def test_silhouette_transform(n_jobs):
sht = Silhouette(n_bins=31, power=1., n_jobs=n_jobs)
X_sht_res = np.array([0., 0.05, 0.1, 0.15, 0.2, 0.25, 0.2, 0.15, 0.1,
0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0., 0.05,
0.1, 0.15, 0.2, 0.25, 0.2, 0.15, 0.1, 0.05, 0.])
assert_almost_equal(sht.fit_transform(X)[0][0], X_sht_res)
def test_silhouette_transform():
sht = Silhouette(n_bins=31, order=1.)
X_sht_res = np.array([0., 0.05, 0.1, 0.15, 0.2, 0.25, 0.2, 0.15, 0.1,
0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0., 0.05,
0.1, 0.15, 0.2, 0.25, 0.2, 0.15, 0.1, 0.05, 0.])
assert_almost_equal(sht.fit_transform(diagram)[0][0], X_sht_res)
def test_silhouette_big_order():
diagrams = np.array([[[0, 2, 0], [1, 4, 0]]])
sht_10 = Silhouette(n_bins=41, power=10.)
X_sht_res = np.array([
0., 0.00170459, 0.00340919, 0.00511378, 0.00681837, 0.00852296,
0.01022756, 0.01193215, 0.01363674, 0.01534133, 0.01704593, 0.11363674,
0.21022756, 0.30681837, 0.40340919, 0.5, 0.59659081, 0.69318163,
0.78977244, 0.88636326, 0.98295407, 1.08124948, 1.17954489, 1.27784029,
1.3761357, 1.47443111, 1.3761357, 1.27784029, 1.17954489, 1.08124948,
0.98295407, 0.88465867, 0.78636326, 0.68806785, 0.58977244, 0.49147704,
0.39318163, 0.29488622, 0.19659081, 0.09829541, 0.
])
assert_almost_equal(sht_10.fit_transform(diagrams)[0][0], X_sht_res)
def test_not_fitted():
with pytest.raises(NotFittedError):
PersistenceEntropy().transform(X)
with pytest.raises(NotFittedError):
BettiCurve().transform(X)
with pytest.raises(NotFittedError):
PersistenceLandscape().transform(X)
with pytest.raises(NotFittedError):
HeatKernel().transform(X)
with pytest.raises(NotFittedError):
PersistenceImage().transform(X)
with pytest.raises(NotFittedError):
Silhouette().transform(X)
def generate_sample_representations(paths_to_patches, labels):
sample_rep_dir = DOTENV_KEY2VAL["GEN_FIGURES_DIR"] + "/sample_rep/"
try:
os.mkdir(sample_rep_dir)
except OSError:
print("Creation of the directory %s failed" % sample_rep_dir)
else:
print("Successfully created the directory %s " % sample_rep_dir)
for i, path in enumerate(paths_to_patches):
patch = np.load(path)
cp = CubicalPersistence(
homology_dimensions=(0, 1, 2),
coeff=2,
periodic_dimensions=None,
infinity_values=None,
reduced_homology=True,
n_jobs=N_JOBS,
)
diagrams_cubical_persistence = cp.fit_transform(
patch.reshape(1, 30, 36, 30)
)
for h_dim in HOMOLOGY_DIMENSIONS:
cp.plot(
diagrams_cubical_persistence,
homology_dimensions=[h_dim],
).update_traces(
marker=dict(size=10, color=HOMOLOGY_CMAP[h_dim]),
).write_image(
sample_rep_dir
+ f"persistence_diagram_{labels[i]}_H_{h_dim}.png",
scale=SCALE,
)
representation_names = [
"Persistence landscape",
"Betti curve",
"Persistence image",
"Heat kernel",
"Silhouette",
]
for j, rep in enumerate(representation_names):
# Have not found a better way of doing this yet.
if rep == "Persistence landscape":
rep = PersistenceLandscape(
n_layers=N_LAYERS, n_bins=VEC_SIZE, n_jobs=N_JOBS
)
elif rep == "Betti curve":
rep = BettiCurve()
elif rep == "Persistence image":
rep = PersistenceImage(
sigma=0.001, n_bins=VEC_SIZE, n_jobs=N_JOBS
)
elif rep == "Heat kernel":
rep = HeatKernel(sigma=0.001, n_bins=VEC_SIZE, n_jobs=N_JOBS)
elif rep == "Silhouette":
rep = Silhouette(power=1.0, n_bins=VEC_SIZE, n_jobs=N_JOBS)
vectorial_representation = rep.fit_transform(
diagrams_cubical_persistence
)
if representation_names[j] in ["Persistence image", "Heat kernel"]:
for h_dim in range(vectorial_representation.shape[1]):
plt.imshow(
vectorial_representation[0:, h_dim, :, :].reshape(
VEC_SIZE, VEC_SIZE
),
cmap=(HOMOLOGY_CMAP[h_dim] + "s").capitalize(),
)
# plt.title(
# f"{representation_names[j]} representation of a "
# f"{labels[i]} patient in h_{image}"
# )
plt.savefig(
sample_rep_dir
+ f"{representation_names[j].replace(' ', '_')}"
f"_{labels[i]}_h_{h_dim}.png",
bbox_inches="tight",
)
else:
rep.plot(vectorial_representation).update_layout(
title=None,
margin=dict(l=0, r=0, b=0, t=0, pad=4),
).write_image(
sample_rep_dir
+ f"{representation_names[j].replace(' ', '_')}"
f"_{labels[i]}.png",
scale=SCALE,
)
print(f"Done plotting {labels[i]} sample")
def test_fit_transform_plot_many_hom_dims(hom_dims):
BettiCurve().fit_transform_plot(X, sample=0, homology_dimensions=hom_dims)
PersistenceLandscape().fit_transform_plot(X,
sample=0,
homology_dimensions=hom_dims)
Silhouette().fit_transform_plot(X, sample=0, homology_dimensions=hom_dims)
pio.renderers.default = 'plotly_mimetype'
X = np.array([[[0., 0., 0.], [0., 1., 0.], [2., 3., 0.],
[4., 6., 1.], [2., 6., 1.]]])
line_plots_traces_params = {"mode": "lines+markers"}
heatmap_trace_params = {"colorscale": "viridis"}
layout_params = {"title": "New title"}
@pytest.mark.parametrize('transformer',
[PersistenceEntropy(), NumberOfPoints(),
ComplexPolynomial(), BettiCurve(),
PersistenceLandscape(), HeatKernel(),
PersistenceImage(), Silhouette()])
def test_not_fitted(transformer):
with pytest.raises(NotFittedError):
transformer.transform(X)
@pytest.mark.parametrize('transformer',
[HeatKernel(), PersistenceImage()])
@pytest.mark.parametrize('hom_dim_idx', [0, 1])
def test_fit_transform_plot_one_hom_dim(transformer, hom_dim_idx):
plotly_params = \
{"trace": heatmap_trace_params, "layout": layout_params}
transformer.fit_transform_plot(
X, sample=0, homology_dimension_idx=hom_dim_idx,
plotly_params=plotly_params
)
[2., 6., 1.]]])
line_plots_traces_params = {"mode": "lines+markers"}
heatmap_trace_params = {"colorscale": "viridis"}
layout_params = {"title": "New title"}
@pytest.mark.parametrize('transformer', [
PersistenceEntropy(),
NumberOfPoints(),
ComplexPolynomial(),
BettiCurve(),
PersistenceLandscape(),
HeatKernel(),
PersistenceImage(),
Silhouette()
])
def test_not_fitted(transformer):
with pytest.raises(NotFittedError):
transformer.transform(X)
@pytest.mark.parametrize('transformer', [HeatKernel(), PersistenceImage()])
@pytest.mark.parametrize('hom_dim_idx', [0, 1])
def test_fit_transform_plot_one_hom_dim(transformer, hom_dim_idx):
plotly_params = \
{"trace": heatmap_trace_params, "layout": layout_params}
transformer.fit_transform_plot(X,
sample=0,
homology_dimension_idx=hom_dim_idx,
plotly_params=plotly_params)