def test_lr_disturbed_sample_weight_half_zero_half_one(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)
len_half = 500
self.model.fit(self.data_anes96.exog,
self.y_disturbed,
sample_weight=np.array(
[1] * len_half + [0] *
(self.data_anes96.exog.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_anes96.exog[:len_half],
self.y_disturbed[:len_half])
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
def test_label_encoder(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, np.ones(3))
np.testing.assert_array_equal(
X_repeated,
np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6],
[4, 5, 6], [7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(sample_weight_repeated,
np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]))
# with sample_weight
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[0.5, 0.25, 0.25], [0.25, 0.5, 0.25], [0.25, 0.25, 0.5]])
sample_weight = np.array([0.25, 0.5, 0.25])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, sample_weight)
np.testing.assert_array_equal(
X_repeated,
np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6],
[4, 5, 6], [7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(
sample_weight_repeated,
np.array([
0.125, 0.0625, 0.0625, 0.125, 0.25, 0.125, 0.0625, 0.0625,
0.125
]))
def test_lr_one_data_point(self):
# with regularization
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[4:5, :],
self.y[4:5, ],
sample_weight=0.5)
# coef
np.testing.assert_array_equal(self.model.coef, np.zeros((4, 1)))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([1, 0]).reshape(-1, 1)),
np.array([0, -np.Infinity]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([1, 1]).reshape(-1, 1)),
np.array([0, 0]),
decimal=3)
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_equal(self.model.coef, np.zeros((4, 1)))
# predict
np.testing.assert_array_equal(
self.model.predict(self.data_spector.exog),
np.array([0] * self.data_spector.endog.shape[0]))
# loglike/_per_sample
np.testing.assert_array_equal(
self.model.loglike_per_sample(
self.data_spector.exog,
np.array([1] * 16 + [0] * 16).reshape(-1, 1)),
np.array([0] * 16 + [-np.Infinity] * 16))
def test_lr_disturbed_two_data_point(self):
# with regularization
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.1,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_spector.exog[4:6, :],
self.y_disturbed[4:6, ],
sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (1, 4))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], self.y_disturbed[4:6, ]),
np.array([-0.503, -0.662]),
decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([[0, 0], [0.99, 0.01]])),
np.array([-np.Infinity, -0.662]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([[0, 0], [0.01, 0.99]])),
np.array([-np.Infinity, -0.725]),
decimal=3)
def test_lr_disturbed_sample_weight_all_half(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_disturbed,
sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-12.327, 2.686, 0.089, 2.258]]),
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_disturbed,
sample_weight=.5),
old_div(-13.366314173353134, 2.),
places=3)
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_regularized(self):
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.01,
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([[-10.66, 2.364, 0.064, 2.142]]),
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),
-13.016861222748515,
places=3)
def test_label_encoder(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, np.ones(3))
np.testing.assert_array_equal(
X_repeated,
np.array([
[1, 2, 3], [1, 2, 3], [1, 2, 3],
[4, 5, 6], [4, 5, 6], [4, 5, 6],
[7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(
Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(
sample_weight_repeated,
np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]))
# with sample_weight
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[0.5, 0.25, 0.25], [0.25, 0.5, 0.25], [0.25, 0.25, 0.5]])
sample_weight = np.array([0.25, 0.5, 0.25])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, sample_weight)
np.testing.assert_array_equal(
X_repeated,
np.array([
[1, 2, 3], [1, 2, 3], [1, 2, 3],
[4, 5, 6], [4, 5, 6], [4, 5, 6],
[7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(
Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(
sample_weight_repeated,
np.array([0.125, 0.0625, 0.0625, 0.125, 0.25, 0.125, 0.0625, 0.0625, 0.125]))
def test_lr_disturbed_multicolinearty(self):
self.model_col = 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)
X = np.hstack([self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])
self.model_col.fit(X,
self.y_disturbed, sample_weight=0.5)
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[:, 0:1],
self.y_disturbed, sample_weight=0.5)
np.testing.assert_array_almost_equal(
self.model_col.coef, np.array([[-9.359, 1.37, 1.37]]), decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y_disturbed),
self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],
self.y_disturbed), decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_spector.exog[:, 0:1]), decimal=3)
def test_lr_sample_weight_all_zero(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.assertRaises(ValueError, self.model.fit, self.data_spector.exog,
self.y, 0)
def test_train_no_covariates(self):
self.model = UnSupervisedIOHMM(num_states=2,
max_EM_iter=100,
EM_tol=1e-6)
self.model.set_models(model_initial=CrossEntropyMNL(solver='lbfgs',
reg_method='l2'),
model_transition=CrossEntropyMNL(
solver='lbfgs', reg_method='l2'),
model_emissions=[OLS()])
self.model.set_inputs(covariates_initial=[],
covariates_transition=[],
covariates_emissions=[[]])
self.model.set_outputs([['rt']])
self.model.set_data([self.data_speed])
self.model.train()
# emission coefficients
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].coef,
np.array([[5.5]]),
decimal=1)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].coef,
np.array([[6.4]]),
decimal=1)
# emission dispersion
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].dispersion,
np.array([[0.037]]),
decimal=2)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].dispersion,
np.array([[0.063]]),
decimal=2)
# transition
np.testing.assert_array_almost_equal(
self.model.model_transition[1].coef, np.array([[2.4]]), decimal=1)
np.testing.assert_array_almost_equal(
self.model.model_transition[0].coef, np.array([[-2]]), decimal=1)
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[1].predict_log_proba(np.array([[]]))),
np.array([[0.08, 0.92]]),
decimal=2)
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[0].predict_log_proba(np.array([[]]))),
np.array([[0.88, 0.12]]),
decimal=2)
def test_lr_sample_weight_all_zero(self):
self.model = DiscreteMNL(
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, classes=None)
self.assertRaises(ValueError, self.model.fit,
self.data_anes96.exog, self.y_disturbed, 0)
def test_lr_sample_weight_all_zero(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.assertRaises(ValueError, self.model.fit,
self.data_spector.exog, self.y, 0)
def test_train_covariates_for_transition(self):
self.model = UnSupervisedIOHMM(num_states=2,
max_EM_iter=100,
EM_tol=1e-6)
self.model.set_models(model_initial=CrossEntropyMNL(solver='newton-cg',
reg_method='l2'),
model_transition=CrossEntropyMNL(
solver='newton-cg', reg_method='l2'),
model_emissions=[OLS()])
self.model.set_inputs(covariates_initial=[],
covariates_transition=['Pacc'],
covariates_emissions=[[]])
self.model.set_outputs([['rt']])
self.model.set_data([self.data_speed])
self.model.train()
# emission coefficients
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].coef,
np.array([[5.5]]),
decimal=1)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].coef,
np.array([[6.4]]),
decimal=1)
# emission dispersion
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].dispersion,
np.array([[0.036]]),
decimal=2)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].dispersion,
np.array([[0.063]]),
decimal=2)
# transition
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[0].predict_log_proba(
self.model.inp_transitions_all_sequences)).sum(axis=0),
np.array([312, 126]),
decimal=0)
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[1].predict_log_proba(
self.model.inp_transitions_all_sequences)).sum(axis=0),
np.array([112, 326]),
decimal=0)
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_multicolinearty(self):
self.model_col = 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)
X = np.hstack(
[self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])
self.model_col.fit(X, self.y, sample_weight=0.5)
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[:, 0:1],
self.y,
sample_weight=0.5)
np.testing.assert_array_almost_equal(
self.model_col.coef,
np.array([[-9.703, 1.42002783, 1.42002783]]),
decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y),
self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],
self.y),
decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_spector.exog[:, 0:1]),
decimal=3)
def test_lr_disturbed_sample_weight_half_zero_half_one(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)
len_half = 500
self.model.fit(self.data_anes96.exog, self.y_disturbed,
sample_weight=np.array([1] * len_half +
[0] * (self.data_anes96.exog.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_anes96.exog[:len_half], self.y_disturbed[:len_half])
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
self.model_half.coef,
decimal=3)
def test_lr_disturbed_sample_weight_all_half(self):
self.model_half = CrossEntropyMNL(solver='newton-cg',
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_half.fit(self.data_anes96.exog,
self.y_disturbed,
sample_weight=.5)
self.model = CrossEntropyMNL(solver='newton-cg',
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_anes96.exog, self.y_disturbed)
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
# predict
self.assertEqual(
np.sum(
self.model_half.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 367)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y_disturbed,
sample_weight=.5),
old_div(-1516.50148, 2.),
places=3)
def test_lr_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_equal(self.model.coef, np.zeros((4, 1)))
# loglike/_per_sample
self.assertEqual(
self.model.loglike(self.data_spector.exog,
self.y,
sample_weight=.5), 0)
def test_lr_disturbed_sample_weight_all_half(self):
self.model_half = CrossEntropyMNL(
solver='newton-cg', 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_half.fit(self.data_anes96.exog, self.y_disturbed, sample_weight=.5)
self.model = CrossEntropyMNL(
solver='newton-cg', 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_anes96.exog, self.y_disturbed)
# coefficient
np.testing.assert_array_almost_equal(self.model.coef, self.model_half.coef, decimal=3)
# predict
self.assertEqual(
np.sum(self.model_half.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 367)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y_disturbed, sample_weight=.5),
old_div(-1516.50148, 2.),
places=3)
def test_lr_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_equal(
self.model.coef,
np.zeros((4, 1)))
# loglike/_per_sample
self.assertEqual(
self.model.loglike(self.data_spector.exog, self.y, sample_weight=.5), 0)
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_disturbed_regularized(self):
self.model = CrossEntropyMNL(solver='newton-cg',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.5,
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_disturbed)
# predict
self.assertEqual(
np.sum(
self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 366)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y_disturbed),
-1519.9521131193064,
places=3)
def test_lr_sample_weight_all_zero(self):
self.model = DiscreteMNL(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,
classes=None)
self.assertRaises(ValueError, self.model.fit, self.data_anes96.exog,
self.y_disturbed, 0)
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_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-13.021, 2.8261, .09515, 2.378]]),
decimal=3)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_spector.exog,
self.y,
sample_weight=.5),
old_div(-12.8896334653335, 2.),
places=3)
def test_lr_disturbed_regularized(self):
self.model = CrossEntropyMNL(
solver='newton-cg', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.5, 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_disturbed)
# predict
self.assertEqual(
np.sum(self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 366)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y_disturbed),
-1519.9521131193064,
places=3)
def test_lr_disturbed(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_disturbed)
# coefficient
# predict
self.assertEqual(
np.sum(
self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 335)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y_disturbed),
-1580.5280532302786,
places=3)
