[WIP] Add capability to serialize and de-serialize dice-ml explainers

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

Author: gaugup

dice_ml/explainer_interfaces/explainer_base.py

@@ -6,6 +6,7 @@
 from collections.abc import Iterable
 
 import numpy as np
+import pickle
 import pandas as pd
 from sklearn.neighbors import KDTree
 from tqdm import tqdm
@@ -805,3 +806,17 @@ def _check_any_counterfactuals_computed(self, cf_examples_arr):
         if no_cf_generated:
             raise UserConfigValidationException(
                 "No counterfactuals found for any of the query points! Kindly check your configuration.")
+
+    def serialize_explainer(self, path):
+        """Serialize the explainer to the file specified by path."""
+        with open(path, "wb") as pickle_file:
+            pickle.dump(self, pickle_file)
+
+    @staticmethod
+    def deserialize_explainer(path):
+        """Reload the explainer into the memroy by reading the file specified by path."""
+        deserialized_exp = None
+        with open(path, "rb") as pickle_file:
+            deserialized_exp = pickle.load(pickle_file)
+
+        return deserialized_exp

tests/test_dice_interface/test_dice_random.py

@@ -3,6 +3,7 @@
 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
 
@@ -55,6 +56,18 @@ 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)])