[GSK-1751] text push always giving None as the most contributing word

python-client/giskard/push/contribution.py

@@ -55,9 +55,7 @@ def _get_model_predictions(model: BaseModel, sliced_ds: Dataset):
     return raw_prediction, correct_prediction
 
 
-def _create_non_text_contribution_push(
-    shap_feature, sliced_ds, ds, model, correct_prediction
-):
+def _create_non_text_contribution_push(shap_feature, sliced_ds, ds, model, correct_prediction):
     """
     Create a ContributionPush object for non-text features with outlier contributions.
 
@@ -72,9 +70,7 @@ def _create_non_text_contribution_push(
         ContributionPush: An object representing the contribution push for non-text features.
     """
     # Calculate bounds for the feature
-    bounds = slice_bounds_relative(
-        feature=shap_feature, value=sliced_ds.df[shap_feature].values[0], ds=ds
-    )
+    bounds = slice_bounds_relative(feature=shap_feature, value=sliced_ds.df[shap_feature].values[0], ds=ds)
 
     # Create the ContributionPush object
     return ContributionPush(
@@ -86,9 +82,7 @@ def _create_non_text_contribution_push(
     )
 
 
-def _create_text_contribution_push(
-    shap_feature, sliced_ds, model, raw_prediction, correct_prediction
-):
+def _create_text_contribution_push(shap_feature, sliced_ds, model, raw_prediction, correct_prediction):
     """
     Create a ContributionPush object for text features.
 
@@ -103,36 +97,36 @@ def _create_text_contribution_push(
         ContributionPush: An object representing the contribution push for text features.
     """
     # Explain the text feature
+    input_df = model.prepare_dataframe(sliced_ds.df, column_dtypes=sliced_ds.column_dtypes, target=sliced_ds.target)
+
     text_explanation = explain_text(
         model=model,
-        input_df=sliced_ds.df,
+        input_df=input_df,
         text_column=shap_feature,
         text_document=sliced_ds.df[shap_feature].iloc[0],
     )
 
     # Create a dictionary mapping words to their importance scores
     if model.meta.model_type == SupportedModelTypes.CLASSIFICATION:
-        text_explanation_map = dict(
-            zip(text_explanation[0], text_explanation[1][raw_prediction])
-        )
+        text_explanation_map = dict(zip(text_explanation[0], text_explanation[1][raw_prediction]))
     else:
         text_explanation_map = dict(zip(text_explanation[0], text_explanation[1]))
 
     # Detect the most important word based on the explanation
     most_important_word = _detect_text_shap_outlier(text_explanation_map)
 
-    return ContributionPush(
-        feature=shap_feature,
-        feature_type="text",
-        value=most_important_word,
-        model_type=model.meta.model_type,
-        correct_prediction=correct_prediction,
-    )
+    # If for any reason, the most_important_word is not found, don't create a push notification
+    if most_important_word is not None:
+        return ContributionPush(
+            feature=shap_feature,
+            feature_type="text",
+            value=most_important_word,
+            model_type=model.meta.model_type,
+            correct_prediction=correct_prediction,
+        )
 
 
-def create_contribution_push(
-    model: BaseModel, ds: Dataset, df: pd.DataFrame
-) -> ContributionPush:
+def create_contribution_push(model: BaseModel, ds: Dataset, df: pd.DataFrame) -> ContributionPush:
     """
     Create contribution notification from SHAP values.
 
@@ -150,45 +144,30 @@ def create_contribution_push(
         ContributionPush if outlier contribution found, else None
     """
     # Check if there is only one feature type in the dataset, and if it's "text"
-    _text_is_the_only_feature = (
-        len(ds.column_types.values()) == 1
-        and list(ds.column_types.values())[0] == "text"
-    )
+    _text_is_the_only_feature = len(ds.column_types.values()) == 1 and list(ds.column_types.values())[0] == "text"
 
     # Get global SHAP values for the model's predictions on the input DataFrame
     global_feature_shap = _get_shap_values(model, ds, df)
 
     # Detect the SHAP feature with outlier contribution, if SHAP values exist
-    shap_feature = (
-        _detect_shap_outlier(global_feature_shap) if _existing_shap_values(ds) else None
-    )
+    shap_feature = _detect_shap_outlier(global_feature_shap) if _existing_shap_values(ds) else None
 
     # Check if a SHAP feature with outlier contribution was detected
     _shap_outlier_detected = shap_feature is not None
 
     # Determine if model predictions are needed based on the presence of outlier SHAP features and target variable
-    _model_predictions_needed = (
-        _shap_outlier_detected or _text_is_the_only_feature
-    ) and ds.target is not None
+    _model_predictions_needed = (_shap_outlier_detected or _text_is_the_only_feature) and ds.target is not None
 
     # If model predictions are needed, continue
     if _model_predictions_needed:
         # Choose the SHAP feature for analysis, considering the case when there's only one text feature
-        shap_feature = (
-            shap_feature
-            if not _text_is_the_only_feature
-            else list(ds.column_types.keys())[0]
-        )
+        shap_feature = shap_feature if not _text_is_the_only_feature else list(ds.column_types.keys())[0]
 
         # Check if the SHAP feature is not a text feature
-        _shap_feature_is_not_text = (
-            _shap_outlier_detected and ds.column_types[shap_feature] != "text"
-        )
+        _shap_feature_is_not_text = _shap_outlier_detected and ds.column_types[shap_feature] != "text"
 
         # Check if the SHAP feature is a text feature or it's the only feature in the dataset
-        _shap_feature_is_text = (
-            _shap_outlier_detected and ds.column_types[shap_feature] == "text"
-        )
+        _shap_feature_is_text = _shap_outlier_detected and ds.column_types[shap_feature] == "text"
 
         # Create a new dataset for analysis, copying the column types and excluding validation
         sliced_ds = Dataset(
@@ -203,15 +182,11 @@ def create_contribution_push(
 
         # If the SHAP feature is not a text feature, create a non-text ContributionPush
         if _shap_feature_is_not_text:
-            return _create_non_text_contribution_push(
-                shap_feature, sliced_ds, ds, model, correct_prediction
-            )
+            return _create_non_text_contribution_push(shap_feature, sliced_ds, ds, model, correct_prediction)
 
         # If the SHAP feature is a text feature or it's the only feature, create a text ContributionPush
         elif _shap_feature_is_text or _text_is_the_only_feature:
-            return _create_text_contribution_push(
-                shap_feature, sliced_ds, model, raw_prediction, correct_prediction
-            )
+            return _create_text_contribution_push(shap_feature, sliced_ds, model, raw_prediction, correct_prediction)
 
 
 def _detect_shap_outlier(global_feature_shap):
@@ -280,9 +255,7 @@ def _get_shap_values(model: BaseModel, ds: Dataset, df: pd.DataFrame):
         Dictionary of SHAP values per feature
     """
     if model.meta.model_type == SupportedModelTypes.CLASSIFICATION:
-        return explain(model, ds, df.iloc[0])["explanations"][
-            model.meta.classification_labels[0]
-        ]
+        return explain(model, ds, df.iloc[0])["explanations"][model.meta.classification_labels[0]]
     elif model.meta.model_type == SupportedModelTypes.REGRESSION:
         return explain(model, ds, df.iloc[0])["explanations"]["default"]
 

python-client/tests/test_push.py

@@ -211,3 +211,12 @@ def test_coltype_to_supported_perturbation_type():
 
     perturbation_type = coltype_to_supported_perturbation_type("text")
     assert perturbation_type == SupportedPerturbationType.TEXT
+
+
+def test_text_explain_in_push(medical_transcript_model, medical_transcript_data):
+
+    problematic_df_entry = medical_transcript_data.df.iloc[[3]]
+    output = create_contribution_push(medical_transcript_model, medical_transcript_data, problematic_df_entry)
+
+    assert output is not None
+    assert output.value is not None