def test_lr_disturbed_three_data_point(self):
# with regularization
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.1,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_anes96.exog[6:9, :],
self.y_disturbed[6:9, ],
sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (7, 6))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], self.y_disturbed[6:9, ]),
np.array([-0.336, -0.389,
-0.398]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :],
label_binarize([3, 1, 4], list(range(7)))),
np.array([-3.415, -4.506,
-2.367]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :],
label_binarize([3, 0, 5], list(range(7)))),
np.array([-3.415, -4.492,
-4.301]),
decimal=3)
def test_lr_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-13.021, 2.8261, .09515, 2.378]]),
decimal=3)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_spector.exog, self.y, sample_weight=.5),
old_div(-12.8896334653335, 2.),
places=3)
def test_lr_disturbed(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_disturbed)
# coefficient
# predict
self.assertEqual(
np.sum(self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 335)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y_disturbed),
-1580.5280532302786,
places=3)
def test_lr_one_data_point(self):
# with regularization
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[4:5, :],
self.y[4:5, ], sample_weight=0.5)
# coef
np.testing.assert_array_equal(
self.model.coef,
np.zeros((4, 1)))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([1, 0]).reshape(-1, 1)),
np.array([0, -np.Infinity]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([1, 1]).reshape(-1, 1)),
np.array([0, 0]), decimal=3)
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_equal(
self.model.coef,
np.zeros((4, 1)))
# predict
np.testing.assert_array_equal(
self.model.predict(self.data_spector.exog),
np.array([0] * self.data_spector.endog.shape[0]))
# loglike/_per_sample
np.testing.assert_array_equal(
self.model.loglike_per_sample(self.data_spector.exog,
np.array([1] * 16 + [0] * 16).reshape(-1, 1)),
np.array([0] * 16 + [-np.Infinity] * 16))
def test_lr_disturbed_three_data_point(self):
# with regularization
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.1, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_anes96.exog[6:9, :],
self.y_disturbed[6:9, ], sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (7, 6))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], self.y_disturbed[6:9, ]),
np.array([-0.336, -0.389, -0.398]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], label_binarize([3, 1, 4], list(range(7)))),
np.array([-3.415, -4.506, -2.367]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], label_binarize([3, 0, 5], list(range(7)))),
np.array([-3.415, -4.492, -4.301]), decimal=3)
def test_lr_disturbed_two_data_point(self):
# with regularization
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.1, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_spector.exog[4:6, :],
self.y_disturbed[4:6, ], sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (1, 4))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], self.y_disturbed[4:6, ]),
np.array([-0.503, -0.662]), decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([[0, 0], [0.99, 0.01]])),
np.array([-np.Infinity, -0.662]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([[0, 0], [0.01, 0.99]])),
np.array([-np.Infinity, -0.725]), decimal=3)
def test_lr_regularized(self):
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.01, 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([[-10.66, 2.364, 0.064, 2.142]]),
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),
-13.016861222748515,
places=3)
def test_lr_disturbed_sample_weight_all_half(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_disturbed, sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-12.327, 2.686, 0.089, 2.258]]),
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_disturbed, sample_weight=.5),
old_div(-13.366314173353134, 2.),
places=3)
class CrossEntropyMNLBinaryTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_spector = sm.datasets.spector.load()
cls.y = np.array([[1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [1,
0], [1, 0],
[1, 0], [1, 0], [0, 1], [1, 0], [1, 0], [1,
0], [0, 1],
[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0,
1], [1, 0],
[0, 1], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1],
[1, 0], [0, 1], [0, 1], [1, 0], [0, 1]])
cls.y_disturbed = np.array([[0.99, 0.01], [0.99, 0.01], [0.99, 0.01],
[0.99, 0.01], [0.01, 0.99], [0.99, 0.01],
[0.99, 0.01], [0.99, 0.01], [0.99, 0.01],
[0.01, 0.99], [0.99, 0.01], [0.99, 0.01],
[0.99, 0.01], [0.01, 0.99], [0.99, 0.01],
[0.99, 0.01], [0.99, 0.01], [0.99, 0.01],
[0.99, 0.01], [0.01, 0.99], [0.99, 0.01],
[0.01, 0.99], [0.99, 0.01], [0.99, 0.01],
[0.01, 0.99], [0.01, 0.99], [0.01, 0.99],
[0.99, 0.01], [0.01, 0.99], [0.01, 0.99],
[0.99, 0.01], [0.01, 0.99]])
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_disturbed(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_disturbed)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-12.327, 2.686, 0.089, 2.258]]),
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_disturbed),
-13.366314173353134,
places=3)
def test_lr_regularized(self):
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.01,
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([[-10.66, 2.364, 0.064, 2.142]]),
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),
-13.016861222748515,
places=3)
def test_lr_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-13.021, 2.8261, .09515, 2.378]]),
decimal=3)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_spector.exog,
self.y,
sample_weight=.5),
old_div(-12.8896334653335, 2.),
places=3)
def test_lr_disturbed_sample_weight_all_half(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_disturbed,
sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-12.327, 2.686, 0.089, 2.258]]),
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_disturbed,
sample_weight=.5),
old_div(-13.366314173353134, 2.),
places=3)
def test_lr_sample_weight_all_zero(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.assertRaises(ValueError, self.model.fit, self.data_spector.exog,
self.y, 0)
def test_lr_sample_weight_half_zero_half_one(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)
len_half = 8
self.model.fit(self.data_spector.exog,
self.y,
sample_weight=np.array([1] * len_half + [0] *
