def confusion(model=None):
emotions = ['joy', 'sadness', 'disgust', 'anger', 'surprise', 'fear']
data = spark.data('semeval', cores=16) \
.map(lambda x: spark.dict_kv(x,
'emotion',
text_nltk.empathy(
text_nltk.mean_vector(
x['text'], model=model),
model=model
)
)).collect()
sample = data[0]
print >>sys.stderr, sample
results = []
for emotion in emotions:
for prediction in sample['emotion'].keys():
x = np.asarray([float(row[emotion]) for row in data])
y = np.asarray([row['emotion'][prediction] for row in data])
ind = np.where((np.isnan(x) + np.isnan(y)) == 0)
z = (emotion, prediction, ) + scipy.stats.pearsonr(x[ind], y[ind])
results.append(
{'emotion': z[0], 'vector': z[1], 'r': z[2], 'p': z[3]})
return results
def all_articles(source, model=None):
if not model:
model = 'vectors_25d'
results = spark.data(source, cores=16).map(lambda x: dict_kv(
x, 'emotion', text_nltk.empathy(x['headline_main_%s' % model], model=model)))
return results.collect()
def all_identities(source, model=None):
if not model:
model = 'vectors_25d'
return spark.data(source, cores=4) \
.map(lambda x: ((x['query'], '%s-01' % x['pub_date'][:7]), text_nltk.empathy(x['headline_main_%s' % model], model=model))) \
.reduceByKey(add_emotion).map(lambda x: {'query': x[0][0], 'pub_date': x[0][1], 'emotion': div_emotion(x[1])}).collect()