Merge branch 'main' of https://github.com/microsoft/responsible-ai-to…

…olbox into agemawat/ea_torch_hotfix
microsoft · Aug 31, 2023 · 7ea9649 · 7ea9649
2 parents dc29e00 + 225bbf1
commit 7ea9649
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Showing 3 changed files with 8 additions and 4 deletions.
diff --git a/responsibleai_text/responsibleai_text/utils/feature_extractors.py b/responsibleai_text/responsibleai_text/utils/feature_extractors.py
@@ -134,7 +134,7 @@ def append_metadata_values(start_meta_index, text_dataset, i,
     for j in range(start_meta_index, text_dataset.shape[1]):
         if has_dropped_features and column_names[j] in dropped_features:
             continue
-        extracted_features.append(text_dataset.iloc[i][j])
+        extracted_features.append(text_dataset.iloc[i, j])
     return extracted_features
 
 

diff --git a/responsibleai_vision/responsibleai_vision/utils/feature_extractors.py b/responsibleai_vision/responsibleai_vision/utils/feature_extractors.py
@@ -45,7 +45,7 @@ def extract_features(image_dataset: pd.DataFrame,
             continue
         feature_names.append(column_names[j])
     for i in tqdm(range(image_dataset.shape[0])):
-        image = image_dataset.iloc[i][0]
+        image = image_dataset.iloc[i, 0]
         if isinstance(image, str):
             image = get_image_from_path(image, image_mode)
         mean_pixel_value = image.mean()
@@ -54,6 +54,6 @@ def extract_features(image_dataset: pd.DataFrame,
         for j in range(start_meta_index, image_dataset.shape[1]):
             if has_dropped_features and column_names[j] in dropped_features:
                 continue
-            row_feature_values.append(image_dataset.iloc[i][j])
+            row_feature_values.append(image_dataset.iloc[i, j])
         results.append(row_feature_values)
     return results, feature_names
diff --git a/responsibleai_vision/tests/test_rai_vision_insights.py b/responsibleai_vision/tests/test_rai_vision_insights.py
@@ -223,7 +223,11 @@ def test_rai_insights_object_detection_fridge_image_transforms(self,
         task_type = ModelTask.OBJECT_DETECTION
         class_names = np.array(['can', 'carton',
                                 'milk_bottle', 'water_bottle'])
-        dropped_features = [i for i in range(0, 10)]
+        dropped_cols_num = [i for i in range(0, 10)]
+        dropped_features = ["{}".format(i) for i in dropped_cols_num]
+        # rename column names to strings since RAI validation fails otherwise
+        data = data.rename(columns={i: j for i, j in zip(
+            dropped_cols_num, dropped_features)}).reset_index(drop=True)
         run_rai_insights(model, data[:3], ImageColumns.LABEL,
                          task_type, class_names,
                          dropped_features=dropped_features)