] - 106s 220us/sample - loss: 1.3415e-04 - val_loss: 0.0011\n",
      "Epoch 5/50\n",
      "482639/482639 [==============================] - 91s 189us/sample - loss: 9.9849e-05 - val_loss: 3.2169e-04\n",
      "Epoch 6/50\n",
      "482639/482639 [==============================] - 89s 184us/sample - loss: 8.5648e-05 - val_loss: 2.1182e-04\n",
      "Epoch 7/50\n",
      "482639/482639 [==============================] - 70s 145us/sample - loss: 8.6777e-05 - val_loss: 1.2553e-04\n",
      "Epoch 8/50\n",
      "482639/482639 [==============================] - 77s 159us/sample - loss: 6.8500e-05 - val_loss: 2.3423e-04\n",
      "Epoch 9/50\n",
      "482639/482639 [==============================] - 78s 163us/sample - loss: 7.1071e-05 - val_loss: 1.8877e-04\n",
      "Epoch 10/50\n",
      "482639/482639 [==============================] - 81s 168us/sample - loss: 5.2909e-05 - val_loss: 4.2821e-05\n",
      "Epoch 11/50\n",
      "482639/482639 [==============================] - 77s 161us/sample - loss: 5.1971e-05 - val_loss: 6.4431e-05\n",
      "Epoch 12/50\n",
      "482639/482639 [==============================] - 88s 183us/sample - loss: 4.4470e-05 - val_loss: 7.3434e-05\n",
      "Epoch 13/50\n",
      "482639/482639 [==============================] - 86s 178us/sample - loss: 4.2536e-05 - val_loss: 8.3145e-05\n",
      "Epoch 14/50\n",
      "482639/482639 [==============================] - 82s 170us/sample - loss: 2.9589e-05 - val_loss: 5.8570e-05\n",
      "Epoch 15/50\n",
      "482639/482639 [==============================] - 85s 175us/sample - loss: 4.0756e-05 - val_loss: 6.1295e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1723690628.py:51: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  save_model(model, \"sae_digital.h5\")\n",
      "C:\\Users\\vgupt\\AppData\\Local\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train_pred before reshaping: (603299, 1, 8)\n",
      "Shape of test_pred before reshaping: (647594, 1, 8)\n",
      "Model: \"model_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_8 (InputLayer)        [(None, 1, 8)]            0         \n",
      "                                                                 \n",
      " conv1d_27 (Conv1D)          (None, 1, 32)             800       \n",
      "                                                                 \n",
      " conv1d_28 (Conv1D)          (None, 1, 16)             1552      \n",
      "                                                                 \n",
      " conv1d_29 (Conv1D)          (None, 1, 6)              294       \n",
      "                                                                 \n",
      " conv1d_transpose_19 (Conv1  (None, 1, 16)             304       \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      " conv1d_transpose_20 (Conv1  (None, 1, 32)             1568      \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      " conv1d_transpose_21 (Conv1  (None, 1, 8)              776       \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5294 (20.68 KB)\n",
      "Trainable params: 5294 (20.68 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\") # change according to where your data is stored\n",
    "    \n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    autoencoder = AutoencoderModels(preprocessor.digital_train, preprocessor.digital_test) # comment out depending on whether digital/analog data is used\n",
    "    # autoencoder = AutoencoderModels(preprocessor.analog_train, preprocessor.analog_test)\n",
    "    # autoencoder.sae_analog() # comment out according to which method you call from the AutoencoderModels class\n",
    "    autoencoder.sae_digital()\n",
    "    # autoencoder.vae_analog()\n",
    "    # autoencoder.vae_digital()\n",
    "    train_pred, test_pred = autoencoder.autoencoder_predict_digital()\n",
    "    with open(\"sae_digital_train_pred\", \"wb\") as file: # name of file path can change depending on where you want to save it\n",
    "        pickle.dump(train_pred, file)\n",
    "    with open(\"sae_digital_test_pred\", \"wb\") as file: # similarly, this file path can be changed\n",
    "        pickle.dump(test_pred, file)\n",
    "    print(autoencoder.model.summary())\n",
