Add tests

Signed-off-by: Gaurav Gupta <gaugup@microsoft.com>

Author: gaugup

tests/test_dice_interface/test_dice_random.py

@@ -3,7 +3,6 @@
 import dice_ml
 from dice_ml.counterfactual_explanations import CounterfactualExplanations
 from dice_ml.diverse_counterfactuals import CounterfactualExamples
-from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 from dice_ml.utils import helpers
 from dice_ml.utils.exception import UserConfigValidationException
 
@@ -56,18 +55,6 @@ def test_random_counterfactual_explanations_output(self, desired_class, sample_c
         assert len(counterfactual_explanations.cf_examples_list) == sample_custom_query_1.shape[0]
         assert counterfactual_explanations.cf_examples_list[0].final_cfs_df.shape[0] == total_CFs
 
-        self.exp.serialize_explainer("random.pkl")
-        new_exp = ExplainerBase.deserialize_explainer("random.pkl")
-
-        assert new_exp is not None
-        counterfactual_explanations = new_exp.generate_counterfactuals(
-            query_instances=sample_custom_query_1, desired_class=desired_class,
-            total_CFs=total_CFs)
-
-        assert counterfactual_explanations is not None
-        assert len(counterfactual_explanations.cf_examples_list) == sample_custom_query_1.shape[0]
-        assert counterfactual_explanations.cf_examples_list[0].final_cfs_df.shape[0] == total_CFs
-
     # When invalid desired_class is given
     @pytest.mark.parametrize(("desired_class", "desired_range", "total_CFs", "features_to_vary", "permitted_range"),
                              [(7, None, 3, "all", None)])

tests/test_dice_interface/test_explainer_base.py

@@ -256,6 +256,22 @@ def test_desired_class(
             assert all(ans.cf_examples_list[0].final_cfs_df_sparse[exp.data_interface.outcome_name].values ==
                        [desired_class] * 2)
 
+        exp.serialize_explainer(method + '.pkl')
+        new_exp = ExplainerBase.deserialize_explainer(method + '.pkl')
+
+        ans = new_exp.generate_counterfactuals(query_instances=sample_custom_query_2,
+                                               features_to_vary='all',
+                                               total_CFs=2, desired_class=desired_class,
+                                               proximity_weight=0.2, sparsity_weight=0.2,
+                                               diversity_weight=5.0,
+                                               categorical_penalty=0.1,
+                                               permitted_range=None)
+        if method != 'kdtree':
+            assert all(ans.cf_examples_list[0].final_cfs_df[new_exp.data_interface.outcome_name].values == [desired_class] * 2)
+        else:
+            assert all(ans.cf_examples_list[0].final_cfs_df_sparse[new_exp.data_interface.outcome_name].values ==
+                       [desired_class] * 2)
+
     @pytest.mark.parametrize(("desired_class", "total_CFs", "permitted_range"),
                              [(1, 1, {'Numerical': [10, 150]})])
     def test_permitted_range(
@@ -349,6 +365,29 @@ def test_desired_class(
                 [desired_class] * total_CFs)
         assert all(i == desired_class for i in exp.cfs_preds)
 
+        exp.serialize_explainer(method + '.pkl')
+        new_exp = ExplainerBase.deserialize_explainer(method + '.pkl')
+
+        if method != 'genetic':
+            ans = new_exp.generate_counterfactuals(
+                    query_instances=sample_custom_query_2,
+                    total_CFs=total_CFs, desired_class=desired_class)
+        else:
+            ans = new_exp.generate_counterfactuals(
+                    query_instances=sample_custom_query_2,
+                    total_CFs=total_CFs, desired_class=desired_class,
+                    initialization=genetic_initialization)
+
+        assert ans is not None
+        if method != 'kdtree':
+            assert all(
+                ans.cf_examples_list[0].final_cfs_df[new_exp.data_interface.outcome_name].values == [desired_class] * total_CFs)
+        else:
+            assert all(
+                ans.cf_examples_list[0].final_cfs_df_sparse[new_exp.data_interface.outcome_name].values ==
+                [desired_class] * total_CFs)
+        assert all(i == desired_class for i in new_exp.cfs_preds)
+
     # When no elements in the desired_class are present in the training data
     @pytest.mark.parametrize(("desired_class", "total_CFs"), [(100, 3), ('opposite', 3)])
     def test_unsupported_multiclass(
@@ -422,6 +461,16 @@ def test_numeric_categories(self, desired_range, method, create_housing_data):
 
         assert cf_explanation is not None
 
+        exp.serialize_explainer("explainer.pkl")
+        new_exp = ExplainerBase.deserialize_explainer("explainer.pkl")
+
+        cf_explanation = new_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: