def plot_diurnal(headers):
"""
Diurnal plot of the emails, with years running along the x axis and times of
day on the y axis.
"""
xday = []
ytime = []
print 'making diurnal plot...'
for h in iterview(headers):
if len(h) > 1:
try:
s = h[1][5:].strip()
x = dateutil.parser.parse(s)
except ValueError:
print
print marquee(' ERROR: skipping ')
print h
print marquee()
continue
timestamp = mktime(x.timetuple()) # convert datetime into floating point number
mailstamp = datetime.fromtimestamp(timestamp)
xday.append(mailstamp)
# Time the email is arrived
# Note that years, month and day are not important here.
y = datetime(2010, 10, 14, mailstamp.hour, mailstamp.minute, mailstamp.second)
ytime.append(y)
plot_date(xday,ytime,'.',alpha=.7)
xticks(rotation=30)
return xday,ytime
def f1(data, name):
print
print 'Phrase-based F1:', name
f1 = F1()
for i, x in enumerate(iterview(data)):
predict = extract_contiguous(model(x))
truth = extract_contiguous(x.truth)
# (i,begin,end) uniquely identifies the span
for (label, begins, ends) in truth:
f1.add_relevant(label, (i, begins, ends))
for (label, begins, ends) in predict:
f1.add_retrieved(label, (i, begins, ends))
print
return f1.scores(verbose=True)
def validate(model, iteration=None):
def f1(data, name):
print
print 'Phrase-based F1:', name
f1 = F1()
for i, x in enumerate(iterview(data)):
predict = extract_contiguous(model(x))
truth = extract_contiguous(x.truth)
# (i,begin,end) uniquely identifies the span
for (label, begins, ends) in truth:
f1.add_relevant(label, (i, begins, ends))
for (label, begins, ends) in predict:
f1.add_retrieved(label, (i, begins, ends))
print
return f1.scores(verbose=True)
f1(train, name='TRAIN')
f1(test, name='TEST')
print
print 'likelihood:', sum(map(crf.likelihood, iterview(train))) / len(train)
print
print
def kl_filter(data, verbose=True, progress=False, out=sys.stdout):
"""
data = (label, [features ...])
KL is a synonym for Information Gain
KL( p(label) || p(label|feature) )
"""
(L, F, data) = integerize(data)
K = len(L)
M = len(F)
if progress:
from 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:
cut = 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], 0.00001) # avoids divide-by-zero
KL[f] = -kl_divergence(label_prior, label_given_f)
# print KL-feature, most-informative first
for i in KL.argsort():
p = counts[:,i] * 1.0 / counts[:,i].sum()
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' % (-KL[i], F.lookup(i)), '\t\033[32m', ' '.join('%s(%s)' % (k,v) for v, k in l), '\033[0m'
yield z