(self.y.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_spector.exog[:len_half],
self.y[:len_half])
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
def test_lr_disturbed_sample_weight_half_zero_half_one(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)
len_half = 8
self.model.fit(
self.data_spector.exog,
self.y_disturbed,
sample_weight=np.array([1] * len_half + [0] *
(self.y_disturbed.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_spector.exog[:len_half],
self.y_disturbed[:len_half])
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
# corner cases
def test_lr_two_data_point(self):
# with regularization
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.1,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_spector.exog[4:6, :],
self.y[4:6, ],
sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (1, 4))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], self.y[4:6, ]),
np.array([-0.495, -0.661]),
decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([[0, 0], [1, 0]])),
np.array([-np.Infinity, -0.661]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([[0, 0], [0, 1]])),
np.array([-np.Infinity, -0.726]),
decimal=3)
def test_lr_disturbed_two_data_point(self):
# with regularization
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.1,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_spector.exog[4:6, :],
self.y_disturbed[4:6, ],
sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (1, 4))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], self.y_disturbed[4:6, ]),
np.array([-0.503, -0.662]),
decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([[0, 0], [0.99, 0.01]])),
np.array([-np.Infinity, -0.662]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([[0, 0], [0.01, 0.99]])),
np.array([-np.Infinity, -0.725]),
decimal=3)
def test_lr_multicolinearty(self):
self.model_col = 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)
X = np.hstack(
[self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])
self.model_col.fit(X, self.y, sample_weight=0.5)
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[:, 0:1],
self.y,
sample_weight=0.5)
np.testing.assert_array_almost_equal(
self.model_col.coef,
np.array([[-9.703, 1.42002783, 1.42002783]]),
decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y),
self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],
self.y),
decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_spector.exog[:, 0:1]),
decimal=3)
def test_lr_disturbed_multicolinearty(self):
self.model_col = 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)
X = np.hstack(
[self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])
self.model_col.fit(X, self.y_disturbed, sample_weight=0.5)
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[:, 0:1],
self.y_disturbed,
sample_weight=0.5)
np.testing.assert_array_almost_equal(self.model_col.coef,
np.array([[-9.359, 1.37, 1.37]]),
decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y_disturbed),
self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],
self.y_disturbed),
decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_spector.exog[:, 0:1]),
decimal=3)
class CrossEntropyMNLMultinomialTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_anes96 = sm.datasets.anes96.load()
cls.y = label_binarize(cls.data_anes96.endog, classes=list(range(7)))
cls.y_disturbed = old_div((cls.y + 0.01), 1.07)
def test_label_encoder(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, np.ones(3))
np.testing.assert_array_equal(
X_repeated,
np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6],
[4, 5, 6], [7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(sample_weight_repeated,
np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]))
# with sample_weight
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[0.5, 0.25, 0.25], [0.25, 0.5, 0.25], [0.25, 0.25, 0.5]])
sample_weight = np.array([0.25, 0.5, 0.25])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, sample_weight)
np.testing.assert_array_equal(
X_repeated,
np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6],
[4, 5, 6], [7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(
sample_weight_repeated,
np.array([
0.125, 0.0625, 0.0625, 0.125, 0.25, 0.125, 0.0625, 0.0625,
0.125
]))
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_disturbed(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_disturbed)
# coefficient
# predict
self.assertEqual(
np.sum(
self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 335)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y_disturbed),
-1580.5280532302786,
places=3)
def test_lr_regularized(self):
self.model = CrossEntropyMNL(solver='newton-cg',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.5,
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)
# predict
self.assertEqual(
np.sum(
self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 369)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y),
-1466.9886103092626,
places=3)
def test_lr_disturbed_regularized(self):
self.model = CrossEntropyMNL(solver='newton-cg',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.5,
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_disturbed)
# predict
self.assertEqual(
np.sum(
self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 366)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y_disturbed),
-1519.9521131193064,
places=3)
def test_lr_sample_weight_all_half(self):
self.model_half = CrossEntropyMNL(solver='newton-cg',
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_half.fit(self.data_anes96.exog, self.y, sample_weight=.5)
self.model = CrossEntropyMNL(solver='newton-cg',
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_anes96.exog, self.y)
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
# predict
self.assertEqual(
np.sum(
self.model_half.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 372)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y,
sample_weight=.5),
old_div(-1461.92274725, 2.),
places=3)
def test_lr_disturbed_sample_weight_all_half(self):
self.model_half = CrossEntropyMNL(solver='newton-cg',
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_half.fit(self.data_anes96.exog,
self.y_disturbed,
sample_weight=.5)
self.model = CrossEntropyMNL(solver='newton-cg',
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_anes96.exog, self.y_disturbed)
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
# predict
self.assertEqual(
np.sum(
self.model_half.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 367)
