def test_hdbscan():
hdbscan = analyze.cluster(reno, columns=columns, method='hdbscan')
assert len(hdbscan.census.hdbscan.unique()) > 27
def test_plot():
fig, ax = plt.subplots()
sd = data.Community(name='sd', source='ltdb', cbsafips='41740')
sd_clusters = analyze.cluster(sd, columns=['median_household_income', 'p_poverty_rate', 'p_edu_college_greater', 'p_unemployment_rate'], method='kmeans')
sd_clusters.plot(column='kmeans', ax=ax)
return fig
def test_aff_prop():
aff_prop = analyze.cluster(reno, columns=columns, method='affinity_propagation',
preference=-100)
assert len(aff_prop.census.affinity_propagation.unique()) == 3
def test_kmeans():
kmeans = analyze.cluster(reno, columns=columns, method='kmeans')
assert len(kmeans.census.kmeans.unique()) == 6
def test_spectral():
spectral = analyze.cluster(reno, columns=columns, method='spectral')
assert len(spectral.census.spectral.unique()) == 6
def test_ward():
ward = analyze.cluster(reno, columns=columns, method='ward')
assert len(ward.census.ward.unique()) == 6
def test_gm():
gm = analyze.cluster(reno, columns=columns, method='gaussian_mixture', best_model=True)
assert len(gm.census.gaussian_mixture.unique()) > 7