Update explainer_base.py

dice_ml/data_interfaces/private_data_interface.py

@@ -182,11 +182,7 @@ def get_features_range(self, permitted_range_input=None, features_dict=None):
 
         ranges = {}
         # Getting default ranges based on the dataset
-        for feature in features_dict:
-            if type(features_dict[feature][0]) is int:  # continuous feature
-                ranges[feature] = features_dict[feature]
-            else:
-                ranges[feature] = features_dict[feature]
+        ranges[feature] = features_dict[feature]
         feature_ranges_orig = ranges.copy()
         # Overwriting the ranges for a feature if input provided
         if permitted_range_input is not None:

dice_ml/explainer_interfaces/explainer_base.py

@@ -250,12 +250,7 @@ def setup(self, features_to_vary, permitted_range, query_instance, feature_weigh
         if features_to_vary == 'all':
             features_to_vary = self.data_interface.feature_names
 
-        if permitted_range is None:  # use the precomputed default
-            self.feature_range = self.data_interface.permitted_range
-            feature_ranges_orig = self.feature_range
-        else:  # compute the new ranges based on user input
-            self.feature_range, feature_ranges_orig = self.data_interface.get_features_range(permitted_range)
-
+        self.feature_range, feature_ranges_orig = self.data_interface.get_features_range(permitted_range)
         self.check_query_instance_validity(features_to_vary, permitted_range, query_instance, feature_ranges_orig)
 
         return features_to_vary

tests/conftest.py

@@ -411,6 +411,34 @@ def _load_custom_vars_dataset_model():
     return model
 
 
+def _load_adult_income_binary_model():
+    dataset = helpers.load_adult_income_dataset()
+    X_train = dataset.drop('income', axis=1)
+    y_train = dataset["income"]
+    num_feature_names = ["age", "hours_per_week"]
+    cat_feature_names = X_train.columns.difference(num_feature_names)
+    model = create_complex_classification_pipeline(
+        X_train, y_train, num_feature_names, cat_feature_names)
+    return model
+
+
+def sample_adult_income_custom_query_11():
+    """
+    Returns multiple query instance for adult income dataset
+    """
+    data_point = 2
+    query_instances = pd.DataFrame({'age': [22]*data_point,
+                                  'workclass': ['Private']*data_point,
+                                  'education': ['HS-grad']*data_point,
+                                  'marital_status': ['Single']*data_point,
+                                  'occupation': ['Service']*data_point,
+                                  'race': ['White']*data_point,
+                                  'gender': ['Female']*data_point,
+                                  'hours_per_week': [45]*data_point}, 
+                                  index=list(range(data_point)))
+    return query_instances
+
+
 @pytest.fixture(scope='session')
 def sample_adultincome_query():
     """

tests/test_dice_interface/test_explainer_base.py

@@ -12,7 +12,10 @@
 from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 from dice_ml.utils import helpers
 
-from ..conftest import _load_custom_testing_binary_model
+from ..conftest import (private_data_object,
+                        sample_adult_income_custom_query_11,
+                        _load_adult_income_binary_model,
+                        _load_custom_testing_binary_model)
 
 
 @pytest.mark.parametrize("method", ['random', 'genetic', 'kdtree'])
@@ -349,6 +352,20 @@ def test_cfs_type_consistency(
                 assert cf_explanations.cf_examples_list[0].final_cfs_df[col].dtype == sample_custom_query[col].dtype
             if cf_explanations.cf_examples_list[0].final_cfs_df_sparse is not None:
                 assert cf_explanations.cf_examples_list[0].final_cfs_df_sparse[col].dtype == sample_custom_query[col].dtype
+
+    @pytest.mark.parametrize("method", ["genetic"])
+    def test_genetic_private_data(method):
+        d = private_data_object()
+        query = sample_adult_income_custom_query_11()
+        model = _load_adult_income_binary_model()
+        m = dice_ml.Model(model=model, backend='sklearn')
+        exp = dice_ml.Dice(d, m, method=method)
+
+        return exp.generate_counterfactuals(
+            query_instances=query,
+            total_CFs=1,
+            desired_class="opposite",
+            initialization="random")
 
 
 @pytest.mark.parametrize("method", ['random', 'genetic', 'kdtree'])