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