Barone-Adesi and Whaley Approximation and FNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2372898",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unnia\\anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\Users\\unnia\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n",
      "C:\\Users\\unnia\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas64__v0.3.23-gcc_10_3_0.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "C:\\Users\\unnia\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.25.0\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import optuna\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import QuantLib as ql\n",
    "import mibian\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from copy import deepcopy\n",
    "from keras.regularizers import l2\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, LSTM, Flatten, Dense, TimeDistributed, Dropout, BatchNormalization, Bidirectional\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from ta import add_all_ta_features\n",
    "from ta.volatility import BollingerBands\n",
    "from ta.momentum import RSIIndicator\n",
    "from tensorflow import keras\n",
    "keras.utils.set_random_seed(812)\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25bc656",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30f9efbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data\n",
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0a7aa9f",
   "metadata": {},
   "source": [
    "## TSLA data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e244ceb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:4: 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",
      "  df_TSLA['SMA'] = df_TSLA['UNDERLYING_LAST'].rolling(window=14).mean()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:7: 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",
      "  df_TSLA['EMA'] = df_TSLA['UNDERLYING_LAST'].ewm(span=14, adjust=False).mean()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:10: 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",
      "  df_TSLA['RSI'] = RSIIndicator(close=df_TSLA['UNDERLYING_LAST'], window=14).rsi()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:14: 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",
      "  df_TSLA['bb_bbm'] = indicator_bb.bollinger_mavg()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:15: 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",
      "  df_TSLA['bb_bbh'] = indicator_bb.bollinger_hband()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:16: 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",
      "  df_TSLA['bb_bbl'] = indicator_bb.bollinger_lband()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:19: 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",
      "  df_TSLA['realized_vol'] = df_TSLA['UNDERLYING_LAST'].pct_change().rolling(window=14).std() * np.sqrt(252)\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:23: 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",
      "  df_TSLA['C_LAST_lag1'] = df_TSLA['C_LAST'].shift(1)\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2518326361.py:25: 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",
      "  df_TSLA['C_IV_lag1'] = df_TSLA['C_IV'].shift(1)\n"
     ]
    }
   ],
   "source": [
    "df_TSLA = df[df['Stock_TSLA'] == 1.0]\n",
    "\n",
    "# Calculate Simple Moving Average\n",
    "df_TSLA['SMA'] = df_TSLA['UNDERLYING_LAST'].rolling(window=14).mean()\n",
    "\n",
    "# Calculate Exponential Moving Average\n",
    "df_TSLA['EMA'] = df_TSLA['UNDERLYING_LAST'].ewm(span=14, adjust=False).mean()\n",
    "\n",
    "# Calculate RSI\n",
    "df_TSLA['RSI'] = RSIIndicator(close=df_TSLA['UNDERLYING_LAST'], window=14).rsi()\n",
    "\n",
    "# Calculate Bollinger Bands\n",
    "indicator_bb = BollingerBands(close=df_TSLA['UNDERLYING_LAST'], window=20, window_dev=2)\n",
    "df_TSLA['bb_bbm'] = indicator_bb.bollinger_mavg()\n",
    "df_TSLA['bb_bbh'] = indicator_bb.bollinger_hband()\n",
    "df_TSLA['bb_bbl'] = indicator_bb.bollinger_lband()\n",
    "\n",
    "# Realized Volatility\n",
    "df_TSLA['realized_vol'] = df_TSLA['UNDERLYING_LAST'].pct_change().rolling(window=14).std() * np.sqrt(252)\n",
    "\n",
    "\n",
    "# For lagged features of the target variable, C_LAST:\n",
    "df_TSLA['C_LAST_lag1'] = df_TSLA['C_LAST'].shift(1)\n",
    "# For other variables, you could do the same:\n",
    "df_TSLA['C_IV_lag1'] = df_TSLA['C_IV'].shift(1)\n",
    "\n",
    "\n",
    "df_TSLA = df_TSLA[['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST', 'STRIKE', 'DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate', 'dividend_rate','C_LAST']]\n",
    "df_TSLA = df_TSLA.dropna()\n",
    "\n",
    "# Find indices where C_IV or DTE is zero\n",
    "indices_to_remove = df_TSLA[(df_TSLA['C_IV'] == 0) | (df_TSLA['DTE'] == 0)].index\n",
    "\n",
    "# Drop these indices from the DataFrame\n",
    "df_TSLA = df_TSLA.drop(indices_to_remove)\n",
    "\n",
    "# Reset the index\n",
    "df_TSLA = df_TSLA.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a48a11b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the features and target variable\n",
    "X_TSLA = df_TSLA[['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST',  'STRIKE','DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate']].values\n",
    "y_TSLA = df_TSLA['C_LAST'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12a3274e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define split points\n",
    "train_split_TSLA = int(len(X_TSLA) * 0.7)  # 70% of the data for training\n",
    "val_split_TSLA = int(len(X_TSLA) * 0.8)    \n",
    "\n",
    "# Split the data into training, validation, and testing sets\n",
    "X_train_TSLA, X_val_test_TSLA = X_TSLA[:train_split_TSLA], X_TSLA[train_split_TSLA:]\n",
    "y_train_TSLA, y_val_test_TSLA = y_TSLA[:train_split_TSLA], y_TSLA[train_split_TSLA:]\n",
    "\n",
    "# Split the remaining 30% into validation and test sets\n",
    "val_size_TSLA = int(len(X_val_test_TSLA) * (10/30))  \n",
    "X_val_TSLA, X_test_TSLA = X_val_test_TSLA[:val_size_TSLA], X_val_test_TSLA[val_size_TSLA:]\n",
    "y_val_TSLA, y_test_TSLA = y_val_test_TSLA[:val_size_TSLA], y_val_test_TSLA[val_size_TSLA:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3656521",
   "metadata": {},
   "source": [
    "## AAPL data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "515c4b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:4: 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",
      "  df_AAPL['SMA'] = df_AAPL['UNDERLYING_LAST'].rolling(window=14).mean()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:7: 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",
      "  df_AAPL['EMA'] = df_AAPL['UNDERLYING_LAST'].ewm(span=14, adjust=False).mean()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:10: 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",
      "  df_AAPL['RSI'] = RSIIndicator(close=df_AAPL['UNDERLYING_LAST'], window=14).rsi()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:14: 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",
      "  df_AAPL['bb_bbm'] = indicator_bb.bollinger_mavg()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:15: 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",
      "  df_AAPL['bb_bbh'] = indicator_bb.bollinger_hband()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:16: 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",
      "  df_AAPL['bb_bbl'] = indicator_bb.bollinger_lband()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:19: 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",
      "  df_AAPL['realized_vol'] = df_AAPL['UNDERLYING_LAST'].pct_change().rolling(window=14).std() * np.sqrt(252)\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:23: 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",
      "  df_AAPL['C_LAST_lag1'] = df_AAPL['C_LAST'].shift(1)\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\2192086726.py:25: 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",
      "  df_AAPL['C_IV_lag1'] = df_AAPL['C_IV'].shift(1)\n"
     ]
    }
   ],
   "source": [
    "df_AAPL = df[df['Stock_AAPL'] == 1.0]\n",
    "\n",
    "# Calculate Simple Moving Average\n",
    "df_AAPL['SMA'] = df_AAPL['UNDERLYING_LAST'].rolling(window=14).mean()\n",
    "\n",
    "# Calculate Exponential Moving Average\n",
    "df_AAPL['EMA'] = df_AAPL['UNDERLYING_LAST'].ewm(span=14, adjust=False).mean()\n",
    "\n",
    "# Calculate RSI\n",
    "df_AAPL['RSI'] = RSIIndicator(close=df_AAPL['UNDERLYING_LAST'], window=14).rsi()\n",
    "\n",
    "# Calculate Bollinger Bands\n",
    "indicator_bb = BollingerBands(close=df_AAPL['UNDERLYING_LAST'], window=20, window_dev=2)\n",
    "df_AAPL['bb_bbm'] = indicator_bb.bollinger_mavg()\n",
    "df_AAPL['bb_bbh'] = indicator_bb.bollinger_hband()\n",
    "df_AAPL['bb_bbl'] = indicator_bb.bollinger_lband()\n",
    "\n",
    "# Realized Volatility\n",
    "df_AAPL['realized_vol'] = df_AAPL['UNDERLYING_LAST'].pct_change().rolling(window=14).std() * np.sqrt(252)\n",
    "\n",
    "\n",
    "# For lagged features of the target variable, C_LAST:\n",
    "df_AAPL['C_LAST_lag1'] = df_AAPL['C_LAST'].shift(1)\n",
    "# For other variables, you could do the same:\n",
    "df_AAPL['C_IV_lag1'] = df_AAPL['C_IV'].shift(1)\n",
    "\n",
    "\n",
    "df_AAPL = df_AAPL[['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST', 'STRIKE', 'DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate', 'C_LAST']]\n",
    "df_AAPL = df_AAPL.dropna()\n",
    "\n",
    "# Find indices where C_IV or DTE is zero\n",
    "indices_to_remove = df_AAPL[(df_AAPL['C_IV'] == 0) | (df_AAPL['DTE'] == 0)].index\n",
    "\n",
    "# Drop these indices from the DataFrame\n",
    "df_AAPL = df_AAPL.drop(indices_to_remove)\n",
    "\n",
    "# Reset the index\n",
    "df_AAPL = df_AAPL.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36e3f852",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the features and target variable\n",
    "X_AAPL = df_AAPL[['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST',  'STRIKE','DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate']].values\n",
    "y_AAPL = df_AAPL['C_LAST'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "171c95f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define split points\n",
    "train_split_AAPL = int(len(X_AAPL) * 0.7)  # 70% of the data for training\n",
    "val_split_AAPL = int(len(X_AAPL) * 0.8)    \n",
    "\n",
    "# Split the data into training, validation, and testing sets\n",
    "X_train_AAPL, X_val_test_AAPL = X_AAPL[:train_split_AAPL], X_AAPL[train_split_AAPL:]\n",
    "y_train_AAPL, y_val_test_AAPL = y_AAPL[:train_split_AAPL], y_AAPL[train_split_AAPL:]\n",
    "\n",
    "# Split the remaining 30% into validation and test sets\n",
    "val_size_AAPL = int(len(X_val_test_AAPL) * (10/30))  \n",
    "X_val_AAPL, X_test_AAPL = X_val_test_AAPL[:val_size_AAPL], X_val_test_AAPL[val_size_AAPL:]\n",
    "y_val_AAPL, y_test_AAPL = y_val_test_AAPL[:val_size_AAPL], y_val_test_AAPL[val_size_AAPL:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8420f36e",
   "metadata": {},
   "source": [
    "## NVDA data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b95d1883",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:4: 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",
      "  df_NVDA['SMA'] = df_NVDA['UNDERLYING_LAST'].rolling(window=14).mean()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:7: 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",
      "  df_NVDA['EMA'] = df_NVDA['UNDERLYING_LAST'].ewm(span=14, adjust=False).mean()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:10: 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",
      "  df_NVDA['RSI'] = RSIIndicator(close=df_NVDA['UNDERLYING_LAST'], window=14).rsi()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:14: 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",
      "  df_NVDA['bb_bbm'] = indicator_bb.bollinger_mavg()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:15: 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",
      "  df_NVDA['bb_bbh'] = indicator_bb.bollinger_hband()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:16: 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",
      "  df_NVDA['bb_bbl'] = indicator_bb.bollinger_lband()\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:19: 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",
      "  df_NVDA['realized_vol'] = df_NVDA['UNDERLYING_LAST'].pct_change().rolling(window=14).std() * np.sqrt(252)\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:23: 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",
      "  df_NVDA['C_LAST_lag1'] = df_NVDA['C_LAST'].shift(1)\n",
      "C:\\Users\\unnia\\AppData\\Local\\Temp\\ipykernel_25328\\1211373322.py:25: 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",
      "  df_NVDA['C_IV_lag1'] = df_NVDA['C_IV'].shift(1)\n"
     ]
    }
   ],
   "source": [
    "df_NVDA = df[df['Stock_NVDA'] == 1.0]\n",
    "\n",
    "# Calculate Simple Moving Average\n",
    "df_NVDA['SMA'] = df_NVDA['UNDERLYING_LAST'].rolling(window=14).mean()\n",
    "\n",
    "# Calculate Exponential Moving Average\n",
    "df_NVDA['EMA'] = df_NVDA['UNDERLYING_LAST'].ewm(span=14, adjust=False).mean()\n",
    "\n",
    "# Calculate RSI\n",
    "df_NVDA['RSI'] = RSIIndicator(close=df_NVDA['UNDERLYING_LAST'], window=14).rsi()\n",
    "\n",
    "# Calculate Bollinger Bands\n",
    "indicator_bb = BollingerBands(close=df_NVDA['UNDERLYING_LAST'], window=20, window_dev=2)\n",
    "df_NVDA['bb_bbm'] = indicator_bb.bollinger_mavg()\n",
    "df_NVDA['bb_bbh'] = indicator_bb.bollinger_hband()\n",
    "df_NVDA['bb_bbl'] = indicator_bb.bollinger_lband()\n",
    "\n",
    "# Realized Volatility\n",
    "df_NVDA['realized_vol'] = df_NVDA['UNDERLYING_LAST'].pct_change().rolling(window=14).std() * np.sqrt(252)\n",
    "\n",
    "\n",
    "# For lagged features of the target variable, C_LAST:\n",
    "df_NVDA['C_LAST_lag1'] = df_NVDA['C_LAST'].shift(1)\n",
