def test_LinearRegression_settings(self):
"""...Test LinearRegression basic settings
"""
# solver
solver_class_map = LinearRegression._solvers
for solver in LinearRegression._solvers.keys():
learner = LinearRegression(solver=solver,
**Test.specific_solver_kwargs(solver))
solver_class = solver_class_map[solver]
self.assertTrue(isinstance(learner._solver_obj, solver_class))
msg = '^``solver`` must be one of agd, gd, svrg, got wrong_name$'
with self.assertRaisesRegex(ValueError, msg):
LinearRegression(solver='wrong_name')
# prox
prox_class_map = LinearRegression._penalties
for penalty in LinearRegression._penalties.keys():
if penalty == 'binarsity':
learner = LinearRegression(penalty=penalty, blocks_start=[0],
blocks_length=[1])
else:
learner = LinearRegression(penalty=penalty)
prox_class = prox_class_map[penalty]
self.assertTrue(isinstance(learner._prox_obj, prox_class))
msg = '^``penalty`` must be one of binarsity, elasticnet, l1, l2, ' \
'none, tv, got wrong_name$'
with self.assertRaisesRegex(ValueError, msg):
LinearRegression(penalty='wrong_name')
def test_score(self):
"""...Test LinearRegression predict
"""
X_train, y_train, _, _ = self.get_train_data(n_samples=2000,
n_features=12)
learner = LinearRegression(random_state=32789, tol=1e-9)
learner.fit(X_train, y_train)
X_test, y_test, _, _ = self.get_train_data(n_samples=200,
n_features=12)
self.assertAlmostEqual(
learner.score(X_test, y_test), 0.793774, places=4)
def test_predict(self):
"""...Test LinearRegression predict
"""
X_train, y_train, _, _ = self.get_train_data(n_samples=200,
n_features=12)
learner = LinearRegression(random_state=32789, tol=1e-9)
learner.fit(X_train, y_train)
X_test, y_test, _, _ = self.get_train_data(n_samples=5, n_features=12)
y_pred = np.array([0.084, -1.4276, -3.1555, 2.6218, 0.3736])
np.testing.assert_array_almost_equal(
learner.predict(X_test), y_pred, decimal=4)
def test_LinearRegression_solver_step(self):
"""...Test LinearRegression setting of step parameter of solver
"""
for solver in LinearRegression._solvers.keys():
learner = LinearRegression(solver=solver, step=self.float_1,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.step, self.float_1)
self.assertEqual(learner._solver_obj.step, self.float_1)
learner.step = self.float_2
self.assertEqual(learner.step, self.float_2)
self.assertEqual(learner._solver_obj.step, self.float_2)
def test_LinearRegression_solver_random_state(self):
"""...Test LinearRegression setting of random_state parameter of solver
"""
for solver in LinearRegression._solvers.keys():
if solver in ['agd', 'gd']:
msg = '^Solver "%s" has no settable random_state$' % solver
with self.assertWarnsRegex(RuntimeWarning, msg):
learner = LinearRegression(
solver=solver, random_state=1,
**Test.specific_solver_kwargs(solver))
self.assertIsNone(learner.random_state)
else:
learner = LinearRegression(
solver=solver, random_state=self.int_1,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.random_state, self.int_1)
self.assertEqual(learner._solver_obj.seed, self.int_1)
msg = '^random_state must be positive, got -1$'
with self.assertRaisesRegex(ValueError, msg):
LinearRegression(solver=solver, random_state=-1,
**Test.specific_solver_kwargs(solver))
msg = '^random_state is readonly in LinearRegression$'
with self.assertRaisesRegex(AttributeError, msg):
learner = LinearRegression(
solver=solver, **Test.specific_solver_kwargs(solver))
learner.random_state = self.int_2
def test_safe_array_cast(self):
"""...Test error and warnings raised by LogLearner constructor
"""
msg = '^Copying array of size \(5, 5\) to convert it in the ' \
'right format$'
with self.assertWarnsRegex(RuntimeWarning, msg):
LinearRegression._safe_array(self.X.astype(int))
msg = '^Copying array of size \(3, 5\) to create a ' \
