def count_training():
counts = {i: 0 for i in range(17)}
pl = PickleLoader(training_dir)
for sense in pl.iterate():
for n in sense.thesaurus_nodes:
thesclass = tdb.get_thesclass(n)
counts[thesclass.level] += 1
for i in range(17):
print '%d\t%d' % (i, counts[i])
def count_classified():
counts = {i: 0 for i in range(17)}
for p in parent_directories:
subdir = os.path.join(p, 'classified')
pl = PickleLoader(subdir)
for sense in pl.iterate():
try:
sense.class_id
except AttributeError:
pass
else:
thesclass = tdb.get_thesclass(sense.class_id)
counts[thesclass.level] += 1
for i in range(17):
print '%d\t%d' % (i, counts[i])
print """
===========================================================
Enter lemma (optionally followed by '-c' or '-u' to specify
classified or unclassified):
"""
lemma = raw_input(">>>")
lemma = lemma.strip()
if lemma.endswith(" -c"):
dirs = ["classified"]
elif lemma.endswith(" -u"):
dirs = ["unclassified"]
else:
dirs = ["classified", "unclassified"]
lemma = re.sub(r" +-.$", "", lemma)
initial = lemma[0].upper()
if initial in letters:
seen = set()
for p in parent_directories:
for d in dirs:
subdir = os.path.join(p, d)
pl = PickleLoader(subdir, letters=initial)
for sense in pl.iterate():
if sense.lemma == lemma and sense.node_id not in seen:
print "----------------------------------------"
print trace_sense(sense)
seen.add(sense.node_id)