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test_preprocessing.py
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test_preprocessing.py
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"""Testing of preprocessing tools for persistence diagrams."""
# License: GNU AGPLv3
import numpy as np
import plotly.io as pio
import pytest
from numpy.testing import assert_almost_equal
from sklearn.exceptions import NotFittedError
from gtda.diagrams import ForgetDimension, Scaler, Filtering
pio.renderers.default = 'plotly_mimetype'
plotly_params = {"trace": {"marker_size": 20},
"layout": {"title": "New title"}}
X_1 = np.array([[[0., 0.36905774, 0],
[0., 0.37293977, 0],
[0., 0.38995215, 0],
[0., 3.00457644, 0],
[0., 3.04772496, 0],
[0., 3.32096624, 0],
[7.97852135, 8.00382805, 1],
[1.79289687, 1.8224113, 1],
[1.69005811, 2.32093406, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.36905774, 0],
[0., 0.37293977, 0],
[0., 0.38995215, 0],
[0., 3.00457644, 0],
[0., 3.04772496, 0],
[0., 3.32096624, 0],
[15.27686119, 24.32133484, 1],
[1.79289687, 1.8224113, 1],
[1.69005811, 2.32093406, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 1],
[25.24895287, 25.36620903, 2],
[25.15629959, 25.18988037, 2],
[25.06381798, 25.23542404, 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 1.32215321, 0],
[0., 1.48069561, 0],
[0., 1.62762213, 0],
[0., 3.18582344, 0],
[0., 3.25349188, 0],
[0., 3.28288555, 0],
[3.32096624, 23.92891693, 1],
[2.93474603, 3.07139683, 1],
[2.83503842, 2.94497037, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 1],
[24.84643745, 25.06381798, 2],
[24.77123451, 25.04314995, 2],
[24.67935562, 24.93212509, 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.13842253, 0],
[0., 0.13912651, 0],
[0., 0.15125643, 0],
[0., 3.30339432, 0],
[0., 3.3078723, 0],
[0., 3.33029914, 0],
[16.82829285, 16.84351158, 1],
[16.8180275, 16.84162521, 1],
[16.80234337, 16.80629158, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.13842253, 0],
[0., 0.13912651, 0],
[0., 0.15125643, 0],
[0., 2.91534185, 0],
[0., 2.93620634, 0],
[0., 3.00776553, 0],
[2.85910106, 3.39503384, 1],
[1.25564897, 1.25871313, 1],
[1.24251938, 1.27403092, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 1],
[3.66717434, 3.69763446, 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.13842253, 0],
[0., 0.13912651, 0],
[0., 0.15125643, 0],
[0., 2.91534185, 0],
[0., 2.93620634, 0],
[0., 3.00776553, 0],
[21.34438705, 24.6866188, 1],
[2.85910106, 2.88541412, 1],
[1.52559161, 3.39503384, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 1],
[25.05523109, 25.27045441, 2],
[24.93939018, 25.25673294, 2],
[24.89836693, 25.20828819, 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]]])
X_2 = np.array([[[0., 0.36905774, 0],
[0., 0.37293977, 0],
[0., 0.38995215, 0],
[0., 3.00457644, 0],
[0., 3.32096624, 0],
[7.97852135, 8.00382805, 1],
[1.79289687, 1.8224113, 1],
[1.69005811, 2.32093406, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.36905774, 0],
[0., 0.37293977, 0],
[0., 3.00457644, 0],
[0., 3.04772496, 0],
[0., 3.32096624, 0],
[15.27686119, 24.32133484, 1],
[1.79289687, 1.8224113, 1],
[1.69005811, 2.32093406, 1],
[0., 0., 1],
[0., 0., 1],
[25.24895287, 25.36620903, 2],
[25.15629959, 25.18988037, 2],
[25.06381798, 25.23542404, 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 1.32215321, 0],
[0., 1.48069561, 0],
[0., 3.18582344, 0],
[0., 3.25349188, 0],
[0., 3.28288555, 0],
[3.32096624, 23.92891693, 1],
[2.93474603, 3.07139683, 1],
[2.83503842, 2.94497037, 1],
[0., 0., 1],
[0., 0., 1],
[24.84643745, 25.06381798, 2],
[24.77123451, 25.04314995, 2],
[24.67935562, 24.93212509, 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.13842253, 0],
[0., 0.13912651, 0],
[0., 0.15125643, 0],
[0., 3.3078723, 0],
[0., 3.33029914, 0],
