[#41] flesh out docstrings and typehints for utils method

sgnlp/models/sentic_asgcn/utils.py

@@ -1,12 +1,10 @@
 import argparse
 import json
-from logging import error
 import math
-from pickle import load
 import pickle
 import random
 import pathlib
-from typing import Dict
+from typing import Dict, Iterable, List
 
 import numpy as np
 import torch
@@ -115,6 +113,10 @@ def build_embedding_matrix(
 
 
 class BucketIterator(object):
+    """
+    Bucket iterator class which provides sorting and padding for input dataset, iterate thru dataset batches
+    """
+
     def __init__(
         self, data, batch_size, sort_key="text_indices", shuffle=True, sort=True
     ):
@@ -124,7 +126,17 @@ def __init__(
         self.batches = self.sort_and_pad(data, batch_size)
         self.batch_len = len(self.batches)
 
-    def sort_and_pad(self, data, batch_size):
+    def sort_and_pad(self, data, batch_size: int) -> List[Dict[str, torch.tensor]]:
+        """
+        Class method to sort and pad data batches
+
+        Args:
+            data ([type]): input data
+            batch_size (int): batch size
+
+        Returns:
+            List[Dict[str, torch.tensor]]: return a list of dictionaries of tensors
+        """
         num_batch = int(math.ceil(len(data) / batch_size))
         sorted_data = (
             sorted(data, key=lambda x: len(x[self.sort_key])) if self.sort else data
@@ -135,7 +147,16 @@ def sort_and_pad(self, data, batch_size):
         ]
         return batches
 
-    def pad_data(self, batch_data):
+    def pad_data(self, batch_data: Iterable) -> Dict[str, torch.tensor]:
+        """
+        Class method to pad data batches
+
+        Args:
+            batch_data (Iterable): An iterable for looping thru input dataset
+
+        Returns:
+            Dict[str, torch.tensor]: return dictionary of tensors from data batches
+        """
         batch_text_indices = []
         batch_context_indices = []
         batch_aspect_indices = []