Address issue with timeouts in unit change tests

The build sometimes randomly fails due to a timeout issue in the unit tests of the unit change methods of the GCM module. While this only happens in the github builds, this is most likely due to the prallelization of the underlying RandomForestRegressors being fitted.

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

tests/gcm/test_unit_change.py

@@ -109,9 +109,9 @@ def test_given_unfitted_mechanisms_when_evaluate_unit_change_methods_then_raises
 
     with pytest.raises(NotFittedError):
         unit_change_nonlinear(
-            SklearnRegressionModel(RFR()),
+            SklearnRegressionModel(RFR(n_jobs=1)),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
-            SklearnRegressionModel(RFR()),
+            SklearnRegressionModel(RFR(n_jobs=1)),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
             ["A", "B"],
             shapley_config=ShapleyConfig(n_jobs=1),
@@ -121,9 +121,9 @@ def test_given_unfitted_mechanisms_when_evaluate_unit_change_methods_then_raises
 def test_given_fitted_nonlinnear_mechanisms_when_evaluate_unit_change_linear_method_then_raises_exception():
     with pytest.raises(AttributeError):
         unit_change_linear(
-            SklearnRegressionModel(RFR().fit(np.random.normal(size=(100, 2)), np.random.normal(size=100))),
+            SklearnRegressionModel(RFR(n_jobs=1).fit(np.random.normal(size=(100, 2)), np.random.normal(size=100))),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
-            SklearnRegressionModel(RFR().fit(np.random.normal(size=(100, 2)), np.random.normal(size=100))),
+            SklearnRegressionModel(RFR(n_jobs=1).fit(np.random.normal(size=(100, 2)), np.random.normal(size=100))),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
             ["A", "B"],
         )
@@ -140,7 +140,7 @@ def test_given_fitted_mechanisms_with_no_input_change_when_evaluate_unit_change_
     foreground_df = pd.DataFrame(data=dict(A=A, B=B, C=C))
 
     actual_contributions = unit_change_nonlinear_input_only(
-        SklearnRegressionModel(RFR().fit(np.column_stack((A, B)), C)),
+        SklearnRegressionModel(RFR(n_jobs=1).fit(np.column_stack((A, B)), C)),
         background_df,
         foreground_df,
         ["A", "B"],
@@ -217,7 +217,7 @@ def test_given_unfitted_mechanisms_when_evaluate_unit_change_input_only_methods_
 
     with pytest.raises(NotFittedError):
         unit_change_nonlinear_input_only(
-            SklearnRegressionModel(RFR()),
+            SklearnRegressionModel(RFR(n_jobs=1)),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
             ["A", "B"],
@@ -228,7 +228,7 @@ def test_given_unfitted_mechanisms_when_evaluate_unit_change_input_only_methods_
 def test_given_fitted_nonlinnear_mechanism_when_evaluate_unit_change_linear_input_only_method_then_raises_exception():
     with pytest.raises(AttributeError):
         unit_change_linear_input_only(
-            SklearnRegressionModel(RFR().fit(np.random.normal(size=(100, 2)), np.random.normal(size=100))),
+            SklearnRegressionModel(RFR(n_jobs=1).fit(np.random.normal(size=(100, 2)), np.random.normal(size=100))),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
             pd.DataFrame(data=dict(A=np.random.normal(size=100), B=np.random.normal(size=100))),
             ["A", "B"],
@@ -256,7 +256,7 @@ def test_given_single_mechanism_with_default_optional_parameters_when_evaluate_u
 
     np.testing.assert_array_almost_equal(actual_contributions, expected_contributions, decimal=1)
 
-    mechanism = SklearnRegressionModel(RFR().fit(np.column_stack((A1, B1)), C1))
+    mechanism = SklearnRegressionModel(RFR(n_jobs=1).fit(np.column_stack((A1, B1)), C1))
 
     actual_contributions = unit_change(background_df, foreground_df, ["A", "B"], mechanism)
     expected_contributions = unit_change_nonlinear_input_only(
@@ -295,8 +295,8 @@ def test_given_two_mechanisms_when_evaluate_unit_change_then_returns_correct_att
 
     np.testing.assert_array_almost_equal(actual_contributions, expected_contributions, decimal=1)
 
-    background_mechanism = SklearnRegressionModel(RFR().fit(np.column_stack((A1, B1)), C1))
-    foreground_mechanism = SklearnRegressionModel(RFR().fit(np.column_stack((A2, B2)), C2))
+    background_mechanism = SklearnRegressionModel(RFR(n_jobs=1).fit(np.column_stack((A1, B1)), C1))
+    foreground_mechanism = SklearnRegressionModel(RFR(n_jobs=1).fit(np.column_stack((A2, B2)), C2))
 
     actual_contributions = unit_change(
         background_df, foreground_df, ["A", "B"], background_mechanism, foreground_mechanism