    "# For other variables, you could do the same:\n",
    "df_NVDA['C_IV_lag1'] = df_NVDA['C_IV'].shift(1)\n",
    "\n",
    "\n",
    "df_NVDA = df_NVDA[['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST', 'STRIKE', 'DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate', 'C_LAST']]\n",
    "df_NVDA = df_NVDA.dropna()\n",
    "\n",
    "# Find indices where C_IV or DTE is zero\n",
    "indices_to_remove = df_NVDA[(df_NVDA['C_IV'] == 0) | (df_NVDA['DTE'] == 0)].index\n",
    "\n",
    "# Drop these indices from the DataFrame\n",
    "df_NVDA = df_NVDA.drop(indices_to_remove)\n",
    "\n",
    "# Reset the index\n",
    "df_NVDA = df_NVDA.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01363902",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the features and target variable\n",
    "X_NVDA = df_NVDA[['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST',  'STRIKE','DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate']].values\n",
    "y_NVDA = df_NVDA['C_LAST'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38fff363",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define split points\n",
    "train_split_NVDA = int(len(X_NVDA) * 0.7)  # 70% of the data for training\n",
    "val_split_NVDA = int(len(X_NVDA) * 0.8)    \n",
    "\n",
    "# Split the data into training, validation, and testing sets\n",
    "X_train_NVDA, X_val_test_NVDA = X_NVDA[:train_split_NVDA], X_NVDA[train_split_NVDA:]\n",
    "y_train_NVDA, y_val_test_NVDA = y_NVDA[:train_split_NVDA], y_NVDA[train_split_NVDA:]\n",
    "\n",
    "# Split the remaining 30% into validation and test sets\n",
    "val_size_NVDA = int(len(X_val_test_NVDA) * (10/30))  \n",
    "X_val_NVDA, X_test_NVDA = X_val_test_NVDA[:val_size_NVDA], X_val_test_NVDA[val_size_NVDA:]\n",
    "y_val_NVDA, y_test_NVDA = y_val_test_NVDA[:val_size_NVDA], y_val_test_NVDA[val_size_NVDA:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c424bb34",
   "metadata": {},
   "source": [
    "### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fc753f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features to use for prediction\n",
    "features = ['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol', 'UNDERLYING_LAST', 'STRIKE', 'DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate']\n",
    "target = 'C_LAST'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1f1403",
   "metadata": {},
   "source": [
    "# Barone-Adesi and Whaley Approximation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9147cfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_american_option(row, option_type='call'):\n",
    "    # Extract parameters from the row\n",
    "    underlying_price = row['UNDERLYING_LAST']\n",
    "    strike_price = row['STRIKE']\n",
    "    interest_rate = row['interest rate'] / 100  # Convert to decimal\n",
    "    days_to_expiration = row['DTE']\n",
    "    volatility = row['C_IV'] / 100  # Convert to decimal\n",
    "    dividend_rate = row['dividend_rate']  # Assuming no dividends, adjust if you have dividend rates\n",
    "\n",
    "    # Setup the calculation date\n",
    "    calculation_date = ql.Date.todaysDate()\n",
    "    ql.Settings.instance().evaluationDate = calculation_date\n",
    "\n",
    "    # Option data\n",
    "    maturity_date = calculation_date + int(days_to_expiration)\n",
    "    day_count = ql.Actual365Fixed()\n",
    "    calendar = ql.UnitedStates(ql.UnitedStates.NYSE)\n",
    "\n",
    "    # Option type\n",
    "    if option_type.lower() == 'call':\n",
    "        option_type = ql.Option.Call\n",
    "    else:\n",
    "        option_type = ql.Option.Put\n",
    "\n",
    "    # Construct the European Option\n",
    "    payoff = ql.PlainVanillaPayoff(option_type, strike_price)\n",
    "    exercise = ql.AmericanExercise(calculation_date, maturity_date)\n",
    "    american_option = ql.VanillaOption(payoff, exercise)\n",
    "\n",
    "    # Construct the Black-Scholes-Merton process\n",
    "    spot_handle = ql.QuoteHandle(ql.SimpleQuote(underlying_price))\n",
    "    flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, interest_rate, day_count))\n",
    "    dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, dividend_rate, day_count))\n",
    "    flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, volatility, day_count))\n",
    "    bsm_process = ql.BlackScholesMertonProcess(spot_handle, dividend_yield, flat_ts, flat_vol_ts)\n",
    "\n",
    "    # Setup the engine and calculate\n",
    "    engine = ql.BaroneAdesiWhaleyApproximationEngine(bsm_process)\n",
    "    american_option.setPricingEngine(engine)\n",
    "\n",
    "    return american_option.NPV()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebd1b69",
   "metadata": {},
   "source": [
    "## Barone-Adesi and Whaley Approximation for TSLA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dbf1159d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for TSLA dataset: 21.12744626008425\n"
     ]
    }
   ],
   "source": [
    "# Apply the function to the DataFrame\n",
    "X_test_df_TSLA = pd.DataFrame(X_test_TSLA, columns=['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST',  'STRIKE','DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate'])\n",
    "X_test_df_TSLA['BlackScholesPrice'] = X_test_df_TSLA.apply(compute_american_option, axis=1)\n",
    "\n",
    "# Get the predicted Black-Scholes prices\n",
    "predicted_prices_TSLA = X_test_df_TSLA['BlackScholesPrice'].values\n",
    "\n",
    "# Compute the RMSE for Black Sholes Method\n",
    "rmse_TSLA = mean_squared_error(y_test_TSLA, predicted_prices_TSLA, squared=False)\n",
    "print(f\"RMSE for TSLA dataset: {rmse_TSLA}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa4a4f1",
   "metadata": {},
   "source": [
    "## Barone-Adesi and Whaley Approximation for AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1a292c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for AAPL dataset: 6.941820707584086\n"
     ]
    }
   ],
   "source": [
    "# Apply the function to the DataFrame\n",
    "X_test_df_AAPL = pd.DataFrame(X_test_AAPL, columns=['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST',  'STRIKE','DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate'])\n",
    "X_test_df_AAPL['BlackScholesPrice'] = X_test_df_AAPL.apply(compute_american_option, axis=1)\n",
    "\n",
    "# Get the predicted Black-Scholes prices\n",
    "predicted_prices_AAPL = X_test_df_AAPL['BlackScholesPrice'].values\n",
    "\n",
    "# Compute the RMSE for Black Sholes Method\n",
    "rmse_AAPL = mean_squared_error(y_test_AAPL, predicted_prices_AAPL, squared=False)\n",
    "print(f\"RMSE for AAPL dataset: {rmse_AAPL}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee63acb",
   "metadata": {},
   "source": [
    "## Barone-Adesi and Whaley Approximation for NVDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "830fcd0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for NVDA dataset: 44.93740398284506\n"
     ]
    }
   ],
   "source": [
    "# Apply the function to the DataFrame\n",
    "X_test_df_NVDA = pd.DataFrame(X_test_NVDA, columns=['C_IV_lag1','C_LAST_lag1','bb_bbm','bb_bbh','bb_bbl','SMA','EMA','RSI', 'realized_vol','UNDERLYING_LAST',  'STRIKE','DTE', 'C_IV', 'C_DELTA', 'C_GAMMA', 'C_VEGA', 'C_THETA', 'C_RHO', 'C_VOLUME', 'STRIKE_DISTANCE', 'STRIKE_DISTANCE_PCT', 'interest rate','dividend_rate'])\n",
    "X_test_df_NVDA['BlackScholesPrice'] = X_test_df_NVDA.apply(compute_american_option, axis=1)\n",
    "\n",
    "# Get the predicted Black-Scholes prices\n",
    "predicted_prices_NVDA = X_test_df_NVDA['BlackScholesPrice'].values\n",
    "\n",
    "# Compute the RMSE for Black Sholes Method\n",
    "rmse_NVDA = mean_squared_error(y_test_NVDA, predicted_prices_NVDA, squared=False)\n",
    "print(f\"RMSE for NVDA dataset: {rmse_NVDA}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4e6da57",
   "metadata": {},
   "source": [
    "# Feedforward Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "768da943",
   "metadata": {},
   "source": [
    "## Scaling and Converting to Tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe730a1f",
   "metadata": {},
   "source": [
    "### TSLA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3538232",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the scaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "# scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler on the training data\n",
    "scaler.fit(X_train_TSLA)\n",
    "\n",
    "# Scale the training data\n",
    "X_train_TSLA_scaled = scaler.transform(X_train_TSLA)\n",
    "\n",
    "# Scale the validation data\n",
    "X_val_TSLA_scaled = scaler.transform(X_val_TSLA)\n",
    "\n",
    "# Scale the test data\n",
    "X_test_TSLA_scaled = scaler.transform(X_test_TSLA)\n",
    "\n",
    "# Convert the scaled data to tensors\n",
    "X_train_TSLA_tensor = torch.tensor(X_train_TSLA_scaled, dtype=torch.float32)\n",
    "y_train_TSLA_tensor = torch.tensor(y_train_TSLA, dtype=torch.float32).unsqueeze(1)\n",
    "\n",
    "X_val_TSLA_tensor = torch.tensor(X_val_TSLA_scaled, dtype=torch.float32)\n",
    "y_val_TSLA_tensor = torch.tensor(y_val_TSLA, dtype=torch.float32).unsqueeze(1)\n",
    "\n",
    "X_test_TSLA_tensor = torch.tensor(X_test_TSLA_scaled, dtype=torch.float32)\n",
    "y_test_TSLA_tensor = torch.tensor(y_test_TSLA, dtype=torch.float32).unsqueeze(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "036e6234",
   "metadata": {},
   "source": [
    "### AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "30dd718e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the scaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "# scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler on the training data\n",
    "scaler.fit(X_train_AAPL)\n",
    "\n",
    "# Scale the training data\n",
    "X_train_AAPL_scaled = scaler.transform(X_train_AAPL)\n",
    "\n",
    "# Scale the validation data\n",
    "X_val_AAPL_scaled = scaler.transform(X_val_AAPL)\n",
    "\n",
    "# Scale the test data\n",
    "X_test_AAPL_scaled = scaler.transform(X_test_AAPL)\n",
    "\n",
    "# Convert the scaled data to tensors\n",
    "X_train_AAPL_tensor = torch.tensor(X_train_AAPL_scaled, dtype=torch.float32)\n",
    "y_train_AAPL_tensor = torch.tensor(y_train_AAPL, dtype=torch.float32).unsqueeze(1)\n",
    "\n",
    "X_val_AAPL_tensor = torch.tensor(X_val_AAPL_scaled, dtype=torch.float32)\n",
    "y_val_AAPL_tensor = torch.tensor(y_val_AAPL, dtype=torch.float32).unsqueeze(1)\n",
    "\n",
    "X_test_AAPL_tensor = torch.tensor(X_test_AAPL_scaled, dtype=torch.float32)\n",
    "y_test_AAPL_tensor = torch.tensor(y_test_AAPL, dtype=torch.float32).unsqueeze(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3335ec1",
   "metadata": {},
   "source": [
    "### NVDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54b42fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the scaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "# scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler on the training data\n",
    "scaler.fit(X_train_NVDA)\n",
    "\n",
    "# Scale the training data\n",
    "X_train_NVDA_scaled = scaler.transform(X_train_NVDA)\n",
    "\n",
    "# Scale the validation data\n",
    "X_val_NVDA_scaled = scaler.transform(X_val_NVDA)\n",
    "\n",
    "# Scale the test data\n",
    "X_test_NVDA_scaled = scaler.transform(X_test_NVDA)\n",
    "\n",
    "# Convert the scaled data to tensors\n",
    "X_train_NVDA_tensor = torch.tensor(X_train_NVDA_scaled, dtype=torch.float32)\n",
    "y_train_NVDA_tensor = torch.tensor(y_train_NVDA, dtype=torch.float32).unsqueeze(1)\n",
    "\n",
    "X_val_NVDA_tensor = torch.tensor(X_val_NVDA_scaled, dtype=torch.float32)\n",
    "y_val_NVDA_tensor = torch.tensor(y_val_NVDA, dtype=torch.float32).unsqueeze(1)\n",
    "\n",
    "X_test_NVDA_tensor = torch.tensor(X_test_NVDA_scaled, dtype=torch.float32)\n",
    "y_test_NVDA_tensor = torch.tensor(y_test_NVDA, dtype=torch.float32).unsqueeze(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10ace82",
   "metadata": {},
   "source": [
    "### Train Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5f2ad34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model with early stopping\n",
    "def train(model, X_train_tensor, y_train_tensor, X_val_tensor, y_val_tensor, loss_fn, optimizer, epochs=100, batch_size=64, patience=10):\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    best_loss = float('inf')\n",
    "    best_model = None\n",
    "    epochs_no_improve = 0\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        for i in range(0, len(X_train_tensor), batch_size):\n",
    "            # Get the minibatch\n",
    "            X_batch = X_train_tensor[i:i+batch_size]\n",
    "            y_batch = y_train_tensor[i:i+batch_size]\n",
    "\n",
    "            # Forward pass\n",
    "            optimizer.zero_grad()\n",
    "            predictions = model(X_batch)\n",
    "            loss = loss_fn(predictions, y_batch)\n",
    "            \n",
    "            epoch_loss += loss.item()\n",
    "\n",
    "            # Backward pass and optimization\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        \n",
    "        # Average loss for this epoch\n",
    "        avg_epoch_loss = epoch_loss / (len(X_train_tensor) / batch_size)\n",
    "        train_losses.append(avg_epoch_loss)\n",
    "\n",
    "        # Validation phase\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            val_predictions = model(X_val_tensor)\n",
    "            val_loss = loss_fn(val_predictions, y_val_tensor).item()\n",
    "            val_losses.append(val_loss)\n",
    "\n",
    "        # Print training and validation loss\n",
    "        print(f'Epoch {epoch+1}/{epochs} - Loss: {loss.item()}, Val Loss: {val_loss}')\n",
    "\n",
    "        # Check for early stopping\n",
    "        if val_loss < best_loss:\n",
    "            best_loss = val_loss\n",
    "            best_model = deepcopy(model.state_dict())  # Save a copy of the model\n",
