def mtf_encode_3_to_4(arr_3d, dim_mult=3):
dim = arr_3d[0].mean(axis=0).shape[1] * dim_mult
transformer_multi = MultivariateTransformer(
MarkovTransitionField(image_size=dim), flatten=False)
mtf_fsdd_4d = (transformer_multi.fit_transform(array.T)
for array in arr_3d)
return mtf_fsdd_4d
def mrp_encode_3_to_4(arr_3d, percentage=60, swap=(2, 2)):
transformer_multi = MultivariateTransformer(RecurrencePlot(
threshold='point', percentage=percentage),
flatten=False)
recplot_isff_4d = (transformer_multi.fit_transform(
array.swapaxes(swap[0], swap[1])) for array in arr_3d)
return recplot_isff_4d
def test_actual_results_without_flatten():
"""Test that the actual results are the expected ones."""
params = {'estimator': RecurrencePlot(dimension=6), 'flatten': False}
arr_actual = MultivariateTransformer(**params).fit_transform(X)
arr_desired = []
for i in range(n_features):
arr_desired.append(params['estimator'].transform(X[:, i]))
arr_desired = np.transpose(arr_desired, axes=(1, 0, 2, 3))
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
def test_actual_results_with_flatten():
"""Test that the actual results are the expected ones."""
params = {'estimator': RecurrencePlot(dimension=6), 'flatten': True}
arr_actual = MultivariateTransformer(**params).fit_transform(X)
arr_desired = []
for i in range(n_features):
arr_desired.append(params['estimator'].transform(X[:, i]).reshape(
(n_samples, -1)))
arr_desired = np.concatenate(arr_desired, axis=1)
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
def test_shapes(params, shape_desired):
"""Test that the shape of the output is the expected one."""
transformer = MultivariateTransformer(**params)
assert transformer.fit(X).transform(X).shape == shape_desired
assert transformer.fit_transform(X).shape == shape_desired
def test_array_conversion_error(X, err_msg):
"""Test the array conversion static method."""
with pytest.raises(ValueError, match=re.escape(err_msg)):
MultivariateTransformer._convert_to_array(X)
def test_array_conversion(X, arr_desired):
"""Test the array conversion static method."""
arr_actual = MultivariateTransformer._convert_to_array(X)
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
def test_parameter_check(params, error, err_msg):
"""Test parameter validation."""
transformer = MultivariateTransformer(**params)
with pytest.raises(error, match=re.escape(err_msg)):
transformer.fit_transform(X)
def test_ndim(params, ndim_desired):
"""Test that the ndim of the output is the expected one."""
transformer = MultivariateTransformer(**params)
assert transformer.fit(X).transform(X).ndim == ndim_desired
assert transformer.fit_transform(X).ndim == ndim_desired
def gaf_encode_3_to_4(arr_3d, swap=(2, 2)):
transformer_multi = MultivariateTransformer(GramianAngularField(),
flatten=False)
gramian_isff_4d = (transformer_multi.fit_transform(
array.swapaxes(swap[0], swap[1])) for array in arr_3d)
return gramian_isff_4d
