226 lines
8.8 KiB
Python
226 lines
8.8 KiB
Python
import random
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import matplotlib.pyplot as plt
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from torch.utils.data import DataLoader, Subset
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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import numpy as np
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import Datasets
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import dataset_helper
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import EarlyStopping
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import ml_helper
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import ml_history
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import ml_train
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SEED = 501
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random.seed(SEED)
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np.random.seed(SEED)
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torch.manual_seed(SEED)
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torch.cuda.manual_seed_all(SEED)
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torch.backends.cudnn.deterministic = True
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class EnhancedCNNRegressor(nn.Module):
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def __init__(self, vocab_size, embedding_dim, filter_sizes, num_filters, embedding_matrix, dropout):
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super(EnhancedCNNRegressor, self).__init__()
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self.embedding = nn.Embedding.from_pretrained(embedding_matrix, freeze=False)
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# Convolutional Schichten mit Batch-Normalisierung
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self.convs = nn.ModuleList([
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nn.Sequential(
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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.Dropout(dropout) # Dropout nach jeder Schicht
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)
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for fs in filter_sizes
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])
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# Fully-Connected Layer
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self.fc1 = nn.Linear(len(filter_sizes) * num_filters, 128) # Erweiterte Dense-Schicht
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self.fc2 = nn.Linear(128, 1) # Ausgangsschicht (Regression)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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x = self.embedding(x).unsqueeze(1) # [Batch, 1, Seq, Embedding]
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conv_outputs = [conv(x).squeeze(3).squeeze(2) for conv in self.convs] # Pooling reduziert Dim
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x = torch.cat(conv_outputs, 1) # Kombiniere Features von allen Filtern
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x = torch.relu(self.fc1(x)) # Zusätzliche Dense-Schicht
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x = self.dropout(x)
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return self.fc2(x).squeeze(1)
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def train_model(model, train_dataset, test_dataset, criterion, optimizer, epochs, batch_size):
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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test_losses, train_losses = [], []
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train_r2_scores, test_r2_scores = [], []
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for epoch in range(epochs):
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model.train()
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running_loss = 0.0
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running_r2 = 0.0
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# Training
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for inputs, labels in train_loader:
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inputs = inputs.to(device)
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labels = labels.to(device)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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running_r2 += r2_score(labels.cpu().numpy(), outputs.cpu().detach().numpy())
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train_losses.append(running_loss / len(train_loader))
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train_r2_scores.append(running_r2 / len(train_loader))
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# Test
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model.eval() # Set model to evaluation mode
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test_loss = 0.0
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test_r2 = 0.0
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with torch.no_grad(): # No gradient calculation for testing
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for inputs, labels in test_loader:
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inputs = inputs.to(device)
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labels = labels.to(device)
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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test_loss += loss.item()
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test_r2 += r2_score(labels.cpu().numpy(), outputs.cpu().detach().numpy())
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test_losses.append(test_loss / len(test_loader))
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test_r2_scores.append(test_r2 / len(test_loader))
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print(f'Epoch {epoch + 1}/{epochs}, Train Loss: {train_losses[-1]:.4f}, Train R²: {train_r2_scores[-1]:.4f}, Test Loss: {test_losses[-1]:.4f}, Test R²: {test_r2_scores[-1]:.4f}')
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return train_losses, test_losses, train_r2_scores, test_r2_scores
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# Bootstrap Aggregation (Bagging) Update
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def bootstrap_aggregation(ModelClass, train_dataset, test_dataset, num_models=5, epochs=10, batch_size=32, learning_rate=0.001):
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models = []
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all_train_losses, all_test_losses = [], []
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all_train_r2_scores, all_test_r2_scores = [], []
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subset_size = len(train_dataset) // num_models
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for i in range(num_models):
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print(f"Training Model {i + 1}/{num_models}...")
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start_idx = i * subset_size
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end_idx = start_idx + subset_size
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subset_indices = list(range(0, start_idx)) + list(range(end_idx, len(train_dataset)))
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subset = Subset(train_dataset, subset_indices)
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model = ModelClass(vocab_size, EMBEDDING_DIM, params["filter_sizes"], params["num_filters"], embedding_matrix, params["dropout"])
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model.to(device)
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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train_losses, test_losses, train_r2_scores, test_r2_scores = train_model(model, subset, test_dataset, criterion, optimizer, epochs, batch_size)
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models.append(model)
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all_train_losses.append(train_losses)
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all_test_losses.append(test_losses)
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all_train_r2_scores.append(train_r2_scores)
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all_test_r2_scores.append(test_r2_scores)
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# Plot für alle Modelle
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plt.figure(figsize=(12, 6))
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for i in range(num_models):
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plt.plot(all_train_losses[i], label=f'Model {i + 1} Train Loss')
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plt.plot(all_test_losses[i], label=f'Model {i + 1} Test Loss', linestyle = 'dashed')
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plt.title("Training and Test Loss for all Models")
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plt.xlabel('Epochs')
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plt.ylabel('Loss')
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plt.legend()
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plt.show()
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plt.figure(figsize=(12, 6))
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for i in range(num_models):
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plt.plot(all_train_r2_scores[i], label=f'Model {i + 1} Train R²')
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plt.plot(all_test_r2_scores[i], label=f'Model {i + 1} Test R²', linestyle = 'dashed')
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plt.title("Training and Test R² for all Models")
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plt.xlabel('Epochs')
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plt.ylabel('R²')
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plt.legend()
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plt.show()
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return models, all_train_losses, all_test_losses, all_train_r2_scores, all_test_r2_scores
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# Ensemble Prediction
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def ensemble_predict(models, test_dataset):
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dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
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all_predictions = []
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with torch.no_grad():
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for inputs, _ in dataloader:
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inputs = inputs.to(device)
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predictions = torch.stack([model(inputs).squeeze() for model in models])
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avg_predictions = predictions.mean(dim=0)
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all_predictions.extend(avg_predictions.cpu().numpy())
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return np.array(all_predictions)
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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": 2,
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"patience": 7,
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"batch_size": 16,
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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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}
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# Configs
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MODEL_NAME = 'CNN.pt'
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HIST_NAME = 'CNN_history'
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GLOVE_PATH = 'data/glove.6B.100d.txt'
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DATA_PATH = 'data/hack.csv'
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EMBEDDING_DIM = 100
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TEST_SIZE = 0.1
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VAL_SIZE = 0.1
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# Daten laden und vorbereiten
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embedding_matrix, word_index, vocab_size, d_model = dataset_helper.get_embedding_matrix(
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gloVe_path=GLOVE_PATH, emb_len=EMBEDDING_DIM)
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X, y = dataset_helper.load_preprocess_data(path_data=DATA_PATH, verbose=True)
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# Aufteilen der Daten
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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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# Bootstrap Aggregation (Bagging) Training
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models, all_train_losses, all_test_losses, all_train_r2_scores, all_test_r2_scores = bootstrap_aggregation(
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EnhancedCNNRegressor, train_dataset, test_dataset, num_models=2, epochs=params["epochs"], batch_size=params["batch_size"], learning_rate=params["learning_rate"])
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# Ensemble Prediction
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test_predictions = ensemble_predict(models, test_dataset)
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# Test Evaluation
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# test_labels = np.array([y for _, y in test_dataset])
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test_mse = mean_squared_error(test_dataset.labels.to_numpy(), test_predictions)
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test_mae = mean_absolute_error(test_dataset.labels.to_numpy(), test_predictions)
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test_r2 = r2_score(test_dataset.labels.to_numpy(), test_predictions)
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print(f"Test RMSE: {test_mse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
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