def test_elastic_net_sklearn_zeros(self): x = PREDICT_ARRAY.copy() y = RESPONSE_ARRAY.copy() raw_result = elasticnet_python.ElasticNetCV(**PARAMS).fit( x, y.flatten()) np.testing.assert_array_equal([0.0, 0.0, 0.0, 0.0, 0.0], raw_result.coef_)
def test_elastic_net_sklearn_nz(self): x = PREDICT_ARRAY.copy() y = RESPONSE_ARRAY.copy() x[:, 2] = np.sort(x[:, 2]) raw_result = elasticnet_python.ElasticNetCV(**PARAMS).fit(x, y.flatten()) print(raw_result.coef_) np.testing.assert_array_equal([False, False, True, False, False], abs(raw_result.coef_) > 0.1)
def test_elastic_net_zeros(self): x = PREDICT_ARRAY.copy() y = RESPONSE_ARRAY.copy() result = sklearn_regression.sklearn_gene( x, y, elasticnet_python.ElasticNetCV(**PARAMS), min_coef=MIN_COEF) self.assertEqual(len(result["pp"]), 5) self.assertEqual(len(result["betas"]), 5) self.assertEqual(len(result["betas_resc"]), 5) pp = np.array([True, True, True, True, True]) betas = ([0.0, 0.0, 0.0, 0.0, 0.0]) betas_resc = ([0.0, 0.0, 0.0, 0.0, 0.0]) check = {'pp': pp, 'betas': betas, 'betas_resc': betas_resc} for component in check.keys(): for idx in range(0, len(check[component])): np.testing.assert_array_almost_equal(result[component][idx], check[component][idx], 2)