    "    autoencoder.plot_losses()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98e3b228",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb7293c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "29da23ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(suppress=True, precision=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25d3c91c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1892ba77",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AutoencoderResults:\n",
    "    def __init__(self, train_data, test_data, analog_train, analog_test, digital_train, digital_test):\n",
    "        self.train_data = train_data\n",
    "        self.test_data = test_data\n",
    "        self.analog_train = analog_train\n",
    "        self.analog_test = analog_test\n",
    "        self.digital_train = digital_train\n",
    "        self.digital_test = digital_test\n",
    "        self.train_pred = None\n",
    "        self.test_pred = None\n",
    "        self.threshold = None\n",
    "\n",
    "    def compute_error_sae_digital(self):\n",
    "        with open(\"sae_digital_train_pred\", \"rb\") as file:\n",
    "            self.train_pred = pickle.load(file)\n",
    "        with open(\"sae_digital_test_pred\", \"rb\") as file:\n",
    "            self.test_pred = pickle.load(file)\n",
    "        reconstruction_error = np.mean(np.square(self.train_pred - self.digital_train), axis=1) # can try mse/rmse/mae etc.\n",
    "        q1 = np.percentile(reconstruction_error, 25)\n",
    "        q3 = np.percentile(reconstruction_error, 75)\n",
    "        q9 = np.percentile(reconstruction_error, 99)\n",
    "        self.threshold = q3 + 500000 * (q3 - q1)\n",
    "        # self.threshold = np.mean(reconstruction_error) + 2 * np.std(reconstruction_error)  # can be adjusted to find the optimal threshold\n",
    "        test_error = np.mean(np.square(self.test_pred - self.digital_test), axis=1)\n",
    "        print(q1)\n",
    "        print(q3)\n",
    "        print(max(reconstruction_error))\n",
    "        print(min(test_error))\n",
    "        q1_test = np.percentile(test_error, 25)\n",
    "        q3_test = np.percentile(test_error, 75)\n",
    "        q9_test = np.percentile(test_error, 99)\n",
    "        print(q1_test)\n",
    "        print(q3_test)\n",
    "        print(q9_test)\n",
    "        filtered_error = self.low_pass_filter(test_error,\n",
    "                                         alpha=0.05)  # can be adjusted to find optimal alpha\n",
    "        # predictions = [1 if error > self.threshold else 0 for error in filtered_error]\n",
    "        predictions = [1 if error > 0.1 else 0 for error in filtered_error]\n",
    "        self.test_data[\"predicted_output\"] = predictions\n",
    "        # pred_output = [0] * len(self.test_data)\n",
    "        # for index in self.create_sequence(predictions):\n",
    "        #     pred_output[index] = 1\n",
    "        # self.test_data[\"predicted_output\"] = pred_output\n",
    "        # print(self.test_data[\"is_anomaly\"].value_counts())\n",
    "        # print(self.test_data[\"predicted_output\"].value_counts())\n",
    "        \n",
    "    def compute_error_vae_digital(self):\n",
    "        with open(\"vae_digital_train_pred\", \"rb\") as file:\n",
    "            self.train_pred = pickle.load(file)\n",
    "        with open(\"vae_digital_test_pred\", \"rb\") as file:\n",
    "            self.test_pred = pickle.load(file)\n",
    "        reconstruction_error = np.mean(np.square(self.train_pred - self.digital_train), axis=1) # can try mse/rmse/mae etc.\n",
    "        q1 = np.percentile(reconstruction_error, 25)\n",
    "        q3 = np.percentile(reconstruction_error, 75)\n",
    "        # self.threshold = q3 + 45000 * (q3 - q1)\n",
    "        self.threshold = 0.122\n",
    "        # self.threshold = np.mean(reconstruction_error) + 2 * np.std(reconstruction_error)  # can be adjusted to find the optimal threshold\n",
    "        test_error = np.mean(np.square(self.test_pred - self.digital_test), axis=1)\n",
    "        \n",
    "        filtered_error = self.low_pass_filter(test_error,\n",
    "                                         alpha=0.05)  # can be adjusted to find optimal alpha\n",
    "        predictions = [1 if error > self.threshold else 0 for error in filtered_error]\n",
