def test_with_X_nan(self, boston_X, boston_y):
boston_X_nan = boston_X.copy()
index = np.random.choice(boston_X_nan.size, 100, replace=False)
boston_X_nan.ravel()[index] = np.nan
assert np.sum(np.isnan(boston_X_nan)) == 100
forest = GRFForestRegressor()
forest.fit(boston_X_nan, boston_y)
pred = forest.predict(boston_X_nan)
assert len(pred) == boston_X_nan.shape[0]
def test_predict(self, boston_X, boston_y):
forest = GRFForestRegressor()
forest.fit(boston_X, boston_y)
pred = forest.predict(boston_X)
assert len(pred) == boston_X.shape[0]