[16.82829285, 16.84351158, 1],
[16.8180275, 16.84162521, 1],
[16.80234337, 16.80629158, 1],
[0., 0., 1],
[0., 0., 1],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.13842253, 0],
[0., 0.13912651, 0],
[0., 0.15125643, 0],
[0., 2.93620634, 0],
[0., 3.00776553, 0],
[2.85910106, 3.39503384, 1],
[1.25564897, 1.25871313, 1],
[1.24251938, 1.27403092, 1],
[0., 0., 1],
[0., 0., 1],
[3.66717434, 3.69763446, 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2],
[0., 0., 2]],
[[0., 0.13842253, 0],
[0., 0.13912651, 0],
[0., 0.15125643, 0],
[0., 2.93620634, 0],
[0., 3.00776553, 0],
[21.34438705, 24.6866188, 1],
[2.85910106, 2.88541412, 1],
[1.52559161, 3.39503384, 1],
[0., 0., 1],
[0., 0., 1],
[25.05523109, 25.27045441, 2],
[24.93939018, 25.25673294, 2],
[24.89836693, 26.20828819, 2],
[0., 0., 2],
[0., 0., 2]]])
def test_not_fitted():
with pytest.raises(NotFittedError):
ForgetDimension().transform(X_1)
with pytest.raises(NotFittedError):
Scaler().transform(X_1)
with pytest.raises(NotFittedError):
Scaler().inverse_transform(X_1)
with pytest.raises(NotFittedError):
Filtering().transform(X_1)
def test_forg_fit_transform_plot():
ForgetDimension().fit_transform_plot(
X_1, sample=0, plotly_params=plotly_params
)
@pytest.mark.parametrize('hom_dims', [None, (0,), (1,)])
def test_fit_transform_plot(hom_dims):
Scaler().fit_transform_plot(
X_1, sample=0, homology_dimensions=hom_dims,
plotly_params=plotly_params
)
Filtering().fit_transform_plot(
X_1, sample=0, homology_dimensions=hom_dims,
plotly_params=plotly_params
)
@pytest.mark.parametrize('X', [X_1, X_2])
def test_forg_transform_shape(X):
forg = ForgetDimension()
X_res = forg.fit_transform(X)
assert X_res.shape == X.shape
parameters_sc = [
('bottleneck', None),
('wasserstein', {'p': 2}),
('betti', {'p': 2.1, 'n_bins': 10}),
('landscape', {'p': 2.1, 'n_bins': 10, 'n_layers': 2}),
('silhouette', {'p': 2.1, 'power': 1.2, 'n_bins': 10}),
('heat', {'p': 2.1, 'sigma': 0.5, 'n_bins': 10}),
('persistence_image',
{'p': 2.1, 'sigma': 0.5, 'n_bins': 10}),
('persistence_image',
{'p': 2.1, 'sigma': 0.5, 'n_bins': 10, 'weight_function': lambda x: x})
]
@pytest.mark.parametrize(('metric', 'metric_params'), parameters_sc)
@pytest.mark.parametrize('X', [X_1, X_2])
def test_sc_transform_shape(X, metric, metric_params):
sc = Scaler(metric=metric, metric_params=metric_params, n_jobs=1)
X_res = sc.fit_transform(X)
assert X_res.shape == X.shape
X_inv_res = sc.inverse_transform(X_res)
assert_almost_equal(X_inv_res, X)
@pytest.mark.parametrize('X', [X_1, X_2])
def test_filt_transform_zero(X):
filt = Filtering(epsilon=0.)
X_res = filt.fit_transform(X[:, [0], :])
assert_almost_equal(X_res, X[:, [0], :])
def total_lifetimes_in_dims(X, dims):
return sum([
np.sum(np.diff(X[X[:, :, 2] == dim], axis=1)[:, 0]) for dim in dims
])
@pytest.mark.parametrize('homology_dimensions', [None, (0, 1, 2), (0,), (1,),
(2,), (0, 1), (0, 2), (1, 2)])
def test_filt_transform_unfiltered_hom_dims(homology_dimensions):
filt = Filtering(epsilon=2., homology_dimensions=homology_dimensions)
X_1_res = filt.fit_transform(X_1)
if homology_dimensions is None:
unfiltered_hom_dims = []
else:
unfiltered_hom_dims = [
dim for dim in filt.homology_dimensions_
if dim not in homology_dimensions
]
assert total_lifetimes_in_dims(X_1, unfiltered_hom_dims) == \
total_lifetimes_in_dims(X_1_res, unfiltered_hom_dims)
lifetimes_1 = X_1[:, :, 1] - X_1[:, :, 0]
epsilons_1 = np.linspace(np.min(lifetimes_1), np.max(lifetimes_1), num=3)
@pytest.mark.parametrize('epsilon', epsilons_1)
def test_filt_transform(epsilon):
filt = Filtering(epsilon=epsilon)
X_res_1 = filt.fit_transform(X_1)
assert X_res_1.shape[1] <= X_1.shape[1]
lifetimes_res_1 = X_res_1[:, :, 1] - X_res_1[:, :, 0]
assert not ((lifetimes_res_1 > 0.) & (lifetimes_res_1 <= epsilon)).any()