def cubical_persistence(images,
title,
plot_diagrams=False,
betti_curves=False,
scaled=False):
homology_dimensions = (0, 1, 2)
cp = CubicalPersistence(
homology_dimensions=homology_dimensions,
coeff=2,
periodic_dimensions=None,
infinity_values=None,
reduced_homology=True,
n_jobs=N_JOBS,
)
diagrams_cubical_persistence = cp.fit_transform(images)
if scaled:
sc = Scaler(metric="bottleneck")
diagrams_cubical_persistence = sc.fit_transform(
diagrams_cubical_persistence)
else:
scaled_diagrams_cubical_persistence = diagrams_cubical_persistence
if plot_diagrams:
fig = cp.plot(diagrams_cubical_persistence)
fig.update_layout(title=title)
fig.show()
if betti_curves:
BC = BettiCurve()
X_betti_curves = BC.fit_transform(diagrams_cubical_persistence)
fig = BC.plot(X_betti_curves)
fig.update_layout(title=title)
fig.show()
if title is not None:
print(f"Computed CP for {title}")
return diagrams_cubical_persistence
def vr_persistent_homology(patch_pc):
homology_dimensions = (0, 1, 2)
VR = VietorisRipsPersistence(
metric="euclidean",
max_edge_length=5,
homology_dimensions=homology_dimensions,
n_jobs=N_JOBS,
)
diagrams_VietorisRips = VR.fit_transform(np.asarray(patch_pc))
VR.plot(diagrams_VietorisRips).show()
BC = BettiCurve()
X_betti_curves = BC.fit_transform(diagrams_VietorisRips)
BC.plot(X_betti_curves).show()
return diagrams_VietorisRips
def extract_feature_(self, persistence_diagram):
betti_curve = BettiCurve(n_jobs=-1).fit_transform(
[persistence_diagram])[0]
return np.array([
np.sum(betti_curve[i, :])
for i in range(int(np.max(persistence_diagram[:, 2])) + 1)
])
def extract_feature_(self, persistence_diagram, n_bins=100):
betti_curve = BettiCurve(n_jobs=-1, n_bins=n_bins).fit_transform(
[persistence_diagram])[0]
max_dim = int(np.max(persistence_diagram[:, 2])) + 1
max_bettis = np.array(
[np.max(betti_curve[i, :]) for i in range(max_dim)])
return np.array([
np.where(betti_curve[i, :] == max_bettis[i])[0][0] /
(n_bins * max_dim) for i in range(max_dim)
])
def bettiCurve_pipe2(img_file):
"""
Pipeline 2: Cubical Perisitance --> Betti Curve
"""
img = cv2.imread(img_file)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# blur the image to reduce noise
figure_size = 9 # the dimension of the x and y axis of the kernal.
# img = cv2.blur(img,(figure_size, figure_size))
shape = img.shape
images = np.zeros((1, *shape))
images[0] = img
p = make_pipeline(CubicalPersistence(), BettiCurve(n_bins=50))
return p.fit_transform(images)
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 bettiCurve_pipe1(img_file):
"""
Pipeline 1: Binarizer --> Height Filtration --> Cubical Persistance --> Betti Curve
"""
img = cv2.imread(img_file)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# blur the image to reduce noise
figure_size = 9 # the dimension of the x and y axis of the kernal.
img = cv2.blur(img, (figure_size, figure_size))
shape = img.shape
images = np.zeros((1, *shape))
images[0] = img
bz = Binarizer(threshold=40 / 255)
binned = bz.fit_transform(images)
p = make_pipeline(HeightFiltration(direction=np.array([1, 1])),
CubicalPersistence(), BettiCurve(n_bins=50))
return p.fit_transform(binned)
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_bc_transform_shape(n_bins):
bc = BettiCurve(n_bins=n_bins)
X_res = bc.fit_transform(X)
assert X_res.shape == (1, bc._n_dimensions, n_bins)
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)
ComplexPolynomial, BettiCurve, PersistenceLandscape, HeatKernel, \
PersistenceImage, Silhouette
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,
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}