    "            epochs_no_improve = 0\n",
    "        else:\n",
    "            epochs_no_improve += 1\n",
    "\n",
    "        if epochs_no_improve == patience:\n",
    "            print('Early stopping triggered')\n",
    "            break\n",
    "\n",
    "    # Load the best model state\n",
    "    model.load_state_dict(best_model)\n",
    "    return model, train_losses, val_losses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1108c062",
   "metadata": {},
   "source": [
    "### Test Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4241f111",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test the model\n",
    "def test(model, X_test_tensor, y_test_tensor):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        y_pred_tensor = model(X_test_tensor)\n",
    "        y_pred = y_pred_tensor.numpy().squeeze()\n",
    "        y_test_original = y_test_tensor.numpy().squeeze() \n",
    "        rmse = mean_squared_error(y_test_original, y_pred, squared=False)\n",
    "        print(f'Root Mean Squared Error (RMSE): {rmse}')\n",
    "        return rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635ac7e7",
   "metadata": {},
   "source": [
    "# Find Best FNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1d7b7e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BaseNN(nn.Module):\n",
    "    def __init__(self, no_features, hidden_dims, activation='relu'):\n",
    "        super(BaseNN, self).__init__()\n",
    "        layers = []\n",
    "        input_dim = no_features\n",
    "        # Define a dictionary to map activation names to their functions\n",
    "        activations = {\n",
    "            'relu': nn.ReLU(),\n",
    "            'leaky_relu': nn.LeakyReLU()\n",
    "        }\n",
    "        # Get the selected activation function from the dictionary\n",
    "        self.activation = activations[activation]\n",
    "        \n",
    "        for hidden_dim in hidden_dims:\n",
    "            layers.append(nn.Linear(input_dim, hidden_dim))\n",
    "            layers.append(self.activation)  # Use the selected activation function\n",
    "            input_dim = hidden_dim  # Set the input for the next layer\n",
    "        layers.append(nn.Linear(input_dim, 1))  # Output layer\n",
    "        self.mlp_stack = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = self.mlp_stack(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e30b5a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100 - Loss: 12836.271484375, Val Loss: 664.485595703125\n",
      "Epoch 2/100 - Loss: 2084.982177734375, Val Loss: 328.3521423339844\n",
      "Epoch 3/100 - Loss: 1057.62109375, Val Loss: 242.59222412109375\n",
      "Epoch 4/100 - Loss: 782.8485717773438, Val Loss: 193.63211059570312\n",
      "Epoch 5/100 - Loss: 657.555908203125, Val Loss: 170.39654541015625\n",
      "Epoch 6/100 - Loss: 612.4159545898438, Val Loss: 159.15834045410156\n",
      "Epoch 7/100 - Loss: 648.5150146484375, Val Loss: 152.8344268798828\n",
      "Epoch 8/100 - Loss: 718.7825317382812, Val Loss: 149.2294158935547\n",
      "Epoch 9/100 - Loss: 753.514892578125, Val Loss: 147.4657440185547\n",
      "Epoch 10/100 - Loss: 782.74609375, Val Loss: 146.257568359375\n",
      "Epoch 11/100 - Loss: 825.372314453125, Val Loss: 145.0177764892578\n",
      "Epoch 12/100 - Loss: 864.65576171875, Val Loss: 144.0819091796875\n",
      "Epoch 13/100 - Loss: 896.0657348632812, Val Loss: 143.4281005859375\n",
      "Epoch 14/100 - Loss: 959.3167724609375, Val Loss: 142.7875518798828\n",
      "Epoch 15/100 - Loss: 1008.3289794921875, Val Loss: 141.64405822753906\n",
      "Epoch 16/100 - Loss: 1028.5509033203125, Val Loss: 139.6105499267578\n",
      "Epoch 17/100 - Loss: 923.4361572265625, Val Loss: 131.10415649414062\n",
      "Epoch 18/100 - Loss: 901.1072387695312, Val Loss: 129.91529846191406\n",
      "Epoch 19/100 - Loss: 887.6273803710938, Val Loss: 129.87106323242188\n",
      "Epoch 20/100 - Loss: 894.4066162109375, Val Loss: 130.1575927734375\n",
      "Epoch 21/100 - Loss: 901.7586669921875, Val Loss: 130.35504150390625\n",
      "Epoch 22/100 - Loss: 911.3432006835938, Val Loss: 130.56199645996094\n",
      "Epoch 23/100 - Loss: 938.0205688476562, Val Loss: 130.82151794433594\n",
      "Epoch 24/100 - Loss: 958.3765258789062, Val Loss: 130.92120361328125\n",
      "Epoch 25/100 - Loss: 972.2109985351562, Val Loss: 131.05398559570312\n",
      "Epoch 26/100 - Loss: 981.8740234375, Val Loss: 131.26480102539062\n",
      "Epoch 27/100 - Loss: 986.25, Val Loss: 131.51861572265625\n",
      "Epoch 28/100 - Loss: 993.012451171875, Val Loss: 131.76202392578125\n",
      "Epoch 29/100 - Loss: 1000.5025634765625, Val Loss: 132.05007934570312\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.396098136901855\n",
      "Epoch 1/100 - Loss: 11.543466567993164, Val Loss: 510.60443115234375\n",
      "Epoch 2/100 - Loss: 557.3247680664062, Val Loss: 331.578369140625\n",
      "Epoch 3/100 - Loss: 1026.672607421875, Val Loss: 250.42474365234375\n",
      "Epoch 4/100 - Loss: 1318.6973876953125, Val Loss: 202.1914825439453\n",
      "Epoch 5/100 - Loss: 1541.361572265625, Val Loss: 177.2417449951172\n",
      "Epoch 6/100 - Loss: 1723.3900146484375, Val Loss: 162.94923400878906\n",
      "Epoch 7/100 - Loss: 1894.3028564453125, Val Loss: 154.34442138671875\n",
      "Epoch 8/100 - Loss: 2044.3931884765625, Val Loss: 147.72605895996094\n",
      "Epoch 9/100 - Loss: 2177.7783203125, Val Loss: 142.69439697265625\n",
      "Epoch 10/100 - Loss: 2300.8837890625, Val Loss: 138.82595825195312\n",
      "Epoch 11/100 - Loss: 2411.922607421875, Val Loss: 136.0188751220703\n",
      "Epoch 12/100 - Loss: 2503.619140625, Val Loss: 133.8609619140625\n",
      "Epoch 13/100 - Loss: 2585.671875, Val Loss: 132.42062377929688\n",
      "Epoch 14/100 - Loss: 2658.519775390625, Val Loss: 131.21388244628906\n",
      "Epoch 15/100 - Loss: 2721.506591796875, Val Loss: 130.2934112548828\n",
      "Epoch 16/100 - Loss: 2786.272705078125, Val Loss: 129.46620178222656\n",
      "Epoch 17/100 - Loss: 2832.279296875, Val Loss: 128.7478790283203\n",
      "Epoch 18/100 - Loss: 2849.2685546875, Val Loss: 128.48741149902344\n",
      "Epoch 19/100 - Loss: 2877.74560546875, Val Loss: 128.25794982910156\n",
      "Epoch 20/100 - Loss: 2907.239501953125, Val Loss: 128.10601806640625\n",
      "Epoch 21/100 - Loss: 2931.4765625, Val Loss: 127.86164093017578\n",
      "Epoch 22/100 - Loss: 2947.299072265625, Val Loss: 127.58602142333984\n",
      "Epoch 23/100 - Loss: 2964.009521484375, Val Loss: 127.47704315185547\n",
      "Epoch 24/100 - Loss: 2981.109130859375, Val Loss: 127.291748046875\n",
      "Epoch 25/100 - Loss: 2989.08056640625, Val Loss: 127.07838439941406\n",
      "Epoch 26/100 - Loss: 2994.938232421875, Val Loss: 126.89266967773438\n",
      "Epoch 27/100 - Loss: 3011.220458984375, Val Loss: 126.92149353027344\n",
      "Epoch 28/100 - Loss: 3025.9619140625, Val Loss: 126.90699768066406\n",
      "Epoch 29/100 - Loss: 3036.888427734375, Val Loss: 126.77924346923828\n",
      "Epoch 30/100 - Loss: 3041.5302734375, Val Loss: 126.68273162841797\n",
      "Epoch 31/100 - Loss: 3050.53955078125, Val Loss: 126.67754364013672\n",
      "Epoch 32/100 - Loss: 3064.675537109375, Val Loss: 126.60833740234375\n",
      "Epoch 33/100 - Loss: 3074.275634765625, Val Loss: 126.53206634521484\n",
      "Epoch 34/100 - Loss: 3086.31494140625, Val Loss: 126.52210235595703\n",
      "Epoch 35/100 - Loss: 3087.68310546875, Val Loss: 126.55586242675781\n",
      "Epoch 36/100 - Loss: 3101.548095703125, Val Loss: 126.60009002685547\n",
      "Epoch 37/100 - Loss: 3111.360595703125, Val Loss: 126.51544952392578\n",
      "Epoch 38/100 - Loss: 3114.02685546875, Val Loss: 126.36946868896484\n",
      "Epoch 39/100 - Loss: 3123.956298828125, Val Loss: 126.2984390258789\n",
      "Epoch 40/100 - Loss: 3134.43994140625, Val Loss: 126.24520874023438\n",
      "Epoch 41/100 - Loss: 3138.786376953125, Val Loss: 126.35144805908203\n",
      "Epoch 42/100 - Loss: 3144.969970703125, Val Loss: 126.2898178100586\n",
      "Epoch 43/100 - Loss: 3140.075439453125, Val Loss: 126.22216796875\n",
      "Epoch 44/100 - Loss: 3151.233642578125, Val Loss: 126.39505004882812\n",
      "Epoch 45/100 - Loss: 3158.590576171875, Val Loss: 126.35877227783203\n",
      "Epoch 46/100 - Loss: 3161.783203125, Val Loss: 126.3800277709961\n",
      "Epoch 47/100 - Loss: 3153.899658203125, Val Loss: 126.26356506347656\n",
      "Epoch 48/100 - Loss: 3154.24755859375, Val Loss: 126.34075164794922\n",
      "Epoch 49/100 - Loss: 3161.608154296875, Val Loss: 126.45709228515625\n",
      "Epoch 50/100 - Loss: 3169.142333984375, Val Loss: 126.534912109375\n",
      "Epoch 51/100 - Loss: 3178.54833984375, Val Loss: 126.59288787841797\n",
      "Epoch 52/100 - Loss: 3182.61181640625, Val Loss: 126.53681182861328\n",
      "Epoch 53/100 - Loss: 3173.306396484375, Val Loss: 126.2703628540039\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.234864234924316\n",
      "Epoch 1/100 - Loss: 179.5019989013672, Val Loss: 454.0506591796875\n",
      "Epoch 2/100 - Loss: 1184.267822265625, Val Loss: 309.9859313964844\n",
      "Epoch 3/100 - Loss: 1572.0927734375, Val Loss: 240.96926879882812\n",
      "Epoch 4/100 - Loss: 1759.597412109375, Val Loss: 201.08860778808594\n",
      "Epoch 5/100 - Loss: 1897.8980712890625, Val Loss: 180.11105346679688\n",
      "Epoch 6/100 - Loss: 1975.2373046875, Val Loss: 168.7057647705078\n",
      "Epoch 7/100 - Loss: 2027.964599609375, Val Loss: 162.02853393554688\n",
      "Epoch 8/100 - Loss: 2070.73974609375, Val Loss: 157.5644073486328\n",
      "Epoch 9/100 - Loss: 2119.05517578125, Val Loss: 154.35948181152344\n",
      "Epoch 10/100 - Loss: 2173.842529296875, Val Loss: 151.91259765625\n",
      "Epoch 11/100 - Loss: 2199.017578125, Val Loss: 150.07009887695312\n",
      "Epoch 12/100 - Loss: 2236.543212890625, Val Loss: 148.22080993652344\n",
      "Epoch 13/100 - Loss: 2245.782958984375, Val Loss: 146.56024169921875\n",
      "Epoch 14/100 - Loss: 2255.385986328125, Val Loss: 144.81427001953125\n",
      "Epoch 15/100 - Loss: 2269.974609375, Val Loss: 143.37326049804688\n",
      "Epoch 16/100 - Loss: 2288.482177734375, Val Loss: 142.23583984375\n",
      "Epoch 17/100 - Loss: 2300.32421875, Val Loss: 141.2354278564453\n",
      "Epoch 18/100 - Loss: 2311.394775390625, Val Loss: 140.3086395263672\n",
      "Epoch 19/100 - Loss: 2331.546142578125, Val Loss: 139.57862854003906\n",
      "Epoch 20/100 - Loss: 2346.21875, Val Loss: 138.86376953125\n",
      "Epoch 21/100 - Loss: 2365.09326171875, Val Loss: 138.1917266845703\n",
      "Epoch 22/100 - Loss: 2385.127197265625, Val Loss: 137.391357421875\n",
      "Epoch 23/100 - Loss: 2410.92626953125, Val Loss: 136.66165161132812\n",
      "Epoch 24/100 - Loss: 2417.587890625, Val Loss: 135.87913513183594\n",
      "Epoch 25/100 - Loss: 2449.322265625, Val Loss: 134.9171905517578\n",
      "Epoch 26/100 - Loss: 2466.602783203125, Val Loss: 133.85076904296875\n",
      "Epoch 27/100 - Loss: 2486.87158203125, Val Loss: 133.0162811279297\n",
      "Epoch 28/100 - Loss: 2501.4013671875, Val Loss: 132.40501403808594\n",
      "Epoch 29/100 - Loss: 2512.2734375, Val Loss: 131.9524688720703\n",
      "Epoch 30/100 - Loss: 2520.2880859375, Val Loss: 131.61769104003906\n",
      "Epoch 31/100 - Loss: 2527.291015625, Val Loss: 131.37527465820312\n",
      "Epoch 32/100 - Loss: 2531.374267578125, Val Loss: 131.16796875\n",
      "Epoch 33/100 - Loss: 2534.654296875, Val Loss: 131.03292846679688\n",
      "Epoch 34/100 - Loss: 2540.29638671875, Val Loss: 130.9752960205078\n",
      "Epoch 35/100 - Loss: 2541.77197265625, Val Loss: 130.91563415527344\n",
      "Epoch 36/100 - Loss: 2543.61767578125, Val Loss: 130.7901611328125\n",
      "Epoch 37/100 - Loss: 2542.880859375, Val Loss: 130.69728088378906\n",
      "Epoch 38/100 - Loss: 2547.805908203125, Val Loss: 130.69618225097656\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39/100 - Loss: 2549.060546875, Val Loss: 130.6299285888672\n",
      "Epoch 40/100 - Loss: 2545.250732421875, Val Loss: 130.53067016601562\n",
      "Epoch 41/100 - Loss: 2544.93701171875, Val Loss: 130.43199157714844\n",
      "Epoch 42/100 - Loss: 2544.7529296875, Val Loss: 130.33905029296875\n",
      "Epoch 43/100 - Loss: 2542.87890625, Val Loss: 130.2485809326172\n",
      "Epoch 44/100 - Loss: 2542.42822265625, Val Loss: 130.16673278808594\n",
      "Epoch 45/100 - Loss: 2542.2734375, Val Loss: 130.09535217285156\n",
      "Epoch 46/100 - Loss: 2543.4697265625, Val Loss: 130.0165557861328\n",
      "Epoch 47/100 - Loss: 2544.8818359375, Val Loss: 129.9248504638672\n",
      "Epoch 48/100 - Loss: 2543.6220703125, Val Loss: 129.83360290527344\n",
      "Epoch 49/100 - Loss: 2543.495849609375, Val Loss: 129.7232208251953\n",
      "Epoch 50/100 - Loss: 2546.76416015625, Val Loss: 129.64910888671875\n",
      "Epoch 51/100 - Loss: 2547.39892578125, Val Loss: 129.52577209472656\n",
      "Epoch 52/100 - Loss: 2549.91162109375, Val Loss: 129.42672729492188\n",
      "Epoch 53/100 - Loss: 2550.30712890625, Val Loss: 129.29287719726562\n",
      "Epoch 54/100 - Loss: 2552.686767578125, Val Loss: 129.1983642578125\n",
      "Epoch 55/100 - Loss: 2552.764404296875, Val Loss: 129.06732177734375\n",
      "Epoch 56/100 - Loss: 2555.2548828125, Val Loss: 128.96224975585938\n",
      "Epoch 57/100 - Loss: 2554.98046875, Val Loss: 128.83834838867188\n",
      "Epoch 58/100 - Loss: 2557.40185546875, Val Loss: 128.74790954589844\n",
      "Epoch 59/100 - Loss: 2559.05322265625, Val Loss: 128.64549255371094\n",
      "Epoch 60/100 - Loss: 2559.187255859375, Val Loss: 128.5283203125\n",
      "Epoch 61/100 - Loss: 2559.576171875, Val Loss: 128.42807006835938\n",
      "Epoch 62/100 - Loss: 2559.45849609375, Val Loss: 128.3194580078125\n",
      "Epoch 63/100 - Loss: 2560.795654296875, Val Loss: 128.2335968017578\n",
      "Epoch 64/100 - Loss: 2560.83251953125, Val Loss: 128.1289520263672\n",
      "Epoch 65/100 - Loss: 2560.9755859375, Val Loss: 128.06846618652344\n",
      "Epoch 66/100 - Loss: 2559.01123046875, Val Loss: 128.0162811279297\n",
      "Epoch 67/100 - Loss: 2559.38525390625, Val Loss: 127.97005462646484\n",
      "Epoch 68/100 - Loss: 2559.06298828125, Val Loss: 127.91959381103516\n",
      "Epoch 69/100 - Loss: 2558.86474609375, Val Loss: 127.86213684082031\n",
      "Epoch 70/100 - Loss: 2559.232421875, Val Loss: 127.81541442871094\n",
      "Epoch 71/100 - Loss: 2560.0302734375, Val Loss: 127.76754760742188\n",
      "Epoch 72/100 - Loss: 2562.1513671875, Val Loss: 127.71719360351562\n",
      "Epoch 73/100 - Loss: 2563.314208984375, Val Loss: 127.64379119873047\n",
      "Epoch 74/100 - Loss: 2560.07421875, Val Loss: 127.53285217285156\n",
      "Epoch 75/100 - Loss: 2558.912109375, Val Loss: 127.42786407470703\n",
      "Epoch 76/100 - Loss: 2559.35302734375, Val Loss: 127.3510971069336\n",
      "Epoch 77/100 - Loss: 2555.423095703125, Val Loss: 127.2150650024414\n",
      "Epoch 78/100 - Loss: 2556.4111328125, Val Loss: 127.14752960205078\n",
      "Epoch 79/100 - Loss: 2556.88916015625, Val Loss: 127.06880187988281\n",
      "Epoch 80/100 - Loss: 2555.54052734375, Val Loss: 126.98004913330078\n",
      "Epoch 81/100 - Loss: 2556.4248046875, Val Loss: 126.92138671875\n",
      "Epoch 82/100 - Loss: 2556.11376953125, Val Loss: 126.84872436523438\n",
      "Epoch 83/100 - Loss: 2556.768310546875, Val Loss: 126.78070068359375\n",
      "Epoch 84/100 - Loss: 2556.83984375, Val Loss: 126.70209503173828\n",
      "Epoch 85/100 - Loss: 2557.73486328125, Val Loss: 126.62296295166016\n",
      "Epoch 86/100 - Loss: 2557.5625, Val Loss: 126.55012512207031\n",
      "Epoch 87/100 - Loss: 2558.154296875, Val Loss: 126.47145080566406\n",
      "Epoch 88/100 - Loss: 2557.041015625, Val Loss: 126.3742904663086\n",
      "Epoch 89/100 - Loss: 2557.333984375, Val Loss: 126.29170227050781\n",
      "Epoch 90/100 - Loss: 2558.3876953125, Val Loss: 126.21757507324219\n",
      "Epoch 91/100 - Loss: 2558.2861328125, Val Loss: 126.14167022705078\n",
      "Epoch 92/100 - Loss: 2557.21044921875, Val Loss: 126.06864929199219\n",
      "Epoch 93/100 - Loss: 2557.47119140625, Val Loss: 125.99867248535156\n",
      "Epoch 94/100 - Loss: 2557.37451171875, Val Loss: 125.95796203613281\n",
      "Epoch 95/100 - Loss: 2558.91796875, Val Loss: 125.90934753417969\n",
      "Epoch 96/100 - Loss: 2558.1396484375, Val Loss: 125.83338165283203\n",
      "Epoch 97/100 - Loss: 2558.64697265625, Val Loss: 125.76648712158203\n",
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      "Root Mean Squared Error (RMSE): 11.205100059509277\n",
      "Epoch 1/100 - Loss: 17.882915496826172, Val Loss: 489.46453857421875\n",
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      "Epoch 53/100 - Loss: 2521.80908203125, Val Loss: 138.29110717773438\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.708808898925781\n",
      "Epoch 1/100 - Loss: 121.29533386230469, Val Loss: 460.47418212890625\n",
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      "Epoch 6/100 - Loss: 1867.782958984375, Val Loss: 188.30615234375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100 - Loss: 1958.966552734375, Val Loss: 182.6675262451172\n",
      "Epoch 8/100 - Loss: 2072.84765625, Val Loss: 177.6640625\n",
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      "Epoch 60/100 - Loss: 2152.687255859375, Val Loss: 150.0186004638672\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.230929374694824\n",
      "Epoch 1/100 - Loss: 6222.73876953125, Val Loss: 486.63409423828125\n",
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      "Epoch 67/100 - Loss: 941.273193359375, Val Loss: 111.58985137939453\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 68/100 - Loss: 932.3463745117188, Val Loss: 111.30538177490234\n",
      "Epoch 69/100 - Loss: 926.9542236328125, Val Loss: 111.07622528076172\n",
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      "Epoch 75/100 - Loss: 897.3414306640625, Val Loss: 109.86690521240234\n",
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      "Epoch 81/100 - Loss: 875.967041015625, Val Loss: 109.07102966308594\n",
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      "Root Mean Squared Error (RMSE): 10.403321266174316\n",
      "Epoch 1/100 - Loss: 163.18312072753906, Val Loss: 556.3458251953125\n",
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      "Epoch 7/100 - Loss: 1667.96142578125, Val Loss: 181.53060913085938\n",
      "Epoch 8/100 - Loss: 1833.111328125, Val Loss: 168.54400634765625\n",
      "Epoch 9/100 - Loss: 1954.44775390625, Val Loss: 161.2822723388672\n",
      "Epoch 10/100 - Loss: 2042.67138671875, Val Loss: 155.8212890625\n",
      "Epoch 11/100 - Loss: 2113.441650390625, Val Loss: 151.87368774414062\n",
      "Epoch 12/100 - Loss: 2172.12939453125, Val Loss: 149.061279296875\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 89/100 - Loss: 2804.529541015625, Val Loss: 119.3267822265625\n",
      "Epoch 90/100 - Loss: 2791.7412109375, Val Loss: 119.43229675292969\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.899765968322754\n",
      "Epoch 1/100 - Loss: 75.22699737548828, Val Loss: 528.4171142578125\n",
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      "Epoch 10/100 - Loss: 2015.9344482421875, Val Loss: 177.73159790039062\n",
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      "Epoch 60/100 - Loss: 2262.90087890625, Val Loss: 150.84710693359375\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.253220558166504\n",
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      "Epoch 58/100 - Loss: 2252.0517578125, Val Loss: 150.48516845703125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/100 - Loss: 2249.365234375, Val Loss: 150.5388641357422\n",
      "Epoch 60/100 - Loss: 2245.81005859375, Val Loss: 150.61859130859375\n",
      "Epoch 61/100 - Loss: 2244.917236328125, Val Loss: 150.6454315185547\n",
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      "Epoch 64/100 - Loss: 2231.42236328125, Val Loss: 150.73353576660156\n",
      "Epoch 65/100 - Loss: 2228.2275390625, Val Loss: 150.7729949951172\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.262642860412598\n",
      "Epoch 1/100 - Loss: 9.951979637145996, Val Loss: 437.4568176269531\n",
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      "Epoch 27/100 - Loss: 2194.5908203125, Val Loss: 152.4279327392578\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.24393081665039\n",
      "Epoch 1/100 - Loss: 12585.1728515625, Val Loss: 723.0399780273438\n",
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      "Epoch 48/100 - Loss: 1062.59423828125, Val Loss: 111.69313049316406\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/100 - Loss: 1053.1478271484375, Val Loss: 111.63469696044922\n",
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      "Epoch 63/100 - Loss: 950.1992797851562, Val Loss: 109.9516830444336\n",
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      "Epoch 89/100 - Loss: 857.2816772460938, Val Loss: 108.67556762695312\n",
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      "Root Mean Squared Error (RMSE): 10.417781829833984\n",
      "Epoch 1/100 - Loss: 1421.3634033203125, Val Loss: 1114.7359619140625\n",
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      "Epoch 68/100 - Loss: 2700.94140625, Val Loss: 131.4351806640625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 69/100 - Loss: 2701.26806640625, Val Loss: 131.2628936767578\n",
      "Epoch 70/100 - Loss: 2702.64501953125, Val Loss: 131.12985229492188\n",
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      "Epoch 92/100 - Loss: 2711.07421875, Val Loss: 127.55443572998047\n",
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      "Root Mean Squared Error (RMSE): 11.257421493530273\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.538601875305176\n",
      "Epoch 1/100 - Loss: 1509.8544921875, Val Loss: 983.8984985351562\n",
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      "Epoch 29/100 - Loss: 2497.90673828125, Val Loss: 140.0714569091797\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/100 - Loss: 2484.6669921875, Val Loss: 138.0917205810547\n",
      "Epoch 31/100 - Loss: 2483.540283203125, Val Loss: 136.6890411376953\n",
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      "Epoch 45/100 - Loss: 2514.4482421875, Val Loss: 138.84393310546875\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.60130500793457\n",
      "Epoch 1/100 - Loss: 306.85302734375, Val Loss: 607.5032958984375\n",
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      "Epoch 32/100 - Loss: 2556.9326171875, Val Loss: 137.95989990234375\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.7366943359375\n",
      "Epoch 1/100 - Loss: 23808.44921875, Val Loss: 1849.5093994140625\n",
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      "Epoch 71/100 - Loss: 1138.017333984375, Val Loss: 137.5106201171875\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 72/100 - Loss: 1137.808349609375, Val Loss: 137.53733825683594\n",
      "Epoch 73/100 - Loss: 1138.7535400390625, Val Loss: 137.5469207763672\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.721660614013672\n",
      "Epoch 1/100 - Loss: 1009.97705078125, Val Loss: 928.4146118164062\n",
      "Epoch 2/100 - Loss: 71.97012329101562, Val Loss: 376.9856872558594\n",
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      "Epoch 9/100 - Loss: 1747.7059326171875, Val Loss: 152.231201171875\n",
      "Epoch 10/100 - Loss: 1855.1280517578125, Val Loss: 147.71432495117188\n",
      "Epoch 11/100 - Loss: 1964.3807373046875, Val Loss: 144.3284149169922\n",
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      "Root Mean Squared Error (RMSE): 10.62281322479248\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/100 - Loss: 2275.8857421875, Val Loss: 164.8782196044922\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.728470802307129\n",
      "Epoch 1/100 - Loss: 4462.18359375, Val Loss: 2046.03857421875\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 87/100 - Loss: 2408.406494140625, Val Loss: 137.7538299560547\n",
      "Epoch 88/100 - Loss: 2407.36279296875, Val Loss: 137.50369262695312\n",
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      "Root Mean Squared Error (RMSE): 11.702215194702148\n",
      "Epoch 1/100 - Loss: 3049.173828125, Val Loss: 1695.779052734375\n",
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      "Epoch 86/100 - Loss: 2432.9765625, Val Loss: 140.19256591796875\n",
      "Epoch 87/100 - Loss: 2433.85107421875, Val Loss: 140.1725616455078\n",
      "Epoch 88/100 - Loss: 2433.239013671875, Val Loss: 140.14955139160156\n",
      "Epoch 89/100 - Loss: 2433.712158203125, Val Loss: 140.13145446777344\n",
      "Epoch 90/100 - Loss: 2433.8359375, Val Loss: 140.12887573242188\n",
      "Epoch 91/100 - Loss: 2432.587646484375, Val Loss: 140.1074981689453\n",
      "Epoch 92/100 - Loss: 2433.02197265625, Val Loss: 140.1023406982422\n",
      "Epoch 93/100 - Loss: 2433.6513671875, Val Loss: 140.10140991210938\n",
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      "Epoch 100/100 - Loss: 2383.81982421875, Val Loss: 139.38108825683594\n",
      "Root Mean Squared Error (RMSE): 11.805976867675781\n",
      "Epoch 1/100 - Loss: 641.7783203125, Val Loss: 631.3405151367188\n",
      "Epoch 2/100 - Loss: 531.5087890625, Val Loss: 414.1797790527344\n",
      "Epoch 3/100 - Loss: 563.7277221679688, Val Loss: 300.5773620605469\n",
      "Epoch 4/100 - Loss: 595.294921875, Val Loss: 230.82879638671875\n",
      "Epoch 5/100 - Loss: 626.5543212890625, Val Loss: 183.92674255371094\n",
      "Epoch 6/100 - Loss: 599.0460815429688, Val Loss: 161.7086181640625\n",
      "Epoch 7/100 - Loss: 607.8215942382812, Val Loss: 150.65464782714844\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/100 - Loss: 603.3936157226562, Val Loss: 146.89381408691406\n",
      "Epoch 9/100 - Loss: 632.6411743164062, Val Loss: 139.37237548828125\n",
      "Epoch 10/100 - Loss: 677.0557250976562, Val Loss: 134.89431762695312\n",
      "Epoch 11/100 - Loss: 685.75, Val Loss: 130.89231872558594\n",
      "Epoch 12/100 - Loss: 692.547607421875, Val Loss: 128.5907440185547\n",
      "Epoch 13/100 - Loss: 710.2473754882812, Val Loss: 125.79833221435547\n",
      "Epoch 14/100 - Loss: 720.4099731445312, Val Loss: 124.43013000488281\n",
      "Epoch 15/100 - Loss: 675.6795043945312, Val Loss: 125.50972747802734\n",
      "Epoch 16/100 - Loss: 697.8961791992188, Val Loss: 123.7131576538086\n",
      "Epoch 17/100 - Loss: 703.1317749023438, Val Loss: 122.18045806884766\n",
      "Epoch 18/100 - Loss: 699.432861328125, Val Loss: 120.86270141601562\n",
      "Epoch 19/100 - Loss: 701.635498046875, Val Loss: 119.76136016845703\n",
      "Epoch 20/100 - Loss: 716.4959106445312, Val Loss: 120.74282836914062\n",
      "Epoch 21/100 - Loss: 702.3834228515625, Val Loss: 119.6601791381836\n",
      "Epoch 22/100 - Loss: 705.5704345703125, Val Loss: 118.81773376464844\n",
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      "Epoch 28/100 - Loss: 706.7504272460938, Val Loss: 114.81586456298828\n",
      "Epoch 29/100 - Loss: 695.93603515625, Val Loss: 114.59838104248047\n",
      "Epoch 30/100 - Loss: 702.6277465820312, Val Loss: 113.91744232177734\n",
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      "Epoch 32/100 - Loss: 710.0343017578125, Val Loss: 113.22492980957031\n",
      "Epoch 33/100 - Loss: 704.2894287109375, Val Loss: 112.65373229980469\n",
      "Epoch 34/100 - Loss: 695.7830810546875, Val Loss: 112.40780639648438\n",
      "Epoch 35/100 - Loss: 696.0890502929688, Val Loss: 112.28624725341797\n",
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      "Epoch 38/100 - Loss: 666.8185424804688, Val Loss: 111.7447738647461\n",
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      "Epoch 43/100 - Loss: 621.20556640625, Val Loss: 111.47248077392578\n",
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      "Epoch 45/100 - Loss: 621.807373046875, Val Loss: 111.49303436279297\n",
