def coherence(word_topic, train, test, top=10, window_with=10):
words_for_probs = set(eval_words_for_probs(word_topic, top))
prob_ungrams = eval_pob_ungrams(train, words_for_probs)
prob_conditn = eval_pob_bigrams(train, word_topic.shape[0], words_for_probs, wind_with=window_with)
pmi = lambda w1, w2: math.log(prob_conditn[w2][w1] / prob_ungrams[w1]) if prob_conditn[w2][w1] != 0.0 else 0.0
pmis_mean = []
pmis_median = []
for t in xrange(word_topic.shape[1]):
topic_wrds = get_topic(word_topic, topic=t, head=top)
pmi_t = [pmi(w1, w2) for w1, w2 in all_combine(topic_wrds) if pmi(w1, w2) != 0.0]
pmis_median.append(np.median(pmi_t))
pmis_mean.append(np.mean(pmi_t))
return 'coherence = mean %.2f median %.2f' % (np.mean(pmis_mean), np.median(pmis_median))
def coherence(word_topic, train, test, top=10, window_with=10):
words_for_probs = set(eval_words_for_probs(word_topic, top))
prob_ungrams = eval_pob_ungrams(train, words_for_probs)
prob_conditn = eval_pob_bigrams(train,
word_topic.shape[0],
words_for_probs,
wind_with=window_with)
pmi = lambda w1, w2: math.log(prob_conditn[w2][w1] / prob_ungrams[w1]
) if prob_conditn[w2][w1] != 0.0 else 0.0
pmis_mean = []
pmis_median = []
for t in xrange(word_topic.shape[1]):
topic_wrds = get_topic(word_topic, topic=t, head=top)
pmi_t = [
pmi(w1, w2) for w1, w2 in all_combine(topic_wrds)
if pmi(w1, w2) != 0.0
]
pmis_median.append(np.median(pmi_t))
pmis_mean.append(np.mean(pmi_t))
return 'coherence = mean %.2f median %.2f' % (np.mean(pmis_mean),
np.median(pmis_median))
def eval_words_for_probs(word_topic, top):
words = []
for t in xrange(word_topic.shape[1]):
words += get_topic(word_topic, topic=t, head=top)
return words
def eval_words_for_probs(word_topic, top):
words = []
for t in xrange(word_topic.shape[1]):
words += get_topic(word_topic, topic=t, head=top)
return words
