Implemented initial training structure, adding further logic soon including loss, stopping, optimisation and loop

src/dataset.py

@@ -43,14 +43,16 @@ class ReviewDataset(Dataset):
         return {
             'input_ids': encoding['input_ids'].squeeze(0),
             'attention_mask': encoding['attention_mask'].squeeze(0),
-            'bug_report': torch.tensor(self.df.iloc[idx]['bug_report']),
+            'bug_report': torch.tensor(self.df.iloc[idx]['bug_report'], dtype=torch.long),
-            'feature_request': torch.tensor(self.df.iloc[idx]['feature_request']),
+            'feature_request': torch.tensor(self.df.iloc[idx]['feature_request'], dtype=torch.long),
-            'aspect': torch.tensor(self.df.iloc[idx]['aspect']),
+            'aspect': torch.tensor(self.df.iloc[idx]['aspect'], dtype=torch.long),
-            'aspect_sentiment': torch.tensor(self.df.iloc[idx]['aspect_sentiment'])
+            'aspect_sentiment': torch.tensor(self.df.iloc[idx]['aspect_sentiment'], dtype=torch.long)
         }
+    
+if __name__ == "__main__":
+    dataset = ReviewDataset("data/processed/original_train.csv", AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base"))
+    print(dataset.__getitem__(1))
 
-# uber = ReviewDataset("data/processed/original_train.csv", AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base"))
-# print(uber.__getitem__(1))
 
     
 

src/train.py

@@ -1,7 +1,69 @@
-#train.py
+# train.py
-
+import torch
+from sklearn.utils.class_weight import compute_class_weight
+import numpy as np
+import torch.nn as nn
+from torch.utils.data import DataLoader
 from transformers import AutoTokenizer
+import pandas as pd
+
+from dataset import ReviewDataset
+from model import Model
+
+# class weights, training loop and early stopping
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
+
+train = "data/processed/original_train.csv"
+val = "data/processed/original_val.csv"
+train_dataset = ReviewDataset(train, tokenizer)
+val_dataset = ReviewDataset(val, tokenizer)
+train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False)
+
+model = Model().to(device)
 
 
-class multiTaskModel():
+# move input_ids, attention_mask and labels to device in each batch
-    tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
+
+# ------------------- Class weights -------------------
+# Using weights inversely proportional to class frequencies to avoid majority class bias, 
+# prioritize useful bug reports / feature requests
+def compute_weights(train_df, column):
+    classes = np.unique(train_df[column])
+    weights = compute_class_weight(class_weight='balanced', classes=classes, y=train_df[column])
+    return torch.tensor(weights, dtype=torch.float).to(device)
+
+# -------------------- Loss functions -------------------
+# just a later idea
+#    1.0 * bug_loss +
+#    1.0 * feature_loss +
+#    0.5 * aspect_loss +
+#    0.5 * sentiment_loss
+
+
+# -------------------- Optimizer and scheduler -------------------
+
+
+
+
+# ------------------- Training loop -------------------
+# For each epoch:
+
+
+
+
+# ------------------- Stopping logic -------------------
+# After each epoch, find mean of 4 macro f1 scores
+# If there is no improvement for 3 epochs consecutively, stop training 
+# Prevents overfitting which saves time and resources
+
+
+
+
+train_df = pd.read_csv(train)
+bug_weights = compute_weights(train_df, 'bug_report')
+feature_weights = compute_weights(train_df, 'feature_request')
+aspect_weights = compute_weights(train_df, 'aspect')    
+aspect_sentiment_weights = compute_weights(train_df, 'aspect_sentiment')