      "Epoch 46/100 - Loss: 627.2717895507812, Val Loss: 111.67375946044922\n",
      "Epoch 47/100 - Loss: 625.0557250976562, Val Loss: 111.40335845947266\n",
      "Epoch 48/100 - Loss: 621.976318359375, Val Loss: 111.14720153808594\n",
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      "Epoch 50/100 - Loss: 623.9033813476562, Val Loss: 111.02459716796875\n",
      "Epoch 51/100 - Loss: 624.6246948242188, Val Loss: 111.30803680419922\n",
      "Epoch 52/100 - Loss: 624.9946899414062, Val Loss: 111.16368103027344\n",
      "Epoch 53/100 - Loss: 627.833740234375, Val Loss: 111.248291015625\n",
      "Epoch 54/100 - Loss: 629.7322998046875, Val Loss: 111.06863403320312\n",
      "Epoch 55/100 - Loss: 633.0587768554688, Val Loss: 111.51043701171875\n",
      "Epoch 56/100 - Loss: 633.7055053710938, Val Loss: 111.521240234375\n",
      "Epoch 57/100 - Loss: 626.5184326171875, Val Loss: 111.4906997680664\n",
      "Epoch 58/100 - Loss: 625.5631713867188, Val Loss: 111.45823669433594\n",
      "Epoch 59/100 - Loss: 619.2993774414062, Val Loss: 111.09435272216797\n",
      "Epoch 60/100 - Loss: 619.33740234375, Val Loss: 111.23576354980469\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.536821365356445\n",
      "Epoch 1/100 - Loss: 1708.007568359375, Val Loss: 623.3740844726562\n",
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      "Epoch 5/100 - Loss: 3007.36328125, Val Loss: 262.48699951171875\n",
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      "Epoch 59/100 - Loss: 2030.078857421875, Val Loss: 127.33804321289062\n",
      "Epoch 60/100 - Loss: 2016.9810791015625, Val Loss: 127.27263641357422\n",
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      "Epoch 63/100 - Loss: 1969.6417236328125, Val Loss: 126.62228393554688\n",
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      "Epoch 65/100 - Loss: 1926.731201171875, Val Loss: 126.16353607177734\n",
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      "Epoch 67/100 - Loss: 1932.2044677734375, Val Loss: 127.20701599121094\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 68/100 - Loss: 1923.401123046875, Val Loss: 126.62905883789062\n",
      "Epoch 69/100 - Loss: 1911.4278564453125, Val Loss: 126.69076538085938\n",
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      "Epoch 71/100 - Loss: 1995.275146484375, Val Loss: 127.6204605102539\n",
      "Epoch 72/100 - Loss: 1989.514404296875, Val Loss: 127.10267639160156\n",
      "Epoch 73/100 - Loss: 1981.5457763671875, Val Loss: 126.6557388305664\n",
      "Epoch 74/100 - Loss: 1948.16845703125, Val Loss: 126.38981628417969\n",
      "Epoch 75/100 - Loss: 1929.9609375, Val Loss: 126.21764373779297\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.232254981994629\n",
      "Epoch 1/100 - Loss: 2004.709716796875, Val Loss: 632.2020874023438\n",
      "Epoch 2/100 - Loss: 2652.494384765625, Val Loss: 477.5811462402344\n",
      "Epoch 3/100 - Loss: 2887.230224609375, Val Loss: 398.2950439453125\n",
      "Epoch 4/100 - Loss: 2936.6337890625, Val Loss: 335.6961975097656\n",
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      "Epoch 6/100 - Loss: 2960.571044921875, Val Loss: 276.20904541015625\n",
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      "Epoch 35/100 - Loss: 2531.21435546875, Val Loss: 162.2481689453125\n",
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      "Epoch 38/100 - Loss: 2525.83154296875, Val Loss: 159.03851318359375\n",
      "Epoch 39/100 - Loss: 2536.015625, Val Loss: 157.91250610351562\n",
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      "Epoch 41/100 - Loss: 2534.35888671875, Val Loss: 155.82376098632812\n",
      "Epoch 42/100 - Loss: 2538.76025390625, Val Loss: 155.1724853515625\n",
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      "Epoch 64/100 - Loss: 2495.61181640625, Val Loss: 149.89871215820312\n",
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      "Epoch 69/100 - Loss: 2480.02783203125, Val Loss: 145.871337890625\n",
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      "Epoch 83/100 - Loss: 2458.732177734375, Val Loss: 151.5027618408203\n",
      "Epoch 84/100 - Loss: 2451.666259765625, Val Loss: 151.5447540283203\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.005882263183594\n",
      "Epoch 1/100 - Loss: 2051.34814453125, Val Loss: 711.4963989257812\n",
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      "Epoch 12/100 - Loss: 3237.6201171875, Val Loss: 180.3365020751953\n",
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      "Epoch 29/100 - Loss: 2946.115966796875, Val Loss: 153.95523071289062\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/100 - Loss: 2926.4345703125, Val Loss: 154.80145263671875\n",
      "Epoch 31/100 - Loss: 2950.81494140625, Val Loss: 152.6956787109375\n",
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      "Epoch 41/100 - Loss: 2939.764892578125, Val Loss: 154.17828369140625\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.357008934020996\n",
      "Epoch 1/100 - Loss: 1926.3236083984375, Val Loss: 624.6715698242188\n",
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      "Epoch 90/100 - Loss: 2214.160888671875, Val Loss: 115.0961685180664\n",
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      "Root Mean Squared Error (RMSE): 10.549424171447754\n",
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      "Epoch 9/100 - Loss: 613.122314453125, Val Loss: 133.8430633544922\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/100 - Loss: 637.2681884765625, Val Loss: 129.53158569335938\n",
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      "Root Mean Squared Error (RMSE): 10.209551811218262\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/100 - Loss: 2705.290283203125, Val Loss: 128.17929077148438\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.286626815795898\n",
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      "Epoch 70/100 - Loss: 2382.455810546875, Val Loss: 136.85687255859375\n",
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      "Epoch 73/100 - Loss: 2310.142578125, Val Loss: 133.6000213623047\n",
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      "Epoch 76/100 - Loss: 2250.5078125, Val Loss: 130.6187744140625\n",
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      "Epoch 80/100 - Loss: 2183.33251953125, Val Loss: 127.9845199584961\n",
      "Epoch 81/100 - Loss: 2235.554931640625, Val Loss: 128.02639770507812\n",
      "Epoch 82/100 - Loss: 2237.142578125, Val Loss: 127.06426239013672\n",
      "Epoch 83/100 - Loss: 2209.92919921875, Val Loss: 127.30438232421875\n",
      "Epoch 84/100 - Loss: 2227.0361328125, Val Loss: 127.28012084960938\n",
      "Epoch 85/100 - Loss: 2217.91162109375, Val Loss: 127.38428497314453\n",
      "Epoch 86/100 - Loss: 2200.3046875, Val Loss: 127.55669403076172\n",
      "Epoch 87/100 - Loss: 2199.038818359375, Val Loss: 127.21846008300781\n",
      "Epoch 88/100 - Loss: 2193.916259765625, Val Loss: 126.42560577392578\n",
      "Epoch 89/100 - Loss: 2179.8515625, Val Loss: 127.1834716796875\n",
      "Epoch 90/100 - Loss: 2167.0625, Val Loss: 127.5914535522461\n",
      "Epoch 91/100 - Loss: 2168.826171875, Val Loss: 125.89186096191406\n",
      "Epoch 92/100 - Loss: 2136.38720703125, Val Loss: 126.61808776855469\n",
      "Epoch 93/100 - Loss: 2126.072265625, Val Loss: 125.90101623535156\n",
      "Epoch 94/100 - Loss: 2111.26806640625, Val Loss: 124.79695129394531\n",
      "Epoch 95/100 - Loss: 2095.339111328125, Val Loss: 125.21883392333984\n",
      "Epoch 96/100 - Loss: 2093.64794921875, Val Loss: 125.27828216552734\n",
      "Epoch 97/100 - Loss: 2087.13525390625, Val Loss: 124.94461059570312\n",
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      "Epoch 99/100 - Loss: 2088.45849609375, Val Loss: 125.47907257080078\n",
      "Epoch 100/100 - Loss: 2077.625, Val Loss: 125.7419662475586\n",
      "Root Mean Squared Error (RMSE): 11.171255111694336\n",
      "Epoch 1/100 - Loss: 2000.903564453125, Val Loss: 649.0682983398438\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100 - Loss: 2597.742431640625, Val Loss: 443.52117919921875\n",
      "Epoch 3/100 - Loss: 2857.82958984375, Val Loss: 352.267822265625\n",
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      "Epoch 15/100 - Loss: 2617.420166015625, Val Loss: 196.36669921875\n",
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      "Epoch 38/100 - Loss: 2493.9990234375, Val Loss: 159.4909210205078\n",
      "Epoch 39/100 - Loss: 2491.8642578125, Val Loss: 158.97471618652344\n",
      "Epoch 40/100 - Loss: 2484.609619140625, Val Loss: 157.50064086914062\n",
      "Epoch 41/100 - Loss: 2478.427734375, Val Loss: 156.99334716796875\n",
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      "Root Mean Squared Error (RMSE): 12.054166793823242\n",
      "Epoch 1/100 - Loss: 1864.9825439453125, Val Loss: 625.1700439453125\n",
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      "Epoch 6/100 - Loss: 3022.041015625, Val Loss: 173.33935546875\n",
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      "Epoch 21/100 - Loss: 2472.4326171875, Val Loss: 156.8419189453125\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.342616081237793\n",
      "Epoch 1/100 - Loss: 1149.2716064453125, Val Loss: 513.7227172851562\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100 - Loss: 738.5056762695312, Val Loss: 282.9766845703125\n",
      "Epoch 3/100 - Loss: 783.4984741210938, Val Loss: 195.92320251464844\n",
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      "Epoch 18/100 - Loss: 748.5287475585938, Val Loss: 125.33058166503906\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.981118202209473\n",
      "Epoch 1/100 - Loss: 868.2318725585938, Val Loss: 515.0067749023438\n",
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      "Epoch 30/100 - Loss: 3099.66357421875, Val Loss: 185.40914916992188\n",
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      "Epoch 38/100 - Loss: 2951.750732421875, Val Loss: 178.64263916015625\n",
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      "Epoch 41/100 - Loss: 2938.528076171875, Val Loss: 177.88729858398438\n",
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      "Epoch 45/100 - Loss: 2899.48291015625, Val Loss: 177.77883911132812\n",
      "Epoch 46/100 - Loss: 2883.1865234375, Val Loss: 177.42263793945312\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 13.272137641906738\n",
      "Epoch 1/100 - Loss: 1244.985595703125, Val Loss: 497.2850646972656\n",
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      "Epoch 8/100 - Loss: 2937.85400390625, Val Loss: 209.16017150878906\n",
      "Epoch 9/100 - Loss: 2979.2919921875, Val Loss: 205.49874877929688\n",
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      "Epoch 14/100 - Loss: 2786.78369140625, Val Loss: 200.61598205566406\n",
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      "Epoch 57/100 - Loss: 2280.912109375, Val Loss: 166.61361694335938\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/100 - Loss: 2270.73388671875, Val Loss: 166.38775634765625\n",
      "Epoch 59/100 - Loss: 2260.718994140625, Val Loss: 165.80783081054688\n",
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      "Epoch 86/100 - Loss: 2262.6376953125, Val Loss: 151.00927734375\n",
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      "Epoch 97/100 - Loss: 2223.5869140625, Val Loss: 147.78338623046875\n",
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      "Epoch 100/100 - Loss: 2218.969482421875, Val Loss: 147.0216522216797\n",
      "Root Mean Squared Error (RMSE): 12.125249862670898\n",
      "Epoch 1/100 - Loss: 1256.242431640625, Val Loss: 527.6399536132812\n",
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      "Epoch 8/100 - Loss: 2901.8310546875, Val Loss: 222.3572235107422\n",
      "Epoch 9/100 - Loss: 2959.88720703125, Val Loss: 217.74212646484375\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 14.756087303161621\n",
      "Epoch 1/100 - Loss: 1280.707763671875, Val Loss: 494.9103088378906\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 14.694884300231934\n",
      "Epoch 1/100 - Loss: 1478.37060546875, Val Loss: 520.7274780273438\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.019893646240234\n",
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      "Epoch 18/100 - Loss: 3317.10693359375, Val Loss: 194.94703674316406\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/100 - Loss: 3292.1708984375, Val Loss: 193.78250122070312\n",
      "Epoch 20/100 - Loss: 3273.819580078125, Val Loss: 193.69076538085938\n",
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      "Epoch 85/100 - Loss: 2313.388916015625, Val Loss: 149.0161590576172\n",
      "Epoch 86/100 - Loss: 2302.13134765625, Val Loss: 149.765625\n",
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      "Root Mean Squared Error (RMSE): 12.050079345703125\n",
      "Epoch 1/100 - Loss: 1119.9632568359375, Val Loss: 513.9930419921875\n",
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      "Epoch 39/100 - Loss: 2912.205322265625, Val Loss: 172.7823944091797\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40/100 - Loss: 2915.13525390625, Val Loss: 172.2804412841797\n",
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      "Epoch 67/100 - Loss: 2922.67578125, Val Loss: 162.55416870117188\n",
      "Epoch 68/100 - Loss: 2927.87548828125, Val Loss: 161.60997009277344\n",
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      "Epoch 79/100 - Loss: 2967.95703125, Val Loss: 159.4265899658203\n",
      "Epoch 80/100 - Loss: 2975.89794921875, Val Loss: 159.66598510742188\n",
      "Epoch 81/100 - Loss: 3004.9365234375, Val Loss: 158.34226989746094\n",
      "Epoch 82/100 - Loss: 2999.02587890625, Val Loss: 159.02781677246094\n",
      "Epoch 83/100 - Loss: 3018.40478515625, Val Loss: 158.57347106933594\n",
      "Epoch 84/100 - Loss: 3019.62890625, Val Loss: 155.05194091796875\n",
