def test_cluster_hdbscan():
try:
from hdbscan import HDBSCAN
_has_hdbscan = True
except:
_has_hdbscan = False
if _has_hdbscan:
hdbscan_labels = cluster(data, cluster='HDBSCAN')
assert len(set(hdbscan_labels)) == 2
else:
with pytest.raises(ImportError):
hdbscan_labels = cluster(data, cluster='HDBSCAN')
def text_cluster_geo():
geo = plot(data, show=False)
hdbscan_labels = cluster(geo, cluster='HDBSCAN')
assert len(set(hdbscan_labels)) == 2
def test_cluster_hdbscan():
# Given well separated clusters this should "just work"
hdbscan_labels = cluster(data, cluster='HDBSCAN')
assert len(set(hdbscan_labels)) == 2
# -*- coding: utf-8 -*-
import numpy as np
from hypertools.tools.cluster import cluster
from hypertools.plot.plot import plot
cluster1 = np.random.multivariate_normal(np.zeros(3), np.eye(3), size=100)
cluster2 = np.random.multivariate_normal(np.zeros(3) + 100,
np.eye(3),
size=100)
data = np.vstack([cluster1, cluster2])
labels = cluster(data, n_clusters=2)
def test_cluster_n_clusters():
assert len(set(labels)) == 2
def test_cluster_returns_list():
assert type(labels) is list
def test_cluster_hdbscan():
# Given well separated clusters this should "just work"
hdbscan_labels = cluster(data, cluster='HDBSCAN')
assert len(set(hdbscan_labels)) == 2
def text_cluster_geo():
geo = plot(data, show=False)
hdbscan_labels = cluster(geo, cluster='HDBSCAN')
# -*- coding: utf-8 -*-
import pytest
from scipy.stats import multivariate_normal
import numpy as np
from hypertools.tools.cluster import cluster
cluster1 = np.random.multivariate_normal(np.zeros(3), np.eye(3), size=100)
cluster2 = np.random.multivariate_normal(np.zeros(3)+100, np.eye(3), size=100)
data = np.vstack([cluster1,cluster2])
labels_kmeans = cluster(data,n_clusters=2)
labels_kmeans_custom=cluster(data,cluster={'model':'KMeans','params':{'n_clusters':3}})
labels_gaussian_prob=cluster(data, cluster='GaussianMixture', n_clusters=2)
labels_gaussian_prob_custom=cluster(data, cluster={'model':'GaussianMixture','params':{'n_components':3}})
labels_bayesian_gaussian_prob=cluster(data, cluster='BayesianGaussianMixture', n_clusters=2)
labels_bayesian_gaussian_prob_custom=cluster(data, cluster={'model':'BayesianGaussianMixture','params':{'n_components':3}})
def test_cluster_n_clusters():
assert (len(set(labels_kmeans))==2) and (len(set(labels_kmeans_custom))==3) and (len(labels_gaussian_prob[0])==2) and \
(len(labels_gaussian_prob_custom[0])==3) and (len(labels_bayesian_gaussian_prob[0])==2) and (len(labels_bayesian_gaussian_prob_custom[0])==3)
def test_cluster_returns_list():
assert (type(labels_kmeans) is list) and (type(labels_kmeans_custom) is list) and (type(labels_gaussian_prob) is list) and \
(type(labels_gaussian_prob_custom) is list) and (type(labels_bayesian_gaussian_prob) is list) and (type(labels_bayesian_gaussian_prob_custom) is list)
def test_cluster_gaussian_returns_list_of_probs():
assert (type(labels_gaussian_prob[0]) is np.ndarray) and (type(labels_gaussian_prob_custom[0]) is np.ndarray) and \
(type(labels_bayesian_gaussian_prob[0]) is np.ndarray) and (type(labels_bayesian_gaussian_prob_custom[0]) is np.ndarray)