class ConllEval(object):
def __init__(self):
self.dep_acc_by_pos = TwoLevelCountDict()
self.head_acc_by_pos = TwoLevelCountDict()
self.long_sent_stats = CountDict()
self.short_sent_stats = CountDict()
self.fields = ["pos_acc", "ul_acc", "l_acc"]
def add(self, k, sent):
self.long_sent_stats.add(k)
if len(sent) < 10:
self.short_sent_stats.add(k)
def pos_stats(self):
for pos in sorted(set(self.dep_acc_by_pos.keys()).union(set(self.head_acc_by_pos.keys()))):
print(
",".join(
[
pos,
str(self.dep_acc_by_pos.sub_distribution(pos).get(True, 0.0)),
str(self.head_acc_by_pos.sub_distribution(pos).get(True, 0.0)),
]
)
)
def acc(self, d, k):
return d[k] / d["words"] * 100
def long_stats(self):
return [self.acc(self.long_sent_stats, k) for k in self.fields]
def short_stats(self):
return [self.acc(self.short_sent_stats, k) for k in self.fields]
def short_ul(self):
return self.acc(self.short_sent_stats, "ul_acc")
def short_ul_count(self):
return self.short_sent_stats.get("ul_acc", 0)
def short_words(self):
return self.short_sent_stats.get("words", 0)
def long_ul(self):
return self.acc(self.long_sent_stats, "ul_acc")
def long_ul_count(self):
return self.long_sent_stats.get("ul_acc", 0)
def long_words(self):
return self.long_sent_stats.get("words", 0)
def info(self):
"""
Print the feature statistics for the given model. (Assumes MaxEnt)
"""
mallet = c['mallet']
env = set_env_lang_utf8()
info_bin = os.path.join(os.path.join(mallet, 'bin'), 'classifier2info')
info_p = sub.Popen([info_bin, '--classifier', self._model],
stdout=sub.PIPE, stdin=sub.PIPE, stderr=sub.PIPE, env=env)
cur_class = None
feats = TwoLevelCountDict()
# Go through and pick out what the features are for
for line in info_p.stdout:
content = line.decode(encoding='utf-8')
class_change = re.search('FEATURES FOR CLASS (.*)', content)
# Set the current class if the section changes
if class_change:
cur_class = class_change.group(1).strip()
continue
# Otherwise, let's catalog the features.
word, prob = content.split()
feats.add(cur_class, word, float(prob))
# Now, print some info
for cur_class in feats.keys():
print(cur_class, end='\t')
print('%s:%.4f' % ('<default>', feats[cur_class]['<default>']), end='\t')
top_10 = feats.top_n(cur_class, n=10, key2_re='^nom')
print('\t'.join(['%s:%.4f' % (w,p) for w,p in top_10]))
def get_prototypes(tagged_path, proto_out, delimeter, ignoretags=[], unambiguous=False, maxproto=0):
encoding = getencoding(tagged_path)
tagged_file = codecs.open(tagged_path, "r", encoding=encoding)
tag_word_dict = TwoLevelCountDict()
word_tag_dict = TwoLevelCountDict()
proto_dict = defaultdict(set)
for line in tagged_file:
tokens = line.split()
for token in tokens:
word, pos = re.search("(^.*)%s(.*?)$" % delimeter, token).groups()
if pos not in ignoretags:
word = word.lower()
tag_word_dict.add(pos, word)
word_tag_dict.add(word, pos)
numproto = 0
# First, let's pick the maxproto most frequent words for a tag.
for tag in tag_word_dict.keys():
words = tag_word_dict[tag].most_frequent(minimum=1, num=None)
found_words = 0
for word in words:
freq_tag = word_tag_dict[word].most_frequent(minimum=1)
if freq_tag and freq_tag[0] == tag:
# if freq_tag:
proto_dict.add(freq_tag[0], word)
numproto += 1
found_words += 1
if maxproto and found_words == maxproto:
break
print("%s Prototypes found." % numproto)
# Now, set up the proto file for writing.
proto_file = open(proto_out, "w")
for tag in proto_dict:
proto_file.write(tag)
for word in proto_dict[tag]:
proto_file.write("\t" + word.lower()) # LOWERCASE for testing
proto_file.write("\n")
proto_file.close()