def test_subset_fit(): """[Subsemble] 'fit' and 'predict' runs correctly.""" meta = OLS() meta.fit(F, y) g = meta.predict(P) ens = Subsemble() ens.add(estimators, partitions=2, folds=3, dtype=np.float64) ens.add_meta(OLS(), dtype=np.float64) ens.fit(X, y) pred = ens.predict(X) np.testing.assert_array_equal(pred, g)
def test_run(): """[Blend] 'fit' and 'predict' runs correctly.""" meta = OLS() meta.fit(F, y[3:]) g = meta.predict(P) ens = BlendEnsemble(test_size=3) ens.add(ESTIMATORS, PREPROCESSING, dtype=np.float64) ens.add(OLS(), meta=True, dtype=np.float64) ens.fit(X, y) pred = ens.predict(X) np.testing.assert_array_equal(pred, g)
def test_ols_weights(): """[Utils] OLS: check weights.""" ols = OLS() ols.fit(X, y) np.testing.assert_array_almost_equal(ols.coef_, np.array([3., -0.5]))