# loglike/_per_sample
self.assertAlmostEqual(self.model.loglike(self.data_anes96.exog,
self.y_disturbed,
sample_weight=.5),
old_div(-1516.50148, 2.),
places=3)
def test_lr_sample_weight_all_zero(self):
self.model = DiscreteMNL(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,
classes=None)
self.assertRaises(ValueError, self.model.fit, self.data_anes96.exog,
self.y_disturbed, 0)
def test_lr_sample_weight_half_zero_half_one(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)
len_half = 500
self.model.fit(self.data_anes96.exog,
self.y,
sample_weight=np.array(
[1] * len_half + [0] *
(self.data_anes96.exog.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_anes96.exog[:len_half],
self.y[:len_half])
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
def test_lr_disturbed_sample_weight_half_zero_half_one(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)
len_half = 500
self.model.fit(self.data_anes96.exog,
self.y_disturbed,
sample_weight=np.array(
[1] * len_half + [0] *
(self.data_anes96.exog.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_anes96.exog[:len_half],
self.y_disturbed[:len_half])
# coefficient
np.testing.assert_array_almost_equal(self.model.coef,
self.model_half.coef,
decimal=3)
# corner cases
def test_lr_three_data_point(self):
# with regularization
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.1,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_anes96.exog[6:9, :],
self.y[6:9, ],
sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (7, 6))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], self.y[6:9, ]),
np.array([-0.015, -0.091,
-0.095]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :],
label_binarize([3, 1, 4], list(range(7)))),
np.array([-4.201, -5.094,
-2.825]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :],
label_binarize([3, 0, 5], list(range(7)))),
np.array([-4.201, -7.352,
-8.957]),
decimal=3)
def test_lr_disturbed_three_data_point(self):
# with regularization
self.model = CrossEntropyMNL(solver='lbfgs',
fit_intercept=True,
est_stderr=True,
reg_method='l2',
alpha=.1,
l1_ratio=0,
tol=1e-4,
max_iter=100,
coef=None,
stderr=None,
n_classes=None)
self.model.fit(self.data_anes96.exog[6:9, :],
self.y_disturbed[6:9, ],
sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (7, 6))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], self.y_disturbed[6:9, ]),
np.array([-0.336, -0.389,
-0.398]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :],
label_binarize([3, 1, 4], list(range(7)))),
np.array([-3.415, -4.506,
-2.367]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :],
label_binarize([3, 0, 5], list(range(7)))),
np.array([-3.415, -4.492,
-4.301]),
decimal=3)
def test_lr_multicolinearty(self):
self.model_col = 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)
X = np.hstack(
[self.data_anes96.exog[:, 0:1], self.data_anes96.exog[:, 0:1]])
self.model_col.fit(X, self.y, sample_weight=0.5)
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_anes96.exog[:, 0:1],
self.y,
sample_weight=0.5)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y),
self.model.loglike_per_sample(self.data_anes96.exog[:, 0:1],
self.y),
decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_anes96.exog[:, 0:1]),
decimal=3)
def test_lr_disturbed_multicolinearty(self):
self.model_col = 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)
X = np.hstack(
[self.data_anes96.exog[:, 0:1], self.data_anes96.exog[:, 0:1]])
self.model_col.fit(X, self.y_disturbed, sample_weight=0.5)
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_anes96.exog[:, 0:1],
self.y_disturbed,
sample_weight=0.5)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y_disturbed),
self.model.loglike_per_sample(self.data_anes96.exog[:, 0:1],
self.y_disturbed),
decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_anes96.exog[:, 0:1]),
decimal=3)
def test_train_multivariate(self):
self.model = UnSupervisedIOHMM(num_states=2,
max_EM_iter=100,
EM_tol=1e-6)
self.model.set_models(
model_initial=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),
model_transition=CrossEntropyMNL(solver='newton-cg',
reg_method='l2'),
model_emissions=[OLS(), DiscreteMNL(reg_method='l2')])
self.model.set_inputs(covariates_initial=[],
covariates_transition=[],
covariates_emissions=[[], ['Pacc']])
self.model.set_outputs([['rt'], ['corr']])
self.model.set_data([self.data_speed])
self.model.train()
# emission coefficients
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].coef,
np.array([[5.5]]),
decimal=1)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].coef,
np.array([[6.4]]),
decimal=1)
# emission dispersion
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].dispersion,
np.array([[0.036]]),
decimal=2)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].dispersion,
np.array([[0.063]]),
decimal=2)
# transition
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[0].predict_log_proba(
self.model.inp_transitions_all_sequences)).sum(axis=0),
np.array([387, 51]),
decimal=0)
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[1].predict_log_proba(
self.model.inp_transitions_all_sequences)).sum(axis=0),
np.array([37, 401.]),
decimal=0)
# to_json
json_dict = self.model.to_json('tests/IOHMM_models/UnSupervisedIOHMM/')
self.assertEqual(json_dict['data_type'], 'UnSupervisedIOHMM')
self.assertSetEqual(
set(json_dict['properties'].keys()),
set([
'num_states', 'EM_tol', 'max_EM_iter', 'covariates_initial',
'covariates_transition', 'covariates_emissions',
'responses_emissions', 'model_initial', 'model_transition',
'model_emissions'
]))
with open('tests/IOHMM_models/UnSupervisedIOHMM/model.json',
'w') as outfile:
json.dump(json_dict, outfile, indent=4, sort_keys=True)
class CrossEntropyMNLUnaryTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_spector = sm.datasets.spector.load()
cls.y = np.ones((cls.data_spector.endog.shape[0], 1))
def test_label_encoder(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1], [1], [1]])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, np.ones(3))
np.testing.assert_array_equal(X_repeated, x)
np.testing.assert_array_equal(Y_repeated, np.array([0, 0, 0]))
np.testing.assert_array_equal(sample_weight_repeated,
np.array([1, 1, 1]))
# with sample_weight
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1], [1], [1]])
sample_weight = np.array([0.25, 0.5, 0.25])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, sample_weight)
np.testing.assert_array_equal(X_repeated, x)
np.testing.assert_array_equal(Y_repeated, np.array([0, 0, 0]))
np.testing.assert_array_equal(sample_weight_repeated, sample_weight)
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_equal(self.model.coef, np.zeros((4, 1)))
# predict
np.testing.assert_array_equal(
self.model.predict(self.data_spector.exog),
np.array([0] * self.data_spector.endog.shape[0]))
# loglike/_per_sample
np.testing.assert_array_equal(
self.model.loglike_per_sample(
self.data_spector.exog,
np.array([1] * 16 + [0] * 16).reshape(-1, 1)),
np.array([0] * 16 + [-np.Infinity] * 16))
def test_lr_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_equal(self.model.coef, np.zeros((4, 1)))
# loglike/_per_sample
self.assertEqual(
self.model.loglike(self.data_spector.exog,
self.y,
sample_weight=.5), 0)
# corner cases
def test_lr_one_data_point(self):
# with regularization
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[4:5, :],
self.y[4:5, ],
sample_weight=0.5)
# coef
np.testing.assert_array_equal(self.model.coef, np.zeros((4, 1)))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([1, 0]).reshape(-1, 1)),
np.array([0, -np.Infinity]),
decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([1, 1]).reshape(-1, 1)),
np.array([0, 0]),
decimal=3)
def test_train_no_covariates(self):
np.random.seed(0)
self.model = SemiSupervisedIOHMM(num_states=4,
max_EM_iter=100,
EM_tol=1e-10)
self.model.set_models(model_initial=CrossEntropyMNL(solver='newton-cg',
reg_method='l2'),
model_transition=CrossEntropyMNL(
solver='newton-cg', reg_method='l2'),
model_emissions=[OLS()])
self.model.set_inputs(covariates_initial=[],
covariates_transition=[],
covariates_emissions=[[]])
self.model.set_outputs([['rt']])
self.model.set_data([[self.data_speed, self.states]])
self.model.train()
# emission coefficients
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].coef, np.array([[0]]), decimal=1)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].coef, np.array([[1]]), decimal=1)
np.testing.assert_array_almost_equal(
self.model.model_emissions[2][0].coef,
np.array([[6.4]]),
decimal=1)
np.testing.assert_array_almost_equal(
self.model.model_emissions[3][0].coef,
np.array([[5.5]]),
decimal=1)
# emission dispersion
np.testing.assert_array_almost_equal(
self.model.model_emissions[0][0].dispersion,
np.array([[0]]),
decimal=2)
np.testing.assert_array_almost_equal(
self.model.model_emissions[1][0].dispersion,
np.array([[0]]),
decimal=2)
np.testing.assert_array_almost_equal(
self.model.model_emissions[2][0].dispersion,
np.array([[0.051]]),
decimal=2)
np.testing.assert_array_almost_equal(
self.model.model_emissions[3][0].dispersion,
np.array([[0.032]]),
decimal=2)
# transition
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[0].predict_log_proba(np.array([[]]))),
np.array([[0.4, 0.6, 0, 0]]),
decimal=1)
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[1].predict_log_proba(np.array([[]]))),
np.array([[0.19, 0.81, 0, 0]]),
decimal=2)
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[2].predict_log_proba(np.array([[]]))),
np.array([[0, 0, 0.93, 0.07]]),
decimal=2)
np.testing.assert_array_almost_equal(np.exp(
self.model.model_transition[3].predict_log_proba(np.array([[]]))),
np.array([[0, 0, 0.11, 0.89]]),
decimal=2)
# to_json
json_dict = self.model.to_json(
'tests/IOHMM_models/SemiSupervisedIOHMM/')
self.assertEqual(json_dict['data_type'], 'SemiSupervisedIOHMM')
self.assertSetEqual(
set(json_dict['properties'].keys()),
set([
'num_states', 'EM_tol', 'max_EM_iter', 'covariates_initial',
'covariates_transition', 'covariates_emissions',
'responses_emissions', 'model_initial', 'model_transition',
'model_emissions'
]))
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json',
'w') as outfile:
json.dump(json_dict, outfile, indent=4, sort_keys=True)
class CrossEntropyMNLBinaryTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_spector = sm.datasets.spector.load()
cls.y = np.array([
[1, 0], [1, 0], [1, 0], [1, 0],
[0, 1], [1, 0], [1, 0], [1, 0],
[1, 0], [0, 1], [1, 0], [1, 0],
[1, 0], [0, 1], [1, 0], [1, 0],
[1, 0], [1, 0], [1, 0], [0, 1],
[1, 0], [0, 1], [1, 0], [1, 0],
[0, 1], [0, 1], [0, 1], [1, 0],
[0, 1], [0, 1], [1, 0], [0, 1]])
cls.y_disturbed = np.array([
[0.99, 0.01], [0.99, 0.01], [0.99, 0.01], [0.99, 0.01],
[0.01, 0.99], [0.99, 0.01], [0.99, 0.01], [0.99, 0.01],
[0.99, 0.01], [0.01, 0.99], [0.99, 0.01], [0.99, 0.01],
[0.99, 0.01], [0.01, 0.99], [0.99, 0.01], [0.99, 0.01],
[0.99, 0.01], [0.99, 0.01], [0.99, 0.01], [0.01, 0.99],
[0.99, 0.01], [0.01, 0.99], [0.99, 0.01], [0.99, 0.01],
[0.01, 0.99], [0.01, 0.99], [0.01, 0.99], [0.99, 0.01],
[0.01, 0.99], [0.01, 0.99], [0.99, 0.01], [0.01, 0.99]])
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_disturbed(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_disturbed)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-12.327, 2.686, 0.089, 2.258]]),
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_disturbed),
-13.366314173353134,
places=3)
def test_lr_regularized(self):
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.01, 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([[-10.66, 2.364, 0.064, 2.142]]),
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),
-13.016861222748515,
places=3)
def test_lr_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-13.021, 2.8261, .09515, 2.378]]),
decimal=3)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_spector.exog, self.y, sample_weight=.5),
old_div(-12.8896334653335, 2.),
places=3)
def test_lr_disturbed_sample_weight_all_half(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_disturbed, sample_weight=.5)
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
np.array([[-12.327, 2.686, 0.089, 2.258]]),
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_disturbed, sample_weight=.5),
old_div(-13.366314173353134, 2.),
places=3)
def test_lr_sample_weight_all_zero(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.assertRaises(ValueError, self.model.fit,
self.data_spector.exog, self.y, 0)
def test_lr_sample_weight_half_zero_half_one(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)
len_half = 8
self.model.fit(self.data_spector.exog, self.y,
sample_weight=np.array([1] * len_half +
[0] * (self.y.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_spector.exog[:len_half], self.y[:len_half])
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
self.model_half.coef,
decimal=3)
