def start_(self):
dataset = Dataset('fourforums',annotation_list=['qr_dependencies', 'topic'])#'topic','dependencies'])
for discussion in dataset.get_discussions(annotation_label='mechanical_turk'):
if 'qr_meta' not in discussion.annotations['mechanical_turk']: continue
topic = discussion.annotations['topic']
for post in discussion.get_posts():
result = self.extract_(post, topic)
if result:
self.feature_vectors.append(result)
def generate_features(self):
dataset = Dataset('convinceme',annotation_list=['topic','dependencies','used_in_wassa2011', 'side'])
directory = "{}/convinceme/output_by_thread".format(data_root_dir)
for discussion in dataset.get_discussions(annotation_label='topic'):
if self.topic != discussion.annotations['topic']:
continue
for post in discussion.get_posts():
feature_vector = defaultdict(int)
post.discussion_id = discussion.id
post.topic_side = get_topic_side(discussion, post.side)
post.key = str((discussion.id,post.id))
feature_vector[self.classification_feature] = post.topic_side
try:
json_file = "{}/{}/{}.json".format(directory, discussion.id, post.id)
pos, parsetree, dep, ident = json.load(open(json_file, 'r'))
result = sorted(feat_vect(dep, pos, feature_vector), key=operator.itemgetter(0))
try:
text = TextObj(post.text.decode('utf-8', 'replace'))
except Exception, e:
continue
self.bounds.add(discussion_id=discussion.id, post_id=post.id, text=text.text, tuples=result)
uni_from_boundaries(text.text, result, feature_vector)
dependency_list = None if 'dependencies' not in post.annotations else post.annotations['dependencies']
if 'unigram' in self.features:
ngrams_from_text(text.text.lower(), feature_vector, prefix="uni_lower_", n=1, style='float')
ngrams_from_text(text.text, feature_vector, prefix="uni_caps_", n=1, style='float')
feats = set(self.features).difference(set(['unigram']))
get_features_by_type(feature_vector=feature_vector, features=feats, text_obj=text, dependency_list=dependency_list)
if None == dependency_list: continue
if 'dependencies' in self.features:
get_dependency_features(feature_vector, dependency_list, generalization='opinion')
if DELETE_QUOTE:
unigrams = map(lambda x: x[8:], filter(lambda x: x.startswith('unigram:'), feature_vector.keys()))
for unigram in unigrams:
key = 'quote: {}'.format(unigram)
if key in feature_vector:
del feature_vector[key]
self.feature_vectors.append(feature_vector)
except IOError, e:
# XXX TODO : we don't have all the parses saved apparently so this sometimes fails.
pass
def start(self):
dataset = Dataset('convinceme',annotation_list=['side', 'topic','dependencies','used_in_wassa2011'])#'topic','dependencies'])
for discussion in dataset.get_discussions(annotation_label='topic'):
topic = discussion.annotations['topic']
for post in discussion.get_posts():
post.discussion_id = discussion.id
post.topic_side = get_topic_side(discussion, post.side)
post.key = str((discussion.id,post.id))
result = self.extract(post, topic)
if result:
self.feature_vectors_by_topic[topic].append(result['all'])
self.collapsed_vectors[topic].append(result['collapsed'])
self.commitment_vectors[topic].append(result['commitment'])
for topic in self.feature_vectors_by_topic.keys():
print '{topic} has {length} elements.'.format(**{'topic': topic, 'length': len(self.feature_vectors_by_topic[topic])})
_feats = {'all': self.feature_vectors_by_topic,
'commitment': self.commitment_vectors,
'collapsed': self.collapsed_vectors,}
fd = open('results', 'w')
print 'Experimental Results:'
fd.write('Experimental Results:\n')
for topic in ['evolution','gay marriage', 'existence of god', 'abortion']:
for featureset, vectors in _feats.iteritems():
self.write(vectors=vectors, topic=topic, featureset=featureset)
classifiers = ['weka.classifiers.functions.SMO']#'weka.classifiers.bayes.NaiveBayes']
arff_folder = 'arffs/{topic}/'.format(topic=topic)
arffs = ['all.arff', 'collapsed.arff', 'commitment.arff']
print '**RESULTS: {topic}'.format(topic=topic)
fd.write('**RESULTS: {topic}\n'.format(topic=topic))
for arff in arffs:
results = defaultdict(dict) #run->classifier->featureset->results
for classifier_name in classifiers:
run_results = weka_interface.cross_validate(arff_folder, [arff], classifier_name=classifier_name, classification_feature=self.label, n=10)
right = sum([1 for entry in run_results.values() if entry['right?']])
run_accuracy = right / float(len(run_results))
print '\t{arff}: accuracy - {accuracy}'.format(arff=arff, accuracy=run_accuracy)
fd.write('\t{arff}: accuracy - {accuracy}\n'.format(arff=arff, accuracy=run_accuracy))
fd.close()