Change default parameter of SVC model in the GCM module

Before, the Support Vector Classifier did not produce probabilities, which are required for different algorithms in the GCM module. This changes the 'probability' parameter to True.

Signed-off-by: Patrick Bloebaum <bloebp@amazon.com>

dowhy/gcm/ml/classification.py

@@ -76,7 +76,7 @@ def create_ada_boost_classifier(**kwargs) -> SklearnClassificationModel:
 
 
 def create_support_vector_classifier(**kwargs) -> SklearnClassificationModel:
-    return SklearnClassificationModel(SVC(**kwargs))
+    return SklearnClassificationModel(SVC(**kwargs, probability=True))
 
 
 def create_knn_classifier(**kwargs) -> SklearnClassificationModel:

tests/gcm/ml/test_classification.py

@@ -2,6 +2,7 @@ import numpy as np
 from flaky import flaky
 
 from dowhy.gcm.ml import create_hist_gradient_boost_classifier, create_polynom_logistic_regression_classifier
+from dowhy.gcm.ml.classification import create_support_vector_classifier
 
 
 @flaky(max_runs=3)
@@ -58,3 +59,9 @@ def test_given_categorical_training_data_with_many_categories_when_fit_classific
     mdl.fit(X_training, Y_training)
 
     assert np.sum(mdl.predict(X_test).reshape(-1) == Y_test) > 950
+
+
+def test_given_svc_model_then_supports_predict_probabilities():
+    mdl = create_support_vector_classifier()
+    mdl.fit(np.random.normal(0, 1, 100), np.random.choice(2, 100).astype(str))
+    mdl.predict_probabilities(np.random.normal(0, 1, 10))