def test_load(self, json_mock, file_mock):
"""Load can recreate an instance from a saved file."""
# Setup
covariance = [[
1.006711409395973, -0.11010327176239865, 0.8776048563471857,
0.823443255069628
],
[
-0.11010327176239865, 1.006711409395972,
-0.4233383520816991, -0.3589370029669186
],
[
0.8776048563471857, -0.4233383520816991,
1.006711409395973, 0.9692185540781536
],
[
0.823443255069628, -0.3589370029669186,
0.9692185540781536, 1.0067114093959735
]]
json_mock.return_value = {
'covariance': covariance,
'distribs': {
'feature_01': {
'mean': 5.843333333333334,
'std': 0.8253012917851409
},
'feature_02': {
'mean': 3.0540000000000003,
'std': 0.4321465800705435
},
'feature_03': {
'mean': 3.758666666666666,
'std': 1.7585291834055212
},
'feature_04': {
'mean': 1.1986666666666668,
'std': 0.7606126185881716
}
}
}
# Run
instance = GaussianMultivariate.load('somefile.json')
# Check
assert (instance.covariance == np.array(
[[
1.006711409395973, -0.11010327176239865, 0.8776048563471857,
0.823443255069628
],
[
-0.11010327176239865, 1.006711409395972, -0.4233383520816991,
-0.3589370029669186
],
[
0.8776048563471857, -0.4233383520816991, 1.006711409395973,
0.9692185540781536
],
[
0.823443255069628, -0.3589370029669186, 0.9692185540781536,
1.0067114093959735
]])).all()
for name, distrib in instance.distribs.items():
assert instance.distribs[name].to_dict(
) == json_mock.return_value['distribs'][name]
def test_load(self, json_mock, open_mock):
"""Load can recreate an instance from a saved file."""
# Setup
covariance = [[
1.006711409395973, -0.11010327176239865, 0.8776048563471857,
0.823443255069628
],
[
-0.11010327176239865, 1.006711409395972,
-0.4233383520816991, -0.3589370029669186
],
[
0.8776048563471857, -0.4233383520816991,
1.006711409395973, 0.9692185540781536
],
[
0.823443255069628, -0.3589370029669186,
0.9692185540781536, 1.0067114093959735
]]
json_mock.return_value = {
'covariance':
covariance,
'fitted':
True,
'type':
'copulas.multivariate.gaussian.GaussianMultivariate',
'distribution':
'copulas.univariate.gaussian.GaussianUnivariate',
'columns':
['feature_01', 'feature_02', 'feature_03', 'feature_04'],
'univariates': [{
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 5.843333333333334,
'std': 0.8253012917851409,
'fitted': True,
}, {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 3.0540000000000003,
'std': 0.4321465800705435,
'fitted': True,
}, {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 3.758666666666666,
'std': 1.7585291834055212,
'fitted': True,
}, {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 1.1986666666666668,
'std': 0.7606126185881716,
'fitted': True,
}]
}
# Run
instance = GaussianMultivariate.load('test.json')
# Check
assert (instance.covariance == np.array(
[[
1.006711409395973, -0.11010327176239865, 0.8776048563471857,
0.823443255069628
],
[
-0.11010327176239865, 1.006711409395972, -0.4233383520816991,
-0.3589370029669186
],
[
0.8776048563471857, -0.4233383520816991, 1.006711409395973,
0.9692185540781536
],
[
0.823443255069628, -0.3589370029669186, 0.9692185540781536,
1.0067114093959735
]])).all()
for a, b in zip(json_mock.columns, instance.columns):
assert a == b
for a, b in zip(json_mock.univariates, instance.univariates):
assert a.to_dict() == b.to_dict()
assert open_mock.called_once_with('test.json', 'r')
