fix: TensorFlow explainers have the same signature as the rest

Right now the TensorFlow gradient explainers do not implement
the `_generate_counterfactuals` method from the `BaseExplainer`
class. This means that:
	1) You cannot instantiate object of class
	`DiceTensorFlow(1/2)` without replacing the `__class__`
	of another method, because it is not a valid child
	of `ExplainerBase`.
	2) By overriding the parent `generate_counterfactuals`
	method, the two classes bypass any validation steps
	that would normally be carried out by the base class
	(e.g. checking that the number of CF queries is non-negative).

Signed-off-by: Asen Dotsinski <[email protected]>

dice_ml/explainer_interfaces/dice_tensorflow1.py

@@ -61,20 +61,22 @@ def __init__(self, data_interface, model_interface):
         self.loss_weights = []  # yloss_type, diversity_loss_type, feature_weights
         self.optimizer_weights = []  # optimizer
 
-    def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opposite", proximity_weight=0.5,
-                                 diversity_weight=1.0, categorical_penalty=0.1, algorithm="DiverseCF",
-                                 features_to_vary="all", permitted_range=None, yloss_type="hinge_loss",
-                                 diversity_loss_type="dpp_style:inverse_dist", feature_weights="inverse_mad",
-                                 optimizer="tensorflow:adam", learning_rate=0.05, min_iter=500, max_iter=5000,
-                                 project_iter=0, loss_diff_thres=1e-5, loss_converge_maxiter=1, verbose=False,
-                                 init_near_query_instance=True, tie_random=False, stopping_threshold=0.5,
-                                 posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm="linear", limit_steps_ls=10000):
+    def _generate_counterfactuals(self, query_instance, total_CFs,
+                                  desired_class="opposite", desired_range=None,
+                                  proximity_weight=0.5,
+                                  diversity_weight=1.0, categorical_penalty=0.1, algorithm="DiverseCF", features_to_vary="all",
+                                  permitted_range=None, yloss_type="hinge_loss", diversity_loss_type="dpp_style:inverse_dist",
+                                  feature_weights="inverse_mad", optimizer="tensorflow:adam", learning_rate=0.05, min_iter=500,
+                                  max_iter=5000, project_iter=0, loss_diff_thres=1e-5, loss_converge_maxiter=1, verbose=False,
+                                  init_near_query_instance=True, tie_random=False, stopping_threshold=0.5,
+                                  posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm="linear", limit_steps_ls=10000):
         """Generates diverse counterfactual explanations
 
         :param query_instance: Test point of interest. A dictionary of feature names and values or a single row dataframe.
         :param total_CFs: Total number of counterfactuals required.
         :param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the
                               outcome class of query_instance for binary classification.
+        :param desired_range: Not supported currently.
         :param proximity_weight: A positive float. Larger this weight, more close the counterfactuals are to the
                                  query_instance.
         :param diversity_weight: A positive float. Larger this weight, more diverse the counterfactuals are.
@@ -159,16 +161,14 @@ def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opp
             loss_diff_thres, loss_converge_maxiter, verbose, init_near_query_instance, tie_random,
             stopping_threshold, posthoc_sparsity_param, posthoc_sparsity_algorithm)
 
-        counterfactual_explanations = exp.CounterfactualExamples(
+        return exp.CounterfactualExamples(
             data_interface=self.data_interface,
             final_cfs_df=final_cfs_df,
             test_instance_df=test_instance_df,
             final_cfs_df_sparse=final_cfs_df_sparse,
             posthoc_sparsity_param=posthoc_sparsity_param,
             desired_class=desired_class)
 
-        return CounterfactualExplanations(cf_examples_list=[counterfactual_explanations])
-
     def do_cf_initializations(self, total_CFs, algorithm, features_to_vary):
         """Intializes TF variables required for CF generation."""
 

dice_ml/explainer_interfaces/dice_tensorflow2.py

@@ -49,20 +49,22 @@ def __init__(self, data_interface, model_interface):
         self.hyperparameters = [1, 1, 1]  # proximity_weight, diversity_weight, categorical_penalty
         self.optimizer_weights = []  # optimizer, learning_rate
 
-    def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opposite", proximity_weight=0.5,
-                                 diversity_weight=1.0, categorical_penalty=0.1, algorithm="DiverseCF",
-                                 features_to_vary="all", permitted_range=None, yloss_type="hinge_loss",
-                                 diversity_loss_type="dpp_style:inverse_dist", feature_weights="inverse_mad",
-                                 optimizer="tensorflow:adam", learning_rate=0.05, min_iter=500, max_iter=5000,
-                                 project_iter=0, loss_diff_thres=1e-5, loss_converge_maxiter=1, verbose=False,
-                                 init_near_query_instance=True, tie_random=False, stopping_threshold=0.5,
-                                 posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm="linear", limit_steps_ls=10000):
+    def _generate_counterfactuals(self, query_instance, total_CFs,
+                                  desired_class="opposite", desired_range=None,
+                                  proximity_weight=0.5,
+                                  diversity_weight=1.0, categorical_penalty=0.1, algorithm="DiverseCF", features_to_vary="all",
+                                  permitted_range=None, yloss_type="hinge_loss", diversity_loss_type="dpp_style:inverse_dist",
+                                  feature_weights="inverse_mad", optimizer="tensorflow:adam", learning_rate=0.05, min_iter=500,
+                                  max_iter=5000, project_iter=0, loss_diff_thres=1e-5, loss_converge_maxiter=1, verbose=False,
+                                  init_near_query_instance=True, tie_random=False, stopping_threshold=0.5,
+                                  posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm="linear", limit_steps_ls=10000):
         """Generates diverse counterfactual explanations
 
         :param query_instance: Test point of interest. A dictionary of feature names and values or a single row dataframe
         :param total_CFs: Total number of counterfactuals required.
         :param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the
                               outcome class of query_instance for binary classification.
+        :param desired_range: Not supported currently.
         :param proximity_weight: A positive float. Larger this weight, more close the counterfactuals are to
                                  the query_instance.
         :param diversity_weight: A positive float. Larger this weight, more diverse the counterfactuals are.
@@ -136,15 +138,14 @@ def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opp
                                       init_near_query_instance, tie_random, stopping_threshold,
                                       posthoc_sparsity_param, posthoc_sparsity_algorithm, limit_steps_ls)
 
-        counterfactual_explanations = exp.CounterfactualExamples(
+        return exp.CounterfactualExamples(
             data_interface=self.data_interface,
             final_cfs_df=final_cfs_df,
             test_instance_df=test_instance_df,
             final_cfs_df_sparse=final_cfs_df_sparse,
             posthoc_sparsity_param=posthoc_sparsity_param,
             desired_class=desired_class)
 
-        return CounterfactualExplanations(cf_examples_list=[counterfactual_explanations])
 
     def predict_fn(self, input_instance):
         """prediction function"""