update docstrings

langfair/generator/counterfactual.py

@@ -334,10 +334,18 @@ async def generate_responses(
         ----------
         dict
             A dictionary with two keys: 'data' and 'metadata'.
+
             'data' : dict
                 A dictionary containing the prompts and responses.
+
+                'prompt' : list
+                    A list of prompts.
+                'response' : list
+                    A list of responses corresponding to the prompts.
+
             'metadata' : dict
                 A dictionary containing metadata about the generation process.
+
                 'non_completion_rate' : float
                     The rate at which the generation process did not complete.
                 'temperature' : float

langfair/generator/generator.py

@@ -197,14 +197,18 @@ async def generate_responses(
         -------
         dict
             A dictionary with two keys: 'data' and 'metadata'.
+
             'data' : dict
                 A dictionary containing the prompts and responses.
+
                 'prompt' : list
                     A list of prompts.
                 'response' : list
                     A list of responses corresponding to the prompts.
+
             'metadata' : dict
                 A dictionary containing metadata about the generation process.
+
                 'non_completion_rate' : float
                     The rate at which the generation process did not complete.
                 'temperature' : float

langfair/metrics/classification/metrics/baseclass/metrics.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import Optional,List
 
 from numpy.typing import ArrayLike
 
@@ -38,7 +38,24 @@ def evaluate(
         pass
 
     @staticmethod
-    def binary_confusion_matrix(y_true, y_pred):
+    def binary_confusion_matrix(y_true, y_pred) -> List[List[float]]:
+        """
+        Method for computing binary confusion matrix    
+
+        Parameters
+        ----------
+        y_true : Array-like
+            Binary labels (ground truth values)
+
+        y_pred : Array-like
+            Binary model predictions    
+
+        Returns
+        -------
+        List[List[float]]
+            2x2 confusion matrix 
+
+        """
         cm = [[0, 0], [0, 0]]
         for i in range(len(y_pred)):
             if y_pred[i] == y_true[i]: