3D Cat Data

This example generates a Mapper built from a point-cloud sampled from a 3D model of a cat.

Visualization of the cat mapper

Out:

KeplerMapper(verbose=2)
..Composing projection pipeline of length 1:
        Projections: sum
        Distance matrices: False
        Scalers: MinMaxScaler()
..Projecting on data shaped (7207, 3)

..Projecting data using: sum

..Scaling with: MinMaxScaler()

Mapping on data shaped (7207, 3) using lens shaped (7207, 1)

Minimal points in hypercube before clustering: 5
Creating 15 hypercubes.
   > Found 2 clusters in hypercube 0.
   > Found 2 clusters in hypercube 1.
   > Found 2 clusters in hypercube 2.
   > Found 1 clusters in hypercube 3.
   > Found 2 clusters in hypercube 4.
   > Found 2 clusters in hypercube 5.
   > Found 1 clusters in hypercube 6.
   > Found 1 clusters in hypercube 7.
   > Found 1 clusters in hypercube 8.
   > Found 1 clusters in hypercube 9.
   > Found 1 clusters in hypercube 10.
   > Found 1 clusters in hypercube 11.
   > Found 1 clusters in hypercube 12.
   > Found 1 clusters in hypercube 13.
   > Found 1 clusters in hypercube 14.

Created 19 edges and 20 nodes in 0:00:00.093830.
Wrote visualization to: output/cat.html
no display found. Using non-interactive Agg backend

import numpy as np
import sklearn
import kmapper as km

data = np.genfromtxt("data/cat-reference.csv", delimiter=",")

mapper = km.KeplerMapper(verbose=2)

lens = mapper.fit_transform(data)

graph = mapper.map(
    lens,
    data,
    clusterer=sklearn.cluster.DBSCAN(eps=0.1, min_samples=5),
    cover=km.Cover(n_cubes=15, perc_overlap=0.2),
)

mapper.visualize(graph, path_html="output/cat.html")

km.draw_matplotlib(graph)

import matplotlib.pyplot as plt

plt.show()