def _entropy_scores(cm):
"""Given a ClusteringMetrics object, calculate three entropy-based metrics
(Alternative implementation for testing)
"""
H_C = fentropy(cm.row_totals)
H_K = fentropy(cm.col_totals)
H_CK = sum(fentropy(col) for col in cm.iter_cols())
H_KC = sum(fentropy(row) for row in cm.iter_rows())
# The '<=' comparisons below both prevent division by zero errors
# and ensure that the scores are non-negative.
homogeneity = 0.0 if H_C <= H_CK else (H_C - H_CK) / H_C
completeness = 0.0 if H_K <= H_KC else (H_K - H_KC) / H_K
nmi_score = harmonic_mean(homogeneity, completeness)
return homogeneity, completeness, nmi_score
def test_entropy_of_counts_zero():
"""Returns zero for empty set
"""
val = fentropy([])
assert_almost_equal(val, 0.0000, 4)
