Added tests to confirm featurebuilder behavior on unhashable columns …

…is correct

src/team_comm_tools/feature_builder.py

@@ -151,9 +151,6 @@ def __init__(
         if not isinstance(input_df, pd.DataFrame):
             raise TypeError(f"Expected a Pandas DataFrame as input_df, but got {type(df).__name__})")
 
-        # if not vector_directory:
-        #     raise ValueError("You must pass in a valid directory to cache vectors! For example: ./vector_data/") # NOTE: This is redundant because we have a default value
-
         print("Initializing Featurization...")
 
         ###### Set all parameters ######

src/team_comm_tools/utils/preprocess.py

@@ -80,8 +80,23 @@ def remove_unhashable_cols(df: pd.DataFrame, column_names: dict) -> pd.DataFrame
     """
     # Check cols with unhashable types
     unhashable_cols = {}
+
+    def is_unhashable(obj):
+        """
+        Small function to test whether a data type is hashable, without storing a hard-coded list of hashable types.
+        
+        :param obj: an object to test hashability
+        :return: Whether or not the object is unhashable
+        :rtype: bool
+        """
+        try:
+            hash(obj)
+            return False
+        except TypeError:
+            return True
+
     for col in df.columns:
-        unhashable_values = df[col].apply(lambda x: isinstance(x, (set, list, dict)))
+        unhashable_values = df[col].apply(lambda x: is_unhashable(x))
         if unhashable_values.any():
             unique_types = df[col][unhashable_values].apply(lambda x: type(x)).unique()
             unhashable_cols[col] = unique_types

tests/run_package_grouping_tests.py

@@ -8,6 +8,8 @@
 # Importing the Feature Generating Class
 from team_comm_tools import FeatureBuilder
 import pandas as pd
+import warnings
+warnings.simplefilter("always", category=UserWarning)
 
 # Main Function
 if __name__ == "__main__":
@@ -231,3 +233,46 @@
         user_aggregation = False,
 	)
 	custom_agg_fb_no_agg.featurize()
+
+
+	"""
+	Test that it's possible to run the FB with unhashable types that are NOT required columns.
+	"""
+
+	print("Testing unhashable types on non-required columns...")	
+	test_df_with_nonrequired_unhashables = pd.DataFrame({
+		"conversation_id": [1, 1, 2, 2, 2],
+		"text": ["test1", "test2", "test3", "test4", "test5"],
+		"speaker_id": [1, 2, 1, 2, 1],
+		"unhashable_col": [{}, set(), ["foo", "bar"], {"foo": "bar"}, {"foo": ["bar1", "bar2", "bar3"]}]
+		})
+
+	fb_nonrequred_unhashable_test = FeatureBuilder(
+			input_df = test_df_with_nonrequired_unhashables,
+			conversation_id_col = "conversation_id",
+			message_col = "text",
+			speaker_id_col = "speaker_id"
+		)
+
+	"""
+	Test that, if we have unhashable types as required columns, we throw a ValueError as expected. 
+	"""
+
+	print("Testing unhashable types on required columns...")
+	test_df_with_required_unhashables = pd.DataFrame({
+		"conversation_id": [[1], [1], [2], [2], [2]], # conversation_id is a required col and contains lists
+		"text": ["test1", "test2", "test3", "test4", "test5"],
+		"speaker_id": [1, 2, 1, 2, 1]		
+		})
+
+	try:
+		fb_required_unhashable_test = FeatureBuilder(
+			input_df = test_df_with_required_unhashables,
+			conversation_id_col = "conversation_id",
+			message_col = "text",
+			speaker_id_col = "speaker_id"
+		)
+	except ValueError as e:
+		assert('has unhashable data types' in str(e)) # make sure we caught the right error!
+		print("Test has properly exited with error.")
+