Implement CounterPlots to DiCE CF generation

dice_ml/counterfactual_explanations.py

@@ -1,8 +1,12 @@
 import json
 import os
 
+
 import jsonschema
+import numpy as np
+import pandas as pd
 from raiutils.exceptions import UserConfigValidationException
+from counterplots import CreatePlot
 
 from dice_ml.constants import _SchemaVersions
 from dice_ml.diverse_counterfactuals import (CounterfactualExamples,
@@ -52,11 +56,12 @@ class CounterfactualExplanations:
         based on the input set of CounterfactualExamples instances
 
     """
-    def __init__(self, cf_examples_list,
+    def __init__(self, cf_examples_list, predict_fn,
                  local_importance=None,
                  summary_importance=None,
                  version=None):
         self._cf_examples_list = cf_examples_list
+        self._predict_fn = predict_fn
         self._local_importance = local_importance
         self._summary_importance = summary_importance
         self._metadata = {'version': version if version is not None else _SchemaVersions.CURRENT_VERSION}
@@ -240,6 +245,7 @@ def from_json(json_str):
 
                 return CounterfactualExplanations(
                         cf_examples_list=cf_examples_list,
+                        predict_fn=None,
                         local_importance=json_dict[_CounterfactualExpV1SchemaConstants.LOCAL_IMPORTANCE],
                         summary_importance=json_dict[_CounterfactualExpV1SchemaConstants.SUMMARY_IMPORTANCE],
                         version=version)
@@ -295,8 +301,50 @@ def from_json(json_str):
 
                 return CounterfactualExplanations(
                         cf_examples_list=cf_examples_list,
+                        predict_fn=None,
                         local_importance=local_importance_list,
                         summary_importance=summary_importance_dict,
                         version=version)
         else:
             return json_dict
+
+    def generete_counterplots(self, predict_fn=None):
+
+        if self._predict_fn is None and predict_fn is None:
+            raise ValueError("predict_fn is required to generate counterplots")
+        elif predict_fn is not None:
+            self._predict_fn = predict_fn
+
+        cf_data = json.loads(self.to_json())
+        factual = self.cf_examples_list[0].test_instance_df.to_numpy()[0][:-1]
+        feature_names = list(self.cf_examples_list[0].test_instance_df.columns)[:-1]
+        df_structure = self.cf_examples_list[0].test_instance_df[:0].loc[:, feature_names]
+        data_types = df_structure.dtypes.apply(lambda x: x.name).to_dict()
+
+        factual_class_name = str(self.cf_examples_list[0].test_pred)
+        cf_class_name = str(self.cf_examples_list[0].new_outcome)
+
+        def adjust_types(x):
+            for i in range(x.shape[1]):
+                if 'int' in list(data_types.values())[i]:
+                    x[:, i] = int(float(x[:, i]))
+            return x
+
+        def model_pred(x):
+            scores = self._predict_fn(df_structure.append(pd.DataFrame(x, columns=feature_names))).numpy()
+            return np.concatenate((1 - scores, scores), axis=1)
+
+        out_exp = []
+
+        for raw_cf in cf_data['cfs_list'][0]:
+            cf = adjust_types(np.array([raw_cf[:-1]]))[0]
+
+            out_exp.append(CreatePlot(
+                factual=np.array(factual),
+                cf=np.array(cf),
+                model_pred=model_pred,
+                feature_names=feature_names,
+                class_names={0: factual_class_name, 1: cf_class_name}))
+
+        return out_exp
+

dice_ml/explainer_interfaces/dice_tensorflow1.py

@@ -167,7 +167,7 @@ def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opp
             posthoc_sparsity_param=posthoc_sparsity_param,
             desired_class=desired_class)
 
-        return CounterfactualExplanations(cf_examples_list=[counterfactual_explanations])
+        return CounterfactualExplanations(cf_examples_list=[counterfactual_explanations], predict_fn=self.predict_fn)
 
