def pipeline1(images):
"""
Binarizer --> Height Filtration, Erosion Filtration, Dilation Filtration --> Cubical Persistance --> Amp, PE
return: Array of pipelines
"""
# Pipeline parameters
bin_thresholds = [np.percentile(images[0], 93) / np.max(images[0])]
directions = [
np.array([np.cos(t), np.sin(t)])
for t in np.linspace(0, 2 * np.pi, 8)[:-1]
]
n_iterations = np.linspace(1, 21, 5).astype(int).tolist()
features = [('bottleneck', Amplitude(metric='bottleneck', n_jobs=-1)),
('PE', PersistenceEntropy(n_jobs=-1))]
# Make filtrations
binned_steps = [('binarizer_{}'.format(t), Binarizer(threshold=t,
n_jobs=-1))
for t in bin_thresholds]
filtrations = [('height_{}'.format(d),
HeightFiltration(direction=d, n_jobs=-1))
for d in directions]
filtrations += [('erosion_{}'.format(i),
ErosionFiltration(n_iterations=i, n_jobs=-1))
for i in n_iterations]
filtrations += [('dilation_{}'.format(i),
DilationFiltration(n_iterations=i, n_jobs=-1))
for i in n_iterations]
# Make pipelines
cubical_lower = ('cubical', CubicalPersistence(n_jobs=-1))
partial_pipeline_steps = []
partial_pipeline_steps.append([cubical_lower])
partial_pipeline_steps.append([('inverter', Inverter(n_jobs=-1)),
cubical_lower])
for b, f in itertools.product(binned_steps, filtrations):
partial_pipeline_steps.append(
[b, f, ('cubical', CubicalPersistence(n_jobs=-1))])
feature_pipelines = []
for s, f in itertools.product(partial_pipeline_steps, features):
feature_pipelines.append(Pipeline(s + [f]))
return feature_pipelines
def test_dilation_fit_transform_plot():
DilationFiltration().fit_transform_plot(images_2D, sample=0)
def test_dilation_transform(n_iterations, images, expected):
dilation = DilationFiltration(n_iterations=n_iterations)
assert_almost_equal(dilation.fit_transform(images), expected)
def test_dilation_errors():
n_iterations = 'a'
dilation = DilationFiltration(n_iterations=n_iterations)
with pytest.raises(TypeError):
dilation.fit(images_2D)
def test_dilation_not_fitted():
dilation = DilationFiltration()
with pytest.raises(NotFittedError):
dilation.transform(images_2D)
X_copy = X_copy.reshape((40000, 28, 28))
X[X <= .4] = 0
X[X > .4] = 1
X = X.reshape((40000, 28, 28))
X_bin = X
# X_index = np.zeros((40000, 28, 28))
# for i in tqdm(range(40000)):
# img = X[i]
# x_arr, y_arr = np.nonzero(img)
# X_index[i] = np.array([x_arr, y_arr]).T
hf = HeightFiltration()
df = DilationFiltration()
rf = RadialFiltration(np.array([8, 15]))
X_used = X_bin
y_used = y
## Create the Filtrations
X_hf = hf.fit_transform(X_used, y_used)
X_df = df.fit_transform(X_used, y_used)
X_rf = rf.fit_transform(X_used, y_used)
fig = plt.figure()
fig.set_size_inches((12, 9))
a = fig.add_subplot(2, 2, 1)
a.set_title("Height filtration")
imgplot = plt.imshow(X_hf[4], cmap="viridis")
a = fig.add_subplot(2,2, 2)
pio.renderers.default = 'plotly_mimetype'
images_2D = np.stack([np.ones((3, 4)),
np.concatenate([np.ones((3, 2)), np.zeros((3, 2))],
axis=1),
np.zeros((3, 4))], axis=0)
images_3D = np.stack([np.ones((3, 4, 2)),
np.concatenate([np.ones((3, 2, 2)),
np.zeros((3, 2, 2))], axis=1),
np.zeros((3, 4, 2))], axis=0)
@pytest.mark.parametrize("transformer",
[HeightFiltration(), RadialFiltration(),
DilationFiltration(), ErosionFiltration(),
SignedDistanceFiltration(), DensityFiltration()])
def test_invalid_input_shape(transformer):
X = np.ones((1, 1, 1, 1, 1))
with pytest.raises(ValueError, match="Input of `fit`"):
transformer.fit(X)
def test_height_not_fitted():
height = HeightFiltration()
with pytest.raises(NotFittedError):
height.transform(images_2D)
def test_height_errors():
direction = 'a'