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# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Binary classifier and corresponding datasets : MR, CR, SUBJ, MPQA
'''
from __future__ import absolute_import, division, unicode_literals
import io
import os
import numpy as np
import logging
from senteval.tools.validation import InnerKFoldClassifier
class BinaryClassifierEval(object):
def __init__(self, pos, neg, seed=1111):
self.seed = seed
self.samples, self.labels = pos + neg, [1] * len(pos) + [0] * len(neg)
self.n_samples = len(self.samples)
def do_prepare(self, params, prepare):
# prepare is given the whole text
return prepare(params, self.samples)
# prepare puts everything it outputs in "params" : params.word2id etc
# Those output will be further used by "batcher".
def loadFile(self, fpath):
with io.open(fpath, 'r', encoding='latin-1') as f:
return [line.split() for line in f.read().splitlines()]
def run(self, params, batcher):
enc_input = []
# Sort to reduce padding
sorted_corpus = sorted(zip(self.samples, self.labels),
key=lambda z: (len(z[0]), z[1]))
sorted_samples = [x for (x, y) in sorted_corpus]
sorted_labels = [y for (x, y) in sorted_corpus]
logging.info('Generating sentence embeddings')
for ii in range(0, self.n_samples, params.batch_size):
batch = sorted_samples[ii:ii + params.batch_size]
embeddings = batcher(params, batch)
enc_input.append(embeddings)
enc_input = np.vstack(enc_input)
logging.info('Generated sentence embeddings')
config = {'nclasses': 2, 'seed': self.seed,
'usepytorch': params.usepytorch,
'classifier': params.classifier,
'nhid': params.nhid, 'kfold': params.kfold}
clf = InnerKFoldClassifier(enc_input, np.array(sorted_labels), config)
devacc, testacc = clf.run()
logging.debug('Dev acc : {0} Test acc : {1}\n'.format(devacc, testacc))
return {'devacc': devacc, 'acc': testacc, 'ndev': self.n_samples,
'ntest': self.n_samples}
class CREval(BinaryClassifierEval):
def __init__(self, task_path, seed=1111):
logging.debug('***** Transfer task : CR *****\n\n')
pos = self.loadFile(os.path.join(task_path, 'custrev.pos'))
neg = self.loadFile(os.path.join(task_path, 'custrev.neg'))
super(self.__class__, self).__init__(pos, neg, seed)
class MREval(BinaryClassifierEval):
def __init__(self, task_path, seed=1111):
logging.debug('***** Transfer task : MR *****\n\n')
pos = self.loadFile(os.path.join(task_path, 'rt-polarity.pos'))
neg = self.loadFile(os.path.join(task_path, 'rt-polarity.neg'))
super(self.__class__, self).__init__(pos, neg, seed)
class SUBJEval(BinaryClassifierEval):
def __init__(self, task_path, seed=1111):
logging.debug('***** Transfer task : SUBJ *****\n\n')
obj = self.loadFile(os.path.join(task_path, 'subj.objective'))
subj = self.loadFile(os.path.join(task_path, 'subj.subjective'))
super(self.__class__, self).__init__(obj, subj, seed)
class MPQAEval(BinaryClassifierEval):
def __init__(self, task_path, seed=1111):
logging.debug('***** Transfer task : MPQA *****\n\n')
pos = self.loadFile(os.path.join(task_path, 'mpqa.pos'))
neg = self.loadFile(os.path.join(task_path, 'mpqa.neg'))
super(self.__class__, self).__init__(pos, neg, seed)
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