'C-contiguous version of it$'
with self.assertWarnsRegex(RuntimeWarning, msg):
LinearRegression._safe_array(self.X[::2])
np.testing.assert_array_equal(self.X,
LinearRegression._safe_array(self.X))
def test_LinearRegression_penalty_elastic_net_ratio(self):
"""...Test LinearRegression setting of parameter of elastic_net_ratio
"""
ratio_1 = 0.6
ratio_2 = 0.3
for penalty in LinearRegression._penalties.keys():
if penalty == 'elasticnet':
learner = LinearRegression(penalty=penalty,
C=self.float_1,
elastic_net_ratio=ratio_1)
self.assertEqual(learner.C, self.float_1)
self.assertEqual(learner.elastic_net_ratio, ratio_1)
self.assertEqual(learner._prox_obj.strength, 1. / self.float_1)
self.assertEqual(learner._prox_obj.ratio, ratio_1)
learner.elastic_net_ratio = ratio_2
self.assertEqual(learner.C, self.float_1)
self.assertEqual(learner.elastic_net_ratio, ratio_2)
self.assertEqual(learner._prox_obj.ratio, ratio_2)
else:
msg = '^Penalty "%s" has no elastic_net_ratio attribute$$' % \
penalty
with self.assertWarnsRegex(RuntimeWarning, msg):
if penalty == 'binarsity':
LinearRegression(penalty=penalty,
elastic_net_ratio=0.8,
blocks_start=[0],
blocks_length=[1])
else:
LinearRegression(penalty=penalty,
elastic_net_ratio=0.8)
if penalty == 'binarsity':
learner = LinearRegression(penalty=penalty,
blocks_start=[0],
blocks_length=[1])
else:
learner = LinearRegression(penalty=penalty)
with self.assertWarnsRegex(RuntimeWarning, msg):
learner.elastic_net_ratio = ratio_1
def test_LinearRegression_warm_start(self):
"""...Test LinearRegression warm start
"""
X, y, weights0, intercept0 = Test.get_train_data()
fit_intercepts = [True, False]
cases = itertools.product(LinearRegression._solvers.keys(),
fit_intercepts)
for solver, fit_intercept in cases:
solver_kwargs = {
'solver': solver,
'max_iter': 2,
'fit_intercept': fit_intercept,
'warm_start': True,
'tol': 0
}
learner = LinearRegression(**solver_kwargs)
learner.fit(X, y)
if fit_intercept:
coeffs_1 = np.hstack((learner.weights, learner.intercept))
else:
coeffs_1 = learner.weights
learner.fit(X, y)
if fit_intercept:
coeffs_2 = np.hstack((learner.weights, learner.intercept))
else:
coeffs_2 = learner.weights
# Thanks to warm start objective should have decreased
self.assertLess(
learner._solver_obj.objective(coeffs_2),
learner._solver_obj.objective(coeffs_1))
def test_LinearRegression_fit(self):
"""...Test LinearRegression fit with different solvers and penalties
"""
fit_intercepts = [False, True]
n_samples = 2000
n_features = 20
X, y, weights0, _ = self.get_train_data(
n_samples=n_samples, n_features=n_features, fit_intercept=False)
intercept0 = -1
for i, (solver, penalty, fit_intercept) \
in enumerate(product(LinearRegression._solvers.keys(),
LinearRegression._penalties.keys(),
fit_intercepts)):
if fit_intercept:
y_ = y + intercept0
else:
y_ = y.copy()
if penalty == 'binarsity':
learner = LinearRegression(
verbose=False, fit_intercept=fit_intercept, solver=solver,
penalty=penalty, tol=1e-10, blocks_start=[0],
blocks_length=[1])
else:
learner = LinearRegression(
verbose=False, fit_intercept=fit_intercept, solver=solver,
penalty=penalty, tol=1e-10)
learner.fit(X, y_)
err = norm(learner.weights - weights0) / n_features ** 0.5
self.assertLess(err, 3e-2)
if fit_intercept:
self.assertLess(abs(learner.intercept - intercept0), 3e-2)
def test_LinearRegression_model_settings(self):
"""...Test LinearRegression setting of parameters of model
"""
for solver in LinearRegression._solvers.keys():
learner = LinearRegression(fit_intercept=True, solver=solver)
self.assertEqual(learner.fit_intercept, True)
self.assertEqual(learner._model_obj.fit_intercept, True)
learner.fit_intercept = False
self.assertEqual(learner.fit_intercept, False)
