def mean_headline(x):
if 'headline' not in x or 'main' not in x['headline'] or not x['headline']['main']:
return None
if 'lead_paragraph' not in x or not x['lead_paragraph']:
return None
words = text_nltk.lemma_tokenize(x['headline']['main'])
if len(words) < 5:
return None
words += text_nltk.lemma_tokenize(x['lead_paragraph'])
return np.nanmean([text_nltk.vectors(w) for w in words], axis=0)
def top_articles(source, emotion):
return article_vector[source] \
.map(_fv(lambda x: np.dot(text_nltk.vectors(emotion), x))) \
.map(fk_(lambda k: k[1])) \
.map(swap).sortByKey(False) \
.map(result_format).take(36)
print happiness
assert happiness[0] >= happiness[
1], 'pleased < delighted: %f %f' % happiness
'''
print test \
.flatMap(k_skip(lambda x: x['query'] if 'query' in x else None)) \
.flatMap(_fv_skip(mean_headline)) \
.map(_fv(empathy)) \
.map(_fv(lambda x: x['happy'])) \
.collect() #map(k_(empathy)).map(k_(max_emotion)).collect()
'''
articles = test.flatMap(
add_skip(lambda x: (x['query'], x['pub_date']) if 'query' in x else None))
scores = articles \
.flatMap(_fv_skip(mean_headline)) \
.map(_fv(lambda x: np.dot(x, text_nltk.vectors('happy'))))
join = articles \
.join(scores) \
.map(fv(lambda x: dict_kv(x[0], 'score', x[1]))) \
.map(add_(lambda x: x['score'])).sortByKey(True).map(v).collect()
assert join[0][
'query'] == 'John Biggs', 'John Biggs is not the least happy after join.'
print >>sys.stderr, 'TEST OK'
'''
.join(test2) \
.map(v(lambda x: dict_kv(x[1], 'score', x[0]))) \
.map(lambda x: dict_kv(x, 'image', '/static/images/%s.png' % x['query'])) \
'''