def test_lr(self):
self.model = CrossEntropyMNL(
solver='newton-cg', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=10, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_anes96.exog, self.y)
# coefficient
# predict
self.assertEqual(
np.sum(self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 333)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y),
-1540.888458338286,
places=3)
# to_json
json_dict = self.model.to_json('./tests/linear_models/CrossentropyMNL/Multinomial/')
self.assertEqual(json_dict['properties']['solver'], 'newton-cg')
# from_json
self.model_from_json = CrossEntropyMNL.from_json(json_dict)
np.testing.assert_array_almost_equal(
self.model.coef,
self.model_from_json.coef,
decimal=3)
np.testing.assert_array_almost_equal(
self.model.classes, np.array(list(range(7))), decimal=3)
self.assertEqual(self.model.n_classes, 7)
def test_lr(self):
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=0,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_spector.exog, self.y)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-13.021, 2.8261, .09515, 2.378]]),
decimal=3)
# predict
np.testing.assert_array_almost_equal(
self.model.predict(self.data_spector.exog),
np.array((0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0.,
0., 0., 0., 0., 1., 1., 0., 1., 0., 1., 1., 0., 1., 0.,
1., 1., 1., 0.)),
decimal=3)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_spector.exog,
self.y),
-12.8896334653335,
places=3)
# to_json
json_dict = self.model.to_json(
'./tests/linear_models/CrossentropyMNL/Binary/')
self.assertEqual(json_dict['properties']['solver'], 'lbfgs')
# from_json
self.model_from_json = CrossEntropyMNL.from_json(json_dict)
np.testing.assert_array_almost_equal(self.model.coef,
self.model_from_json.coef,
decimal=3)
np.testing.assert_array_almost_equal(self.model.classes,
np.array([0, 1]),
decimal=3)
self.assertEqual(self.model.n_classes, 2)
def test_lr(self):
self.model = CrossEntropyMNL(solver='newton-cg',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=10,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_anes96.exog, self.y)
# coefficient
# predict
self.assertEqual(
np.sum(
self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 333)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y),
-1540.888458338286,
places=3)
# to_json
json_dict = self.model.to_json(
'./tests/linear_models/CrossentropyMNL/Multinomial/')
self.assertEqual(json_dict['properties']['solver'], 'newton-cg')
# from_json
self.model_from_json = CrossEntropyMNL.from_json(json_dict)
np.testing.assert_array_almost_equal(self.model.coef,
self.model_from_json.coef,
decimal=3)
np.testing.assert_array_almost_equal(self.model.classes,
np.array(list(range(7))),
decimal=3)
self.assertEqual(self.model.n_classes, 7)
def test_lr(self):
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=0, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_spector.exog, self.y)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-13.021, 2.8261, .09515, 2.378]]),
decimal=3)
# predict
np.testing.assert_array_almost_equal(
self.model.predict(self.data_spector.exog),
np.array((0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0.,
0., 0., 0., 0., 0., 1., 1., 0., 1., 0., 1., 1., 0.,
1., 0., 1., 1., 1., 0.)),
decimal=3)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_spector.exog, self.y),
-12.8896334653335,
places=3)
# to_json
json_dict = self.model.to_json('./tests/linear_models/CrossentropyMNL/Binary/')
self.assertEqual(json_dict['properties']['solver'], 'lbfgs')
# from_json
self.model_from_json = CrossEntropyMNL.from_json(json_dict)
np.testing.assert_array_almost_equal(
self.model.coef,
self.model_from_json.coef,
decimal=3)
np.testing.assert_array_almost_equal(
self.model.classes, np.array([0, 1]), decimal=3)
self.assertEqual(self.model.n_classes, 2)