def test_lr_disturbed_sample_weight_half_zero_half_one(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)
len_half = 8
self.model.fit(self.data_spector.exog, self.y_disturbed,
sample_weight=np.array([1] * len_half +
[0] * (self.y_disturbed.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_spector.exog[:len_half], self.y_disturbed[:len_half])
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
self.model_half.coef,
decimal=3)
# corner cases
def test_lr_two_data_point(self):
# with regularization
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.1, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_spector.exog[4:6, :],
self.y[4:6, ], sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (1, 4))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], self.y[4:6, ]),
np.array([-0.495, -0.661]), decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([[0, 0], [1, 0]])),
np.array([-np.Infinity, -0.661]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([[0, 0], [0, 1]])),
np.array([-np.Infinity, -0.726]), decimal=3)
def test_lr_disturbed_two_data_point(self):
# with regularization
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.1, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_spector.exog[4:6, :],
self.y_disturbed[4:6, ], sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (1, 4))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], self.y_disturbed[4:6, ]),
np.array([-0.503, -0.662]), decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([[0, 0], [0.99, 0.01]])),
np.array([-np.Infinity, -0.662]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :],
np.array([[0, 0], [0.01, 0.99]])),
np.array([-np.Infinity, -0.725]), decimal=3)
def test_lr_multicolinearty(self):
self.model_col = 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)
X = np.hstack([self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])
self.model_col.fit(X,
self.y, sample_weight=0.5)
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[:, 0:1],
self.y, sample_weight=0.5)
np.testing.assert_array_almost_equal(
self.model_col.coef, np.array([[-9.703, 1.42002783, 1.42002783]]), decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y),
self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],
self.y), decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_spector.exog[:, 0:1]), decimal=3)
def test_lr_disturbed_multicolinearty(self):
self.model_col = 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)
X = np.hstack([self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])
self.model_col.fit(X,
self.y_disturbed, sample_weight=0.5)
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[:, 0:1],
self.y_disturbed, sample_weight=0.5)
np.testing.assert_array_almost_equal(
self.model_col.coef, np.array([[-9.359, 1.37, 1.37]]), decimal=3)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y_disturbed),
self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],
self.y_disturbed), decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_spector.exog[:, 0:1]), decimal=3)
class CrossEntropyMNLUnaryTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_spector = sm.datasets.spector.load()
cls.y = np.ones((cls.data_spector.endog.shape[0], 1))
def test_label_encoder(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1], [1], [1]])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, np.ones(3))
np.testing.assert_array_equal(X_repeated, x)
np.testing.assert_array_equal(
Y_repeated, np.array([0, 0, 0]))
np.testing.assert_array_equal(
sample_weight_repeated,
np.array([1, 1, 1]))
# with sample_weight
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1], [1], [1]])
sample_weight = np.array([0.25, 0.5, 0.25])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, sample_weight)
np.testing.assert_array_equal(X_repeated, x)
np.testing.assert_array_equal(
Y_repeated, np.array([0, 0, 0]))
np.testing.assert_array_equal(
sample_weight_repeated, sample_weight)
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_equal(
self.model.coef,
np.zeros((4, 1)))
# predict
np.testing.assert_array_equal(
self.model.predict(self.data_spector.exog),
np.array([0] * self.data_spector.endog.shape[0]))
# loglike/_per_sample
np.testing.assert_array_equal(
self.model.loglike_per_sample(self.data_spector.exog,
np.array([1] * 16 + [0] * 16).reshape(-1, 1)),
np.array([0] * 16 + [-np.Infinity] * 16))
def test_lr_sample_weight_all_half(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, sample_weight=.5)
# coefficient
np.testing.assert_array_equal(
self.model.coef,
np.zeros((4, 1)))
# loglike/_per_sample
self.assertEqual(
self.model.loglike(self.data_spector.exog, self.y, sample_weight=.5), 0)
# corner cases
def test_lr_one_data_point(self):
# with regularization
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[4:5, :],
self.y[4:5, ], sample_weight=0.5)
# coef
np.testing.assert_array_equal(
self.model.coef,
np.zeros((4, 1)))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([1, 0]).reshape(-1, 1)),
np.array([0, -np.Infinity]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_spector.exog[4:6, :], np.array([1, 1]).reshape(-1, 1)),
np.array([0, 0]), decimal=3)
class CrossEntropyMNLMultinomialTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_anes96 = sm.datasets.anes96.load()
cls.y = label_binarize(cls.data_anes96.endog, classes=list(range(7)))
cls.y_disturbed = old_div((cls.y + 0.01), 1.07)
def test_label_encoder(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, np.ones(3))
np.testing.assert_array_equal(
X_repeated,
np.array([
[1, 2, 3], [1, 2, 3], [1, 2, 3],
[4, 5, 6], [4, 5, 6], [4, 5, 6],
[7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(
Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(
sample_weight_repeated,
np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]))
# with sample_weight
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[0.5, 0.25, 0.25], [0.25, 0.5, 0.25], [0.25, 0.25, 0.5]])
sample_weight = np.array([0.25, 0.5, 0.25])
X_repeated, Y_repeated, sample_weight_repeated = \
CrossEntropyMNL._label_encoder(x, y, sample_weight)
np.testing.assert_array_equal(
X_repeated,
np.array([
[1, 2, 3], [1, 2, 3], [1, 2, 3],
[4, 5, 6], [4, 5, 6], [4, 5, 6],
[7, 8, 9], [7, 8, 9], [7, 8, 9]]))
np.testing.assert_array_equal(
Y_repeated,
np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))
np.testing.assert_array_equal(
sample_weight_repeated,
np.array([0.125, 0.0625, 0.0625, 0.125, 0.25, 0.125, 0.0625, 0.0625, 0.125]))
