def test_from_json(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json'
) as json_data:
json_dict = json.load(json_data)
self.model = SemiSupervisedIOHMM.from_json(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
# 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)
def test_from_json(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json') as json_data:
json_dict = json.load(json_data)
self.model = SemiSupervisedIOHMM.from_json(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
# 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)
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)
def test_from_config(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json'
) as json_data:
json_dict = json.load(json_data)
json_dict['properties'].update({
'model_initial': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_transition': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_emissions': [
{
'data_type': 'OLS',
'properties': {}
},
]
})
print(json_dict['properties']['model_initial'])
self.model = SemiSupervisedIOHMM.from_config(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
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)
class SemiSupervisedIOHMMTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_speed = pd.read_csv('examples/data/speed.csv')
cls.states = cls._mock_states()
@classmethod
def _mock_states(cls):
states = {}
corr = np.array(cls.data_speed['corr'])
for i in range(int(old_div(len(corr), 2))):
if corr[i] == 'cor':
states[i] = np.array([0, 1, 0, 0])
cls.data_speed.at[i, 'rt'] = 1
else:
states[i] = np.array([1, 0, 0, 0])
cls.data_speed.at[i, 'rt'] = 0
return states
def setUp(self):
np.random.seed(0)
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)
def test_from_json(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json'
) as json_data:
json_dict = json.load(json_data)
self.model = SemiSupervisedIOHMM.from_json(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
# 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)
def test_from_config(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json'
) as json_data:
json_dict = json.load(json_data)
json_dict['properties'].update({
'model_initial': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_transition': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_emissions': [
{
'data_type': 'OLS',
'properties': {}
},
]
})
print(json_dict['properties']['model_initial'])
self.model = SemiSupervisedIOHMM.from_config(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
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)
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 SemiSupervisedIOHMMTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_speed = pd.read_csv('examples/data/speed.csv')
cls.states = cls._mock_states()
@classmethod
def _mock_states(cls):
states = {}
corr = np.array(cls.data_speed['corr'])
for i in range(int(old_div(len(corr), 2))):
if corr[i] == 'cor':
states[i] = np.array([0, 1, 0, 0])
cls.data_speed.set_value(i, 'rt', 1)
else:
states[i] = np.array([1, 0, 0, 0])
cls.data_speed.set_value(i, 'rt', 0)
return states
def setUp(self):
np.random.seed(0)
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)
def test_from_json(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json') as json_data:
json_dict = json.load(json_data)
self.model = SemiSupervisedIOHMM.from_json(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
# 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)
def test_from_config(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json') as json_data:
json_dict = json.load(json_data)
json_dict['properties'].update({
'model_initial': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_transition': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_emissions': [
{
'data_type': 'OLS',
'properties': {}
},
]})
print(json_dict['properties']['model_initial'])
self.model = SemiSupervisedIOHMM.from_config(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
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)
def test_from_config(self):
with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json') as json_data:
json_dict = json.load(json_data)
json_dict['properties'].update({
'model_initial': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_transition': {
'data_type': 'CrossEntropyMNL',
'properties': {
'reg_method': 'l2',
'solver': 'newton-cg'
}
},
'model_emissions': [
{
'data_type': 'OLS',
'properties': {}
},
]})
print(json_dict['properties']['model_initial'])
self.model = SemiSupervisedIOHMM.from_config(json_dict)
self.assertEqual(type(self.model), SemiSupervisedIOHMM)
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)