Utku-TabTransformer-Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-12T06:30:12.914610Z",
     "start_time": "2024-12-12T06:29:59.301037Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# ------------------------- Step 1: Load Dataset -------------------------\n",
    "\n",
    "file_path = \"realtor-data.csv\"  \n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "print(\"Initial Dataset Information:\")\n",
    "print(data.info())\n",
    "\n",
    "# ------------------------- Step 2: Handle Missing Values -------------------------\n",
    "\n",
    "#Filling in the missing values with the median\n",
    "num_cols = ['price', 'bed', 'bath', 'acre_lot', 'house_size']\n",
    "for col in num_cols:\n",
    "    data[col] = data[col].fillna(data[col].median())\n",
    "\n",
    "#Filling in the missing categorical values with the mode\n",
    "cat_cols = ['status', 'state', 'city', 'zip_code']\n",
    "for col in cat_cols:\n",
    "    data[col] = data[col].fillna(data[col].mode()[0])\n",
    "\n",
    "#Handling previous sold dte, converting it to datetime, filling in invalid/missing dates\n",
    "print(\"\\nHandling `prev_sold_date` column...\")\n",
    "def clean_dates(date_series):\n",
    "    return pd.to_datetime(date_series, errors='coerce')\n",
    "\n",
    "data['prev_sold_date'] = clean_dates(data['prev_sold_date'])\n",
    "invalid_dates = data['prev_sold_date'].isna().sum()\n",
    "print(f\"Invalid/Missing dates: {invalid_dates}\")\n",
    "\n",
    "if invalid_dates > 0:\n",
    "    valid_dates = data['prev_sold_date'].dropna()\n",
    "    median_days_since_sold = (pd.Timestamp.now() - valid_dates).dt.days.median()\n",
    "    data['prev_sold_date'] = data['prev_sold_date'].fillna(\n",
    "        pd.Timestamp.now() - pd.Timedelta(days=median_days_since_sold)\n",
    "    )\n",
    "\n",
    "data['days_since_sold'] = (pd.Timestamp.now() - data['prev_sold_date']).dt.days\n",
    "\n",
    "data = data.drop(columns=['prev_sold_date'])  # Drop original column\n",
    "\n",
    "# ------------------------- Step 3: Outlier Detection and Removal -------------------------\n",
    "\n",
    "def remove_outliers(df, columns):\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        initial_len = len(df)\n",
    "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
    "        removed = initial_len - len(df)\n",
    "        print(f\"Outliers removed from {col}: {removed}\")\n",
    "    return df\n",
    "\n",
    "print(\"\\nRemoving outliers...\")\n",
    "data = remove_outliers(data, ['price', 'bed', 'bath', 'acre_lot', 'house_size'])\n",
    "\n",
    "# ------------------------- Step 4: Feature Engineering -------------------------\n",
    "\n",
    "#Deriving features\n",
    "print(\"\\nCreating derived features...\")\n",
    "data['price_per_sqft'] = data['price'] / (data['house_size'] + 1e-5)\n",
    "data['bed_bath_ratio'] = data['bed'] / (data['bath'] + 1e-5)\n",
    "data['space_efficiency'] = data['house_size'] / (data['acre_lot'] + 1e-5)\n",
    "\n",
    "#Extracting the temporal features to be used\n",
    "data['year_sold'] = pd.to_datetime(data['days_since_sold'], unit='D', errors='coerce').dt.year\n",
    "data['month_sold'] = pd.to_datetime(data['days_since_sold'], unit='D', errors='coerce').dt.month\n",
    "\n",
    "# ------------------------- Step 5: Encoding Categorical Variables -------------------------\n",
    "\n",
    "#Encoding the labels for the categorical variables\n",
    "print(\"\\nEncoding categorical variables...\")\n",
    "label_encoder = LabelEncoder()\n",
    "for col in cat_cols:\n",
    "    data[col] = label_encoder.fit_transform(data[col])\n",
    "\n",
    "#Target encoding the city based on price\n",
    "city_avg_price = data.groupby('city')['price'].mean()\n",
    "data['city_encoded'] = data['city'].map(city_avg_price)\n",
    "\n",
    "# ------------------------- Step 6: Clustering Geographic Features -------------------------\n",
    "\n",
    "print(\"\\nClustering zip codes...\")\n",
    "kmeans = KMeans(n_clusters=10, random_state=42)\n",
    "data['zip_cluster'] = kmeans.fit_predict(data[['zip_code']])\n",
    "\n",
    "# ------------------------- Step 7: Normalization -------------------------\n",
    "\n",
    "print(\"\\nNormalizing numerical features...\")\n",
    "scaler = StandardScaler()\n",
    "data[num_cols + ['price_per_sqft', 'bed_bath_ratio', 'space_efficiency']] = scaler.fit_transform(\n",