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_disturbed(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_disturbed)
# coefficient
# predict
self.assertEqual(
np.sum(self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 335)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y_disturbed),
-1580.5280532302786,
places=3)
def test_lr_regularized(self):
self.model = CrossEntropyMNL(
solver='newton-cg', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.5, 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)
# predict
self.assertEqual(
np.sum(self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 369)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y),
-1466.9886103092626,
places=3)
def test_lr_disturbed_regularized(self):
self.model = CrossEntropyMNL(
solver='newton-cg', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.5, 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_disturbed)
# predict
self.assertEqual(
np.sum(self.model.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 366)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y_disturbed),
-1519.9521131193064,
places=3)
def test_lr_sample_weight_all_half(self):
self.model_half = CrossEntropyMNL(
solver='newton-cg', 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_half.fit(self.data_anes96.exog, self.y, sample_weight=.5)
self.model = CrossEntropyMNL(
solver='newton-cg', 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_anes96.exog, self.y)
# coefficient
np.testing.assert_array_almost_equal(self.model.coef, self.model_half.coef, decimal=3)
# predict
self.assertEqual(
np.sum(self.model_half.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 372)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y, sample_weight=.5),
old_div(-1461.92274725, 2.),
places=3)
def test_lr_disturbed_sample_weight_all_half(self):
self.model_half = CrossEntropyMNL(
solver='newton-cg', 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_half.fit(self.data_anes96.exog, self.y_disturbed, sample_weight=.5)
self.model = CrossEntropyMNL(
solver='newton-cg', 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_anes96.exog, self.y_disturbed)
# coefficient
np.testing.assert_array_almost_equal(self.model.coef, self.model_half.coef, decimal=3)
# predict
self.assertEqual(
np.sum(self.model_half.predict(self.data_anes96.exog) ==
self.data_anes96.endog), 367)
# loglike/_per_sample
self.assertAlmostEqual(
self.model.loglike(self.data_anes96.exog, self.y_disturbed, sample_weight=.5),
old_div(-1516.50148, 2.),
places=3)
def test_lr_sample_weight_all_zero(self):
self.model = DiscreteMNL(
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, classes=None)
self.assertRaises(ValueError, self.model.fit,
self.data_anes96.exog, self.y_disturbed, 0)
def test_lr_sample_weight_half_zero_half_one(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)
len_half = 500
self.model.fit(self.data_anes96.exog, self.y,
sample_weight=np.array([1] * len_half +
[0] * (self.data_anes96.exog.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_anes96.exog[:len_half], self.y[:len_half])
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
self.model_half.coef,
decimal=3)
def test_lr_disturbed_sample_weight_half_zero_half_one(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)
len_half = 500
self.model.fit(self.data_anes96.exog, self.y_disturbed,
sample_weight=np.array([1] * len_half +
[0] * (self.data_anes96.exog.shape[0] - len_half)))
self.model_half = 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_half.fit(self.data_anes96.exog[:len_half], self.y_disturbed[:len_half])
# coefficient
np.testing.assert_array_almost_equal(
self.model.coef,
self.model_half.coef,
decimal=3)
# corner cases
def test_lr_three_data_point(self):
# with regularization
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.1, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_anes96.exog[6:9, :],
self.y[6:9, ], sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (7, 6))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], self.y[6:9, ]),
np.array([-0.015, -0.091, -0.095]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], label_binarize([3, 1, 4], list(range(7)))),
np.array([-4.201, -5.094, -2.825]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], label_binarize([3, 0, 5], list(range(7)))),
np.array([-4.201, -7.352, -8.957]), decimal=3)
def test_lr_disturbed_three_data_point(self):
# with regularization
self.model = CrossEntropyMNL(
solver='lbfgs', fit_intercept=True, est_stderr=True,
reg_method='l2', alpha=.1, l1_ratio=0, tol=1e-4, max_iter=100,
coef=None, stderr=None, n_classes=None)
self.model.fit(self.data_anes96.exog[6:9, :],
self.y_disturbed[6:9, ], sample_weight=0.5)
# coef
self.assertEqual(self.model.coef.shape, (7, 6))
# loglike_per_sample
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], self.y_disturbed[6:9, ]),
np.array([-0.336, -0.389, -0.398]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], label_binarize([3, 1, 4], list(range(7)))),
np.array([-3.415, -4.506, -2.367]), decimal=3)
np.testing.assert_array_almost_equal(self.model.loglike_per_sample(
self.data_anes96.exog[6:9, :], label_binarize([3, 0, 5], list(range(7)))),
np.array([-3.415, -4.492, -4.301]), decimal=3)
def test_lr_multicolinearty(self):
self.model_col = 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)
X = np.hstack([self.data_anes96.exog[:, 0:1], self.data_anes96.exog[:, 0:1]])
self.model_col.fit(X,
self.y, sample_weight=0.5)
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_anes96.exog[:, 0:1],
self.y, sample_weight=0.5)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y),
self.model.loglike_per_sample(self.data_anes96.exog[:, 0:1],
self.y), decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_anes96.exog[:, 0:1]), decimal=3)
def test_lr_disturbed_multicolinearty(self):
self.model_col = 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)
X = np.hstack([self.data_anes96.exog[:, 0:1], self.data_anes96.exog[:, 0:1]])
self.model_col.fit(X,
self.y_disturbed, sample_weight=0.5)
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_anes96.exog[:, 0:1],
self.y_disturbed, sample_weight=0.5)
# loglike_per_sample
np.testing.assert_array_almost_equal(
self.model_col.loglike_per_sample(X, self.y_disturbed),
self.model.loglike_per_sample(self.data_anes96.exog[:, 0:1],
self.y_disturbed), decimal=3)
np.testing.assert_array_almost_equal(
self.model_col.predict(X),
self.model.predict(self.data_anes96.exog[:, 0:1]), decimal=3)