self.assertEqual(learner._model_obj.fit_intercept, False)
learner = LinearRegression(fit_intercept=False, solver=solver)
self.assertEqual(learner.fit_intercept, False)
self.assertEqual(learner._model_obj.fit_intercept, False)
learner.fit_intercept = True
self.assertEqual(learner.fit_intercept, True)
self.assertEqual(learner._model_obj.fit_intercept, True)
def test_LinearRegression_solver_basic_settings(self):
"""...Test LinearRegression setting of basic parameters of solver
"""
for solver in LinearRegression._solvers.keys():
# tol
learner = LinearRegression(solver=solver, tol=self.float_1,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.tol, self.float_1)
self.assertEqual(learner._solver_obj.tol, self.float_1)
learner.tol = self.float_2
self.assertEqual(learner.tol, self.float_2)
self.assertEqual(learner._solver_obj.tol, self.float_2)
# max_iter
learner = LinearRegression(solver=solver, max_iter=self.int_1,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.max_iter, self.int_1)
self.assertEqual(learner._solver_obj.max_iter, self.int_1)
learner.max_iter = self.int_2
self.assertEqual(learner.max_iter, self.int_2)
self.assertEqual(learner._solver_obj.max_iter, self.int_2)
# verbose
learner = LinearRegression(solver=solver, verbose=True,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.verbose, True)
self.assertEqual(learner._solver_obj.verbose, True)
learner.verbose = False
self.assertEqual(learner.verbose, False)
self.assertEqual(learner._solver_obj.verbose, False)
learner = LinearRegression(solver=solver, verbose=False,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.verbose, False)
self.assertEqual(learner._solver_obj.verbose, False)
learner.verbose = True
self.assertEqual(learner.verbose, True)
self.assertEqual(learner._solver_obj.verbose, True)
# print_every
learner = LinearRegression(solver=solver, print_every=self.int_1,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.print_every, self.int_1)
self.assertEqual(learner._solver_obj.print_every, self.int_1)
learner.print_every = self.int_2
self.assertEqual(learner.print_every, self.int_2)
self.assertEqual(learner._solver_obj.print_every, self.int_2)
# record_every
learner = LinearRegression(solver=solver, record_every=self.int_1,
**Test.specific_solver_kwargs(solver))
self.assertEqual(learner.record_every, self.int_1)
self.assertEqual(learner._solver_obj.record_every, self.int_1)
learner.record_every = self.int_2
self.assertEqual(learner.record_every, self.int_2)
self.assertEqual(learner._solver_obj.record_every, self.int_2)
def test_LinearRegression_penalty_C(self):
"""...Test LinearRegression setting of parameter of C
"""
for penalty in LinearRegression._penalties.keys():
if penalty != 'none':
if penalty == 'binarsity':
learner = LinearRegression(penalty=penalty, C=self.float_1,
blocks_start=[0],
blocks_length=[1])
else:
learner = LinearRegression(penalty=penalty, C=self.float_1)
self.assertEqual(learner.C, self.float_1)
self.assertEqual(learner._prox_obj.strength, 1. / self.float_1)
learner.C = self.float_2
self.assertEqual(learner.C, self.float_2)
self.assertEqual(learner._prox_obj.strength, 1. / self.float_2)
msg = '^``C`` must be positive, got -1$'
with self.assertRaisesRegex(ValueError, msg):
if penalty == 'binarsity':
LinearRegression(penalty=penalty, C=-1,
blocks_start=[0], blocks_length=[1])
else:
LinearRegression(penalty=penalty, C=-1)
else:
pass
msg = '^You cannot set C for penalty "%s"$' % penalty
with self.assertWarnsRegex(RuntimeWarning, msg):
LinearRegression(penalty=penalty, C=self.float_1)
learner = LinearRegression(penalty=penalty)
with self.assertWarnsRegex(RuntimeWarning, msg):
learner.C = self.float_1
msg = '^``C`` must be positive, got -2$'
with self.assertRaisesRegex(ValueError, msg):
learner.C = -2
