def test__get_model_dict(self):
"""_get_model_dict returns a pandas.Series with all the params to recreate a model."""
# Setup
X = np.eye(3)
expected_result = {
'covariance__0__0': 1.5000000000000009,
'covariance__1__0': -0.7500000000000003,
'covariance__1__1': 1.5000000000000009,
'covariance__2__0': -0.7500000000000003,
'covariance__2__1': -0.7500000000000003,
'covariance__2__2': 1.5000000000000007,
'distribs__0__mean': 0.33333333333333331,
'distribs__0__std': -0.7520386983881371,
'distribs__1__mean': 0.33333333333333331,
'distribs__1__std': -0.7520386983881371,
'distribs__2__mean': 0.33333333333333331,
'distribs__2__std': -0.7520386983881371,
}
data_navigator = MagicMock()
modeler = Modeler(data_navigator)
# Run
result = modeler._get_model_dict(X)
# Check
assert result == expected_result
def test__get_model_dict_default_model(self, log_mock):
"""Test get flatten model dict with default model"""
# Setup
model_fitted = Mock()
model_fitted.covariance = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
distrib1 = Mock()
distrib1.std = 0.2
distrib2 = Mock()
distrib2.std = None
distrib3 = Mock()
distrib3.std = 0.5
distrib4 = Mock()
distrib4.std = 0.3
model_fitted.distribs = {
'distrib1': distrib1,
'distrib2': distrib2,
'distrib3': distrib3,
'distrib4': distrib4
}
# Run
modeler = Mock(spec=Modeler)
modeler._fit_model.return_value = model_fitted
modeler._flatten_dict.return_value = 'result'
data = pd.DataFrame({'data': [1, 2, 3]})
result = Modeler._get_model_dict(modeler, data)
# Asserts
expected_log_mock_call = [
call(0.2),
call(0.5),
call(0.3)
]
assert result == 'result'
assert sorted(log_mock.call_args_list) == sorted(expected_log_mock_call)
pd.testing.assert_frame_equal(
modeler._fit_model.call_args[0][0],
pd.DataFrame({'data': [1, 2, 3]})
)