    "    data[num_cols + ['price_per_sqft', 'bed_bath_ratio', 'space_efficiency']]\n",
    ")\n",
    "\n",
    "# ------------------------- Step 8: Splitting the Dataset -------------------------\n",
    "\n",
    "print(\"\\nSplitting the dataset into training, validation, and test sets...\")\n",
    "train_data, temp_data = train_test_split(data, test_size=0.2, random_state=42)\n",
    "val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)\n",
    "\n",
    "print(f\"Training set size: {len(train_data)}\")\n",
    "print(f\"Validation set size: {len(val_data)}\")\n",
    "print(f\"Test set size: {len(test_data)}\")\n",
    "\n",
    "# Save splits\n",
    "train_data.to_csv(\"train_data.csv\", index=False)\n",
    "val_data.to_csv(\"val_data.csv\", index=False)\n",
    "test_data.to_csv(\"test_data.csv\", index=False)\n",
    "print(\"\\nTraining, validation, and test sets saved as 'train_data.csv', 'val_data.csv', and 'test_data.csv'.\")\n",
    "\n",
    "# ------------------------- Final Steps -------------------------\n",
    "\n",
    "print(\"\\nFinal dataset inspection:\")\n",
    "print(data.info())\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Dataset Information:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2226382 entries, 0 to 2226381\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Dtype  \n",
      "---  ------          -----  \n",
      " 0   brokered_by     float64\n",
      " 1   status          object \n",
      " 2   price           float64\n",
      " 3   bed             float64\n",
      " 4   bath            float64\n",
      " 5   acre_lot        float64\n",
      " 6   street          float64\n",
      " 7   city            object \n",
      " 8   state           object \n",
      " 9   zip_code        float64\n",
      " 10  house_size      float64\n",
      " 11  prev_sold_date  object \n",
      "dtypes: float64(8), object(4)\n",
      "memory usage: 203.8+ MB\n",
      "None\n",
      "\n",
      "Handling `prev_sold_date` column...\n",
      "Invalid/Missing dates: 734298\n",
      "\n",
      "Removing outliers...\n",
      "Outliers removed from price: 171600\n",
      "Outliers removed from bed: 96375\n",
      "Outliers removed from bath: 23880\n",
      "Outliers removed from acre_lot: 315673\n",
      "Outliers removed from house_size: 92135\n",
      "\n",
      "Creating derived features...\n",
      "\n",
      "Encoding categorical variables...\n",
      "\n",
      "Clustering zip codes...\n",
      "\n",
      "Normalizing numerical features...\n",
      "\n",
      "Splitting the dataset into training, validation, and test sets...\n",
      "Training set size: 1221375\n",
      "Validation set size: 152672\n",
      "Test set size: 152672\n",
      "\n",
      "Training, validation, and test sets saved as 'train_data.csv', 'val_data.csv', and 'test_data.csv'.\n",
      "\n",
      "Final dataset inspection:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1526719 entries, 0 to 2226380\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count    Dtype  \n",
      "---  ------            --------------    -----  \n",
      " 0   brokered_by       1523286 non-null  float64\n",
      " 1   status            1526719 non-null  int64  \n",
      " 2   price             1526719 non-null  float64\n",
      " 3   bed               1526719 non-null  float64\n",
      " 4   bath              1526719 non-null  float64\n",
      " 5   acre_lot          1526719 non-null  float64\n",
      " 6   street            1520615 non-null  float64\n",
      " 7   city              1526719 non-null  int64  \n",
      " 8   state             1526719 non-null  int64  \n",
      " 9   zip_code          1526719 non-null  int64  \n",
      " 10  house_size        1526719 non-null  float64\n",
      " 11  days_since_sold   1526719 non-null  int64  \n",
      " 12  price_per_sqft    1526719 non-null  float64\n",
      " 13  bed_bath_ratio    1526719 non-null  float64\n",
      " 14  space_efficiency  1526719 non-null  float64\n",
      " 15  year_sold         1526719 non-null  int32  \n",
      " 16  month_sold        1526719 non-null  int32  \n",
      " 17  city_encoded      1526719 non-null  float64\n",
      " 18  zip_cluster       1526719 non-null  int32  \n",
      "dtypes: float64(11), int32(3), int64(5)\n",
      "memory usage: 215.5 MB\n",
      "None\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Model v3 - TabTransformer with Enhanced Generalization and Regularization\n",
    "\n",
    "This version of the TabTransformer model is configured to have maximum generalization, robustness, and predictive power. It embodies state-of-the-art architectural improvements, regularization techniques, and best hyperparameters, culled from multiple iterations of refinement.\n",
    "\n",
    "Model Architecture:\n",
    "\n",
    "    Embedding Layers:\n",
    "    Handles categorical variables by embedding each category into a dense representation.\n",
    "    Embedding dimension: 64.\n",
    "    Transformer Encoder:\n",
    "    Stacked 4 layers of transformer blocks with multi-head attention (4 heads).\n",
    "    Each block includes:\n",
    "    Self-attention mechanisms for learning relationships between features.\n",
    "    Feedforward layers with 512 hidden units for feature transformation.\n",
    "    Numerical Processing:\n",
    "    A dense layer maps numerical features to a high-dimensional space.\n",
    "    Regression Head:\n",
    "    Fully connected layers with ReLU activation predict the target price.\n",
    "\n",
    "Regularization:\n",
    "\n",
    "    Dropout:\n",
    "    Applied to embeddings (p=0.2) and hidden layers (p=0.2) to reduce overfitting.\n",
    "    Weight Decay:\n",
    "    L2 regularization via weight decay (1e-5) in the optimizer.\n",
    "\n",
    "Learning Strategy:\n",
    "\n",
    "    Optimizer: AdamW with learning rate 5e-4.\n",
    "    Scheduler: ReduceLROnPlateau reduces learning rate when validation loss plateaus, enhancing convergence.\n",
    "    Early Stopping:\n",
    "    Monitors validation loss to halt training after 3 epochs without improvement.\n",
    "\n",
    "Data Augmentation:\n",
    "\n",
    "    Gaussian noise added to numerical features during training improves robustness.\n",
    "\n",
    "Data Scaling:\n",
    "\n",
    "    StandardScaler applied to numerical features ensures uniform scaling for better optimization.\n",
    "\n",
    "Advanced Training:\n",
    "\n",
    "    Batch size: 1024.\n",
    "    Trained for up to 20 epochs, with early stopping at epoch 19 due to stabilization of validation loss."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-12T23:02:08.896417Z",
     "start_time": "2024-12-12T22:50:56.624909Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ------------------------- Step 1: Define PyTorch Dataset -------------------------\n",
    "\n",
    "class RealEstateDataset(Dataset):\n",
    "    def __init__(self, data, categorical_cols, numerical_cols, target_col):\n",
    "        self.data = data\n",
    "        self.categorical_cols = categorical_cols\n",
    "        self.numerical_cols = numerical_cols\n",
    "        self.target_col = target_col\n",
    "\n",
    "        self.categorical_data = self.data[categorical_cols].values\n",
    "        self.numerical_data = self.data[numerical_cols].values\n",
    "        self.targets = self.data[target_col].values\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return {\n",
    "            'categorical': torch.tensor(self.categorical_data[idx], dtype=torch.long),\n",
    "            'numerical': torch.tensor(self.numerical_data[idx], dtype=torch.float32),\n",
    "            'target': torch.tensor(self.targets[idx], dtype=torch.float32)\n",
    "        }\n",
    "\n",
    "# ------------------------- Step 2: Define TabTransformer Model -------------------------\n",
    "\n",
    "class TabTransformer(nn.Module):\n",
    "    def __init__(self, num_categories, num_numerical_features, embedding_dim, transformer_layers, hidden_dim):\n",
    "        super(TabTransformer, self).__init__()\n",
    "\n",
    "        #Embedding  the layers for categorical features\n",
    "        self.embeddings = nn.ModuleList([\n",
    "            nn.Embedding(num_classes, embedding_dim) for num_classes in num_categories\n",
    "        ])\n",
    "\n",
    "        #Transformer Encoder Part\n",
    "        self.transformer = nn.TransformerEncoder(\n",
    "            nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=4, dim_feedforward=hidden_dim, batch_first=True),\n",
    "            num_layers=transformer_layers\n",
    "        )\n",
    "\n",
    "        #Numerical feature processing\n",
