def test_fit_sample(self):
model = TruncatedGaussian(min=1, max=5)
model.fit(self.data)
np.testing.assert_allclose(model._params['loc'], 1.0, atol=0.2)
np.testing.assert_allclose(model._params['scale'], 1.0, atol=0.2)
np.testing.assert_allclose(model._params['a'], 0.0, atol=0.2)
np.testing.assert_allclose(model._params['b'], 4.0, atol=0.2)
sampled_data = model.sample(50)
assert isinstance(sampled_data, np.ndarray)
assert sampled_data.shape == (50, )
def test_to_dict_constant(self):
model = TruncatedGaussian(min=1, max=5)
model.fit(self.constant)
params = model.to_dict()
assert params == {
'type': 'copulas.univariate.truncated_gaussian.TruncatedGaussian',
'loc': 5,
'scale': 0,
'a': 5,
'b': 5,
}
def test_save_load(self):
model = TruncatedGaussian()
model.fit(self.data)
sampled_data = model.sample(50)
path_to_model = os.path.join(self.test_dir.name, "model.pkl")
model.save(path_to_model)
model2 = TruncatedGaussian.load(path_to_model)
pdf = model.probability_density(sampled_data)
pdf2 = model2.probability_density(sampled_data)
assert np.all(np.isclose(pdf, pdf2, atol=0.01))
cdf = model.cumulative_distribution(sampled_data)
cdf2 = model2.cumulative_distribution(sampled_data)
assert np.all(np.isclose(cdf, cdf2, atol=0.01))
def test_truncated(self):
"""
Suppose the data follows a truncated normal distribution. The KS statistic should be
larger for a Gaussian model than a TruncatedGaussian model (since the fit is worse).
"""
model = select_univariate(
self.truncated_data,
[GaussianUnivariate(), TruncatedGaussian()])
assert isinstance(model, TruncatedGaussian)
def test_binary(self):
"""
Suppose the data follows a Bernoulli distribution. The KS statistic should be larger
for a TruncatedGaussian model than a GaussianKDE model which can somewhat capture a
Bernoulli distribution as it resembles a bimodal distribution.
"""
model = select_univariate(
self.binary_data,
[GaussianKDE(), TruncatedGaussian()])
assert isinstance(model, GaussianKDE)
def test_truncated(self):
"""
Suppose the data follows a truncated normal distribution. The KS statistic should be
larger for a Gaussian model than a TruncatedGaussian model (since the fit is worse).
"""
gaussian_likelihood = ks_statistic(GaussianUnivariate(),
self.truncated_data)
truncated_likelihood = ks_statistic(TruncatedGaussian(),
self.truncated_data)
assert truncated_likelihood < gaussian_likelihood
def test_binary(self):
"""
Suppose the data follows a Bernoulli distribution. The KS statistic should be larger
for a TruncatedGaussian model than a GaussianKDE model which can somewhat capture a
Bernoulli distribution as it resembles a bimodal distribution.
"""
kde_likelihood = ks_statistic(GaussianKDE(), self.binary_data)
truncated_likelihood = ks_statistic(TruncatedGaussian(),
self.binary_data)
assert kde_likelihood < truncated_likelihood
def test_to_dict_from_dict(self):
model = TruncatedGaussian()
model.fit(self.data)
sampled_data = model.sample(50)
params = model.to_dict()
model2 = TruncatedGaussian.from_dict(params)
pdf = model.probability_density(sampled_data)
pdf2 = model2.probability_density(sampled_data)
assert np.all(np.isclose(pdf, pdf2, atol=0.01))
cdf = model.cumulative_distribution(sampled_data)
cdf2 = model2.cumulative_distribution(sampled_data)
assert np.all(np.isclose(cdf, cdf2, atol=0.01))
def test_to_dict_from_dict_constant(self):
model = TruncatedGaussian()
model.fit(self.constant)
sampled_data = model.sample(50)
pdf = model.probability_density(sampled_data)
cdf = model.cumulative_distribution(sampled_data)
params = model.to_dict()
model2 = TruncatedGaussian.from_dict(params)
np.testing.assert_equal(np.full(50, 5), sampled_data)
np.testing.assert_equal(np.full(50, 5), model2.sample(50))
np.testing.assert_equal(np.full(50, 1), pdf)
np.testing.assert_equal(np.full(50, 1), model2.probability_density(sampled_data))
np.testing.assert_equal(np.full(50, 1), cdf)
np.testing.assert_equal(np.full(50, 1), model2.cumulative_distribution(sampled_data))
def test_pdf(self):
model = TruncatedGaussian()
model.fit(self.data)
sampled_data = model.sample(50)
# Test PDF
pdf = model.probability_density(sampled_data)
assert (0 < pdf).all()
def test_cdf(self):
model = TruncatedGaussian()
model.fit(self.data)
sampled_data = model.sample(50)
# Test the CDF
cdf = model.cumulative_distribution(sampled_data)
assert (0 <= cdf).all() and (cdf <= 1).all()
# Test CDF increasing function
sorted_data = sorted(sampled_data)
cdf = model.cumulative_distribution(sorted_data)
assert (np.diff(cdf) >= 0).all()
def test_fit_sample_constant(self):
model = TruncatedGaussian()
model.fit(self.constant)
sampled_data = model.sample(50)
assert isinstance(sampled_data, np.ndarray)
assert sampled_data.shape == (50, )
assert model._constant_value == 5
np.testing.assert_equal(np.full(50, 5), model.sample(50))