      "Epoch 85/100 - Loss: 2989.3056640625, Val Loss: 156.97259521484375\n",
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      "Epoch 89/100 - Loss: 2943.583984375, Val Loss: 164.1912841796875\n",
      "Epoch 90/100 - Loss: 2935.68310546875, Val Loss: 164.96713256835938\n",
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      "Epoch 94/100 - Loss: 2905.86474609375, Val Loss: 164.17477416992188\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.451984405517578\n",
      "Epoch 1/100 - Loss: 1075.780517578125, Val Loss: 522.1171875\n",
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      "Epoch 67/100 - Loss: 2048.77587890625, Val Loss: 136.41201782226562\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 68/100 - Loss: 2040.15966796875, Val Loss: 135.00331115722656\n",
      "Epoch 69/100 - Loss: 2043.467041015625, Val Loss: 134.44894409179688\n",
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      "Epoch 82/100 - Loss: 1953.110595703125, Val Loss: 133.67884826660156\n",
      "Epoch 83/100 - Loss: 1945.42041015625, Val Loss: 133.8525390625\n",
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      "Epoch 85/100 - Loss: 1961.794189453125, Val Loss: 132.71575927734375\n",
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      "Epoch 88/100 - Loss: 1942.5203857421875, Val Loss: 131.7848663330078\n",
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      "Root Mean Squared Error (RMSE): 11.390437126159668\n",
      "Epoch 1/100 - Loss: 1203.6868896484375, Val Loss: 478.9391174316406\n",
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      "Epoch 20/100 - Loss: 2567.05908203125, Val Loss: 157.78941345214844\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.223877906799316\n",
      "Epoch 1/100 - Loss: 317.1927490234375, Val Loss: 1543.5806884765625\n",
      "Epoch 2/100 - Loss: 440.912841796875, Val Loss: 605.8611450195312\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.168207168579102\n",
      "Epoch 1/100 - Loss: 3515.62890625, Val Loss: 1144.744384765625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/100 - Loss: 2440.110595703125, Val Loss: 189.71466064453125\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.76318645477295\n",
      "Epoch 1/100 - Loss: 3565.666748046875, Val Loss: 1241.92041015625\n",
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      "Epoch 72/100 - Loss: 1366.07568359375, Val Loss: 134.5955352783203\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.047874450683594\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/100 - Loss: 3464.189208984375, Val Loss: 320.96527099609375\n",
      "Epoch 9/100 - Loss: 3461.4013671875, Val Loss: 315.5684509277344\n",
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      "Epoch 54/100 - Loss: 1973.443115234375, Val Loss: 126.61862182617188\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.585392951965332\n",
      "Epoch 1/100 - Loss: 3688.197265625, Val Loss: 1239.2479248046875\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/100 - Loss: 1991.517578125, Val Loss: 161.3247528076172\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 12.046134948730469\n",
      "Epoch 1/100 - Loss: 359.2654724121094, Val Loss: 1832.5081787109375\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.143460273742676\n",
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      "Epoch 59/100 - Loss: 1568.7998046875, Val Loss: 118.59771728515625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 60/100 - Loss: 1520.421630859375, Val Loss: 114.3159408569336\n",
      "Epoch 61/100 - Loss: 1552.1239013671875, Val Loss: 118.41139221191406\n",
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      "Epoch 65/100 - Loss: 1474.56884765625, Val Loss: 120.71286010742188\n",
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      "Epoch 70/100 - Loss: 1592.9825439453125, Val Loss: 117.85735321044922\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.691863059997559\n",
      "Epoch 1/100 - Loss: 3578.82177734375, Val Loss: 1303.03271484375\n",
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      "Epoch 25/100 - Loss: 2439.83935546875, Val Loss: 175.16177368164062\n",
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      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.31447696685791\n",
      "Epoch 1/100 - Loss: 3796.52587890625, Val Loss: 1750.9517822265625\n",
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      "Epoch 24/100 - Loss: 2885.46728515625, Val Loss: 187.70016479492188\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100 - Loss: 2672.68310546875, Val Loss: 176.53492736816406\n",
      "Epoch 26/100 - Loss: 2668.213623046875, Val Loss: 177.71080017089844\n",
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      "Epoch 49/100 - Loss: 2156.07080078125, Val Loss: 152.93251037597656\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.825867652893066\n",
      "Epoch 1/100 - Loss: 3694.343017578125, Val Loss: 1661.059814453125\n",
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      "Epoch 14/100 - Loss: 3176.7587890625, Val Loss: 264.3910217285156\n",
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      "Epoch 16/100 - Loss: 3039.8798828125, Val Loss: 244.1373291015625\n",
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      "Epoch 54/100 - Loss: 2139.435546875, Val Loss: 130.69436645507812\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.172917366027832\n",
      "Epoch 1/100 - Loss: 448.0079650878906, Val Loss: 1830.9732666015625\n",
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      "Epoch 23/100 - Loss: 537.8526611328125, Val Loss: 133.3028106689453\n",
      "Epoch 24/100 - Loss: 506.09619140625, Val Loss: 126.71379089355469\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 11.035534858703613\n",
      "Epoch 1/100 - Loss: 3192.81396484375, Val Loss: 1319.716796875\n",
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      "Epoch 13/100 - Loss: 2605.560791015625, Val Loss: 241.8710174560547\n",
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      "Epoch 16/100 - Loss: 2558.103515625, Val Loss: 216.5087127685547\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/100 - Loss: 2478.028564453125, Val Loss: 208.77212524414062\n",
      "Epoch 18/100 - Loss: 2367.224365234375, Val Loss: 209.88143920898438\n",
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      "Epoch 20/100 - Loss: 2356.179931640625, Val Loss: 208.32424926757812\n",
      "Epoch 21/100 - Loss: 2378.273681640625, Val Loss: 208.62461853027344\n",
      "Epoch 22/100 - Loss: 2385.242431640625, Val Loss: 220.43557739257812\n",
      "Epoch 23/100 - Loss: 2456.5654296875, Val Loss: 228.3984375\n",
      "Epoch 24/100 - Loss: 2432.06884765625, Val Loss: 208.02951049804688\n",
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      "Epoch 26/100 - Loss: 2138.16357421875, Val Loss: 225.87969970703125\n",
      "Epoch 27/100 - Loss: 2423.006591796875, Val Loss: 213.451171875\n",
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      "Epoch 29/100 - Loss: 2389.8544921875, Val Loss: 230.3739471435547\n",
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      "Epoch 40/100 - Loss: 2172.927490234375, Val Loss: 210.0767059326172\n",
      "Epoch 41/100 - Loss: 2170.0888671875, Val Loss: 201.23239135742188\n",
      "Epoch 42/100 - Loss: 2127.916748046875, Val Loss: 209.92391967773438\n",
      "Epoch 43/100 - Loss: 2204.163330078125, Val Loss: 204.2965087890625\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 14.090999603271484\n",
      "Epoch 1/100 - Loss: 3130.77880859375, Val Loss: 1016.6553344726562\n",
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      "Epoch 10/100 - Loss: 3233.92578125, Val Loss: 505.4803161621094\n",
      "Epoch 11/100 - Loss: 3094.770751953125, Val Loss: 360.4858093261719\n",
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      "Epoch 13/100 - Loss: 2644.093017578125, Val Loss: 274.63397216796875\n",
      "Epoch 14/100 - Loss: 2795.59375, Val Loss: 258.72833251953125\n",
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      "Epoch 22/100 - Loss: 2335.8515625, Val Loss: 173.47146606445312\n",
      "Epoch 23/100 - Loss: 2322.278564453125, Val Loss: 173.24017333984375\n",
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      "Epoch 30/100 - Loss: 2351.251953125, Val Loss: 181.84385681152344\n",
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      "Epoch 32/100 - Loss: 2701.339599609375, Val Loss: 223.777587890625\n",
      "Epoch 33/100 - Loss: 2703.90576171875, Val Loss: 231.76092529296875\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 13.162073135375977\n",
      "Epoch 1/100 - Loss: 3489.458984375, Val Loss: 1413.123046875\n",
      "Epoch 2/100 - Loss: 3706.598388671875, Val Loss: 759.52294921875\n",
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      "Epoch 6/100 - Loss: 3481.42333984375, Val Loss: 408.07708740234375\n",
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      "Epoch 8/100 - Loss: 3224.4921875, Val Loss: 459.8560485839844\n",
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      "Epoch 10/100 - Loss: 3551.74609375, Val Loss: 384.7882995605469\n",
      "Epoch 11/100 - Loss: 3084.20068359375, Val Loss: 342.8564147949219\n",
      "Epoch 12/100 - Loss: 2892.10498046875, Val Loss: 290.74127197265625\n",
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      "Epoch 33/100 - Loss: 2579.13916015625, Val Loss: 225.32762145996094\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 14.179692268371582\n",
      "Epoch 1/100 - Loss: 3199.1435546875, Val Loss: 1267.005126953125\n",
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      "Epoch 9/100 - Loss: 2700.7373046875, Val Loss: 423.4371337890625\n",
      "Epoch 10/100 - Loss: 2473.83056640625, Val Loss: 328.41668701171875\n",
      "Epoch 11/100 - Loss: 2478.943359375, Val Loss: 297.14892578125\n",
      "Epoch 12/100 - Loss: 2600.8623046875, Val Loss: 275.9189453125\n",
      "Epoch 13/100 - Loss: 2456.826416015625, Val Loss: 262.3931579589844\n",
      "Epoch 14/100 - Loss: 2495.2978515625, Val Loss: 244.5017852783203\n",
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      "Epoch 24/100 - Loss: 2547.154296875, Val Loss: 210.88841247558594\n",
      "Epoch 25/100 - Loss: 2598.612060546875, Val Loss: 205.39356994628906\n",
      "Epoch 26/100 - Loss: 2502.115234375, Val Loss: 203.21311950683594\n",
      "Epoch 27/100 - Loss: 2491.93310546875, Val Loss: 208.19622802734375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/100 - Loss: 2492.09033203125, Val Loss: 213.88125610351562\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 14.195241928100586\n",
      "Epoch 1/100 - Loss: 443.04931640625, Val Loss: 1988.2574462890625\n",
      "Epoch 2/100 - Loss: 478.43218994140625, Val Loss: 699.9407958984375\n",
      "Epoch 3/100 - Loss: 443.2587585449219, Val Loss: 337.2893981933594\n",
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      "Epoch 5/100 - Loss: 358.2972412109375, Val Loss: 180.4075927734375\n",
      "Epoch 6/100 - Loss: 356.220703125, Val Loss: 175.85842895507812\n",
      "Epoch 7/100 - Loss: 385.1915283203125, Val Loss: 156.09779357910156\n",
      "Epoch 8/100 - Loss: 506.9664306640625, Val Loss: 126.43142700195312\n",
      "Epoch 9/100 - Loss: 508.1634216308594, Val Loss: 121.30132293701172\n",
      "Epoch 10/100 - Loss: 549.17333984375, Val Loss: 120.44486236572266\n",
      "Epoch 11/100 - Loss: 520.91015625, Val Loss: 119.98141479492188\n",
      "Epoch 12/100 - Loss: 486.6717529296875, Val Loss: 123.534912109375\n",
      "Epoch 13/100 - Loss: 544.4086303710938, Val Loss: 121.22085571289062\n",
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      "Epoch 15/100 - Loss: 508.54669189453125, Val Loss: 121.75872802734375\n",
      "Epoch 16/100 - Loss: 538.8489379882812, Val Loss: 121.64137268066406\n",
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      "Epoch 18/100 - Loss: 438.77386474609375, Val Loss: 121.89613342285156\n",
      "Epoch 19/100 - Loss: 459.9125671386719, Val Loss: 122.21761322021484\n",
      "Epoch 20/100 - Loss: 468.37542724609375, Val Loss: 121.8839111328125\n",
      "Epoch 21/100 - Loss: 422.97894287109375, Val Loss: 123.62310028076172\n",
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      "Epoch 23/100 - Loss: 449.0944519042969, Val Loss: 122.7820816040039\n",
      "Epoch 24/100 - Loss: 453.5289306640625, Val Loss: 125.01444244384766\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.878928184509277\n",
      "Epoch 1/100 - Loss: 3093.63818359375, Val Loss: 1486.4947509765625\n",
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      "Epoch 8/100 - Loss: 2918.15087890625, Val Loss: 372.63446044921875\n",
      "Epoch 9/100 - Loss: 2887.2060546875, Val Loss: 342.1465148925781\n",
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      "Epoch 14/100 - Loss: 2647.76953125, Val Loss: 216.15353393554688\n",
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      "Epoch 17/100 - Loss: 2371.555908203125, Val Loss: 207.7991943359375\n",
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      "Epoch 26/100 - Loss: 2149.609130859375, Val Loss: 199.7294921875\n",
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      "Epoch 28/100 - Loss: 2016.37255859375, Val Loss: 190.63011169433594\n",
      "Epoch 29/100 - Loss: 1860.8094482421875, Val Loss: 178.48097229003906\n",
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      "Epoch 62/100 - Loss: 1025.8416748046875, Val Loss: 121.80552673339844\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 10.516725540161133\n",
      "Epoch 1/100 - Loss: 3081.18994140625, Val Loss: 1557.5596923828125\n",
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      "Epoch 31/100 - Loss: 2743.56298828125, Val Loss: 256.5108337402344\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/100 - Loss: 2656.86474609375, Val Loss: 250.2193145751953\n",
      "Epoch 33/100 - Loss: 2717.1455078125, Val Loss: 246.32337951660156\n",
