Clean up some minor issues (like iterating over the DataSet) & simplify

categorise.py

@@ -24,7 +24,7 @@ from torchtext.vocab import build_vocab_from_iterator
 from torch.utils.data import Dataset, DataLoader
 
 parser = argparse.ArgumentParser(
-  description='Classify text data according to categories', 
+  description='Classify text data according to categories',
   add_help=True,
 )
 parser.add_argument('action', help='train or classify')
@@ -64,27 +64,22 @@ data.dropna(axis='index', inplace=True)
 #sys.exit(0)
 
 '''
-#######################################################
-#               Create Training and Validation sets
-#######################################################
-
-# create a list of ints till len of data
+  Create Training and Validation sets
+'''
+# Create a list of ints till len of data
 data_idx = list(range(len(data)))
 np.random.shuffle(data_idx)
 
-# get indexes for validation and train
-val_frac = 0.1 # precentage of data in validation set
-val_split_idx = int(len(data)*val_frac) # index on which to split (10% of data)
-val_idx, train_idx = data_idx[:val_split_idx], data_idx[val_split_idx:]
-print('len of train: ', len(train_idx))
-print('len of val: ', len(val_idx))
+# Get indexes for validation and train
+split_percent = 0.95
+num_train = int(len(data) * split_percent)
+valid_idx, train_idx = data_idx[num_train:], data_idx[:num_train]
+print("Length of train_data: {}".format(len(train_idx)))
+print("Length of valid_data: {}".format(len(valid_idx)))
 
-# create the training and validation sets, as dataframes
-train_data = data.iloc[train_idx].reset_index().drop('index',axis=1)
-valid_data = data.iloc[val_idx].reset_index().drop('index',axis=1)
-
-# Next, we create Pytorch Datasets and Dataloaders for these dataframes
-'''
+# Create the training and validation sets, as dataframes
+train_data = data.iloc[train_idx].reset_index().drop('index', axis=1)
+valid_data = data.iloc[valid_idx].reset_index().drop('index', axis=1)
 
 
 '''
@@ -118,7 +113,7 @@ class TextCategoriesDataset(Dataset):
     #         replaced by this token
     self.itos = {0: '<pad>', 1:'<sos>', 2:'<eos>', 3: '<unk>'}
     # token-to-index dict
-    self.stoi = {k:j for j,k in self.itos.items()} 
+    self.stoi = {k:j for j, k in self.itos.items()}
 
     # Create vocabularies upon initialisation
     self.text_vocab = build_vocab_from_iterator(
@@ -143,6 +138,10 @@ class TextCategoriesDataset(Dataset):
     return len(self.df)
 
   def __getitem__(self, idx):
+    # Enable use as a plain iterator
+    if idx not in self.df.index:
+      raise(StopIteration)
+
     if torch.is_tensor(idx):
       idx = idx.tolist()
 
@@ -187,6 +186,7 @@ class TextCategoriesDataset(Dataset):
       T.AddToken(2, begin=False)
     )
 
+'''
 dataset = TextCategoriesDataset(df=data,
   text_column="content",
   cats_column="categories",
@@ -200,9 +200,8 @@ valid_dataset = TextCategoriesDataset(df=valid_data,
   text_column="content",
   cats_column="categories",
 )
-'''
 #print(dataset[2])
-#for text, cat in dataset:
+#for text, cat in enumerate(valid_dataset):
 #  print(text, cat)
 #sys.exit(0)
 
@@ -212,7 +211,7 @@ valid_dataset = TextCategoriesDataset(df=valid_data,
   which can batch, shuffle, and load the data in parallel
 '''
 
-class Collate:
+class CollateBatch:
   '''
   We need to pad shorter sentences in a batch to make all the sequences
   in a batch of equal length. We can do this a collate_fn callback class,
@@ -220,37 +219,55 @@ class Collate:
   '''
   def __init__(self, pad_idx):
     self.pad_idx = pad_idx
-  
+
   def __call__(self, batch):
     # T.ToTensor(0) returns a transform that converts the sequence
     # to a torch.tensor and also applies padding.
-    # pad_idx is passed to the constructor to specify the
-    # index of the "<pad>" token in the vocabulary.
+    #
+    # pad_idx is passed to the constructor to specify the index of
+    # the "<pad>" token in the vocabulary.
     return (
       T.ToTensor(self.pad_idx)(list(batch[0])),
       T.ToTensor(self.pad_idx)(list(batch[1])),
     )
 
+
+# Hyperparameters
+EPOCHS = 10       # epoch
+LR = 5            # learning rate
+BATCH_SIZE = 64   # batch size for training
+
+# Get cpu, gpu or mps device for training.
+# Move tensor to the NVIDIA GPU if available
+device = (
+  "cuda" if torch.cuda.is_available()
+  else "xps" if hasattr(torch, "xpu") and torch.xpu.is_available()
+  else "mps" if torch.backends.mps.is_available()
+  else "cpu"
+)
+print(f"Using {device} device")
+
+
+'''
 dataloader = DataLoader(dataset,
   batch_size=4,
   shuffle=True,
   num_workers=0,
-  collate_fn=Collate(pad_idx=dataset.stoi['<pad>']),
+  collate_fn=CollateBatch(pad_idx=dataset.stoi['<pad>']),
 )
 '''
 train_dataloader = DataLoader(train_dataset,
-  batch_size=4,
+  batch_size=BATCH_SIZE,
   shuffle=True,
   num_workers=0,
-  collate_fn=Collate(pad_idx=dataset.stoi['<pad>']),
+  collate_fn=CollateBatch(pad_idx=train_dataset.stoi['<pad>']),
 )
 valid_dataloader = DataLoader(valid_dataset,
-  batch_size=4,
+  batch_size=BATCH_SIZE,
   shuffle=True,
   num_workers=0,
-  collate_fn=Collate(pad_idx=dataset.stoi['<pad>']),
+  collate_fn=CollateBatch(pad_idx=valid_dataset.stoi['<pad>']),
 )
-'''
 #for i_batch, sample_batched in enumerate(dataloader):
 #  print(i_batch, sample_batched[0], sample_batched[1])
 #sys.exit(0)