    "        self.test_data[\"predicted_output\"] = predictions\n",
    "\n",
    "    def compute_error_sae_analog(self):\n",
    "        with open(\"sae_analog_train_pred\", \"rb\") as file:\n",
    "            self.train_pred = pickle.load(file)\n",
    "        with open(\"sae_analog_test_pred\", \"rb\") as file:\n",
    "            self.test_pred = pickle.load(file)\n",
    "        reconstruction_error = np.mean(np.square(self.train_pred - self.analog_train), axis=1) # can try mse/rmse/mae etc.\n",
    "        q1 = np.percentile(reconstruction_error, 25)\n",
    "        q3 = np.percentile(reconstruction_error, 75)\n",
    "        self.threshold = q3 + 2 * (q3 - q1)\n",
    "        # self.threshold = np.mean(reconstruction_error) + 2 * np.std(reconstruction_error)  # can be adjusted to find the optimal threshold\n",
    "        test_error = np.mean(np.square(self.test_pred - self.analog_test), axis=1)\n",
    "        filtered_error = self.low_pass_filter(test_error,\n",
    "                                         alpha=0.1)  # can be adjusted to find optimal alpha\n",
    "        predictions = [1 if error > self.threshold else 0 for error in filtered_error]\n",
    "        self.test_data[\"predicted_output\"] = predictions\n",
    "    \n",
    "    \n",
    "    def compute_error_vae_analog(self):\n",
    "        with open(\"vae_analog_train_pred\", \"rb\") as file:\n",
    "            self.train_pred = pickle.load(file)\n",
    "        with open(\"vae_analog_test_pred\", \"rb\") as file:\n",
    "            self.test_pred = pickle.load(file)\n",
    "        reconstruction_error = np.mean(np.square(self.train_pred - self.analog_train), axis=1) # can try mse/rmse/mae etc.\n",
    "        q1 = np.percentile(reconstruction_error, 25)\n",
    "        q3 = np.percentile(reconstruction_error, 75)\n",
    "        self.threshold = q3 + 2 * (q3 - q1)\n",
    "        # self.threshold = np.mean(reconstruction_error) + 2 * np.std(reconstruction_error)  # can be adjusted to find the optimal threshold\n",
    "        test_error = np.mean(np.square(self.test_pred - self.analog_test), axis=1)\n",
    "        filtered_error = self.low_pass_filter(test_error,\n",
    "                                         alpha=0.1)  # can be adjusted to find optimal alpha\n",
    "        predictions = [1 if error > self.threshold else 0 for error in filtered_error]\n",
    "        self.test_data[\"predicted_output\"] = predictions\n",
    "    \n",
    "    \n",
    "    def low_pass_filter(self, error, alpha): # test_error is passed through low_pass_filter to improve evaluation metrics\n",
    "        error = error.reset_index(drop = True)\n",
    "        output = [error[0]]  # initialize the output with the first input value\n",
    "        for i in range(1, len(error)):\n",
    "            output.append(output[i - 1] + alpha * (error[i] - output[i - 1]))\n",
    "        return output\n",
    "\n",
    "    def create_sequence(self, anomalies):  # check if anomaly data points appear consecutively\n",
    "    # if a point appears by itself, it has a high chance of simply being random noise rather than a part of any failure\n",
    "    # returns index of anomalies\n",
    "        tmp1 = []\n",
    "        tmp2 = []\n",
    "        lst = []\n",
    "        for i in range(len(anomalies)):\n",
    "            if anomalies[i] == 1:\n",
    "                tmp1.append(i)\n",
    "        for j in range(len(tmp1) - 1):\n",
    "            if tmp1[j] + 1 == tmp1[j + 1]:  # check if the next index is the same as the previous index + 1 (eg. 501 == 500 + 1)\n",
    "                tmp2.append(tmp1[j])\n",
    "            if tmp1[j + 1] != tmp1[j] + 1:\n",
    "                if len(tmp2) >= 2:  # sequences have to be x seconds or more - can be adjusted to affect precision/recall\n",
    "                    tmp2.append(tmp1[j])\n",
    "                    lst.extend(tmp2)\n",
    "                tmp2 = []\n",
    "        return lst\n",
    "\n",
    "    def plot_cm_SAE_Digital(self):\n",
    "        cm = confusion_matrix(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"])\n",
    "        tp = cm[1, 1]\n",
    "        tn = cm[0, 0]\n",
    "        fp = cm[0, 1]\n",
    "        fn = cm[1, 0]\n",
    "        print(classification_report(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"]))\n",
    "        precision = tp / (tp + fp)\n",
    "        recall = tp / (tp + fn)\n",
    "        f1_score = 2 * (precision * recall) / (precision + recall)\n",
    "        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"Normal\", \"Anomaly\"])\n",
    "        disp.plot(values_format='d', cmap=\"Reds\")\n",
    "        plt.title(\"Sparse Autoencoder - Digital (alpha=0.05)\\nPrecision={:.2f}, Recall={:.2f}, F1 Score={:.2f}\".format(precision, recall, f1_score))\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "    def plot_cm_VAE_Digital(self):\n",
    "        cm = confusion_matrix(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"])\n",
    "        tp = cm[1, 1]\n",
    "        tn = cm[0, 0]\n",
    "        fp = cm[0, 1]\n",
    "        fn = cm[1, 0]\n",
    "        print(classification_report(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"]))\n",
    "        precision = tp / (tp + fp)\n",
    "        recall = tp / (tp + fn)\n",
    "        f1_score = 2 * (precision * recall) / (precision + recall)\n",
    "        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"Normal\", \"Anomaly\"])\n",
    "        disp.plot(values_format='d', cmap=\"Reds\")\n",
    "        plt.title(\"Variational Autoencoder - Digital (alpha=0.05)\\nPrecision={:.2f}, Recall={:.2f}, F1 Score={:.2f}\".format(precision, recall, f1_score))\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "    def plot_cm_SAE_Analog(self):\n",
    "        cm = confusion_matrix(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"])\n",
    "        tp = cm[1, 1]\n",
    "        tn = cm[0, 0]\n",
    "        fp = cm[0, 1]\n",
    "        fn = cm[1, 0]\n",
    "        print(classification_report(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"]))\n",
    "        precision = tp / (tp + fp)\n",
    "        recall = tp / (tp + fn)\n",
    "        f1_score = 2 * (precision * recall) / (precision + recall)\n",
    "        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"Normal\", \"Anomaly\"])\n",
    "        disp.plot(values_format='d', cmap=\"Reds\")\n",
    "        plt.title(\"Sparse Autoencoder - Analog (alpha=0.05)\\nPrecision={:.2f}, Recall={:.2f}, F1 Score={:.2f}\".format(precision, recall, f1_score))\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "    def plot_cm_VAE_Analog(self):\n",
    "        cm = confusion_matrix(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"])\n",
    "        tp = cm[1, 1]\n",
    "        tn = cm[0, 0]\n",
    "        fp = cm[0, 1]\n",
    "        fn = cm[1, 0]\n",
    "        print(classification_report(self.test_data[\"is_anomaly\"], self.test_data[\"predicted_output\"]))\n",
    "        precision = tp / (tp + fp)\n",
    "        recall = tp / (tp + fn)\n",
    "        f1_score = 2 * (precision * recall) / (precision + recall)\n",
    "        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"Normal\", \"Anomaly\"])\n",
    "        disp.plot(values_format='d', cmap=\"Reds\")\n",
    "        plt.title(\"Variational Autoencoder - Analog (alpha=0.05)\\nPrecision={:.2f}, Recall={:.2f}, F1 Score={:.2f}\".format(precision, recall, f1_score))\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a777a82c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.900196514983019e-74\n",
      "4.900196514983019e-74\n",
      "0.24773759255062133\n",
      "4.900196514983019e-74\n",
      "4.900196514983019e-74\n",
      "9.809930645587883e-13\n",
      "0.12500000000019584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\2005856560.py:39: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data[\"predicted_output\"] = predictions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    543011\n",
      "           1       1.00      1.00      1.00    104583\n",
      "\n",
      "    accuracy                           1.00    647594\n",
      "   macro avg       1.00      1.00      1.00    647594\n",
      "weighted avg       1.00      1.00      1.00    647594\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sae_digital\n",
    "\n",
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\")\n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    autoencoder_results = AutoencoderResults(preprocessor.train_data, preprocessor.test_data, preprocessor.analog_train,\n",
