def test_with_X_nan(self, causal_X, causal_y, causal_w):
causal_X_nan = causal_X.copy()
index = np.random.choice(causal_X_nan.size, 100, replace=False)
causal_X_nan.ravel()[index] = np.nan
assert np.sum(np.isnan(causal_X_nan)) == 100
tree = GRFTreeInstrumentalRegressor()
tree.fit(causal_X_nan, causal_y, causal_w, causal_w)
pred = tree.predict(causal_X_nan)
assert len(pred) == causal_X_nan.shape[0]
def test_predict(self, causal_X, causal_y, causal_w):
tree = GRFTreeInstrumentalRegressor()
tree.fit(causal_X, causal_y, causal_w, causal_w)
pred = tree.predict(causal_X)
assert len(pred) == causal_X.shape[0]