Extend categorical value check to numeric values (#212)

* Extend categorical value check to numeric values Signed-off-by: gaugup <[email protected]> * Add unit tests Signed-off-by: gaugup <[email protected]>
interpretml · Aug 20, 2021 · 3122320 · 3122320
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Showing 3 changed files with 38 additions and 7 deletions.
diff --git a/dice_ml/explainer_interfaces/explainer_base.py b/dice_ml/explainer_interfaces/explainer_base.py
@@ -168,7 +168,8 @@ def check_query_instance_validity(self, features_to_vary, permitted_range, query
                 raise ValueError("Feature", feature, "not present in training data!")
 
         for feature in self.data_interface.categorical_feature_names:
-            if query_instance[feature].values[0] not in feature_ranges_orig[feature]:
+            if query_instance[feature].values[0] not in feature_ranges_orig[feature] and \
+                    str(query_instance[feature].values[0]) not in feature_ranges_orig[feature]:
                 raise ValueError("Feature", feature, "has a value outside the dataset.")
 
             if feature not in features_to_vary and permitted_range is not None:

diff --git a/tests/conftest.py b/tests/conftest.py
@@ -1,8 +1,9 @@
-import pytest
 from collections import OrderedDict
 import pandas as pd
+import pytest
 from sklearn.datasets import load_iris, load_boston
 from sklearn.model_selection import train_test_split
+
 import dice_ml
 from dice_ml.utils import helpers
 
@@ -194,4 +195,6 @@ def create_boston_data():
     x_train, x_test, y_train, y_test = train_test_split(
         boston.data, boston.target,
         test_size=0.2, random_state=7)
-    return x_train, x_test, y_train, y_test, boston.feature_names
+    x_train = pd.DataFrame(data=x_train, columns=boston.feature_names)
+    x_test = pd.DataFrame(data=x_test, columns=boston.feature_names)
+    return x_train, x_test, y_train, y_test, boston.feature_names.tolist()
diff --git a/tests/test_dice_interface/test_explainer_base.py b/tests/test_dice_interface/test_explainer_base.py
@@ -1,6 +1,8 @@
 import pandas as pd
 import pytest
+from sklearn.ensemble import RandomForestRegressor
 
+import dice_ml
 from dice_ml.utils.exception import UserConfigValidationException
 from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 
@@ -157,16 +159,41 @@ def test_zero_totalcfs(self, desired_class, multi_classification_exp_object, sam
 
 class TestExplainerBaseRegression:
 
-    @pytest.mark.parametrize("desired_class, regression_exp_object",
-                             [(1, 'random'), (1, 'genetic'), (1, 'kdtree')],
+    @pytest.mark.parametrize("desired_range, regression_exp_object",
+                             [([10, 100], 'random'), ([10, 100], 'genetic'), ([10, 100], 'kdtree')],
                              indirect=['regression_exp_object'])
-    def test_zero_totalcfs(self, desired_class, regression_exp_object, sample_custom_query_1):
+    def test_zero_totalcfs(self, desired_range, regression_exp_object, sample_custom_query_1):
         exp = regression_exp_object  # explainer object
         with pytest.raises(UserConfigValidationException):
             exp.generate_counterfactuals(
                     query_instances=[sample_custom_query_1],
                     total_CFs=0,
-                    desired_class=desired_class)
+                    desired_range=desired_range)
+
+    @pytest.mark.parametrize("desired_range, method",
+                             [([10, 100], 'random')])
+    def test_numeric_categories(self, desired_range, method, create_boston_data):
+        x_train, x_test, y_train, y_test, feature_names = \
+            create_boston_data
+
+        rfc = RandomForestRegressor(n_estimators=10, max_depth=4,
+                                    random_state=777)
+        model = rfc.fit(x_train, y_train)
+
+        dataset_train = x_train.copy()
+        dataset_train['Outcome'] = y_train
+        feature_names.remove('CHAS')
+
+        d = dice_ml.Data(dataframe=dataset_train, continuous_features=feature_names, outcome_name='Outcome')
+        m = dice_ml.Model(model=model, backend='sklearn', model_type='regressor')
+        exp = dice_ml.Dice(d, m, method=method)
+
+        cf_explanation = exp.generate_counterfactuals(
+            query_instances=x_test.iloc[0:1],
+            total_CFs=10,
+            desired_range=desired_range)
+
+        assert cf_explanation is not None
 
 
 class TestExplainerBase: