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# Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import unittest
import numpy as np
from sklearn import linear_model
from Orange.data import Table
from Orange.regression import (LinearRegressionLearner,
RidgeRegressionLearner,
LassoRegressionLearner,
ElasticNetLearner,
ElasticNetCVLearner,
MeanLearner)
from Orange.evaluation import CrossValidation, RMSE
class TestLinearRegressionLearner(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.housing = Table("housing")
def test_LinearRegression(self):
nrows = 1000
ncols = 3
x = np.random.randint(-20, 51, (nrows, ncols))
c = np.random.rand(ncols, 1) * 10 - 3
e = np.random.rand(nrows, 1) - 0.5
y = np.dot(x, c) + e
x1, x2 = np.split(x, 2)
y1, y2 = np.split(y, 2)
t = Table.from_numpy(None, x1, y1)
learn = LinearRegressionLearner()
clf = learn(t)
z = clf(x2)
self.assertTrue((abs(z.reshape(-1, 1) - y2) < 2.0).all())
def test_Regression(self):
ridge = RidgeRegressionLearner()
lasso = LassoRegressionLearner()
elastic = ElasticNetLearner()
elasticCV = ElasticNetCVLearner()
mean = MeanLearner()
learners = [ridge, lasso, elastic, elasticCV, mean]
cv = CrossValidation(k=2)
res = cv(self.housing, learners)
rmse = RMSE(res)
for i in range(len(learners) - 1):
self.assertLess(rmse[i], rmse[-1])
def test_linear_scorer(self):
learner = LinearRegressionLearner()
scores = learner.score_data(self.housing)
self.assertEqual(
'LSTAT', self.housing.domain.attributes[np.argmax(scores[0])].name)
self.assertEqual(scores.shape[1], len(self.housing.domain.attributes))
def test_scorer(self):
learners = [LinearRegressionLearner(),
RidgeRegressionLearner(),
LassoRegressionLearner(alpha=0.01),
ElasticNetLearner(alpha=0.01)]
for learner in learners:
scores = learner.score_data(self.housing)
self.assertEqual(
'LSTAT',
self.housing.domain.attributes[np.argmax(scores[0])].name)
self.assertEqual(scores.shape[1],
len(self.housing.domain.attributes))
def test_scorer_feature(self):
learners = [LinearRegressionLearner(),
RidgeRegressionLearner(),
LassoRegressionLearner(alpha=0.01),
ElasticNetLearner(alpha=0.01)]
for learner in learners:
scores = learner.score_data(self.housing)
for i, attr in enumerate(self.housing.domain.attributes):
score = learner.score_data(self.housing, attr)
np.testing.assert_array_almost_equal(score, scores[:, i])
def test_coefficients(self):
data = Table.from_numpy(None, [[11], [12], [13]], [0, 1, 2])
model = LinearRegressionLearner()(data)
self.assertAlmostEqual(float(model.intercept), -11)
self.assertEqual(len(model.coefficients), 1)
self.assertAlmostEqual(float(model.coefficients[0]), 1)
def test_comparison_with_sklearn(self):
alphas = [0.001, 0.1, 1, 10, 100]
learners = [(LassoRegressionLearner, linear_model.Lasso),
(RidgeRegressionLearner, linear_model.Ridge),
(ElasticNetLearner, linear_model.ElasticNet)]
for o_learner, s_learner in learners:
for a in alphas:
lr = o_learner(alpha=a)
o_model = lr(self.housing)
s_model = s_learner(alpha=a, fit_intercept=True)
s_model.fit(self.housing.X, self.housing.Y)
delta = np.sum(s_model.coef_ - o_model.coefficients)
self.assertAlmostEqual(delta, 0.0)
def test_comparison_elastic_net(self):
alphas = [0.001, 0.1, 1, 10, 100]
for a in alphas:
lasso = LassoRegressionLearner(alpha=a)
lasso_model = lasso(self.housing)
en = ElasticNetLearner(alpha=a, l1_ratio=1)
en_model = en(self.housing)
np.testing.assert_allclose(
lasso_model.coefficients, en_model.coefficients, atol=1e-07)
def test_linear_regression_repr(self):
learner = LinearRegressionLearner()
repr_text = repr(learner)
learner2 = eval(repr_text)
self.assertIsInstance(learner2, LinearRegressionLearner)
def test_supports_weights(self):
self.assertTrue(LinearRegressionLearner().supports_weights)
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