def confusion_worker(queue, animation=False):
"""Matplotlib worker."""
config = get_config()
titles = json.loads(config.get('sentiment', 'titles'))
def init():
clear_annotations(annotations)
for ax, image, title in zip(axes, images, titles):
empty_confusion = [[0] * 3] * 3
image.set_data(empty_confusion)
# annotate_confusion_matrix(ax, empty_confusion, annotations)
return images
fig, axes, images, annotations = make_confusion()
# Important to assign it to a variable, even if we don't use it.
anim = FuncAnimation(fig=fig,
func=update_confusion,
frames=lambda: get_data(queue),
fargs=(images, axes, annotations),
interval=200,
repeat=False,
init_func=init,
blit=False)
if animation:
anim.save('confusion.mp4', fps=10, extra_args=['-vcodec', 'libx264'])
else:
plt.show()
def parse_artfestival():
config = get_config()
with open(config.get('art_festival', 'corpus')) as f:
for line in f:
if line.startswith('#'):
continue
data = line.rstrip().split('\t')
author, tweet, sentiment = [x.strip() for x in data[:3]]
yield Experiment.DataInstance(tweet, sentiment)
def update_axes(count, axes):
"""Rescale axes to fit the new data."""
config = get_config()
chunk_size = config.getint('sentiment', 'chunk_size')
def round_nearest(x, base):
return int(base * round(float(x) / base))
for ax in axes:
ax.set_xbound(0, round_nearest(count + chunk_size, chunk_size))
def update_images(data, images, axes, annotations):
clear_annotations(annotations)
annotations = []
config = get_config()
titles = json.loads(config.get('sentiment', 'titles'))
for ax, image, title in zip(axes, images, titles):
confusion_matrix = data[title]['confusion']._confusion
norm_conf = normalized_confusion_matrix(confusion_matrix)
image.set_data(norm_conf)
image.norm.vmin, image.norm.vmax = 0.0, 1.0
annotations.append(annotate_confusion_matrix(ax, confusion_matrix,
annotations))
return annotations
def _train_data(self):
for x in super(TrainSemEvalSelfLearning, self)._train_data():
yield x
config = get_config()
for tweet in tweet_generator(config.get('twitter_corpus', 'emoticons')):
features = self.extractor.extract(tweet.text)
label, probability, = self._predict(features)
if label == 'neutral':
continue
if probability > 0.8:
yield self.DataInstance(tweet.text, label)
def setup_axes(axes, empty_confusion):
"""Setup look and feel of the axes."""
config = get_config()
titles = json.loads(config.get('sentiment', 'titles'))
width = len(empty_confusion)
height = len(empty_confusion[0])
for ax, title in zip(axes, titles):
ax.set_title(title)
ax.set_aspect(1)
alphabet = ['Neg', 'Neu', 'Pos']
ax.set_xticks(range(width))
ax.set_xticklabels(alphabet[:width])
ax.set_yticks(range(height))
ax.set_yticklabels(alphabet[:height])
def _test_data(self):
config = get_config()
with codecs.open(config.get('sanders', 'corpus'),
encoding='utf-8') as f:
for line in f:
line = line.split(',', 5)
if len(line) != 5:
continue
sentiment = line[1][1:-1].encode('utf-8')
if sentiment not in ['positive', 'negative', 'neutral']:
continue
text = line[4].encode('utf-8')
# Strip surrounding '"'
text = text[1:-1]
yield self.DataInstance(text, sentiment)
def _train_data(self):
config = get_config()
with open(config.get('stanford', 'corpus')) as f:
for line in f:
line = line.split(',', 5)
if len(line) != 6:
continue
sentiment = int(line[0][1:-1])
if sentiment == 0:
sentiment = 'negative'
elif sentiment == 2:
sentiment = 'neutral'
elif sentiment == 4:
sentiment = 'positive'
text = line[5]
# Strip surrounding '"'
text = text[1:-1]
yield self.DataInstance(text, sentiment)
def _train_data(self):
for count, x in enumerate(super(TrainSemEvalBoosting, self)
._train_data(), start=1):
yield x
config = get_config()
self.chunk_size /= 5
while 1:
with open(config.get('semeval', 'training')) as f:
old_count = count
for tweet in task_b_generator(f):
document = tweet.text
features = self.extractor.extract(document)
if tweet.label != self._predict(features)[0]:
yield self.DataInstance(tweet.text, tweet.label)
count += 1
if count < (old_count + 10):
break
def make_lines(axes):
"""Create the lines for each axes."""