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      "Epoch 38/100 - Loss: 2521.08447265625, Val Loss: 232.7020721435547\n",
      "Epoch 39/100 - Loss: 2371.283447265625, Val Loss: 216.8556671142578\n",
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      "Epoch 46/100 - Loss: 2200.16796875, Val Loss: 204.386962890625\n",
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      "Epoch 48/100 - Loss: 2032.5537109375, Val Loss: 193.30795288085938\n",
      "Epoch 49/100 - Loss: 2045.619140625, Val Loss: 186.85406494140625\n",
      "Epoch 50/100 - Loss: 1999.24609375, Val Loss: 192.29368591308594\n",
      "Epoch 51/100 - Loss: 1969.95703125, Val Loss: 191.36630249023438\n",
      "Epoch 52/100 - Loss: 1892.169677734375, Val Loss: 180.747802734375\n",
      "Epoch 53/100 - Loss: 1949.4744873046875, Val Loss: 191.8544464111328\n",
      "Epoch 54/100 - Loss: 2031.2412109375, Val Loss: 178.69943237304688\n",
      "Epoch 55/100 - Loss: 1883.6417236328125, Val Loss: 187.7503662109375\n",
      "Epoch 56/100 - Loss: 1951.489013671875, Val Loss: 169.4647979736328\n",
      "Epoch 57/100 - Loss: 1915.793212890625, Val Loss: 184.09812927246094\n",
      "Epoch 58/100 - Loss: 1893.03955078125, Val Loss: 174.4963836669922\n",
      "Epoch 59/100 - Loss: 1829.3609619140625, Val Loss: 187.345458984375\n",
      "Epoch 60/100 - Loss: 1970.912109375, Val Loss: 187.0797119140625\n",
      "Epoch 61/100 - Loss: 2066.85400390625, Val Loss: 177.43455505371094\n",
      "Epoch 62/100 - Loss: 1995.86962890625, Val Loss: 177.82077026367188\n",
      "Epoch 63/100 - Loss: 2066.7373046875, Val Loss: 182.56553649902344\n",
      "Epoch 64/100 - Loss: 2106.6337890625, Val Loss: 183.18104553222656\n",
      "Epoch 65/100 - Loss: 1918.96875, Val Loss: 174.09632873535156\n",
      "Epoch 66/100 - Loss: 2037.57373046875, Val Loss: 178.62408447265625\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 13.017864227294922\n",
      "Epoch 1/100 - Loss: 3340.8203125, Val Loss: 1675.259765625\n",
      "Epoch 2/100 - Loss: 3761.80224609375, Val Loss: 823.7142944335938\n",
      "Epoch 3/100 - Loss: 3769.595703125, Val Loss: 671.0771484375\n",
      "Epoch 4/100 - Loss: 3806.993408203125, Val Loss: 595.0117797851562\n",
      "Epoch 5/100 - Loss: 3708.72607421875, Val Loss: 519.066162109375\n",
      "Epoch 6/100 - Loss: 3400.79833984375, Val Loss: 416.17413330078125\n",
      "Epoch 7/100 - Loss: 3129.802734375, Val Loss: 390.5815734863281\n",
      "Epoch 8/100 - Loss: 3175.3857421875, Val Loss: 404.3417053222656\n",
      "Epoch 9/100 - Loss: 3237.8642578125, Val Loss: 432.8767395019531\n",
      "Epoch 10/100 - Loss: 3391.75732421875, Val Loss: 455.7598571777344\n",
      "Epoch 11/100 - Loss: 3314.123046875, Val Loss: 422.6762390136719\n",
      "Epoch 12/100 - Loss: 3132.663330078125, Val Loss: 347.280517578125\n",
      "Epoch 13/100 - Loss: 3114.754150390625, Val Loss: 312.2354736328125\n",
      "Epoch 14/100 - Loss: 2992.57763671875, Val Loss: 270.96209716796875\n",
      "Epoch 15/100 - Loss: 2989.874267578125, Val Loss: 257.3593444824219\n",
      "Epoch 16/100 - Loss: 3061.20654296875, Val Loss: 254.6856689453125\n",
      "Epoch 17/100 - Loss: 3065.653076171875, Val Loss: 246.62176513671875\n",
      "Epoch 18/100 - Loss: 2989.323486328125, Val Loss: 256.24676513671875\n",
      "Epoch 19/100 - Loss: 2996.27685546875, Val Loss: 242.94664001464844\n",
      "Epoch 20/100 - Loss: 2966.55908203125, Val Loss: 238.556640625\n",
      "Epoch 21/100 - Loss: 2884.391845703125, Val Loss: 226.63209533691406\n",
      "Epoch 22/100 - Loss: 2886.03515625, Val Loss: 221.90826416015625\n",
      "Epoch 23/100 - Loss: 2813.5673828125, Val Loss: 222.86129760742188\n",
      "Epoch 24/100 - Loss: 3112.6044921875, Val Loss: 242.9385528564453\n",
      "Epoch 25/100 - Loss: 2971.60400390625, Val Loss: 227.8461151123047\n",
      "Epoch 26/100 - Loss: 3038.555908203125, Val Loss: 239.49546813964844\n",
      "Epoch 27/100 - Loss: 2952.10205078125, Val Loss: 250.3873748779297\n",
      "Epoch 28/100 - Loss: 2931.5283203125, Val Loss: 230.67938232421875\n",
      "Epoch 29/100 - Loss: 3017.93798828125, Val Loss: 236.29286193847656\n",
      "Epoch 30/100 - Loss: 2808.760498046875, Val Loss: 244.82125854492188\n",
      "Epoch 31/100 - Loss: 2839.52392578125, Val Loss: 239.22837829589844\n",
      "Epoch 32/100 - Loss: 2792.72509765625, Val Loss: 245.39727783203125\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 14.896585464477539\n",
      "Epoch 1/100 - Loss: 3080.28662109375, Val Loss: 1527.0841064453125\n",
      "Epoch 2/100 - Loss: 3401.521484375, Val Loss: 834.81396484375\n",
      "Epoch 3/100 - Loss: 3371.14892578125, Val Loss: 677.8233642578125\n",
      "Epoch 4/100 - Loss: 3320.30322265625, Val Loss: 606.5444946289062\n",
      "Epoch 5/100 - Loss: 3174.9853515625, Val Loss: 532.4093017578125\n",
      "Epoch 6/100 - Loss: 3043.08740234375, Val Loss: 481.9212646484375\n",
      "Epoch 7/100 - Loss: 2876.738037109375, Val Loss: 433.3048095703125\n",
      "Epoch 8/100 - Loss: 2798.3515625, Val Loss: 423.54974365234375\n",
      "Epoch 9/100 - Loss: 2782.236328125, Val Loss: 432.4835205078125\n",
      "Epoch 10/100 - Loss: 2718.14013671875, Val Loss: 387.2405700683594\n",
      "Epoch 11/100 - Loss: 2798.05322265625, Val Loss: 350.0915832519531\n",
      "Epoch 12/100 - Loss: 2619.890625, Val Loss: 351.44085693359375\n",
      "Epoch 13/100 - Loss: 2601.5751953125, Val Loss: 315.81988525390625\n",
      "Epoch 14/100 - Loss: 2614.72607421875, Val Loss: 298.5479736328125\n",
      "Epoch 15/100 - Loss: 2586.9033203125, Val Loss: 315.63104248046875\n",
      "Epoch 16/100 - Loss: 2730.79296875, Val Loss: 273.35675048828125\n",
      "Epoch 17/100 - Loss: 2649.52685546875, Val Loss: 255.3769989013672\n",
      "Epoch 18/100 - Loss: 2587.38134765625, Val Loss: 243.0502471923828\n",
      "Epoch 19/100 - Loss: 2537.837158203125, Val Loss: 231.44561767578125\n",
      "Epoch 20/100 - Loss: 2490.53955078125, Val Loss: 223.42835998535156\n",
      "Epoch 21/100 - Loss: 2484.96142578125, Val Loss: 218.05392456054688\n",
      "Epoch 22/100 - Loss: 2434.6123046875, Val Loss: 209.7313690185547\n",
      "Epoch 23/100 - Loss: 2386.7275390625, Val Loss: 213.755126953125\n",
      "Epoch 24/100 - Loss: 2431.77392578125, Val Loss: 212.8298797607422\n",
      "Epoch 25/100 - Loss: 2312.24951171875, Val Loss: 205.02999877929688\n",
      "Epoch 26/100 - Loss: 2376.19921875, Val Loss: 206.95452880859375\n",
      "Epoch 27/100 - Loss: 2238.275390625, Val Loss: 201.16610717773438\n",
      "Epoch 28/100 - Loss: 2169.68896484375, Val Loss: 203.2661895751953\n",
      "Epoch 29/100 - Loss: 2264.59521484375, Val Loss: 189.57699584960938\n",
      "Epoch 30/100 - Loss: 2182.73095703125, Val Loss: 197.1444549560547\n",
      "Epoch 31/100 - Loss: 2199.72314453125, Val Loss: 192.90965270996094\n",
      "Epoch 32/100 - Loss: 2080.72705078125, Val Loss: 192.74325561523438\n",
      "Epoch 33/100 - Loss: 2145.1748046875, Val Loss: 198.98876953125\n",
      "Epoch 34/100 - Loss: 2084.197509765625, Val Loss: 193.82510375976562\n",
      "Epoch 35/100 - Loss: 2128.97119140625, Val Loss: 196.04812622070312\n",
      "Epoch 36/100 - Loss: 2179.6572265625, Val Loss: 196.63064575195312\n",
      "Epoch 37/100 - Loss: 2171.69091796875, Val Loss: 185.95138549804688\n",
      "Epoch 38/100 - Loss: 2155.74365234375, Val Loss: 194.1222381591797\n",
      "Epoch 39/100 - Loss: 2152.96826171875, Val Loss: 191.77272033691406\n",
      "Epoch 40/100 - Loss: 2038.482666015625, Val Loss: 181.93197631835938\n",
      "Epoch 41/100 - Loss: 2033.0797119140625, Val Loss: 184.42832946777344\n",
      "Epoch 42/100 - Loss: 2008.32177734375, Val Loss: 170.60470581054688\n",
      "Epoch 43/100 - Loss: 1993.0958251953125, Val Loss: 173.7808380126953\n",
      "Epoch 44/100 - Loss: 2125.92724609375, Val Loss: 183.69140625\n",
      "Epoch 45/100 - Loss: 2184.12939453125, Val Loss: 174.01686096191406\n",
      "Epoch 46/100 - Loss: 1953.09912109375, Val Loss: 177.99046325683594\n",
      "Epoch 47/100 - Loss: 2106.517822265625, Val Loss: 185.97898864746094\n",
      "Epoch 48/100 - Loss: 2174.329345703125, Val Loss: 199.66004943847656\n",
      "Epoch 49/100 - Loss: 2247.78564453125, Val Loss: 201.9656524658203\n",
      "Epoch 50/100 - Loss: 2123.376708984375, Val Loss: 210.15573120117188\n",
      "Epoch 51/100 - Loss: 2062.2080078125, Val Loss: 199.0153045654297\n",
      "Epoch 52/100 - Loss: 2067.496826171875, Val Loss: 179.99851989746094\n",
      "Early stopping triggered\n",
      "Root Mean Squared Error (RMSE): 13.061572074890137\n"
     ]
    }
   ],
   "source": [
    "hidden_dims_list = [[10], [128], [128, 128]]\n",
    "batch_sizes = [32, 64]\n",
    "activation_functions = ['relu', 'leaky_relu']\n",
    "\n",
    "# Keep track of experiments\n",
    "experiment_log = []\n",
    "\n",
    "# Cross-validation setup\n",
    "k_folds = 5\n",
    "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
    "\n",
    "# Hyperparameter search loop\n",
    "for hidden_dims in hidden_dims_list:\n",
    "    for batch_size in batch_sizes:\n",
    "        for activation in activation_functions:\n",
    "            # Cross-validation scores\n",
    "            cv_scores = []\n",
    "            \n",
    "            for train_index, val_index in kf.split(X_train_TSLA_tensor):\n",
    "                # Split the data into current training and validation sets for this fold\n",
    "                X_train_fold, X_val_fold = X_train_TSLA_tensor[train_index], X_train_TSLA_tensor[val_index]\n",
    "                y_train_fold, y_val_fold = y_train_TSLA_tensor[train_index], y_train_TSLA_tensor[val_index]\n",
    "                \n",
    "                # Initialize model, loss, and optimizer\n",
    "                model = BaseNN(X_train_fold.shape[1], hidden_dims, activation)\n",
    "                criterion = nn.MSELoss()\n",
    "                optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-6)\n",
    "                \n",
    "                # Train the model on the current fold\n",
    "                model = train(model, X_train_fold, y_train_fold, X_val_fold, y_val_fold, criterion, optimizer, epochs=100, batch_size=batch_size)\n",
    "                \n",
    "                # Evaluate the model on the current validation fold\n",
    "                val_rmse = test(model, X_val_fold, y_val_fold)\n",
    "                cv_scores.append(val_rmse)\n",
    "            \n",
    "            # Compute the average RMSE over all folds\n",
    "            average_rmse = np.mean(cv_scores)\n",
    "            \n",
    "            # Log the experiment\n",
    "            experiment_log.append({\n",
    "                'hidden_dims': hidden_dims,\n",
    "                'batch_size': batch_size,\n",
    "                'learning_rate': 0.001,\n",
    "                'activation': activation,\n",
    "                'cv_rmse': average_rmse\n",
    "            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "37bf344e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best experiment: {'hidden_dims': [128, 128], 'batch_size': 32, 'learning_rate': 0.001, 'activation': 'leaky_relu', 'cv_rmse': 10.829717}\n"
     ]
    }
   ],
   "source": [
    "# Find the best hyperparameter set based on cross-validation RMSE\n",
    "best_hyperparams = min(experiment_log, key=lambda x: x['cv_rmse'])\n",
    "print(\"Best experiment:\", best_hyperparams)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33109fe2",
   "metadata": {},
   "source": [
    "### The best model architecture is 2 layers of 128 neurons each with an activation function of leaky relu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1beb61",
   "metadata": {},
   "source": [
    "# Train and test model for TSLA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb15e7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_TSLA = BaseNN(X_train_TSLA_tensor.shape[1], [128, 128], 'leaky_relu')\n",
    "\n",
    "# Weight decay for L2 regularization\n",
    "weight_decay = 1e-6\n",
    "optimizer = torch.optim.Adam(model_TSLA.parameters(), lr=0.001, weight_decay=weight_decay)\n",
    "loss_fn = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a288f0ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100 - Loss: 3737.5771484375, Val Loss: 215.61671447753906\n",
      "Epoch 2/100 - Loss: 3795.26123046875, Val Loss: 206.6848907470703\n",
      "Epoch 3/100 - Loss: 3494.0048828125, Val Loss: 185.41049194335938\n",
      "Epoch 4/100 - Loss: 3220.97119140625, Val Loss: 178.26881408691406\n",
      "Epoch 5/100 - Loss: 3203.7626953125, Val Loss: 176.90737915039062\n",
      "Epoch 6/100 - Loss: 3091.760498046875, Val Loss: 175.04763793945312\n",
      "Epoch 7/100 - Loss: 3136.6376953125, Val Loss: 172.68142700195312\n",
      "Epoch 8/100 - Loss: 3026.3876953125, Val Loss: 174.0966033935547\n",
      "Epoch 9/100 - Loss: 3039.880615234375, Val Loss: 173.57273864746094\n",
      "Epoch 10/100 - Loss: 3048.43115234375, Val Loss: 175.06866455078125\n",
      "Epoch 11/100 - Loss: 3072.11767578125, Val Loss: 174.4716033935547\n",
      "Epoch 12/100 - Loss: 3177.45849609375, Val Loss: 174.75819396972656\n",
      "Epoch 13/100 - Loss: 3138.4091796875, Val Loss: 172.17495727539062\n",
      "Epoch 14/100 - Loss: 2952.97119140625, Val Loss: 173.50344848632812\n",
      "Epoch 15/100 - Loss: 2988.865478515625, Val Loss: 170.11553955078125\n",
      "Epoch 16/100 - Loss: 3071.2890625, Val Loss: 172.08395385742188\n",
      "Epoch 17/100 - Loss: 2873.923828125, Val Loss: 174.00845336914062\n",
      "Epoch 18/100 - Loss: 2910.3037109375, Val Loss: 178.33322143554688\n",
      "Epoch 19/100 - Loss: 2995.46484375, Val Loss: 181.071044921875\n",
      "Epoch 20/100 - Loss: 2915.1904296875, Val Loss: 181.28102111816406\n",
      "Epoch 21/100 - Loss: 2712.10205078125, Val Loss: 190.81301879882812\n",
      "Epoch 22/100 - Loss: 2814.93603515625, Val Loss: 185.90296936035156\n",
      "Epoch 23/100 - Loss: 2646.67041015625, Val Loss: 185.61257934570312\n",
