Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Correct bracket for exponential #814

Open
wants to merge 5 commits into
base: master
Choose a base branch
from
Open

Correct bracket for exponential #814

Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
be08c60
Correct bracket for exponential
wreise Feb 13, 2023
7238d02
Add kernel_approx tests; Wasserstein with pot & hera comparisons
wreise Feb 15, 2023
c6fd175
Correct kernel approx tests after Marcs' comments
wreise Mar 6, 2023
c0d8dd0
Merge branch 'master' into tests_kernels
wreise Oct 16, 2023
d09218c
Fix tests
wreise Oct 16, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
44 changes: 31 additions & 13 deletions src/python/test/test_representations.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import random

from sklearn.cluster import KMeans
from sklearn.kernel_approximation import RBFSampler

# Vectorization
from gudhi.representations import (Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
Expand Down Expand Up @@ -48,9 +49,12 @@ def _n_diags(n):
"bottleneck": (BottleneckDistance(epsilon=0.00001),
dict(e=0.00001),
dict(abs=1e-5)),
"wasserstein": (WassersteinDistance(order=2, internal_p=2, n_jobs=4),
"pot_wasserstein": (WassersteinDistance(order=2, mode="pot", internal_p=2, n_jobs=4),
dict(order=2, internal_p=2, n_jobs=4),
dict(rel=1e-3)),
"hera_wasserstein": (WassersteinDistance(order=2, mode="hera", delta=0.001, internal_p=2, n_jobs=4),
dict(order=2, internal_p=2, n_jobs=4, delta=0.001,),
dict(rel=1e-3)),
"sliced_wasserstein": (SlicedWassersteinDistance(num_directions=100, n_jobs=4),
dict(num_directions=100),
dict(rel=1e-3)),
Expand All @@ -75,34 +79,48 @@ def test_distance_transform_consistency():

kernel_dict = {
"sliced_wasserstein": (SlicedWassersteinKernel(num_directions=10, bandwidth=4., n_jobs=4),
dict(num_directions=10), dict(rel=1e-3)),
dict(num_directions=10)),
"persistence_fisher": (PersistenceFisherKernel(bandwidth_fisher=3., bandwidth=1.),
dict(bandwidth=3.), # corresponds to bandwidth_fisher in the kernel class
dict(rel=1e-3)),
"persistence_weighted_gaussian": (PersistenceWeightedGaussianKernel(bandwidth=4.,
dict(bandwidth=3.),), # corresponds to bandwidth_fisher in the kernel class
"persistence_weighted_gaussian": (PersistenceWeightedGaussianKernel(bandwidth=2.,
weight=lambda x: x[1]-x[0]),
dict(bandwidth=4., weight=lambda x: x[1]-x[0]),
dict(rel=1e-3)),
"persistence_scale_space": (PersistenceScaleSpaceKernel(bandwidth=4.),
dict(bandwidth=4.),
dict(rel=1e-3)),
dict(bandwidth=2., weight=lambda x: x[1]-x[0]),),
"persistence_scale_space": (PersistenceScaleSpaceKernel(bandwidth=3.),
dict(bandwidth=3.)),
}

def test_kernel_from_distance():
l1, l2 = _n_diags(9), _n_diags(11)
tolerance = dict(rel=1e-3)
for kernelName in ["sliced_wasserstein", "persistence_fisher"]:
kernelClass, kernelParams, tolerance = kernel_dict[kernelName]
kernelClass, kernelParams = kernel_dict[kernelName]
f1 = kernelClass.fit_transform(l1)
d1 = pairwise_persistence_diagram_distances(l1, metric=kernelName, **kernelParams)
assert np.exp(-d1/kernelClass.bandwidth == pytest.approx(f1, **tolerance))
assert np.exp(-d1/kernelClass.bandwidth) == pytest.approx(f1, **tolerance)

def test_kernel_distance_consistency():
l1, l2 = _n_diags(9), _n_diags(11)
for kernelName, (kernelClass, kernelParams, tolerance) in kernel_dict.items():
tolerance = dict(rel=1e-3)
for kernelName, (kernelClass, kernelParams) in kernel_dict.items():
_ = kernelClass.fit(l1)
f2 = kernelClass.transform(l2)
f12 = np.array([[kernelClass(l1_, l2_) for l1_ in l1] for l2_ in l2])
assert f12 == pytest.approx(f2, **tolerance)

def test_kernel_approximation():
l1, l2 = _n_diags(3), _n_diags(3)
tolerance_approximate_kernel = dict(rel=1e-1)
for kernelName in ["persistence_weighted_gaussian", "persistence_scale_space"]:
kernelClass, kernelParams = kernel_dict[kernelName]
_ = kernelClass.fit(l1)
f2 = kernelClass.transform(l2)
gamma = 0.5*1./(kernelParams["bandwidth"]**2)
kernel_approx = RBFSampler(gamma=gamma, n_components=1000).fit(np.array([[0., 2.]]))
kernelClass.kernel_approx = kernel_approx
_ = kernelClass.fit(l1)
f2_approx = kernelClass.transform(l2)
assert f2_approx == pytest.approx(f2, **tolerance_approximate_kernel)

def test_sliced_wasserstein_distance_value():
diag1 = np.array([[0., 1.], [0., 2.]])
diag2 = np.array([[1., 0.]])
Expand Down