added grid search
parent
35251933d7
commit
891df1d889
147
CNN.py
147
CNN.py
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@ -8,6 +8,7 @@ from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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import numpy as np
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from datetime import datetime
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import json
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import itertools
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import Datasets
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import dataset_helper
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@ -34,7 +35,7 @@ class EnhancedCNNRegressor(nn.Module):
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nn.Conv2d(1, num_filters, (fs, embedding_dim)),
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nn.BatchNorm2d(num_filters), # Batch-Normalisierung
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nn.ReLU(),
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nn.MaxPool2d((params["max_len"] - fs + 1, 1)),
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nn.MaxPool2d((MAX_LEN - fs + 1, 1)),
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nn.Dropout(dropout) # Dropout nach jeder Schicht
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)
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for fs in filter_sizes
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@ -56,20 +57,27 @@ class EnhancedCNNRegressor(nn.Module):
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if __name__ == '__main__':
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# Hyperparameter und Konfigurationen
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params = {
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# Config
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"max_len": 280,
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# Training
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"epochs": 5,
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"patience": 7,
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"batch_size": 32,
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"learning_rate": 0.001,
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"weight_decay": 5e-4 ,
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"epochs": [5],
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"patience": [7],
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"learning_rate": [0.001],
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"weight_decay": [5e-4] ,
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# Model
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"filter_sizes": [2, 3, 4, 5],
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"num_filters": 150,
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"dropout": 0.6
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"filter_sizes": [[2, 3, 4, 5]],
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"num_filters": [150],
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"dropout": [0.6]
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}
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# Generate permutations of hyperparameters
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keys, values = zip(*params.items())
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grid_params = [dict(zip(keys, v)) for v in itertools.product(*values)]
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best_params = {}
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best_params_rmse = -1
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# Example usage of grid_params
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# for param_set in grid_params:
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# print(param_set)
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print('Number of grid_params:', len(grid_params))
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# Configs
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GLOVE_PATH = 'data/glove.6B.100d.txt'
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DATA_PATH = 'data/hack.csv'
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@ -77,7 +85,11 @@ if __name__ == '__main__':
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TEST_SIZE = 0.1
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VAL_SIZE = 0.1
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N_MODELS = 1
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MAX_LEN = 280
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BATCH_SIZE = 32
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N_MODELS = 2
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USE_GIRD_SEARCH = False
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models = []
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timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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@ -92,17 +104,30 @@ if __name__ == '__main__':
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data_split = dataset_helper.split_data(X, y, test_size=TEST_SIZE, val_size=VAL_SIZE)
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# Dataset und DataLoader
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train_dataset = Datasets.GloveDataset(data_split['train']['X'], data_split['train']['y'], word_index, max_len=params["max_len"])
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val_dataset = Datasets.GloveDataset(data_split['val']['X'], data_split['val']['y'], word_index, max_len=params["max_len"])
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test_dataset = Datasets.GloveDataset(data_split['test']['X'], data_split['test']['y'], word_index, max_len=params["max_len"])
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train_dataset = Datasets.GloveDataset(data_split['train']['X'], data_split['train']['y'], word_index, max_len=MAX_LEN)
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val_dataset = Datasets.GloveDataset(data_split['val']['X'], data_split['val']['y'], word_index, max_len=MAX_LEN)
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test_dataset = Datasets.GloveDataset(data_split['test']['X'], data_split['test']['y'], word_index, max_len=MAX_LEN)
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train_loader = DataLoader(train_dataset, batch_size=params["batch_size"], shuffle=True)
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val_loader = DataLoader(val_dataset, batch_size=params["batch_size"], shuffle=False)
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test_loader = DataLoader(test_dataset, batch_size=params["batch_size"], shuffle=False)
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train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
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val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)
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test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
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subset_size = len(train_dataset) // N_MODELS
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device = ml_helper.get_device(verbose=True, include_mps=False)
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# assert if N_MODLES > 1, than grid_params should be len 1
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if N_MODELS > 1 and len(grid_params) > 1 or N_MODELS > 1 and USE_GIRD_SEARCH:
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raise ValueError("If N_MODELS > 1, than grid_params should be len 1")
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if not USE_GIRD_SEARCH:
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print('Using best params')
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# load best params
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params_name = f'models/best_params_CNN.json'
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with open(params_name, 'r') as f:
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best_params = json.load(f)
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grid_params = [best_params]
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for i in range(N_MODELS):
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model_name = f'CNN.pt'
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hist_name = f'CNN_history'
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@ -113,52 +138,66 @@ if __name__ == '__main__':
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subset_indices = dataset_helper.ensemble_data_idx(train_dataset.labels, N_MODELS, i, methods='bootstrap')
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train_dataset_sub = Subset(train_dataset, subset_indices)
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train_loader = DataLoader(train_dataset_sub, batch_size=params["batch_size"], shuffle=True)
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train_loader = DataLoader(train_dataset_sub, batch_size=BATCH_SIZE, shuffle=True)
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model = EnhancedCNNRegressor(
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vocab_size=vocab_size,
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embedding_dim=EMBEDDING_DIM,
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filter_sizes=params["filter_sizes"],
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num_filters=params["num_filters"],
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embedding_matrix=embedding_matrix,
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dropout=params["dropout"]
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)
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model = model.to(device)
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"], weight_decay=params["weight_decay"])
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early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=model_name)
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for para_idx, params in enumerate(grid_params):
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if len(grid_params) > 1:
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model_name = f'CNN_{i}_param_{para_idx}.pt'
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hist_name = f'CNN_{i}_param_{para_idx}_history'
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hist = ml_history.History()
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model = EnhancedCNNRegressor(
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vocab_size=vocab_size,
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embedding_dim=EMBEDDING_DIM,
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filter_sizes=params["filter_sizes"],
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num_filters=params["num_filters"],
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embedding_matrix=embedding_matrix,
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dropout=params["dropout"]
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)
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model = model.to(device)
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"], weight_decay=params["weight_decay"])
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early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=model_name)
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# Training und Validierung
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for epoch in range(params["epochs"]):
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ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"])
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hist = ml_history.History()
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val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist)
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# Training und Validierung
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for epoch in range(params["epochs"]):
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ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"])
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early_stopping(val_rmse, model)
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if early_stopping.early_stop:
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print("Early stopping triggered.")
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break
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val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist)
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# Load best model
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model.load_state_dict(torch.load('models/checkpoints/' + model_name, weights_only=False))
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models.append(model)
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early_stopping(val_rmse, model)
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if early_stopping.early_stop:
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print("Early stopping triggered.")
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break
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# Test Evaluation
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test_labels, test_preds = ml_train.test_loop(model, test_loader, device)
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# Load best model
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model.load_state_dict(torch.load('models/checkpoints/' + model_name, weights_only=False))
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models.append(model)
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hist.add_test_results(test_labels, test_preds)
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# Test Evaluation
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test_labels, test_preds = ml_train.test_loop(model, test_loader, device)
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# save training history
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hist.save_history(hist_name, timestamp)
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hist.add_test_results(test_labels, test_preds)
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# RMSE, MAE und R²-Score für das Test-Set
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test_mae = mean_absolute_error(test_labels, test_preds)
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test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
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test_r2 = r2_score(test_labels, test_preds)
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print(f"Model: {model_name} Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
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# save training history
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hist.save_history(hist_name, timestamp)
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# RMSE, MAE und R²-Score für das Test-Set
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test_mae = mean_absolute_error(test_labels, test_preds)
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test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
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test_r2 = r2_score(test_labels, test_preds)
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print(f"Model: {model_name} Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
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if test_rmse > best_params_rmse:
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best_params_rmse = test_rmse
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best_params = params
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if len(grid_params) > 1:
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best_params_name = f'models/best_params_CNN.json'
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with open(best_params_name, 'w') as f:
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json.dump(best_params, f)
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if N_MODELS >1:
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# Ensemble Prediction
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