Run the experiment with given configuration. Args: config: dict with optional keys: - hidden_dims: list of hidden layer dimensions (default: [64, 32]) - dropout: dropout rate (default: 0.0) - epochs: number of training epochs (default: 100)
(config=None)
| 104 | |
| 105 | |
| 106 | def run_experiment(config=None): |
| 107 | """ |
| 108 | Run the experiment with given configuration. |
| 109 | |
| 110 | Args: |
| 111 | config: dict with optional keys: |
| 112 | - hidden_dims: list of hidden layer dimensions (default: [64, 32]) |
| 113 | - dropout: dropout rate (default: 0.0) |
| 114 | - epochs: number of training epochs (default: 100) |
| 115 | - lr: learning rate (default: 0.001) |
| 116 | - batch_size: batch size (default: 32) |
| 117 | |
| 118 | Returns: |
| 119 | dict with metrics and training info |
| 120 | """ |
| 121 | # Default configuration |
| 122 | if config is None: |
| 123 | config = {} |
| 124 | |
| 125 | hidden_dims = config.get('hidden_dims', [64, 32]) |
| 126 | dropout = config.get('dropout', 0.0) |
| 127 | epochs = config.get('epochs', 100) |
| 128 | lr = config.get('lr', 0.001) |
| 129 | batch_size = config.get('batch_size', 32) |
| 130 | |
| 131 | # Device setup |
| 132 | device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| 133 | print(f"Using device: {device}") |
| 134 | |
| 135 | # Generate data |
| 136 | print("Generating synthetic data...") |
| 137 | X, y = generate_data() |
| 138 | X_train, X_test, y_train, y_test = train_test_split( |
| 139 | X, y, test_size=0.2, random_state=42 |
| 140 | ) |
| 141 | X_train, X_val, y_train, y_val = train_test_split( |
| 142 | X_train, y_train, test_size=0.2, random_state=42 |
| 143 | ) |
| 144 | |
| 145 | # Create data loaders |
| 146 | train_dataset = TensorDataset(torch.FloatTensor(X_train), torch.FloatTensor(y_train)) |
| 147 | val_dataset = TensorDataset(torch.FloatTensor(X_val), torch.FloatTensor(y_val)) |
| 148 | train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) |
| 149 | val_loader = DataLoader(val_dataset, batch_size=batch_size) |
| 150 | |
| 151 | # Create and train model |
| 152 | print(f"Training MLP with hidden_dims={hidden_dims}, dropout={dropout}") |
| 153 | model = SimpleMLP(input_dim=20, hidden_dims=hidden_dims, dropout=dropout) |
| 154 | |
| 155 | start_time = time.time() |
| 156 | model, history = train_model( |
| 157 | model, train_loader, val_loader, |
| 158 | epochs=epochs, lr=lr, device=device |
| 159 | ) |
| 160 | training_time = time.time() - start_time |
| 161 | print(f"Training completed in {training_time:.2f} seconds") |
| 162 | |
| 163 | # Evaluate |
no test coverage detected