    "        self.numerical_layer = nn.Linear(num_numerical_features, hidden_dim)\n",
    "\n",
    "        #Dropout for regularization - further optimization\n",
    "        self.dropout = nn.Dropout(p=0.2)\n",
    "\n",
    "        #The final regression layer\n",
    "        self.regressor = nn.Sequential(\n",
    "            nn.Linear(hidden_dim + embedding_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.2),\n",
    "            nn.Linear(hidden_dim, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, categorical_data, numerical_data):\n",
    "        #Processing for the categorical features\n",
    "        embeddings = [embedding(categorical_data[:, i]) for i, embedding in enumerate(self.embeddings)]\n",
    "        embeddings = torch.stack(embeddings, dim=1)  # (batch_size, num_categories, embedding_dim)\n",
    "        embeddings = self.dropout(embeddings)  #Applying the dropout to embeddings\n",
    "\n",
    "        #Applying the transformer\n",
    "        transformer_output = self.transformer(embeddings)  \n",
    "        transformer_output = transformer_output.mean(dim=1)  \n",
    "\n",
    "        #Processing numerical features\n",
    "        numerical_output = self.numerical_layer(numerical_data)\n",
    "\n",
    "        #Concatenate and regress\n",
    "        combined_output = torch.cat([transformer_output, numerical_output], dim=1)\n",
    "        output = self.regressor(combined_output)\n",
    "        return output.squeeze()\n",
    "\n",
    "# ------------------------- Step 3: Prepare Data -------------------------\n",
    "\n",
    "#Loading the datasets\n",
    "train_data = pd.read_csv(\"train_data.csv\")\n",
    "val_data = pd.read_csv(\"val_data.csv\")\n",
    "test_data = pd.read_csv(\"test_data.csv\")\n",
    "\n",
    "#Columns\n",
    "categorical_cols = ['status', 'city', 'state', 'zip_cluster']\n",
    "numerical_cols = ['price_per_sqft', 'bed_bath_ratio', 'space_efficiency', 'days_since_sold']\n",
    "target_col = 'price'\n",
    "\n",
    "#Preprocessing\n",
    "\n",
    "#Adjusting the embedding size for `city` to include every unique values\n",
    "num_categories = [train_data[col].nunique() for col in categorical_cols]\n",
    "num_categories[categorical_cols.index('city')] = train_data['city'].max() + 1\n",
    "\n",
    "scaler = StandardScaler()\n",
    "train_data[numerical_cols] = scaler.fit_transform(train_data[numerical_cols])\n",
    "val_data[numerical_cols] = scaler.transform(val_data[numerical_cols])\n",
    "test_data[numerical_cols] = scaler.transform(test_data[numerical_cols])\n",
    "\n",
    "#Adding Gaussian noise to numerical data - data augmentation\n",
    "train_data[numerical_cols] += np.random.normal(0, 0.01, train_data[numerical_cols].shape)\n",
    "\n",
    "#Ensurubg no invalid categorical values exist\n",
    "for col in categorical_cols:\n",
    "    train_data[col] = train_data[col].astype(int)\n",
    "    val_data[col] = val_data[col].astype(int)\n",
    "    test_data[col] = test_data[col].astype(int)\n",
    "\n",
    "#Datasets\n",
    "train_dataset = RealEstateDataset(train_data, categorical_cols, numerical_cols, target_col)\n",
    "val_dataset = RealEstateDataset(val_data, categorical_cols, numerical_cols, target_col)\n",
    "test_dataset = RealEstateDataset(test_data, categorical_cols, numerical_cols, target_col)\n",
    "\n",
    "#Dataloaders\n",
    "train_loader = DataLoader(train_dataset, batch_size=1024, shuffle=True, num_workers=0, pin_memory=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=1024, shuffle=False, num_workers=0, pin_memory=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=1024, shuffle=False, num_workers=0, pin_memory=True)\n",
    "\n",
    "# ------------------------- Step 4: Training Process -------------------------\n",
    "\n",
    "def train_model(model, train_loader, val_loader, epochs, learning_rate, device):\n",
    "    model.to(device)\n",
    "\n",
    "    criterion = nn.MSELoss()\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1e-5)  # Add weight decay\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2)  # Dynamic learning rate\n",
    "\n",
    "    print(f\"Training begins. Total epochs: {epochs}\")\n",
    "    print(f\"Model is on device: {next(model.parameters()).device}\")\n",
    "\n",
    "    best_val_loss = float('inf')\n",
    "    patience_counter = 0\n",
