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FIX Fixes issue with exatly_zero_info_score #19179

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Merged
merged 15 commits into from
Jan 16, 2021
Merged

FIX Fixes issue with exatly_zero_info_score #19179

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240de07
ENH Fixes issue with exatly_zero_info_score [scipy-dev]
thomasjpfan Jan 14, 2021
637681c
ENH Remove unneeded line [scipy-dev]
thomasjpfan Jan 14, 2021
d6fa33a
WIP Keep types [scipy-dev]
thomasjpfan Jan 14, 2021
98e9480
REV Smaller diff [scipy-dev]
thomasjpfan Jan 15, 2021
8178a8b
WIP Expand mutual_info_score [scipy-dev]
thomasjpfan Jan 15, 2021
4065ad4
WIP Removes float casting [scipy-dev]
thomasjpfan Jan 15, 2021
e4d17f1
WIP Adds casting back in
thomasjpfan Jan 15, 2021
e311d89
CI [scipy-dev]
thomasjpfan Jan 15, 2021
6600968
WIP Casting is not needed [scipy-dev]
thomasjpfan Jan 15, 2021
d261ff1
WIP Only clip [scipy-dev]
thomasjpfan Jan 15, 2021
f67c5cf
REV Smaller diff [scipy-dev]
thomasjpfan Jan 15, 2021
ea3094a
WIP Place expected_mutual_information diff back [scipy-dev]
thomasjpfan Jan 15, 2021
d1e3a49
ENH Uses around
thomasjpfan Jan 15, 2021
4572831
WIP Use where again [scipy-dev]
thomasjpfan Jan 15, 2021
0e705f6
ENH Adjust comments to match code
thomasjpfan Jan 16, 2021
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14 changes: 7 additions & 7 deletions sklearn/metrics/cluster/_expected_mutual_info_fast.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ def expected_mutual_information(contingency, int n_samples):
cdef DOUBLE N, gln_N, emi, term2, term3, gln
cdef np.ndarray[DOUBLE] gln_a, gln_b, gln_Na, gln_Nb, gln_nij, log_Nnij
cdef np.ndarray[DOUBLE] nijs, term1
cdef np.ndarray[DOUBLE, ndim=2] log_ab_outer
cdef np.ndarray[DOUBLE] log_a, log_b
cdef np.ndarray[np.int32_t] a, b
#cdef np.ndarray[int, ndim=2] start, end
R, C = contingency.shape
Expand All @@ -37,10 +37,10 @@ def expected_mutual_information(contingency, int n_samples):
# term1 is nij / N
term1 = nijs / N
# term2 is log((N*nij) / (a * b)) == log(N * nij) - log(a * b)
# term2 uses the outer product
log_ab_outer = np.log(a)[:, np.newaxis] + np.log(b)
# term2 uses N * nij
log_Nnij = np.log(N * nijs)
log_a = np.log(a)
log_b = np.log(b)
Comment on lines +40 to +41
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Slightly more memory efficient because we would not need to create the 2d array anymore.

# term2 uses log(N * nij) = log(N) + log(nij)
log_Nnij = np.log(N) + np.log(nijs)
# term3 is large, and involved many factorials. Calculate these in log
# space to stop overflows.
gln_a = gammaln(a + 1)
Expand All @@ -54,12 +54,12 @@ def expected_mutual_information(contingency, int n_samples):
start = np.maximum(start, 1)
end = np.minimum(np.resize(a, (C, R)).T, np.resize(b, (R, C))) + 1
# emi itself is a summation over the various values.
emi = 0
emi = 0.0
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emi is defined as DOUBLE, not sure why you've added the .0

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it is more explicit if you did not read the definition :)

cdef Py_ssize_t i, j, nij
for i in range(R):
for j in range(C):
for nij in range(start[i,j], end[i,j]):
term2 = log_Nnij[nij] - log_ab_outer[i,j]
term2 = log_Nnij[nij] - log_a[i] - log_b[j]
# Numerators are positive, denominators are negative.
gln = (gln_a[i] + gln_b[j] + gln_Na[i] + gln_Nb[j]
- gln_N - gln_nij[nij] - lgamma(a[i] - nij + 1)
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1 change: 1 addition & 0 deletions sklearn/metrics/cluster/_supervised.py
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Expand Up @@ -795,6 +795,7 @@ def mutual_info_score(labels_true, labels_pred, *, contingency=None):
log_outer = -np.log(outer) + log(pi.sum()) + log(pj.sum())
mi = (contingency_nm * (log_contingency_nm - log(contingency_sum)) +
contingency_nm * log_outer)
mi = np.where(np.abs(mi) < np.finfo(mi.dtype).eps, 0.0, mi)
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fair enough, but I wonder what other places we need to be doing it!

return np.clip(mi.sum(), 0.0, None)


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