def test_pdf(self):
model = GaussianKDE()
model.fit(self.data)
sampled_data = model.sample(50)
# Test PDF
pdf = model.probability_density(sampled_data)
assert (0 < pdf).all()
def test_probability_density(self, kde_mock):
"""Sample calls the gaussian_kde.resample method."""
instance = GaussianKDE()
instance.fit(np.array([1, 2, 3, 4]))
model = kde_mock.return_value
model.evaluate.return_value = np.array([0.1, 0.2, 0.3])
pdf = instance.probability_density(np.array([1, 2, 3]))
assert instance._model.evaluate.call_count == 1
input_array = instance._model.evaluate.call_args[0][0]
np.testing.assert_equal(input_array, np.array([1, 2, 3]))
np.testing.assert_equal(pdf, np.array([0.1, 0.2, 0.3]))
def test_to_dict_from_dict(self):
model = GaussianKDE()
model.fit(self.data)
sampled_data = model.sample(50)
params = model.to_dict()
model2 = GaussianKDE.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_save_load(self):
model = GaussianKDE()
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 = GaussianKDE.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_to_dict_from_dict_constant(self):
model = GaussianKDE()
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 = GaussianKDE.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_probability_density_constant(self, pdf_mock):
"""If constant_value, probability_density uses the degenerate version."""
# Setup
instance = GaussianKDE()
instance.fitted = True
instance.constant_value = 3
instance._replace_constant_methods()
X = np.array([0, 1, 2, 3, 4, 5])
expected_result = np.array([0, 0, 1, 0, 0])
pdf_mock.return_value = np.array([0, 0, 1, 0, 0])
# Run
result = instance.probability_density(X)
# Check
compare_nested_iterables(result, expected_result)
pdf_mock.assert_called_once_with(instance, X)
def test_probability_density(self, kde_mock):
"""probability_density evaluates with the model."""
# Setup
model_mock = kde_mock.return_value
model_mock.evaluate.return_value = np.array([0.0, 0.5, 1.0])
fit_data = np.array([1, 2, 3, 4, 5])
instance = GaussianKDE()
instance.fit(fit_data)
call_data = np.array([-10, 0, 10])
expected_result = np.array([0.0, 0.5, 1.0])
# Run
result = instance.probability_density(call_data)
# Check
compare_nested_iterables(result, expected_result)
kde_mock.assert_called_once_with(fit_data)
model_mock.evaluate.assert_called_once_with(call_data)