    "    patience = 3\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        train_loss = 0.0\n",
    "\n",
    "        for batch_idx, batch in enumerate(train_loader):\n",
    "            categorical = batch['categorical'].to(device)\n",
    "            numerical = batch['numerical'].to(device)\n",
    "            targets = batch['target'].to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(categorical, numerical)\n",
    "            loss = criterion(outputs, targets)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            train_loss += loss.item()\n",
    "\n",
    "        train_loss /= len(train_loader)\n",
    "\n",
    "        #Validation Part \n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        with torch.no_grad():\n",
    "            for batch in val_loader:\n",
    "                categorical = batch['categorical'].to(device)\n",
    "                numerical = batch['numerical'].to(device)\n",
    "                targets = batch['target'].to(device)\n",
    "\n",
    "                outputs = model(categorical, numerical)\n",
    "                loss = criterion(outputs, targets)\n",
    "                val_loss += loss.item()\n",
    "\n",
    "        val_loss /= len(val_loader)\n",
    "        scheduler.step(val_loss)  # Update learning rate based on validation loss\n",
    "\n",
    "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}\")\n",
    "\n",
    "        # Early Stopping\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            patience_counter = 0\n",
    "            torch.save(model.state_dict(), \"best_model.pth\")  # Save best model\n",
    "        else:\n",
    "            patience_counter += 1\n",
    "            if patience_counter >= patience:\n",
    "                print(f\"Early stopping at epoch {epoch + 1}\")\n",
    "                break\n",
    "\n",
    "# ------------------------- Step 5: Initialize and Train -------------------------\n",
    "\n",
    "#The Model\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = TabTransformer(num_categories, len(numerical_cols), embedding_dim=64, transformer_layers=4, hidden_dim=512)\n",
    "\n",
    "#Training - Commented out because this one is without tracking\n",
    "#train_model(model, train_loader, val_loader, epochs=20, learning_rate=5e-4, device=device)\n",
    "\n",
    "# ------------------------- Step 6: Evaluate -------------------------\n",
    "\n",
    "def evaluate_model(model, test_loader, device):\n",
    "    model.load_state_dict(torch.load(\"best_model.pth\"))  # Load best model\n",
    "    model.eval()\n",
    "    test_loss = 0.0\n",
    "    criterion = nn.MSELoss()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in test_loader:\n",
    "            categorical = batch['categorical'].to\n",
    "            \n",
    "            \n",
    "# ------------------------- Step 7: Visualizing Training Progress -------------------------\n",
    "\n",
    "def plot_training_progress(training_losses, validation_losses, learning_rates):\n",
    "    \"\"\"\n",
    "    Plots the training and validation losses across epochs and learning rate schedule.\n",
    "    \"\"\"\n",
    "    epochs = range(1, len(training_losses) + 1)\n",
    "\n",
    "    #Plotting train and val Loss\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(epochs, training_losses, label='Training Loss', marker='o')\n",
    "    plt.plot(epochs, validation_losses, label='Validation Loss', marker='x')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss (MSE)')\n",
    "    plt.title('Training and Validation Loss Across Epochs')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    #LR scheduling visualization\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(epochs, learning_rates, label='Learning Rate', marker='s', color='orange')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Learning Rate')\n",
    "    plt.title('Learning Rate Across Epochs')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "#Adding the training loss, validation loss, and learning rate tracking during training\n",
    "training_losses = []\n",
    "validation_losses = []\n",
    "learning_rates = []\n",
    "\n",
    "def train_model_with_tracking(model, train_loader, val_loader, epochs, learning_rate, device):\n",
    "    \"\"\"\n",
    "    Modified training function with loss and learning rate tracking for visualization.\n",
    "    \"\"\"\n",
