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)