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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.
#
'''
probing tasks
'''
from __future__ import absolute_import, division, unicode_literals
import os
import io
import copy
import logging
import numpy as np
from senteval.tools.validation import SplitClassifier
class PROBINGEval(object):
def __init__(self, task, task_path, seed=1111):
self.seed = seed
self.task = task
logging.debug('***** (Probing) Transfer task : %s classification *****', self.task.upper())
self.task_data = {'train': {'X': [], 'y': []},
'dev': {'X': [], 'y': []},
'test': {'X': [], 'y': []}}
self.loadFile(task_path)
logging.info('Loaded %s train - %s dev - %s test for %s' %
(len(self.task_data['train']['y']), len(self.task_data['dev']['y']),
len(self.task_data['test']['y']), self.task))
def do_prepare(self, params, prepare):
samples = self.task_data['train']['X'] + self.task_data['dev']['X'] + \
self.task_data['test']['X']
return prepare(params, samples)
def loadFile(self, fpath):
self.tok2split = {'tr': 'train', 'va': 'dev', 'te': 'test'}
with io.open(fpath, 'r', encoding='utf-8') as f:
for line in f:
line = line.rstrip().split('\t')
self.task_data[self.tok2split[line[0]]]['X'].append(line[-1].split())
self.task_data[self.tok2split[line[0]]]['y'].append(line[1])
labels = sorted(np.unique(self.task_data['train']['y']))
self.tok2label = dict(zip(labels, range(len(labels))))
self.nclasses = len(self.tok2label)
for split in self.task_data:
for i, y in enumerate(self.task_data[split]['y']):
self.task_data[split]['y'][i] = self.tok2label[y]
def run(self, params, batcher):
task_embed = {'train': {}, 'dev': {}, 'test': {}}
bsize = params.batch_size
logging.info('Computing embeddings for train/dev/test')
for key in self.task_data:
# Sort to reduce padding
sorted_data = sorted(zip(self.task_data[key]['X'],
self.task_data[key]['y']),
key=lambda z: (len(z[0]), z[1]))
self.task_data[key]['X'], self.task_data[key]['y'] = map(list, zip(*sorted_data))
task_embed[key]['X'] = []
for ii in range(0, len(self.task_data[key]['y']), bsize):
batch = self.task_data[key]['X'][ii:ii + bsize]
embeddings = batcher(params, batch)
task_embed[key]['X'].append(embeddings)
task_embed[key]['X'] = np.vstack(task_embed[key]['X'])
task_embed[key]['y'] = np.array(self.task_data[key]['y'])
logging.info('Computed embeddings')
config_classifier = {'nclasses': self.nclasses, 'seed': self.seed,
'usepytorch': params.usepytorch,
'classifier': params.classifier}
if self.task == "WordContent" and params.classifier['nhid'] > 0:
config_classifier = copy.deepcopy(config_classifier)
config_classifier['classifier']['nhid'] = 0
print(params.classifier['nhid'])
clf = SplitClassifier(X={'train': task_embed['train']['X'],
'valid': task_embed['dev']['X'],
'test': task_embed['test']['X']},
y={'train': task_embed['train']['y'],
'valid': task_embed['dev']['y'],
'test': task_embed['test']['y']},
config=config_classifier)
devacc, testacc = clf.run()
logging.debug('\nDev acc : %.1f Test acc : %.1f for %s classification\n' % (devacc, testacc, self.task.upper()))
return {'devacc': devacc, 'acc': testacc,
'ndev': len(task_embed['dev']['X']),
'ntest': len(task_embed['test']['X'])}
"""
Surface Information
"""
class LengthEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'sentence_length.txt')
# labels: bins
PROBINGEval.__init__(self, 'Length', task_path, seed)
class WordContentEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'word_content.txt')
# labels: 200 target words
PROBINGEval.__init__(self, 'WordContent', task_path, seed)
"""
Latent Structural Information
"""
class DepthEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'tree_depth.txt')
# labels: bins
PROBINGEval.__init__(self, 'Depth', task_path, seed)
class TopConstituentsEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'top_constituents.txt')
# labels: 'PP_NP_VP_.' .. (20 classes)
PROBINGEval.__init__(self, 'TopConstituents', task_path, seed)
class BigramShiftEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'bigram_shift.txt')
# labels: 0 or 1
PROBINGEval.__init__(self, 'BigramShift', task_path, seed)
# TODO: Voice?
"""
Latent Semantic Information
"""
class TenseEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'past_present.txt')
# labels: 'PRES', 'PAST'
PROBINGEval.__init__(self, 'Tense', task_path, seed)
class SubjNumberEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'subj_number.txt')
# labels: 'NN', 'NNS'
PROBINGEval.__init__(self, 'SubjNumber', task_path, seed)
class ObjNumberEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'obj_number.txt')
# labels: 'NN', 'NNS'
PROBINGEval.__init__(self, 'ObjNumber', task_path, seed)
class OddManOutEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'odd_man_out.txt')
# labels: 'O', 'C'
PROBINGEval.__init__(self, 'OddManOut', task_path, seed)
class CoordinationInversionEval(PROBINGEval):
def __init__(self, task_path, seed=1111):
task_path = os.path.join(task_path, 'coordination_inversion.txt')
# labels: 'O', 'I'
PROBINGEval.__init__(self, 'CoordinationInversion', task_path, seed)
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