Fix issue with categorical inputs to gcm ProductRegressor

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

dowhy/gcm/ml/regression.py

@@ -144,11 +144,14 @@ class InvertibleLogarithmicFunction(InvertibleFunction):
 
 
 class ProductRegressor(PredictionModel):
+    def __init__(self):
+        self._one_hot_encoders = {}
+
     def fit(self, X, Y):
-        # Nothing to fit here.
-        pass
+        self._one_hot_encoders = fit_one_hot_encoders(X)
 
     def predict(self, X):
+        X = apply_one_hot_encoding(X, self._one_hot_encoders)
         return np.prod(X, axis=1).reshape(-1, 1)
 
     def clone(self):

tests/gcm/ml/test_regression.py

@@ -4,9 +4,19 @@ from _pytest.python_api import approx
 from dowhy.gcm.ml.regression import create_product_regressor
 
 
-def test_given_product_regressor_then_computes_correct_values():
+def test_when_use_product_regressor_then_computes_correct_values():
     X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
 
     mdl = create_product_regressor()
+    # No fit needed
 
     assert mdl.predict(X).reshape(-1) == approx(np.array([6, 120, 504]))
+
+
+def test_when_input_is_categorical_when_use_product_regressor_then_computes_correct_values():
+    X = np.column_stack([np.array(["Class 1", "Class 2"]).astype(object), np.array([1, 2])]).astype(object)
+
+    mdl = create_product_regressor()
+    mdl.fit(X, np.zeros(2))  # Need to fit one-hot-encoder
+
+    assert mdl.predict(X).reshape(-1) == approx(np.array([0, 2]))