def test_quantile_regression(self, boston_X, boston_y):
X_train, X_test, y_train, y_test = train_test_split(boston_X, boston_y)
forest = RangerForestRegressor(quantiles=False)
forest.fit(X_train, y_train)
assert not hasattr(forest, "random_node_values_")
with pytest.raises(ValueError):
forest.predict_quantiles(X_test, quantiles=[0.2, 0.5, 0.8])
forest = RangerForestRegressor(quantiles=True)
forest.fit(X_train, y_train)
assert hasattr(forest, "random_node_values_")
quantiles_lower = forest.predict_quantiles(X_test, quantiles=[0.1])
quantiles_upper = forest.predict_quantiles(X_test, quantiles=[0.9])
assert np.less(quantiles_lower, quantiles_upper).all()
assert quantiles_upper.ndim == 1
quantiles = forest.predict_quantiles(X_test, quantiles=[0.1, 0.9])
assert quantiles.shape == (X_test.shape[0], 2)
assert np.sum(np.isnan(quantiles_lower)) == 0
assert np.sum(np.isnan(quantiles_upper)) == 0
# test predict method
pred = forest.predict(X_test, quantiles=[0.2, 0.5])
assert pred.shape == (X_test.shape[0], 2)
assert np.sum(np.isnan(pred)) == 0
pred = forest.predict(X_test, quantiles=[0.2])
assert pred.ndim == 1
assert np.sum(np.isnan(pred)) == 0
# test with single record
boston_X_record = boston_X[0:1, :]
pred = forest.predict(boston_X_record, quantiles=[0.2, 0.5])
assert pred.shape == (1, 2)
assert np.sum(np.isnan(pred)) == 0
def test_quantile_regression(self, boston_X, boston_y):
X_train, X_test, y_train, y_test = train_test_split(boston_X, boston_y)
rfr = RangerForestRegressor(quantiles=False)
rfr.fit(X_train, y_train)
assert not hasattr(rfr, "random_node_values_")
with pytest.raises(ValueError):
rfr.predict_quantiles(X_test)
rfr = RangerForestRegressor(quantiles=True)
rfr.fit(X_train, y_train)
assert hasattr(rfr, "random_node_values_")
quantiles_lower = rfr.predict_quantiles(X_test, quantiles=[0.1])
quantiles_upper = rfr.predict_quantiles(X_test, quantiles=[0.9])
assert np.less(quantiles_lower, quantiles_upper).all()
assert quantiles_upper.ndim == 1
quantiles = rfr.predict_quantiles(X_test, quantiles=[0.1, 0.9])
assert quantiles.ndim == 2