config = get_config()
labels = json.loads(config.get('sentiment', 'labels'))
lines = []
marker = itertools.cycle('o^vds')
for ax in axes:
ax_lines = []
for label in labels:
x, y = [0], [0]
line, = ax.plot(x, y, label=label) # comma for unpacking.
line.set_marker(next(marker))
line.set_alpha(0.75)
if label == 'Accuracy':
line.set_color('black')
line.set_zorder(0) # Drawn first, so is underneath.
ax_lines.append((line, x, y))
lines.append(ax_lines)
return lines
def main():
"""Starts the web server as a user interface to the system."""
config = get_config()
setup_logging(config)
logger = logging.getLogger('ui.web')
git_version, git_commit = get_git_version()
if git_version:
logger.info('Version: {0} ({1})'.format(git_version, git_commit))
else:
logger.warning('Could not detect current Git commit.')
twitter = Twitter(config=config)
logger.info('Starting web server on port {}'.format(config.getint('web',
'port')))
start_server(config=config, twitter=twitter,
git_version=(git_version, git_commit))
def setup_axes(axes):
"""Setup look and feel of the axes."""
config = get_config()
titles = json.loads(config.get('sentiment', 'titles'))
labels = json.loads(config.get('sentiment', 'labels'))
for ax, title in zip(axes, titles):
ax.set_title(title)
ax.set_xlabel('training instances')
ax.set_ylabel('performance')
ax.set_xbound(0, 100) # bound will change as needed.
ax.set_ylim(0, 1) # limit won't change automatically.
# ax.xaxis.set_major_locator(MaxNLocator(10))
# ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_major_locator(MaxNLocator(10))
ax.yaxis.set_minor_locator(AutoMinorLocator(2))
ax.grid(True)
# Use current line labels to build legend.
ax.legend(loc='upper center', ncol=len(labels))
def write_semeval_predictions(experiment, final=False):
config = get_config()
twitter_test = config.get('semeval', 'twitter_test')
twitter_predict = config.get('semeval', 'twitter_predict')
sms_test = config.get('semeval', 'sms_test')
sms_predict = config.get('semeval', 'sms_predict')
# task2-B-twitter
with open(twitter_test) as f, \
open(twitter_predict + ('.final' if final else ''), mode='w') as w:
for instance in task_b_generator(f):
sid, uid, label, text = instance
features = experiment.extractor.extract(instance.text)
label, probability = experiment._predict(features)
w.write('\t'.join([sid, uid, label, text]) + '\n')
# task2-B-SMS
with open(sms_test) as f, \
open(sms_predict + ('.final' if final else ''), mode='w') as w:
for instance in task_b_generator(f):
sid, uid, label, text = instance
features = experiment.extractor.extract(instance.text)
label, probability = experiment._predict(features)
w.write('\t'.join([sid, uid, label, text]) + '\n')
def main():
plot_queue = multiprocessing.Queue()
confusion_queue = multiprocessing.Queue()
start_plot(plot_queue, confusion_queue)
first = (SingleClassifier, semeval.TrainSemEvalSelfLearning, semeval.TestSemEval)
second = (HierarchicalClassifier, semeval.TrainSemEvalSelfLearning, semeval.TestSemEval)
extractor = FeatureExtractor(tokenizer=tokenizer)
extractor.min_n, extractor.max_n = 1, 2
config = get_config()
chunk_size = config.getint("sentiment", "chunk_size")
first_chunk = config.getint("sentiment", "first_chunk")
titles = json.loads(config.get("sentiment", "titles"))
experiment = run_experiment(first, second, extractor, chunk_size, first_chunk)
try:
for data in experiment:
data[titles[0]] = parse_performance(data[titles[0]])
data[titles[1]] = parse_performance(data[titles[1]])
plot_queue.put(data)
confusion_queue.put(data)
print data[titles[0]]["count"], data[titles[0]]["SemEval"], data[titles[1]]["SemEval"], data[titles[0]][
"vocab"
], data[titles[1]]["vocab"]
except KeyboardInterrupt:
pass
finally:
plot_queue.put(None)
plot_queue.close()
confusion_queue.put(None)
confusion_queue.close()
print "Done processing."
def main():
config = get_config()
path = config.get('sentiment', 'path')
sentiment_fname = config.get('sentiment', 'classifier')
sentiment_classifier_location = os.path.join(path, sentiment_fname)
print 'Loading classifier (may take a few minutes) ... ',
assert namedtuple and tokenizer
with open(sentiment_classifier_location, mode='rb') as f:
sentiment_classifier = pickle.load(f)
print 'DONE'
extractor = sentiment_classifier[0]
subjective = sentiment_classifier[1]
polarity = sentiment_classifier[2]
def train(*documents):
for document, label in documents:
if label != 'neutral':
assert label in set(['positive', 'negative'])
polarity.train((document, label))
label = 'subjective'
assert label in set(['neutral', 'subjective'])
subjective.train((document, label))
print 'Training (pickled) classifier using misclassified instances...',
with open(config.get('web', 'misclassified_file')) as f:
for line in f:
date, user, flag, mislabel, text = line.rstrip().split('\t')
features = extractor.extract(text)
train((features, flag))
print 'DONE'
print 'Training (pickled) classifier using active learning instances...',
with open(config.get('web', 'active_file')) as f:
for line in f:
date, user, flag, original, text = line.rstrip().split('\t')
features = extractor.extract(text)
train((features, flag))
print 'DONE'
class SentimentService(rpyc.Service):
def exposed_extract(self, document):
return extractor.extract(document)
def exposed_subjective_classify(self, features):
return subjective.classify(features)
def exposed_subjective_conditional(self, feature, label):
return subjective.conditional(feature, label)
def exposed_polarity_classify(self, features):
return polarity.classify(features)
def exposed_polarity_conditional(self, feature, label):
return polarity.conditional(feature, label)
def exposed_train(self, *documents):
train(*documents)
rpc_port = int(config.get('sentiment', 'rpc_port'))
t = ThreadedServer(SentimentService, port=rpc_port)
t.start()
def _test_data(self):
config = get_config()
with open(config.get('semeval', 'development')) as f:
for instance in task_b_generator(f):
yield instance
def _train_data(self):
config = get_config()
with open(config.get('semeval', 'training')) as f:
for instance in task_b_generator(f):
yield instance
def test_get_config_create():
get_config(fname=TEST_FNAME, create=True, exit=False)
assert get_config(fname=TEST_FNAME, create=False, exit=False)
os.remove(TEST_FNAME)
def test_get_config_none():
assert_raises(OSError, os.remove, TEST_FNAME)
result = get_config(fname=TEST_FNAME, create=False, exit=False)
assert result is None
def run_experiment(first, second, extractor, chunk_size, first_chunk=0):
config = get_config()
titles = json.loads(config.get('sentiment', 'titles'))
test_scale = config.getint('sentiment', 'test_scale')
Approach = type('_'.join(x.__name__ for x in first), first, {})
singular_classifier = Approach(extractor, chunk_size, first_chunk,
test_scale=test_scale,
evaluator=evaluate)
print repr(singular_classifier)
Approach = type('_'.join(x.__name__ for x in second), second, {})
hierarchical_classifier = Approach(extractor, chunk_size, first_chunk,
test_scale=test_scale,
evaluator=evaluate)
print repr(hierarchical_classifier)
# hierarchical_classifier = OldClassifier(extractor)
best_performance = None, None, None
p1, p2 = [], [] # Declare in case the try block raises an exception.
try:
for single, hierarchy in itertools.izip(singular_classifier,
hierarchical_classifier):
new_best = None
c1, p1 = single
c2, p2 = hierarchy
data = dict()
data[titles[0]] = p1
data[titles[0]]['count'] = c1
# data[titles[0]]['vocab'] = singular_classifier.nb._vocab_size#, len(singular_classifier.nb._most_common['positive'].store)
data[titles[0]]['vocab'] = singular_classifier.nb._vocab_size # , len(singular_classifier.polarity._most_common['positive'].store)
data[titles[1]] = p2
data[titles[1]]['count'] = c2
data[titles[1]]['vocab'] = hierarchical_classifier.polarity._vocab_size # , len(hierarchical_classifier.polarity._most_common['positive'].store)
if data[titles[0]]['semeval f_measure'] > best_performance[0]:
new_best = data[titles[0]]['semeval f_measure'], singular_classifier, data[titles[0]]
best_performance = new_best
if data[titles[1]]['semeval f_measure'] > best_performance[0]:
new_best = data[titles[1]]['semeval f_measure'], hierarchical_classifier, data[titles[1]]
best_performance = new_best
# if new_best:
# print 'New Best! (see below):', new_best[1].__class__.__name__
# pprint(new_best[2])
# with open(r"D:\semeval-best.pickle", mode='wb') as f:
# f.write(new_best[1].pickle_dumps())
# write_semeval_predictions(new_best[1])
yield data
except KeyboardInterrupt:
raise
finally:
print 'Final performance:'
try:
for label, performance in zip(titles, (p1, p2)):
confusion_matrix = performance['confusionmatrix'].pp()
# del performance['confusion']
print label
pprint(performance)
print confusion_matrix
except:
print 'ERROR: Unavailable.'