    "    model.to(device)\n",
    "\n",
    "    criterion = nn.MSELoss()\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1e-5)\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2)\n",
    "\n",
    "    print(f\"Training begins. Total epochs: {epochs}\")\n",
    "    print(f\"Model is on device: {next(model.parameters()).device}\")\n",
    "\n",
    "    best_val_loss = float('inf')\n",
    "    patience_counter = 0\n",
    "    patience = 3\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        train_loss = 0.0\n",
    "\n",
    "        for batch_idx, batch in enumerate(train_loader):\n",
    "            categorical = batch['categorical'].to(device)\n",
    "            numerical = batch['numerical'].to(device)\n",
    "            targets = batch['target'].to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(categorical, numerical)\n",
    "            loss = criterion(outputs, targets)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            train_loss += loss.item()\n",
    "\n",
    "        train_loss /= len(train_loader)\n",
    "        training_losses.append(train_loss)  #tracking training loss\n",
    "\n",
    "        #Validation\n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        with torch.no_grad():\n",
    "            for batch in val_loader:\n",
    "                categorical = batch['categorical'].to(device)\n",
    "                numerical = batch['numerical'].to(device)\n",
    "                targets = batch['target'].to(device)\n",
    "\n",
    "                outputs = model(categorical, numerical)\n",
    "                loss = criterion(outputs, targets)\n",
    "                val_loss += loss.item()\n",
    "\n",
    "        val_loss /= len(val_loader)\n",
    "        validation_losses.append(val_loss)  #tracking val loss\n",
    "        scheduler.step(val_loss)\n",
    "\n",
    "        #Tracking the lr\n",
    "        current_lr = optimizer.param_groups[0]['lr']\n",
    "        learning_rates.append(current_lr)\n",
    "\n",
    "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}, Learning Rate: {current_lr:.6f}\")\n",
    "\n",
    "        #Early stop if training isn't progressing\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            patience_counter = 0\n",
    "            torch.save(model.state_dict(), \"best_model.pth\")\n",
    "        else:\n",
    "            patience_counter += 1\n",
    "            if patience_counter >= patience:\n",
    "                print(f\"Early stopping at epoch {epoch + 1}\")\n",
    "                break\n",
    "\n",
    "    #Plotting the training progress\n",
    "    plot_training_progress(training_losses, validation_losses, learning_rates)\n",
    "\n",
    "\n",
    "#Train the model with the new function\n",
    "train_model_with_tracking(model, train_loader, val_loader, epochs=20, learning_rate=5e-4, device=device)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training begins. Total epochs: 20\n",
      "Model is on device: cuda:0\n",
      "Epoch 1/20, Train Loss: 0.1983, Validation Loss: 0.1732, Learning Rate: 0.000500\n",
      "Epoch 2/20, Train Loss: 0.1805, Validation Loss: 0.1664, Learning Rate: 0.000500\n",
      "Epoch 3/20, Train Loss: 0.1751, Validation Loss: 0.1607, Learning Rate: 0.000500\n",
      "Epoch 4/20, Train Loss: 0.1714, Validation Loss: 0.1582, Learning Rate: 0.000500\n",
      "Epoch 5/20, Train Loss: 0.1685, Validation Loss: 0.1586, Learning Rate: 0.000500\n",
      "Epoch 6/20, Train Loss: 0.1664, Validation Loss: 0.1550, Learning Rate: 0.000500\n",
      "Epoch 7/20, Train Loss: 0.1648, Validation Loss: 0.1576, Learning Rate: 0.000500\n",
      "Epoch 8/20, Train Loss: 0.1632, Validation Loss: 0.1502, Learning Rate: 0.000500\n",
      "Epoch 9/20, Train Loss: 0.1623, Validation Loss: 0.1496, Learning Rate: 0.000500\n",
      "Epoch 10/20, Train Loss: 0.1611, Validation Loss: 0.1536, Learning Rate: 0.000500\n",
      "Epoch 11/20, Train Loss: 0.1602, Validation Loss: 0.1491, Learning Rate: 0.000500\n",
      "Epoch 12/20, Train Loss: 0.1592, Validation Loss: 0.1475, Learning Rate: 0.000500\n",
      "Epoch 13/20, Train Loss: 0.1584, Validation Loss: 0.1483, Learning Rate: 0.000500\n",
      "Epoch 14/20, Train Loss: 0.1580, Validation Loss: 0.1478, Learning Rate: 0.000500\n",
      "Epoch 15/20, Train Loss: 0.1574, Validation Loss: 0.1501, Learning Rate: 0.000250\n",
