For this example notebook, we’ll need to install Ripser.py to create the persistence diagrams:

pip install Cython ripser
In [1]:
from sklearn import datasets
import numpy as np
import persim
import persim.plot
import ripser
import matplotlib.pyplot as plt
In [2]:
data_clean = datasets.make_circles()[0]
data_noisy = datasets.make_circles(noise=0.1)[0]
In [3]:
plt.scatter(data_clean[:,0], data_clean[:,1], label="clean data")
plt.scatter(data_noisy[:,0], data_noisy[:,1], label="noisy data")
<matplotlib.collections.PathCollection at 0x119767668>

Generate \(H_1\) diagrams for each of the data sets

In [4]:
dgm_clean = ripser.ripser(data_clean)['dgms'][1]
dgm_noisy = ripser.ripser(data_noisy)['dgms'][1]
In [5]:
ripser.plot_dgms([dgm_clean, dgm_noisy] , labels=['Clean $H_1$', 'Noisy $H_1$'])

Compute and visualize Bottleneck distance

The bottleneck function has the option of returning the matching when the parameter matching is set to True. With the returned data, we can use the plot.bottleneck_matching function to visualize which persistence points contributed to the distance.

In [6]:
distance_bottleneck, (matching, D) = persim.bottleneck(dgm_clean, dgm_noisy, matching=True)
In [7]:
persim.plot.bottleneck_matching(dgm_clean, dgm_noisy, matching, D, labels=['Clean $H_1$', 'Noisy $H_1$'])

The default option of matching=False will return just the distance if that is all you’re interested in.

In [8]:
persim.bottleneck(dgm_clean, dgm_noisy)
In [9]:
dgm1 = np.array([
    [0.5, 1],
    [0.6, 1.1]
dgm2 = np.array([
    [0.5, 1.1],
#     [0.7,0.8]

d, (matching, D) = persim.bottleneck(

persim.plot.bottleneck_matching(dgm1, dgm2, matching, D, labels=['Clean $H_1$', 'Noisy $H_1$'])
plt.title(f"Distance {d:.3f}")
Text(0.5, 1.0, 'Distance 0.100')
In [10]:
matching, D
([(0, 0), (1, 1), (2, 2)], array([[0.1 , 0.25, 0.1 ],
        [0.1 , 0.1 , 0.25],
        [0.3 , 0.  , 0.  ]]))

Sliced Wasserstein distance

Sliced Wasserstein Kernels for persistence diagrams were introduced by Carriere et al, 2017 and implemented by Alice Patania.

The general idea is to compute an approximation of the Wasserstein distance by computing the distance in 1-dimension repeatedly, and use the results as measure. To do so, the points of each persistence diagram are projected onto M lines that pass through (0,0) and forms an angle theta with the axis x.

In [11]:
persim.sliced_wasserstein(dgm_clean, dgm_noisy)

The parameter M controls the number of iterations to run

In [12]:
Ms = range(5, 100, 2)
ds = [persim.sliced_wasserstein(dgm_clean, dgm_noisy, M=M) for M in Ms]
In [13]:
plt.plot(Ms, ds)
plt.xlabel("M Iterations")
plt.ylabel("Approximate Distance")
plt.title("Relationship between M and distance")

Heat Kernel Distance

We also implement the heat kernel distance

In [14]:
persim.heat(dgm_clean, dgm_noisy)

The parameter sigma controls the heat diffusion.

In [15]:
sigmas = np.linspace(0.1, 10, 100)
ds = [persim.heat(dgm_clean, dgm_noisy, sigma=s) for s in sigmas]
In [16]:
plt.plot(sigmas, ds)
plt.xlabel("Heat diffusion parameter")
plt.ylabel("Approximate Distance")
plt.title("Relationship between sigma and distance")
In [ ]: