{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f66b6199",
   "metadata": {},
   "source": [
    "# Lesson 29: PyTorch training loop activity - SOLUTION\n",
    "\n",
    "This notebook contains the solution for adding batching and validation to the PyTorch training loop.\n",
    "\n",
    "## Notebook set-up\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c056632b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Third party imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "torch.manual_seed(315)\n",
    "np.random.seed(315)\n",
    "\n",
    "# Check for GPU availability\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Using device: {device}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace158b4",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5a4ebc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "learning_rate = 1e-3\n",
    "num_epochs = 100\n",
    "print_every = 10 # Print training progress every n epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66df12fb",
   "metadata": {},
   "source": [
    "## 1. Load preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8381fcea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 15480\n",
      "Testing samples: 5160\n",
      "Features: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n",
      "Label: MedHouseVal\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_pickle('https://gperdrizet.github.io/FSA_devops/assets/data/unit4/preprocessed_housing_data.pkl')\n",
    "\n",
    "training_df = data['training_df']\n",
    "testing_df = data['testing_df']\n",
    "features = data['features']\n",
    "label = data['label']\n",
    "\n",
    "print(f'Training samples: {len(training_df)}')\n",
    "print(f'Testing samples: {len(testing_df)}')\n",
    "print(f'Features: {features}')\n",
    "print(f'Label: {label}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b45497b",
   "metadata": {},
   "source": [
    "## 2. Prepare PyTorch tensors and DataLoaders - SOLUTION\n",
    "\n",
    "This section creates tensors, splits training data into train/validation sets, and creates DataLoaders for batching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d0a9d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert dataframes to PyTorch tensors and move to device\n",
    "X_train_full = torch.tensor(training_df[features].values, dtype=torch.float32).to(device)\n",
    "y_train_full = torch.tensor(training_df[label].values, dtype=torch.float32).unsqueeze(1).to(device)\n",
    "X_test = torch.tensor(testing_df[features].values, dtype=torch.float32).to(device)\n",
    "y_test = torch.tensor(testing_df[label].values, dtype=torch.float32).unsqueeze(1).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9189499e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split training data into train and validation sets (80/20)\n",
    "n_samples = X_train_full.shape[0]\n",
    "n_val = int(n_samples * 0.2)\n",
    "n_train = n_samples - n_val\n",
    "\n",
    "# Shuffle indices for random split\n",
    "indices = torch.randperm(n_samples)\n",
    "train_indices = indices[:n_train]\n",
    "val_indices = indices[n_train:]\n",
    "\n",
    "# Create train and validation tensors\n",
    "X_train = X_train_full[train_indices]\n",
    "y_train = y_train_full[train_indices]\n",
    "X_val = X_train_full[val_indices]\n",
    "y_val = y_train_full[val_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c2f12da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 12384\n",
      "Validation samples: 3096\n",
      "Batch size: 32\n",
      "Training batches per epoch: 387\n"
     ]
    }
   ],
   "source": [
    "# Create DataLoaders for batching\n",
    "train_dataset = TensorDataset(X_train, y_train)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "val_dataset = TensorDataset(X_val, y_val)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "print(f'Training samples: {n_train}')\n",
    "print(f'Validation samples: {n_val}')\n",
    "print(f'Batch size: {batch_size}')\n",
    "print(f'Training batches per epoch: {len(train_loader)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c36ca09",
   "metadata": {},
   "source": [
    "## 3. Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4caf32f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Linear(in_features=8, out_features=64, bias=True)\n",
      "  (1): ReLU()\n",
      "  (2): Dropout(p=0.2, inplace=False)\n",
      "  (3): Linear(in_features=64, out_features=32, bias=True)\n",
      "  (4): ReLU()\n",
      "  (5): Dropout(p=0.2, inplace=False)\n",
      "  (6): Linear(in_features=32, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = nn.Sequential(\n",
    "    nn.Linear(8, 64),\n",
    "    nn.ReLU(),\n",
    "    nn.Dropout(0.2),\n",
    "    nn.Linear(64, 32),\n",
    "    nn.ReLU(),\n",
    "    nn.Dropout(0.2),\n",
    "    nn.Linear(32, 1)\n",
    ").to(device)\n",
    "\n",
    "# Define loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4fcac59",
   "metadata": {},
   "source": [
    "## 4. Training function - SOLUTION\n",
    "\n",
    "This solution accepts DataLoaders and iterates over batches, computing validation metrics after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38211a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(\n",
    "    model: nn.Module,\n",
    "    train_loader: DataLoader,\n",
    "    val_loader: DataLoader,\n",
    "    criterion: nn.Module,\n",
    "    optimizer: optim.Optimizer,\n",
    "    epochs: int = 50,\n",
    "    print_every: int = 5\n",
    ") -> dict[str, list[float]]:\n",
    "    '''Training loop with batching and validation.\n",
    "    \n",
    "    Args:\n",
    "        model: PyTorch model to train\n",
    "        train_loader: DataLoader for training batches\n",
    "        val_loader: DataLoader for validation data\n",
    "        criterion: Loss function\n",
    "        optimizer: Optimizer\n",
    "        epochs: Number of training epochs\n",
    "        print_every: Print progress every N epochs\n",
    "    \n",
    "    Returns:\n",
    "        Dictionary containing training history\n",
    "    '''\n",
    "    \n",
    "    history = {\n",
    "        'loss': [], \n",
    "        'val_loss': []\n",
    "    }\n",
    "    \n",
    "    # Outer epoch loop\n",
    "    for epoch in range(epochs):\n",
    "\n",
    "        # Zero epoch losses & batch count\n",
    "        epoch_train_loss = 0.0\n",
    "        epoch_val_loss = 0.0\n",
    "        n_batches = 0\n",
    "\n",
    "        # Set model to training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Inner batch loop\n",
    "        for (X_train, y_train), (X_val, y_val) in zip(train_loader, val_loader):\n",
    "\n",
    "            # Forward pass\n",
    "            optimizer.zero_grad()\n",
    "            train_predictions = model(X_train)\n",
    "            train_loss = criterion(train_predictions, y_train)\n",
    "            \n",
    "            # Backward pass\n",
    "            train_loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            # Sum training loss\n",
    "            epoch_train_loss += train_loss.item()\n",
    "\n",
    "            # Calculate validation loss\n",
    "            val_predictions = model(X_val)\n",
    "            val_loss = criterion(val_predictions, y_val)\n",
    "            epoch_val_loss += val_loss.item()\n",
    "\n",
    "            n_batches += 1\n",
    "        \n",
    "        # Average training loss for the epoch\n",
    "        history['loss'].append(epoch_train_loss / n_batches)\n",
    "        history['val_loss'].append(epoch_val_loss / n_batches)\n",
    "\n",
    "        if (epoch + 1) % print_every == 0:\n",
    "            print(f'Epoch [{epoch+1}/{epochs}] - '\n",
    "                  f'Train Loss: {history[\"loss\"][-1]:.4f} - '\n",
    "                  f'Val Loss: {history[\"val_loss\"][-1]:.4f}')\n",
    "    \n",
    "    print('\\nTraining complete.')\n",
    "\n",
    "    return history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b60be0",
   "metadata": {},
   "source": [
    "## 5. Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4aa46b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/100] - Train Loss: 0.3302 - Val Loss: 0.3381\n",
      "Epoch [20/100] - Train Loss: 0.2862 - Val Loss: 0.3124\n",
      "Epoch [30/100] - Train Loss: 0.2684 - Val Loss: 0.2996\n",
      "Epoch [40/100] - Train Loss: 0.2566 - Val Loss: 0.2851\n",
      "Epoch [50/100] - Train Loss: 0.2743 - Val Loss: 0.2758\n",
      "Epoch [60/100] - Train Loss: 0.2481 - Val Loss: 0.2741\n",
      "Epoch [70/100] - Train Loss: 0.2552 - Val Loss: 0.2665\n",
      "Epoch [80/100] - Train Loss: 0.2462 - Val Loss: 0.2670\n",
      "Epoch [90/100] - Train Loss: 0.2411 - Val Loss: 0.2635\n",
      "Epoch [100/100] - Train Loss: 0.2483 - Val Loss: 0.2596\n",
      "\n",
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "history = train_model(\n",
    "    model=model,\n",
    "    train_loader=train_loader,\n",
    "    val_loader=val_loader,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer,\n",
    "    epochs=num_epochs,\n",
    "    print_every=print_every\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2abdbd",
   "metadata": {},
   "source": [
    "## 6. Learning curves\n",
    "\n",
    "Now showing both training and validation loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "059ddbed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Loss')\n",
    "plt.plot(history['loss'], label='Training')\n",
    "plt.plot(history['val_loss'], label='Validation')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss (MSE)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d9affc",
   "metadata": {},
   "source": [
    "## 7. Test set evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2f73b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model R² on test set: 0.7678\n"
     ]
    }
   ],
   "source": [
    "# Set model to evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# Make predictions (no gradient calculation needed)\n",
    "with torch.no_grad():\n",
    "    predictions = model(X_test).cpu().numpy().flatten()\n",
    "\n",
    "# Calculate R²\n",
    "ss_res = np.sum((testing_df[label].values - predictions) ** 2)\n",
    "ss_tot = np.sum((testing_df[label].values - np.mean(testing_df[label].values)) ** 2)\n",
    "rsquared = 1 - (ss_res / ss_tot)\n",
    "\n",
    "print(f'Model R² on test set: {rsquared:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1fb5b7",
   "metadata": {},
   "source": [
    "## 8. Performance analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "377ddd9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "axes[0].set_title('Model predictions')\n",
    "axes[0].scatter(\n",
    "    testing_df[label], predictions,\n",
    "    c='black', s=0.5, alpha=0.5\n",
    ")\n",
    "axes[0].plot(\n",
    "    [testing_df[label].min(), testing_df[label].max()],\n",
    "    [testing_df[label].min(), testing_df[label].max()],\n",
    "    color='red', linestyle='--'\n",
    ")\n",
    "axes[0].set_xlabel('True values (standardized)')\n",
    "axes[0].set_ylabel('Predicted values (standardized)')\n",
    "\n",
    "axes[1].set_title('Residuals vs predicted values')\n",
    "axes[1].scatter(\n",
    "    predictions, testing_df[label] - predictions,\n",
    "    c='black', s=0.5, alpha=0.5\n",
    ")\n",
    "axes[1].axhline(0, color='red', linestyle='--')\n",
    "axes[1].set_xlabel('Predicted values (standardized)')\n",
    "axes[1].set_ylabel('Residuals (standardized)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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