def test__trasnform(self):
# setup
instance = MagicMock()
trials = np.array([[0.1, 0.8, 0.2], [0.7, 0.6, 0.4], [0.4, 0.2, 0.3],
[0.7, 0.8, 0.9], [0.2, 0.3, 0.4]])
distributions = []
for column in trials.T:
distribution = Univariate()
distribution.fit(column)
distributions.append(distribution)
instance._distributions = distributions
# run
result = GaussianCopulaProcessMetaModel._transform(instance, trials)
# assert
expected_result = np.array([[-0.93051407, 0.72584367, -1.05823838],
[0.78865964, 0.1155097, 0.3732126],
[-0.04619363, -0.96388991, -0.49278658],
[0.78865964, 0.72584367, 1.82607428],
[-0.60472608, -0.66069297, 0.3732126]])
np.testing.assert_allclose(result, expected_result)
def from_dict(cls, copula_dict):
"""Create a new instance from a parameters dictionary.
Args:
params (dict):
Parameters of the distribution, in the same format as the one
returned by the ``to_dict`` method.
Returns:
Multivariate:
Instance of the distribution defined on the parameters.
"""
instance = cls()
instance.univariates = []
instance.columns = copula_dict['columns']
for parameters in copula_dict['univariates']:
instance.univariates.append(Univariate.from_dict(parameters))
instance.covariance = np.array(copula_dict['covariance'])
instance.fitted = True
warnings.warn('`covariance` will be renamed to `correlation` in v0.4.0',
DeprecationWarning)
return instance
def from_dict(cls, copula_dict):
"""Set attributes with provided values."""
instance = cls()
instance.univariates = []
instance.columns = copula_dict['columns']
for parameters in copula_dict['univariates']:
instance.univariates.append(Univariate.from_dict(parameters))
instance.covariance = np.array(copula_dict['covariance'])
instance.fitted = copula_dict['fitted']
instance.distribution = copula_dict['distribution']
if isinstance(instance.distribution, dict):
for k, v in instance.distribution.items():
instance.distribution[k] = Univariate.from_dict(v)
return instance
def test_fit_sample_distribution_dict_multiple(self):
data = sample_trivariate_xyz()
model = GaussianMultivariate(
distribution={
'x': Univariate(parametric=ParametricType.PARAMETRIC),
'y': BetaUnivariate(),
'z': GaussianKDE()
})
model.fit(data)
sampled_data = model.sample(10)
assert sampled_data.shape == (10, 3)
def from_dict(cls, copula_dict):
"""Set attributes with provided values."""
instance = cls()
instance.distribs = {}
for name, parameters in copula_dict['distribs'].items():
instance.distribs[name] = Univariate.from_dict(parameters)
instance.covariance = np.array(copula_dict['covariance'])
instance.fitted = copula_dict['fitted']
instance.distribution = copula_dict['distribution']
return instance
def _fit(self, trials, scores):
self._distributions = []
for column in trials.T:
distribution = Univariate()
distribution.fit(column)
self._distributions.append(distribution)
distribution = Univariate()
distribution.fit(scores)
self._score_distribution = distribution
trans_trials = self._transform(trials)
trans_scores = scipy.stats.norm.ppf(
self._score_distribution.cdf(scores).clip(0 + EPSILON, 1 - EPSILON)
)
super()._fit(trans_trials, trans_scores)