def test_shape_trained_model(random_xy_dataset_regr):
X, y = random_xy_dataset_regr
mod_no_intercept = ProbWeightRegression()
assert mod_no_intercept.fit(X, y).coefs_.shape == (X.shape[1],)
np.testing.assert_approx_equal(
mod_no_intercept.fit(X, y).coefs_.sum(), 1.0, significant=4
)
def test_estimator_checks(test_fn):
regr_min_zero = ProbWeightRegression(non_negative=True)
test_fn(ProbWeightRegression.__name__ + "_min_zero_true", regr_min_zero)
regr_not_min_zero = ProbWeightRegression(non_negative=False)
test_fn(ProbWeightRegression.__name__ + "_min_zero_true_false", regr_not_min_zero)