def run(name, anchors):
topics = ankura.recover_topics(dataset, anchors)
features = ankura.topic_combine(topics, dataset)
train, test = ankura.pipeline.train_test_split(features, .9)
vw_contingency = ankura.measure.vowpal_contingency(train, test, 'dirname')
print(name, 'accuracy:', ankura.measure.vowpal_accuracy(train, test, 'dirname'))
print(name, 'f-Measure:', vw_contingency.fmeasure())
print(name, 'ari:', vw_contingency.ari())
print(name, 'rand:', vw_contingency.rand())
print(name, 'vi:', vw_contingency.vi())
coherence = []
for topic in ankura.topic.topic_summary_indices(topics, dataset, 10):
coherence.append(ankura.measure.topic_coherence(topic, dataset))
print(name, 'coherence-10:', numpy.mean(coherence))
coherence = []
for topic in ankura.topic.topic_summary_indices(topics, dataset, 15):
coherence.append(ankura.measure.topic_coherence(topic, dataset))
print(name, 'coherence-15:', numpy.mean(coherence))
coherence = []
for topic in ankura.topic.topic_summary_indices(topics, dataset, 20):
coherence.append(ankura.measure.topic_coherence(topic, dataset))
print(name, 'coherence-20:', numpy.mean(coherence))
def run(name, anchors):
topics = ankura.recover_topics(dataset, anchors)
features = ankura.topic_combine(topics, dataset)
train, test = ankura.pipeline.train_test_split(features, .9)
vw_contingency = ankura.measure.vowpal_contingency(
train, test, 'dirname')
print(name, 'accuracy:',
ankura.measure.vowpal_accuracy(train, test, 'dirname'))
print(name, 'f-Measure:', vw_contingency.fmeasure())
print(name, 'ari:', vw_contingency.ari())
print(name, 'rand:', vw_contingency.rand())
print(name, 'vi:', vw_contingency.vi())
coherence = []
for topic in ankura.topic.topic_summary_indices(topics, dataset, 10):
coherence.append(ankura.measure.topic_coherence(topic, dataset))
print(name, 'coherence-10:', numpy.mean(coherence))
coherence = []
for topic in ankura.topic.topic_summary_indices(topics, dataset, 15):
coherence.append(ankura.measure.topic_coherence(topic, dataset))
print(name, 'coherence-15:', numpy.mean(coherence))
coherence = []
for topic in ankura.topic.topic_summary_indices(topics, dataset, 20):
coherence.append(ankura.measure.topic_coherence(topic, dataset))
print(name, 'coherence-20:', numpy.mean(coherence))
def demo():
"""Runs the newsgroups demo"""
dataset = get_newsgroups()
anchors = ankura.gramschmidt_anchors(dataset, 20, 500)
topics = ankura.recover_topics(dataset, anchors)
for topic in ankura.topic.topic_summary_tokens(topics, dataset, 20):
print(' '.join(topic))
def topic_inference(raw_anchors):
"""Returns infered topic info from raw anchors"""
dataset = args.get_dataset()
if raw_anchors is None:
anchor_tokens, anchors = args.default_anchors()
else:
anchor_tokens = ankura.util.tuplize(json.loads(raw_anchors))
anchors = user_anchors(anchor_tokens)
topics = ankura.recover_topics(dataset, anchors, epsilon=1e-6)
topic_summary = ankura.topic.topic_summary_tokens(topics, dataset, n=15)
return topics, topic_summary, anchor_tokens
def topic_inference(raw_anchors):
"""Returns infered topic info from raw anchors"""
dataset = args.get_dataset()
if raw_anchors is None:
anchor_tokens, anchors = args.default_anchors()
else:
anchor_tokens = ankura.util.tuplize(json.loads(raw_anchors))
anchors = user_anchors(anchor_tokens)
topics = ankura.recover_topics(dataset, anchors, epsilon=1e-6)
topic_summary = ankura.topic.topic_summary_tokens(topics, dataset, n=15)
return topics, topic_summary, anchor_tokens
