Delete train.py

train.py

@@ -1,105 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from torch_geometric.data import Batch
-from sklearn.model_selection import train_test_split
-import pickle
-
-from OmicsConfig import OmicsConfig
-from MultiOmicsGraphAttentionAutoencoderModel import MultiOmicsGraphAttentionAutoencoderModel
-from GATv2EncoderModel import GATv2EncoderModel
-from GATv2DecoderModel import GATv2DecoderModel
-from EdgeWeightPredictorModel import EdgeWeightPredictorModel
-
-def collate_graph_data(batch):
-    return Batch.from_data_list(batch)
-
-def create_data_loader(graph_data_dict, batch_size=1, shuffle=True):
-    graph_data = list(graph_data_dict.values())
-    return DataLoader(graph_data, batch_size=batch_size, shuffle=shuffle, collate_fn=collate_graph_data)
-
-# Load your data
-graph_data_dict = torch.load('data/graph_data_dictN.pth')
-
-# Split the data
-train_data, temp_data = train_test_split(list(graph_data_dict.items()), train_size=0.6, random_state=42)
-val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)
-
-# Convert lists back into dictionaries
-train_data = dict(train_data)
-val_data = dict(val_data)
-test_data = dict(test_data)
-
-# Define the configuration for the model
-autoencoder_config = OmicsConfig(
-    in_channels=17,
-    edge_attr_channels=1,
-    out_channels=1,
-    original_feature_size=17,
-    learning_rate=0.01,
-    num_layers=2,
-    edge_decoder_hidden_sizes=[128, 64],
-    edge_decoder_activations=['ReLU', 'ReLU']
-)
-
-# Initialize the model
-autoencoder_model = MultiOmicsGraphAttentionAutoencoderModel(autoencoder_config)
-
-# Create data loaders
-train_loader = create_data_loader(train_data, batch_size=4, shuffle=True)
-val_loader = create_data_loader(val_data, batch_size=4, shuffle=False)
-test_loader = create_data_loader(test_data, batch_size=4, shuffle=False)
-
-# Define the device
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# Training process
-def train_autoencoder(autoencoder_model, train_loader, validation_loader, epochs, device):
-    autoencoder_model.to(device)
-    train_losses = []
-    val_losses = []
-
-    for epoch in range(epochs):
-        # Train
-        autoencoder_model.train()
-        train_loss, train_cosine_similarity = autoencoder_model.train_model(train_loader, device)
-        print(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Train Cosine Similarity: {train_cosine_similarity:.4f}")
-        train_losses.append(train_loss)
-
-        # Validate
-        autoencoder_model.eval()
-        val_loss, val_cosine_similarity = autoencoder_model.validate(validation_loader, device)
-        print(f"Epoch {epoch+1}/{epochs}, Validation Loss: {val_loss:.4f}, Validation Cosine Similarity: {val_cosine_similarity:.4f}")
-        val_losses.append(val_loss)
-
-    # Save the trained encoder weights
-    trained_encoder_path = "lc_models/MultiOmicsAutoencoder/trained_encoder"
-    autoencoder_model.encoder.save_pretrained(trained_encoder_path)
-
-    # Save the trained decoder weights
-    trained_decoder_path = "lc_models/MultiOmicsAutoencoder/trained_decoder"
-    autoencoder_model.decoder.save_pretrained(trained_decoder_path)
-
-    # Save the trained edge weight predictor weights (if needed separately)
-    trained_edge_weight_predictor_path = "lc_models/MultiOmicsAutoencoder/trained_edge_weight_predictor"
-    autoencoder_model.decoder.edge_weight_predictor.save_pretrained(trained_edge_weight_predictor_path)
-
-    # Optionally save the entire autoencoder again if you want to have a complete package
-    trained_autoencoder_path = "lc_models/MultiOmicsAutoencoder/trained_autoencoder"
-    autoencoder_model.save_pretrained(trained_autoencoder_path)
-
-    return train_losses, val_losses
-
-# Train and save the model
-train_losses, val_losses = train_autoencoder(autoencoder_model, train_loader, val_loader, epochs=10, device=device)
-
-# Evaluate the model
-test_loss, test_accuracy = autoencoder_model.evaluate(test_loader, device)
-print(f"Test Loss: {test_loss:.4f}")
-print(f"Test Accuracy: {test_accuracy:.4%}")
-
-# Save the training and validation losses
-with open('./results/train_loss.pkl', 'wb') as f:
-    pickle.dump(train_losses, f)
-
-with open('./results/val_loss.pkl', 'wb') as f:
-    pickle.dump(val_losses, f)