      "Early stopping at epoch 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-12T23:03:25.945686Z",
     "start_time": "2024-12-12T23:03:25.927805Z"
    }
   },
   "cell_type": "code",
   "source": "torch.save(model.state_dict(), \"utku-final-tab-transformer.pth\")",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Testing the Model with the Test Set, Printing Out Metrics"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-12T23:11:18.121547Z",
     "start_time": "2024-12-12T23:11:14.666406Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "# ------------------------- Test the Model -------------------------\n",
    "\n",
    "def evaluate_model(model, test_loader, device, model_path=\"utku-final-tab-transformer.pth\"):\n",
    "    \"\"\"\n",
    "    Evaluate the model on the test dataset, compute metrics, and generate visualizations.\n",
    "    Args:\n",
    "        model: The TabTransformer model to test.\n",
    "        test_loader: DataLoader for the test dataset.\n",
    "        device: The device (CPU or GPU) to use for evaluation.\n",
    "        model_path: Path to the saved model weights.\n",
    "    \"\"\"\n",
    "    # Load the best model with `weights_only=True`\n",
    "    model.load_state_dict(torch.load(model_path, weights_only=True))\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "\n",
    "    criterion = nn.MSELoss()\n",
    "    test_loss = 0.0\n",
    "\n",
    "    # Collect predictions and true targets for metrics and plots\n",
    "    all_predictions = []\n",
    "    all_targets = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in test_loader:\n",
    "            categorical = batch['categorical'].to(device)\n",
    "            numerical = batch['numerical'].to(device)\n",
    "            targets = batch['target'].to(device)\n",
    "\n",
    "            # Forward pass\n",
    "            outputs = model(categorical, numerical)\n",
    "            loss = criterion(outputs, targets)\n",
    "            test_loss += loss.item()\n",
    "\n",
    "            # Store predictions and targets\n",
    "            all_predictions.extend(outputs.cpu().numpy())\n",
    "            all_targets.extend(targets.cpu().numpy())\n",
    "\n",
    "    # Compute average test loss\n",
    "    test_loss /= len(test_loader)\n",
    "    print(f\"Test Loss (MSE): {test_loss:.4f}\")\n",
    "\n",
    "    # Convert to NumPy arrays for metric calculations\n",
    "    all_predictions = np.array(all_predictions)\n",
    "    all_targets = np.array(all_targets)\n",
    "\n",
    "    # Compute additional metrics\n",
    "    rmse = np.sqrt(mean_squared_error(all_targets, all_predictions))\n",
    "    mae = mean_absolute_error(all_targets, all_predictions)\n",
    "    r2 = r2_score(all_targets, all_predictions)\n",
    "\n",
    "    print(f\"Test RMSE: {rmse:.4f}\")\n",
    "    print(f\"Test MAE: {mae:.4f}\")\n",
    "    print(f\"R-Squared: {r2:.4f}\")\n",
    "\n",
    "    # ------------------------- Visualizations -------------------------\n",
    "    # Scatter Plot: Predictions vs Targets\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.scatter(all_targets, all_predictions, alpha=0.5, label=\"Predicted vs True\")\n",
    "    plt.plot([min(all_targets), max(all_targets)], [min(all_targets), max(all_targets)], color='red', label=\"Ideal Fit\")\n",
    "    plt.xlabel(\"True Prices\")\n",
    "    plt.ylabel(\"Predicted Prices\")\n",
    "    plt.title(\"Predicted vs True Prices\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    # Error Distribution\n",
    "    errors = all_predictions - all_targets\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.hist(errors, bins=50, alpha=0.7, label=\"Prediction Errors\")\n",
    "    plt.axvline(0, color='red', linestyle='dashed', linewidth=1, label=\"Zero Error\")\n",
    "    plt.xlabel(\"Error\")\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.title(\"Distribution of Prediction Errors\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    return test_loss, rmse, mae, r2\n",
    "\n",
    "# Evaluate the model and generate reports\n",
    "test_loss, rmse, mae, r2 = evaluate_model(model, test_loader, device)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss (MSE): 0.1517\n",
      "Test RMSE: 0.3898\n",
      "Test MAE: 0.2627\n",
      "R-Squared: 0.8484\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}