added userconfig exception for totalcfs<1 (#125)

* added userconfig exception for totalcfs<1

* added unit test for explainer base

dice_ml/explainer_interfaces/dice_random.py

@@ -163,7 +163,7 @@ def _generate_counterfactuals(self, query_instance, total_CFs, desired_range,  d
                       m, 'min %02d' % s, 'sec')
         else:
             if self.total_cfs_found == 0 :
-                print('No Counterfactuals found for the given configuation, perhaps try with different parameters...', '; total time taken: %02d' % m, 'min %02d' % s, 'sec')
+                print('No Counterfactuals found for the given configuration, perhaps try with different parameters...', '; total time taken: %02d' % m, 'min %02d' % s, 'sec')
             else:
                 print('Only %d (required %d) Diverse Counterfactuals found for the given configuration, perhaps try with different parameters...' % (self.total_cfs_found, self.total_CFs), '; total time taken: %02d' % m, 'min %02d' % s, 'sec')
 

dice_ml/explainer_interfaces/explainer_base.py

@@ -68,7 +68,8 @@ def generate_counterfactuals(self, query_instances, total_CFs,
         :returns: A CounterfactualExplanations object that contains the list of
         counterfactual examples per query_instance as one of its attributes.
         """
-
+        if total_CFs <= 0:
+            raise UserConfigValidationException("The number of counterfactuals generated per query instance (total_CFs) should be a positive integer.")
         cf_examples_arr = []
         query_instances_list = []
         if isinstance(query_instances, pd.DataFrame):

tests/conftest.py

@@ -4,6 +4,40 @@
 import dice_ml
 from dice_ml.utils import helpers
 
+@pytest.fixture
+def binary_classification_exp_object(method="random"):
+    backend = 'sklearn'
+    dataset = helpers.load_custom_testing_dataset_binary()
+    d = dice_ml.Data(dataframe=dataset, continuous_features=['Numerical'], outcome_name='Outcome')
+    ML_modelpath = helpers.get_custom_dataset_modelpath_pipeline_binary()
+    m = dice_ml.Model(model_path=ML_modelpath, backend=backend)
+    exp = dice_ml.Dice(d, m, method=method)
+    return exp
+
+
+@pytest.fixture
+def multi_classification_exp_object(method="random"):
+    backend = 'sklearn'
+    dataset = helpers.load_custom_testing_dataset_multiclass()
+    d = dice_ml.Data(dataframe=dataset, continuous_features=['Numerical'], outcome_name='Outcome')
+    ML_modelpath = helpers.get_custom_dataset_modelpath_pipeline_multiclass()
+    m = dice_ml.Model(model_path=ML_modelpath, backend=backend)
+    exp = dice_ml.Dice(d, m, method=method)
+    return exp
+
+
+@pytest.fixture
+def regression_exp_object(method="random"):
+    backend = 'sklearn'
+    dataset = helpers.load_custom_testing_dataset_regression()
+    d = dice_ml.Data(dataframe=dataset, continuous_features=['Numerical'], outcome_name='Outcome')
+    ML_modelpath = helpers.get_custom_dataset_modelpath_pipeline_regression()
+    m = dice_ml.Model(model_path=ML_modelpath, backend=backend, model_type='regressor')
+    exp = dice_ml.Dice(d, m, method=method)
+    return exp
+
+
+
 @pytest.fixture
 def public_data_object():
     """

tests/test_dice_interface/test_explainer_base.py

@@ -0,0 +1,37 @@
+import pytest
+from dice_ml.utils.exception import UserConfigValidationException
+
+
+class TestExplainerBaseBinaryClassification:
+
+    @pytest.mark.parametrize("desired_class, binary_classification_exp_object", [(1, 'random'),(1,'genetic'),(1,'kdtree')], indirect=['binary_classification_exp_object'])
+    def test_zero_totalcfs(self, desired_class, binary_classification_exp_object, sample_custom_query_1):
+        exp = binary_classification_exp_object  # explainer object
+        with pytest.raises(UserConfigValidationException):
+            exp.generate_counterfactuals(
+                    query_instances=[sample_custom_query_1],
+                    total_CFs=0,
+                    desired_class=desired_class)
+
+class TestExplainerBaseMultiClassClassification:
+
+    @pytest.mark.parametrize("desired_class, multi_classification_exp_object", [(1, 'random'),(1,'genetic'),(1,'kdtree')], indirect=['multi_classification_exp_object'])
+    def test_zero_totalcfs(self, desired_class, multi_classification_exp_object, sample_custom_query_1):
+        exp = multi_classification_exp_object  # explainer object
+        with pytest.raises(UserConfigValidationException):
+            exp.generate_counterfactuals(
+                    query_instances=[sample_custom_query_1],
+                    total_CFs=0,
+                    desired_class=desired_class)
+
+
+class TestExplainerBaseRegression:
+
+    @pytest.mark.parametrize("desired_class, regression_exp_object", [(1, 'random'),(1,'genetic'),(1,'kdtree')], indirect=['regression_exp_object'])
+    def test_zero_totalcfs(self, desired_class, 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)