remove target_column requirement for genai tasks

responsibleai/responsibleai/managers/error_analysis_manager.py

@@ -253,8 +253,11 @@ def __init__(self, model: Any, dataset: pd.DataFrame, target_column: str,
                             for evaluating the model.
         :type dropped_features: Optional[List[str]]
         """
-        self._true_y = dataset[target_column]
-        self._dataset = dataset.drop(columns=[target_column])
+        self._true_y = None if target_column is None else dataset[target_column]
+        if self._true_y is None:
+            self._dataset = dataset.copy()
+        else:
+            self._dataset = dataset.drop(columns=[target_column])
         self._feature_names = list(self._dataset.columns)
         self._model_task = model_task
         self._classes = classes

responsibleai_text/responsibleai_text/managers/error_analysis_manager.py

@@ -231,7 +231,8 @@ def _create_index_predictor(model, dataset, target_column,
         :return: A wrapped predictor that uses index to retrieve text data.
         :rtype: WrappedIndexPredictorModel
         """
-        dataset = dataset.drop(columns=[target_column])
+        if target_column is not None:
+            dataset = dataset.drop(columns=[target_column])
         dataset = get_text_columns(dataset, text_column)
         index_predictor = WrappedIndexPredictorModel(
             model, dataset, is_multilabel, task_type, classes)

responsibleai_text/responsibleai_text/managers/explainer_manager.py

@@ -74,10 +74,13 @@ def __init__(self, model: Any, evaluation_examples: pd.DataFrame,
         """
         self._model = model
         self._target_column = target_column
-        if not isinstance(target_column, list):
+        if not isinstance(target_column, (list, type(None))):
             target_column = [target_column]
-        self._evaluation_examples = \
-            evaluation_examples.drop(columns=target_column)
+        if target_column is None:
+            self._evaluation_examples = evaluation_examples
+        else:
+            self._evaluation_examples = \
+                evaluation_examples.drop(columns=target_column)
         self._is_run = False
         self._is_added = False
         self._features = list(self._evaluation_examples.columns)

responsibleai_text/responsibleai_text/rai_text_insights/rai_text_insights.py

@@ -183,7 +183,8 @@ def __init__(self, model: Any, test: pd.DataFrame,
         self._ext_test = ext_test
         self._ext_features = ext_features
         self._ext_test_df = pd.DataFrame(ext_test, columns=ext_features)
-        self._ext_test_df[target_column] = test[target_column]
+        if target_column is not None:
+            self._ext_test_df[target_column] = test[target_column]
         self.predict_output = None
 
         super(RAITextInsights, self).__init__(
@@ -266,11 +267,13 @@ def _validate_model(self, model: Any, test: pd.DataFrame,
             an exception will be raised.
         :type text_column: str or list[str]
         """
-        if not isinstance(target_column, list):
-            target_column = [target_column]
-        # Pick one row from test data
-        small_test_data = test.iloc[0:1].drop(
-            target_column, axis=1)
+        small_test_data = test.iloc[0:1]
+        if target_column is not None:
+            if not isinstance(target_column, list):
+                target_column = [target_column]
+            # Pick one row from test data
+            small_test_data = small_test_data.drop(
+                target_column, axis=1)
         small_test_data = get_text_columns(small_test_data, text_column)
         small_test_data = small_test_data.iloc[0]
         if task_type not in [
@@ -369,6 +372,9 @@ def _validate_rai_insights_input_parameters(
             if not target_columns_set.issubset(set(test.columns)):
                 raise UserConfigValidationException(
                     'The list of target_column(s) should be in test data')
+        elif task_type==ModelTask.GENERATIVE_TEXT.value and target_column is None:
+            # target column is optional for generative text
+            pass
         else:
             if target_column not in list(test.columns):
                 raise UserConfigValidationException(
@@ -522,7 +528,10 @@ def _get_test_text_data(self, is_classification_task):
         elif self.task_type == ModelTask.QUESTION_ANSWERING:
             dataset = self.test.drop([self.target_column], axis=1)
         elif self.task_type == ModelTask.GENERATIVE_TEXT:
-            dataset = self.test.drop([self.target_column], axis=1)
+            if self.target_column is None:
+                dataset = self.test.copy()
+            else:
+                dataset = self.test.drop([self.target_column], axis=1)
         else:
             raise ValueError("Unknown task type: {}".format(self.task_type))
         dataset = get_text_columns(dataset, self._text_column)
@@ -578,13 +587,16 @@ def _get_dataset(self):
 
         dashboard_dataset.features = self._ext_test
 
-        true_y = self.test[self.target_column]
-        if true_y is not None and len(true_y) == row_length:
-            true_y = convert_to_list(true_y)
-            if is_classification_task:
-                true_y = self._convert_labels(
-                    true_y, dashboard_dataset.class_names)
-            dashboard_dataset.true_y = true_y
+        if self.target_column is None:
+            dashboard_dataset.true_y = None
+        else:
+            true_y = self.test[self.target_column]
+            if true_y is not None and len(true_y) == row_length:
+                true_y = convert_to_list(true_y)
+                if is_classification_task:
+                    true_y = self._convert_labels(
+                        true_y, dashboard_dataset.class_names)
+                dashboard_dataset.true_y = true_y
 
         dashboard_dataset.feature_names = self._ext_features
         dashboard_dataset.target_column = self.target_column

responsibleai_text/responsibleai_text/utils/feature_extractors.py

@@ -13,7 +13,8 @@
 
 from nlp_feature_extractors import attribute_extractors as exts
 from responsibleai_text.common.constants import (ModelTask,
-                                                 QuestionAnsweringFields)
+                                                 QuestionAnsweringFields,
+                                                 GenerativeTextFields)
 
 nlp = None
 
@@ -62,27 +63,21 @@ def extract_features(text_dataset: pd.DataFrame,
         feature_names.append("context_overlap")
     elif task_type == ModelTask.GENERATIVE_TEXT:
         # TODO: Add feature names for generative text
-        start_meta_index += 1
-        feature_names = []
-        prefixes = [QuestionAnsweringFields.CONTEXT + "_",
-                    QuestionAnsweringFields.QUESTION + "_"]
-        for prefix in prefixes:
-            for feature_name in base_feature_names:
-                feature_names.append(prefix + feature_name)
-            feature_names.append(prefix + "average_parse_tree_depth")
-            feature_names.append(prefix + "maximum_parse_tree_depth")
-        feature_names.append("question_type")
-        feature_names.append("context_overlap")
+        feature_names = base_feature_names
     else:
         raise ValueError("Unknown task type: {}".format(task_type))
     # copy over the metadata column names
     for j in range(start_meta_index, text_dataset.shape[1]):
         if has_dropped_features and column_names[j] in dropped_features:
             continue
         feature_names.append(column_names[j])
-    if not isinstance(target_column, list):
+
+    if not isinstance(target_column, (list, type(None))):
         target_column = [target_column]
-    text_features = text_dataset.drop(target_column, axis=1)
+
+    text_features = text_dataset.copy()
+    if target_column is not None:
+        text_features = text_features.drop(target_column, axis=1)
 
     if task_type in single_text_col_tasks:
         sentences = text_features.iloc[:, 0].tolist()
@@ -116,7 +111,17 @@ def extract_features(text_dataset: pd.DataFrame,
             results.append(extracted_features)
     elif task_type == ModelTask.GENERATIVE_TEXT:
         # TODO: Add feature extraction for generative text
-        pass
+        for i, row in tqdm(text_features.iterrows(), desc='feature extraction'):
+            extracted_features = []
+            add_extracted_features_for_sentence(
+                row[GenerativeTextFields.PROMPT], extracted_features,
+                task_type)
+
+            # append all other metadata features
+            append_metadata_values(start_meta_index, text_dataset, i,
+                                   extracted_features, has_dropped_features,
+                                   dropped_features, column_names)
+            results.append(extracted_features)
     else:
         raise ValueError("Unknown task type: {}".format(task_type))
     return results, feature_names