     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

@@ -144,7 +144,7 @@ def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opp
             posthoc_sparsity_param=posthoc_sparsity_param,
             desired_class=desired_class)
 
-        return CounterfactualExplanations(cf_examples_list=[counterfactual_explanations])
+        return CounterfactualExplanations(cf_examples_list=[counterfactual_explanations], predict_fn=self.predict_fn)
 
     def predict_fn(self, input_instance):
         """prediction function"""

dice_ml/explainer_interfaces/explainer_base.py

@@ -172,7 +172,7 @@ def generate_counterfactuals(self, query_instances, total_CFs,
             cf_examples_arr.append(res)
         self._check_any_counterfactuals_computed(cf_examples_arr=cf_examples_arr)
 
-        return CounterfactualExplanations(cf_examples_list=cf_examples_arr)
+        return CounterfactualExplanations(cf_examples_list=cf_examples_arr, predict_fn=self.predict_fn)
 
     @abstractmethod
     def _generate_counterfactuals(self, query_instance, total_CFs,
@@ -475,6 +475,7 @@ def feature_importance(self, query_instances, cf_examples_list=None,
 
         return CounterfactualExplanations(
             cf_examples_list,
+            predict_fn=self.predict_fn,
             local_importance=local_importances,
             summary_importance=summary_importance)
 

requirements.txt

@@ -4,3 +4,4 @@ pandas<2.0.0
 scikit-learn
 tqdm
 raiutils>=0.4.0
+counterplots

tests/test_counterfactual_explanations.py

@@ -3,11 +3,17 @@
 import pytest
 from raiutils.exceptions import UserConfigValidationException
 
+
+import numpy as np
+import dice_ml
 from dice_ml.counterfactual_explanations import CounterfactualExplanations
 
 
 class TestCounterfactualExplanations:
 
+    def __init__(self):
+        self.predict_fn = lambda x: np.array([1.0 for _ in range(np.array(x).shape[0])])
+
     def test_sorted_summary_importance_counterfactual_explanations(self):
 
         unsorted_summary_importance = {
@@ -34,6 +40,7 @@ def test_sorted_summary_importance_counterfactual_explanations(self):
 
         counterfactual_explanations = CounterfactualExplanations(
             cf_examples_list=[],
+            predict_fn=self.predict_fn,
             local_importance=None,
             summary_importance=unsorted_summary_importance)
 
@@ -94,6 +101,7 @@ def test_sorted_local_importance_counterfactual_explanations(self):
         counterfactual_explanations = CounterfactualExplanations(
             cf_examples_list=[],
             local_importance=unsorted_local_importance,
+            predict_fn=self.predict_fn,
             summary_importance=None)
 
         for index in range(0, len(unsorted_local_importance)):
@@ -108,6 +116,10 @@ def test_sorted_local_importance_counterfactual_explanations(self):
 
 
 class TestSerializationCounterfactualExplanations:
+
+    def __init__(self):
+        self.predict_fn = lambda x: np.array([1.0 for _ in range(np.array(x).shape[0])])
+
     @pytest.fixture(autouse=True)
     def _initiate_exp_object(self, binary_classification_exp_object):
         self.exp = binary_classification_exp_object  # explainer object
@@ -211,6 +223,7 @@ def test_empty_counterfactual_explanations_object(self, version):
 
         counterfactual_explanations = CounterfactualExplanations(
             cf_examples_list=[],
+            predict_fn=self.predict_fn,
             local_importance=None,
             summary_importance=None,
             version=version)
@@ -311,6 +324,7 @@ def test_unsupported_versions_from_json(self, unsupported_version):
     def test_unsupported_versions_to_json(self, unsupported_version):
         counterfactual_explanations = CounterfactualExplanations(
             cf_examples_list=[],
+            predict_fn=self.predict_fn,
             local_importance=None,
             summary_importance=None,
             version=unsupported_version)