def kl_filter(data,
verbose=True,
progress=False,
out=sys.stdout,
feature_label_cuttoff=0,
feature_count_cuttoff=0,
do_label_count=False):
"""
data = (label, [features ...])
KL is a synonym for Information Gain
KL( p(label) || p(label|feature) )
"""
(L, F, data) = integerize(data)
if do_label_count:
label_count = defaultdict(int)
for label, features in data:
label_count[label] += 1
label_count = label_count.items()
label_count.sort(key=lambda x: -x[1]) # sort by count
print 'label count'
for k,v in label_count:
print '%20s => %s' % (k, v)
sys.exit(0)
K = len(L)
M = len(F)
if progress:
from arsenal.iterextras import iterview
else:
iterview = lambda x, *a, **kw: x
if progress:
print >> sys.stderr, '\nTally'
# label-feature tally (note: we ignore dulicate features)
counts = zeros((K,M))
for y, fv in iterview(data, every=5000):
counts[y, fv] += 1
feature_counts = counts.sum(axis=0)
if feature_count_cuttoff > 0:
cut = feature_counts < feature_count_cuttoff
#if verbose:
print >> sys.stderr, '%s of %s below cutoff of %s' \
% (cut.sum(), len(feature_counts), feature_count_cuttoff)
if progress:
print >> sys.stderr, '%s / %s (%.2f%%) features below cuttoff' % \
(cut.sum(), M, cut.sum()*100.0/M)
# zero-out features below cuttoff
counts[:, cut] = 0
if feature_label_cuttoff:
cut = counts < feature_label_cuttoff
if progress:
print >> sys.stderr, '%s / %s (%.2f%%) feature-label pairs below cuttoff' % \
(cut.sum(), K*M, cut.sum()*100.0/(K*M))
# zero-out features below cuffoff
counts[cut] = 0
label_prior = normalize(counts.sum(axis=1))
# compute KL
if progress:
print >> sys.stderr, '\nKL'
KL = zeros(M)
for f in iterview(xrange(M), every=5000):
label_given_f = lidstone(counts[:,f], 0.0001) # avoids divide-by-zero
KL[f] = -kl_divergence(label_prior, label_given_f)
# print KL-feature, most-informative first
for i in KL.argsort():
z = counts[:,i].sum()
if z == 0:
continue
p = counts[:,i] * 1.0 / z
l = [(v, k) for k,v in zip(L, p) if v > 0]
l.sort()
z = (-KL[i], F.lookup(i), l)
if verbose:
print >> out, '%8.6f\t%s\t%s' % (-KL[i], int(counts[:,i].sum()), F.lookup(i)), '\t\033[32m', ' '.join('%s(%s)' % (k,v) for v, k in l), '\033[0m'
yield z
def kl_filter(data,
verbose=True,
progress=False,
out=sys.stdout,
feature_label_cuttoff=0,
feature_count_cuttoff=0,
do_label_count=False):
"""
data = (label, [features ...])
KL is a synonym for Information Gain
KL( p(label) || p(label|feature) )
"""
(L, F, data) = integerize(data)
if do_label_count:
label_count = defaultdict(int)
for label, features in data:
label_count[label] += 1
label_count = label_count.items()
label_count.sort(key=lambda x: -x[1]) # sort by count
print 'label count'
for k, v in label_count:
print '%20s => %s' % (k, v)
sys.exit(0)
K = len(L)
M = len(F)
if progress:
from arsenal.iterextras import iterview
else:
iterview = lambda x, *a, **kw: x
if progress:
print >> sys.stderr, '\nTally'
# label-feature tally (note: we ignore dulicate features)
counts = zeros((K, M))
for y, fv in iterview(data, every=5000):
counts[y, fv] += 1
feature_counts = counts.sum(axis=0)
if feature_count_cuttoff > 0:
cut = feature_counts < feature_count_cuttoff
#if verbose:
print >> sys.stderr, '%s of %s below cutoff of %s' \
% (cut.sum(), len(feature_counts), feature_count_cuttoff)
if progress:
print >> sys.stderr, '%s / %s (%.2f%%) features below cuttoff' % \
(cut.sum(), M, cut.sum()*100.0/M)
# zero-out features below cuttoff
counts[:, cut] = 0
if feature_label_cuttoff:
cut = counts < feature_label_cuttoff
if progress:
print >> sys.stderr, '%s / %s (%.2f%%) feature-label pairs below cuttoff' % \
(cut.sum(), K*M, cut.sum()*100.0/(K*M))
# zero-out features below cuffoff
counts[cut] = 0
label_prior = normalize(counts.sum(axis=1))
# compute KL
if progress:
print >> sys.stderr, '\nKL'
KL = zeros(M)
for f in iterview(xrange(M), every=5000):
label_given_f = lidstone(counts[:, f], 0.0001) # avoids divide-by-zero
KL[f] = -kl_divergence(label_prior, label_given_f)
# print KL-feature, most-informative first
for i in KL.argsort():
z = counts[:, i].sum()
if z == 0:
continue
p = counts[:, i] * 1.0 / z
l = [(v, k) for k, v in zip(L, p) if v > 0]
l.sort()
z = (-KL[i], F.lookup(i), l)
if verbose:
print >> out, '%8.6f\t%s\t%s' % (-KL[i], int(
counts[:, i].sum()), F.lookup(i)), '\t\033[32m', ' '.join(
'%s(%s)' % (k, v) for v, k in l), '\033[0m'
yield z