    "                                             preprocessor.analog_test, preprocessor.digital_train, preprocessor.digital_test)\n",
    "    autoencoder_results.compute_error_sae_digital()\n",
    "    autoencoder_results.plot_cm_SAE_Digital()\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1c181355",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\3179309103.py:63: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data[\"predicted_output\"] = predictions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97    543011\n",
      "           1       0.78      1.00      0.88    104583\n",
      "\n",
      "    accuracy                           0.95    647594\n",
      "   macro avg       0.89      0.97      0.92    647594\n",
      "weighted avg       0.96      0.95      0.96    647594\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vae_digital\n",
    "\n",
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\")\n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    autoencoder_results = AutoencoderResults(preprocessor.train_data, preprocessor.test_data, preprocessor.analog_train,\n",
    "                                             preprocessor.analog_test, preprocessor.digital_train, preprocessor.digital_test)\n",
    "    autoencoder_results.compute_error_vae_digital()\n",
    "    autoencoder_results.plot_cm_VAE_Digital()\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6764b98a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\2005856560.py:79: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data[\"predicted_output\"] = predictions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.63      0.73    543011\n",
      "           1       0.24      0.60      0.34    104583\n",
      "\n",
      "    accuracy                           0.62    647594\n",
      "   macro avg       0.56      0.61      0.54    647594\n",
      "weighted avg       0.78      0.62      0.67    647594\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sae_analog\n",
    "\n",
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\")\n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    autoencoder_results = AutoencoderResults(preprocessor.train_data, preprocessor.test_data, preprocessor.analog_train,\n",
    "                                             preprocessor.analog_test, preprocessor.digital_train, preprocessor.digital_test)\n",
    "    autoencoder_results.compute_error_sae_analog()\n",
    "    autoencoder_results.plot_cm_SAE_Analog()\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "37f090de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.train_data.drop(self.train_data.columns[0], axis=1,\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\1428266579.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data.drop(self.test_data.columns[0], axis=1, inplace=True)\n",
      "C:\\Users\\vgupt\\AppData\\Local\\Temp\\ipykernel_38140\\2005856560.py:96: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.test_data[\"predicted_output\"] = predictions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.80      0.84    543011\n",
      "           1       0.32      0.48      0.38    104583\n",
      "\n",
      "    accuracy                           0.75    647594\n",
      "   macro avg       0.60      0.64      0.61    647594\n",
      "weighted avg       0.80      0.75      0.77    647594\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vae_analog\n",
    "\n",
    "def main():\n",
    "    preprocessor = DataPreprocessor(\"dataset_train_processed.csv\")\n",
    "    preprocessor.preprocessing_autoencoder()\n",
    "    autoencoder_results = AutoencoderResults(preprocessor.train_data, preprocessor.test_data, preprocessor.analog_train,\n",
    "                                             preprocessor.analog_test, preprocessor.digital_train, preprocessor.digital_test)\n",
    "    autoencoder_results.compute_error_vae_analog()\n",
    "    autoencoder_results.plot_cm_VAE_Analog()\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "236bcefd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0357830f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2d4fcc6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}