      "Epoch 24/100 - Loss: 2618.58154296875, Val Loss: 185.7356719970703\n",
      "Epoch 25/100 - Loss: 2433.03857421875, Val Loss: 184.144287109375\n",
      "Early stopping triggered\n"
     ]
    }
   ],
   "source": [
    "model_TSLA, train_losses_TSLA, val_losses_TSLA = train(model_TSLA, X_train_TSLA_tensor, y_train_TSLA_tensor, X_val_TSLA_tensor, y_val_TSLA_tensor, loss_fn, optimizer, 100, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a54f2d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_losses_TSLA, label='Training Loss for TSLA')\n",
    "plt.plot(val_losses_TSLA, label='Validation Loss for TSLA')\n",
    "plt.title('Training and Validation Loss per Epoch for TSLA')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76b3b54",
   "metadata": {},
   "source": [
    "## RMSE for TSLA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4cac002f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 12.716267585754395\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12.716268"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test(model_TSLA, X_test_TSLA_tensor, y_test_TSLA_tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2d1083",
   "metadata": {},
   "source": [
    "# Train and test model for AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "db64651a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_AAPL = BaseNN(X_train_AAPL_tensor.shape[1], [128, 128], 'leaky_relu')\n",
    "\n",
    "# Weight decay for L2 regularization\n",
    "weight_decay = 1e-6\n",
    "optimizer = torch.optim.Adam(model_AAPL.parameters(), lr=0.001, weight_decay=weight_decay)\n",
    "loss_fn = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c22122f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100 - Loss: 0.6493483781814575, Val Loss: 44.847251892089844\n",
      "Epoch 2/100 - Loss: 0.10234221071004868, Val Loss: 37.927947998046875\n",
      "Epoch 3/100 - Loss: 0.01304763276129961, Val Loss: 43.27386474609375\n",
      "Epoch 4/100 - Loss: 0.1321849375963211, Val Loss: 46.56121063232422\n",
      "Epoch 5/100 - Loss: 0.07497379183769226, Val Loss: 47.85300064086914\n",
      "Epoch 6/100 - Loss: 0.038256801664829254, Val Loss: 46.953704833984375\n",
      "Epoch 7/100 - Loss: 0.007210710551589727, Val Loss: 41.39629364013672\n",
      "Epoch 8/100 - Loss: 0.1057877168059349, Val Loss: 38.20830535888672\n",
      "Epoch 9/100 - Loss: 0.023214465007185936, Val Loss: 36.24909210205078\n",
      "Epoch 10/100 - Loss: 0.1075524315237999, Val Loss: 35.53549575805664\n",
      "Epoch 11/100 - Loss: 0.06614483147859573, Val Loss: 35.54018783569336\n",
      "Epoch 12/100 - Loss: 0.015846647322177887, Val Loss: 42.679969787597656\n",
      "Epoch 13/100 - Loss: 0.08601455390453339, Val Loss: 35.018524169921875\n",
      "Epoch 14/100 - Loss: 0.17191585898399353, Val Loss: 35.15006637573242\n",
      "Epoch 15/100 - Loss: 0.12706318497657776, Val Loss: 35.079185485839844\n",
      "Epoch 16/100 - Loss: 0.06706804782152176, Val Loss: 35.31107711791992\n",
      "Epoch 17/100 - Loss: 0.06757474690675735, Val Loss: 35.32166290283203\n",
      "Epoch 18/100 - Loss: 0.08320547640323639, Val Loss: 35.57514572143555\n",
      "Epoch 19/100 - Loss: 0.08907440304756165, Val Loss: 35.475650787353516\n",
      "Epoch 20/100 - Loss: 0.13311021029949188, Val Loss: 35.63223648071289\n",
      "Epoch 21/100 - Loss: 0.09591539204120636, Val Loss: 35.560455322265625\n",
      "Epoch 22/100 - Loss: 0.11967364698648453, Val Loss: 35.398555755615234\n",
      "Epoch 23/100 - Loss: 0.09307587891817093, Val Loss: 35.537841796875\n",
      "Early stopping triggered\n"
     ]
    }
   ],
   "source": [
    "model_AAPL, train_losses_AAPL, val_losses_AAPL = train(model_AAPL, X_train_AAPL_tensor, y_train_AAPL_tensor, X_val_AAPL_tensor, y_val_AAPL_tensor, loss_fn, optimizer, 100, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fae3e521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_losses_AAPL, label='Training Loss for AAPL')\n",
    "plt.plot(val_losses_AAPL, label='Validation Loss for AAPL')\n",
    "plt.title('Training and Validation Loss per Epoch for AAPL')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2874a9f6",
   "metadata": {},
   "source": [
    "## RMSE for AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2c0aa267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 4.034947395324707\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.0349474"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test(model_AAPL, X_test_AAPL_tensor, y_test_AAPL_tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbebc096",
   "metadata": {},
   "source": [
    "# Train and test model for NVDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aecb3afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_NVDA = BaseNN(X_train_NVDA_tensor.shape[1], [128, 128], 'leaky_relu')\n",
    "\n",
    "# Weight decay for L2 regularization\n",
    "weight_decay = 1e-6\n",
    "optimizer = torch.optim.Adam(model_NVDA.parameters(), lr=0.001, weight_decay=weight_decay)\n",
    "loss_fn = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "20cd7382",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100 - Loss: 8.19725227355957, Val Loss: 813.4305419921875\n",
      "Epoch 2/100 - Loss: 14.570976257324219, Val Loss: 635.0573120117188\n",
      "Epoch 3/100 - Loss: 12.215679168701172, Val Loss: 623.6893920898438\n",
      "Epoch 4/100 - Loss: 17.245689392089844, Val Loss: 658.5140991210938\n",
      "Epoch 5/100 - Loss: 14.559423446655273, Val Loss: 669.2105102539062\n",
      "Epoch 6/100 - Loss: 12.563297271728516, Val Loss: 666.947265625\n",
      "Epoch 7/100 - Loss: 8.562870979309082, Val Loss: 654.8234252929688\n",
      "Epoch 8/100 - Loss: 6.283475875854492, Val Loss: 641.9869384765625\n",
      "Epoch 9/100 - Loss: 5.874760150909424, Val Loss: 634.0049438476562\n",
      "Epoch 10/100 - Loss: 4.069833278656006, Val Loss: 611.6654052734375\n",
      "Epoch 11/100 - Loss: 3.4334237575531006, Val Loss: 603.8296508789062\n",
      "Epoch 12/100 - Loss: 2.4804465770721436, Val Loss: 597.669921875\n",
      "Epoch 13/100 - Loss: 2.3735172748565674, Val Loss: 592.4000854492188\n",
      "Epoch 14/100 - Loss: 2.474311590194702, Val Loss: 586.5309448242188\n",
      "Epoch 15/100 - Loss: 2.562211036682129, Val Loss: 586.1162109375\n",
      "Epoch 16/100 - Loss: 3.753026008605957, Val Loss: 582.7750854492188\n",
      "Epoch 17/100 - Loss: 3.0203704833984375, Val Loss: 575.1201782226562\n",
      "Epoch 18/100 - Loss: 2.985109806060791, Val Loss: 574.452880859375\n",
      "Epoch 19/100 - Loss: 2.7824981212615967, Val Loss: 573.869384765625\n",
      "Epoch 20/100 - Loss: 4.700251579284668, Val Loss: 575.8862915039062\n",
      "Epoch 21/100 - Loss: 3.522383689880371, Val Loss: 575.231201171875\n",
      "Epoch 22/100 - Loss: 3.0169482231140137, Val Loss: 573.08740234375\n",
      "Epoch 23/100 - Loss: 2.6941094398498535, Val Loss: 571.2571411132812\n",
      "Epoch 24/100 - Loss: 3.170727491378784, Val Loss: 574.9301147460938\n",
      "Epoch 25/100 - Loss: 2.4518959522247314, Val Loss: 569.8729248046875\n",
      "Epoch 26/100 - Loss: 2.4034674167633057, Val Loss: 566.54931640625\n",
      "Epoch 27/100 - Loss: 3.2569406032562256, Val Loss: 570.7799682617188\n",
      "Epoch 28/100 - Loss: 3.114201784133911, Val Loss: 569.0350952148438\n",
      "Epoch 29/100 - Loss: 3.290545701980591, Val Loss: 570.6994018554688\n",
      "Epoch 30/100 - Loss: 3.513327121734619, Val Loss: 573.6304931640625\n",
      "Epoch 31/100 - Loss: 2.9156994819641113, Val Loss: 570.85302734375\n",
      "Epoch 32/100 - Loss: 3.507056474685669, Val Loss: 570.654052734375\n",
      "Epoch 33/100 - Loss: 3.007868528366089, Val Loss: 567.9502563476562\n",
      "Epoch 34/100 - Loss: 2.194570302963257, Val Loss: 571.429443359375\n",
      "Epoch 35/100 - Loss: 2.433302402496338, Val Loss: 563.6939697265625\n",
      "Epoch 36/100 - Loss: 2.423743963241577, Val Loss: 568.92724609375\n",
      "Epoch 37/100 - Loss: 2.274714469909668, Val Loss: 566.5562133789062\n",
      "Epoch 38/100 - Loss: 2.2391490936279297, Val Loss: 566.8274536132812\n",
      "Epoch 39/100 - Loss: 2.5529439449310303, Val Loss: 572.5850830078125\n",
      "Epoch 40/100 - Loss: 2.029054641723633, Val Loss: 576.5655517578125\n",
      "Epoch 41/100 - Loss: 2.6671128273010254, Val Loss: 585.4161376953125\n",
      "Epoch 42/100 - Loss: 3.126923084259033, Val Loss: 570.4202270507812\n",
      "Epoch 43/100 - Loss: 2.947613000869751, Val Loss: 570.7133178710938\n",
      "Epoch 44/100 - Loss: 2.2859597206115723, Val Loss: 569.0498657226562\n",
      "Epoch 45/100 - Loss: 2.9131758213043213, Val Loss: 558.1088256835938\n",
      "Epoch 46/100 - Loss: 3.062429189682007, Val Loss: 566.4219360351562\n",
      "Epoch 47/100 - Loss: 2.580726385116577, Val Loss: 563.4284057617188\n",
      "Epoch 48/100 - Loss: 2.798337459564209, Val Loss: 555.3384399414062\n",
      "Epoch 49/100 - Loss: 2.8500330448150635, Val Loss: 555.3270874023438\n",
      "Epoch 50/100 - Loss: 2.9015369415283203, Val Loss: 554.8270874023438\n",
      "Epoch 51/100 - Loss: 3.852511405944824, Val Loss: 551.7685546875\n",
      "Epoch 52/100 - Loss: 3.344959020614624, Val Loss: 551.3622436523438\n",
      "Epoch 53/100 - Loss: 3.0456576347351074, Val Loss: 552.14990234375\n",
      "Epoch 54/100 - Loss: 3.0059866905212402, Val Loss: 546.2151489257812\n",
      "Epoch 55/100 - Loss: 2.857161045074463, Val Loss: 549.524169921875\n",
      "Epoch 56/100 - Loss: 3.9858596324920654, Val Loss: 551.8367919921875\n",
      "Epoch 57/100 - Loss: 2.622548818588257, Val Loss: 547.0567016601562\n",
      "Epoch 58/100 - Loss: 4.193068504333496, Val Loss: 553.4344482421875\n",
      "Epoch 59/100 - Loss: 4.162112712860107, Val Loss: 540.0348510742188\n",
      "Epoch 60/100 - Loss: 5.064830303192139, Val Loss: 544.2498168945312\n",
      "Epoch 61/100 - Loss: 3.16896915435791, Val Loss: 540.8269653320312\n",
      "Epoch 62/100 - Loss: 2.941070318222046, Val Loss: 539.8428344726562\n",
      "Epoch 63/100 - Loss: 4.611254692077637, Val Loss: 537.2498779296875\n",
      "Epoch 64/100 - Loss: 7.303502082824707, Val Loss: 535.239990234375\n",
      "Epoch 65/100 - Loss: 5.830748081207275, Val Loss: 541.1284790039062\n",
      "Epoch 66/100 - Loss: 6.25990629196167, Val Loss: 551.6055297851562\n",
      "Epoch 67/100 - Loss: 8.558274269104004, Val Loss: 533.9826049804688\n",
      "Epoch 68/100 - Loss: 4.2475786209106445, Val Loss: 520.7095947265625\n",
      "Epoch 69/100 - Loss: 5.960981369018555, Val Loss: 520.8523559570312\n",
      "Epoch 70/100 - Loss: 5.0088605880737305, Val Loss: 520.4466552734375\n",
      "Epoch 71/100 - Loss: 5.520730018615723, Val Loss: 524.8236083984375\n",
      "Epoch 72/100 - Loss: 4.175112724304199, Val Loss: 525.4544677734375\n",
      "Epoch 73/100 - Loss: 4.656717300415039, Val Loss: 518.223876953125\n",
      "Epoch 74/100 - Loss: 5.577816009521484, Val Loss: 519.246826171875\n",
      "Epoch 75/100 - Loss: 7.492040634155273, Val Loss: 512.9964599609375\n",
      "Epoch 76/100 - Loss: 5.207108974456787, Val Loss: 505.9795837402344\n",
      "Epoch 77/100 - Loss: 5.000011444091797, Val Loss: 504.8903503417969\n",
      "Epoch 78/100 - Loss: 5.066494464874268, Val Loss: 520.5611572265625\n",
      "Epoch 79/100 - Loss: 4.287623405456543, Val Loss: 526.4788818359375\n",
      "Epoch 80/100 - Loss: 4.316094398498535, Val Loss: 498.63446044921875\n",
      "Epoch 81/100 - Loss: 4.360790252685547, Val Loss: 507.614013671875\n",
      "Epoch 82/100 - Loss: 4.190577983856201, Val Loss: 503.66375732421875\n",
      "Epoch 83/100 - Loss: 3.3783721923828125, Val Loss: 515.2901611328125\n",
      "Epoch 84/100 - Loss: 6.923399925231934, Val Loss: 499.7333068847656\n",
      "Epoch 85/100 - Loss: 3.7901854515075684, Val Loss: 501.5477600097656\n",
      "Epoch 86/100 - Loss: 4.568527698516846, Val Loss: 503.0348205566406\n",
      "Epoch 87/100 - Loss: 5.493670463562012, Val Loss: 491.05755615234375\n",
      "Epoch 88/100 - Loss: 5.749414920806885, Val Loss: 500.3299865722656\n",
      "Epoch 89/100 - Loss: 6.149114608764648, Val Loss: 509.81884765625\n",
      "Epoch 90/100 - Loss: 7.435008525848389, Val Loss: 499.53497314453125\n",
      "Epoch 91/100 - Loss: 6.239455699920654, Val Loss: 484.54571533203125\n",
      "Epoch 92/100 - Loss: 5.477599143981934, Val Loss: 488.9029235839844\n",
      "Epoch 93/100 - Loss: 4.62665319442749, Val Loss: 488.56866455078125\n",
      "Epoch 94/100 - Loss: 5.7798542976379395, Val Loss: 491.98651123046875\n",
      "Epoch 95/100 - Loss: 9.01220703125, Val Loss: 490.3599548339844\n",
      "Epoch 96/100 - Loss: 4.956003665924072, Val Loss: 487.9129638671875\n",
      "Epoch 97/100 - Loss: 4.904714107513428, Val Loss: 487.0563659667969\n",
      "Epoch 98/100 - Loss: 4.586346626281738, Val Loss: 486.60784912109375\n",
      "Epoch 99/100 - Loss: 4.981246471405029, Val Loss: 493.0572509765625\n",
      "Epoch 100/100 - Loss: 6.918284893035889, Val Loss: 493.4186706542969\n"
     ]
    }
   ],
   "source": [
    "model_NVDA, train_losses_NVDA, val_losses_NVDA = train(model_NVDA, X_train_NVDA_tensor, y_train_NVDA_tensor, X_val_NVDA_tensor, y_val_NVDA_tensor, loss_fn, optimizer, 100, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b6e58bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_losses_NVDA, label='Training Loss for NVDA')\n",
    "plt.plot(val_losses_NVDA, label='Validation Loss for NVDA')\n",
    "plt.title('Training and Validation Loss per Epoch for NVDA')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61bea3cd",
   "metadata": {},
   "source": [
    "## RMSE for NVDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "92f99343",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 23.618427276611328\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "23.618427"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test(model_NVDA, X_test_NVDA_tensor, y_test_NVDA_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7db6c3e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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