fix nightly build breaking due to new scikit-learn package which brea…

…ks model function serialization (#455)

python/interpret_community/common/model_wrapper.py

@@ -191,13 +191,49 @@ def predict_proba(self, dataset):
         return self.predict(dataset)
 
 
-class WrappedClassificationModel(object):
-    """A class for wrapping a classification model."""
+class BaseWrappedModel(object):
+    """A base class for WrappedClassificationModel and WrappedRegressionModel."""
 
-    def __init__(self, model, eval_function):
+    def __init__(self, model, eval_function, examples, model_task):
         """Initialize the WrappedClassificationModel with the model and evaluation function."""
         self._eval_function = eval_function
         self._model = model
+        self._examples = examples
+        self._model_task = model_task
+
+    def __getstate__(self):
+        """Influence how BaseWrappedModel is pickled.
+
+        Removes _eval_function which may not be serializable.
+
+        :return state: The state to be pickled, with _eval_function removed.
+        :rtype: dict
+        """
+        odict = self.__dict__.copy()
+        if self._examples is not None:
+            del odict['_eval_function']
+        return odict
+
+    def __setstate__(self, state):
+        """Influence how BaseWrappedModel is unpickled.
+
+        Re-adds _eval_function which may not be serializable.
+
+        :param dict: A dictionary of deserialized state.
+        :type dict: dict
+        """
+        self.__dict__.update(state)
+        if self._examples is not None:
+            eval_function, _ = _eval_model(self._model, self._examples, self._model_task)
+            self._eval_function = eval_function
+
+
+class WrappedClassificationModel(BaseWrappedModel):
+    """A class for wrapping a classification model."""
+
+    def __init__(self, model, eval_function, examples=None):
+        """Initialize the WrappedClassificationModel with the model and evaluation function."""
+        super(WrappedClassificationModel, self).__init__(model, eval_function, examples, ModelTask.Classification)
 
     def predict(self, dataset):
         """Predict the output using the wrapped classification model.
@@ -235,13 +271,12 @@ def predict_proba(self, dataset):
         return proba_preds
 
 
-class WrappedRegressionModel(object):
+class WrappedRegressionModel(BaseWrappedModel):
     """A class for wrapping a regression model."""
 
-    def __init__(self, model, eval_function):
+    def __init__(self, model, eval_function, examples=None):
         """Initialize the WrappedRegressionModel with the model and evaluation function."""
-        self._eval_function = eval_function
-        self._model = model
+        super(WrappedRegressionModel, self).__init__(model, eval_function, examples, ModelTask.Regression)
 
     def predict(self, dataset):
         """Predict the output using the wrapped regression model.
@@ -342,9 +377,9 @@ def _wrap_model(model, examples, model_task, is_function):
             model = WrappedClassificationWithoutProbaModel(model)
         eval_function, eval_ml_domain = _eval_model(model, examples, model_task)
         if eval_ml_domain == ModelTask.Classification:
-            return WrappedClassificationModel(model, eval_function), eval_ml_domain
+            return WrappedClassificationModel(model, eval_function, examples), eval_ml_domain
         else:
-            return WrappedRegressionModel(model, eval_function), eval_ml_domain
+            return WrappedRegressionModel(model, eval_function, examples), eval_ml_domain
 
 
 def _classifier_without_proba(model):

test/test_serialize_explainer.py

@@ -47,7 +47,7 @@ def test_serialize_kernel(self):
         with open(explainer_name, 'wb') as stream:
             dump(explainer.explainer.explainer, stream)
         with open(model_name, 'wb') as stream:
-            dump(explainer.model.predict_proba, stream)
+            dump(explainer.model, stream)
         assert path.exists(model_name)
         assert path.exists(explainer_name)
 
@@ -69,7 +69,7 @@ def test_serialize_mimic_lightgbm(self):
         surrogate_name = 'surrogate_model.joblib'
         tree_explainer_name = 'tree_explainer_model.joblib'
         with open(model_name, 'wb') as stream:
-            dump(explainer.model.predict_proba, stream)
+            dump(explainer.model, stream)
         with open(surrogate_name, 'wb') as stream:
             dump(explainer.surrogate_model.model, stream)
         with open(tree_explainer_name, 'wb') as stream: