update

cnn.py

@@ -5,31 +5,24 @@ import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split
 from nltk.tokenize import word_tokenize
-import gensim
 from torch.utils.data import DataLoader, Dataset
 from sklearn.metrics import accuracy_score
+import gensim
+import nltk
+import time
 import matplotlib.pyplot as plt
 
-# NLTK downloads
-import nltk
-nltk.download('punkt_tab')
-nltk.download('punkt')
-
-# Check if GPU is available (CUDA for NVIDIA or MPS for Apple devices)
-if torch.cuda.is_available():
-    DEVICE = torch.device('cuda')  # Use CUDA if available
-elif torch.backends.mps.is_available():
-    DEVICE = torch.device('mps')  # Use MPS if available
-else:
-    DEVICE = torch.device('cpu')  # Default to CPU if no GPU support is available
+# NLTK Downloads
+nltk.download('punkt')  # Entferne punkt_tab, da es nicht existiert
 
+# Check if GPU is available
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 print('Using device:', DEVICE)
 
 # Maximum sequence length
 MAX_LEN = 100
 
-
-# Data processing helpers
+# Data helpers
 def get_embedding(model, word):
     if word in model.wv:
         return model.wv.key_to_index[word]
@@ -40,11 +33,9 @@ def encode_tokens(tokens):
     return [get_embedding(model_embedding, token) for token in tokens]
 
 def pad_sequences(sequences, MAX_LEN):
-    return np.array([
-        np.pad(seq, (0, MAX_LEN - len(seq)), mode='constant', constant_values=unk_index)
-        if len(seq) < MAX_LEN else seq[:MAX_LEN]
-        for seq in sequences
-    ])
+    return np.array([np.pad(seq, (0, MAX_LEN - len(seq)), mode='constant', constant_values=unk_index)
+                     if len(seq) < MAX_LEN else seq[:MAX_LEN] for seq in sequences])
+
 # Dataset class
 class HumorDataset(Dataset):
     def __init__(self, encodings, labels):
@@ -83,23 +74,41 @@ class CNNBinaryClassifier(nn.Module):
         logits = self.out(fc_out)
         return self.sigmoid(logits)
 
-# Main
+# Main script
 if __name__ == "__main__":
     # Load and process data
-    df = pd.read_csv('ANLP_WS24_CA2/data/hack.csv')  # Ensure this file exists
+    df = pd.read_csv('/content/hack.csv')
     print(f"Loaded dataset: {df.shape}")
 
-    X = df['text']
+    X = df['text'].fillna("unknown").astype(str)
     y = df['is_humor']
 
+    # Train-test split
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-    # Tokenize the datapp
-    train_tokens = [word_tokenize(text.lower()) for text in X_train]
-    test_tokens = [word_tokenize(text.lower()) for text in X_test]
+    # Tokenization with error handling
+    train_tokens = []
+    test_tokens = []
+
+    for text in X_train:
+        try:
+            train_tokens.append(word_tokenize(text.lower()))
+        except Exception as e:
+            print(f"Error tokenizing: {text}. Error: {e}")
+            train_tokens.append(["unknown"])
+
+    for text in X_test:
+        try:
+            test_tokens.append(word_tokenize(text.lower()))
+        except Exception as e:
+            print(f"Error tokenizing: {text}. Error: {e}")
+            test_tokens.append(["unknown"])
+
+    print("Sample tokenization (Train):", train_tokens[:2])
+    print("Sample tokenization (Test):", test_tokens[:2])
 
     # Train Word2Vec model
-    model_embedding = gensim.models.Word2Vec(train_tokens, window=5, min_count=5, workers=4)
+    model_embedding = gensim.models.Word2Vec(train_tokens, vector_size=100, window=5, min_count=1, workers=4)
 
     # Add unknown token
     model_embedding.wv.add_vector('<UNK>', np.zeros(model_embedding.vector_size))
@@ -109,10 +118,13 @@ if __name__ == "__main__":
     train_encodings = [encode_tokens(tokens) for tokens in train_tokens]
     test_encodings = [encode_tokens(tokens) for tokens in test_tokens]
 
-    # Pad sequences
+    # Pad sequences with validation
     train_encodings = pad_sequences(train_encodings, MAX_LEN)
     test_encodings = pad_sequences(test_encodings, MAX_LEN)
 
+    if len(train_encodings) == 0 or len(test_encodings) == 0:
+        raise ValueError("Tokenization or padding failed. Please check your input data.")
+
     # Create datasets
     train_dataset = HumorDataset(train_encodings, y_train.reset_index(drop=True))
     test_dataset = HumorDataset(test_encodings, y_test.reset_index(drop=True))
@@ -132,20 +144,21 @@ if __name__ == "__main__":
     batch_size = 8
     learning_rate = 2e-5
 
+    # Optimizer and loss function
     optimizer = optim.Adam(model.parameters(), lr=learning_rate)
     criterion = nn.BCELoss()
 
+    # Data loaders
     train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
     test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 
     # Move model to device
     model.to(DEVICE)
 
-    # Training loop with loss visualization
     print("Starting training...")
     train_losses = []
-    val_losses = []
 
+    # Training loop
     for epoch in range(epochs):
         epoch_loss = 0
         model.train()
@@ -161,25 +174,20 @@ if __name__ == "__main__":
 
         train_loss = epoch_loss / len(train_loader)
         train_losses.append(train_loss)
+        print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}")
 
-        # Evaluation during training
-        model.eval()
-        val_loss = 0
-        with torch.no_grad():
-            for batch in test_loader:
-                input_ids = batch['input_ids'].to(DEVICE)
-                labels = batch['labels'].unsqueeze(1).to(DEVICE)
-                outputs = model(input_ids)
-                loss = criterion(outputs, labels)
-                val_loss += loss.item()
+    # Visualize training loss
+    plt.figure(figsize=(10, 6))
+    plt.plot(range(1, epochs + 1), train_losses, marker='o', linestyle='-', label='Train Loss')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.title('Training Loss Over Epochs')
+    plt.legend()
+    plt.grid(True)
+    plt.show()
 
-        val_loss /= len(test_loader)
-        val_losses.append(val_loss)
-
-        print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}")
-
-    # Final evaluation
     print("Starting evaluation...")
+    # Evaluation
     model.eval()
     predictions, true_labels = [], []
     with torch.no_grad():
@@ -193,13 +201,3 @@ if __name__ == "__main__":
 
     accuracy = accuracy_score(true_labels, predictions)
     print(f"Final Accuracy: {accuracy:.4f}")
-
-    # Visualize Losses
-    # Visualize Losses with custom colors
-plt.plot(train_losses, label="Train Loss", color='#0032ff')  # Blue color for train loss
-plt.plot(val_losses, label="Validation Loss", color='#0fff00')  # Green color for validation loss
-plt.xlabel("Epochs")
-plt.ylabel("Loss")
-plt.title("Loss Curve")
-plt.legend()
-plt.show()