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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.
#
'''
STS-{2012,2013,2014,2015,2016} (unsupervised) and
STS-benchmark (supervised) tasks
'''
from __future__ import absolute_import, division, unicode_literals
import os
import io
import numpy as np
import logging
from scipy.stats import spearmanr, pearsonr
from senteval.utils import cosine
from senteval.sick import SICKEval
class STSEval(object):
def loadFile(self, fpath):
self.data = {}
self.samples = []
for dataset in self.datasets:
sent1, sent2 = zip(*[l.split("\t") for l in
io.open(fpath + '/STS.input.%s.txt' % dataset,
encoding='utf8').read().splitlines()])
raw_scores = np.array([x for x in
io.open(fpath + '/STS.gs.%s.txt' % dataset,
encoding='utf8')
.read().splitlines()])
not_empty_idx = raw_scores != ''
gs_scores = [float(x) for x in raw_scores[not_empty_idx]]
sent1 = np.array([s.split() for s in sent1])[not_empty_idx]
sent2 = np.array([s.split() for s in sent2])[not_empty_idx]
# sort data by length to minimize padding in batcher
sorted_data = sorted(zip(sent1, sent2, gs_scores),
key=lambda z: (len(z[0]), len(z[1]), z[2]))
sent1, sent2, gs_scores = map(list, zip(*sorted_data))
self.data[dataset] = (sent1, sent2, gs_scores)
self.samples += sent1 + sent2
def do_prepare(self, params, prepare):
if 'similarity' in params:
self.similarity = params.similarity
else: # Default similarity is cosine
self.similarity = lambda s1, s2: np.nan_to_num(cosine(np.nan_to_num(s1), np.nan_to_num(s2)))
return prepare(params, self.samples)
def run(self, params, batcher):
results = {}
all_sys_scores = []
all_gs_scores = []
for dataset in self.datasets:
sys_scores = []
input1, input2, gs_scores = self.data[dataset]
for ii in range(0, len(gs_scores), params.batch_size):
batch1 = input1[ii:ii + params.batch_size]
batch2 = input2[ii:ii + params.batch_size]
# we assume get_batch already throws out the faulty ones
if len(batch1) == len(batch2) and len(batch1) > 0:
enc1 = batcher(params, batch1)
enc2 = batcher(params, batch2)
for kk in range(enc2.shape[0]):
sys_score = self.similarity(enc1[kk], enc2[kk])
sys_scores.append(sys_score)
all_sys_scores.extend(sys_scores)
all_gs_scores.extend(gs_scores)
results[dataset] = {'pearson': pearsonr(sys_scores, gs_scores),
'spearman': spearmanr(sys_scores, gs_scores),
'nsamples': len(sys_scores)}
logging.debug('%s : pearson = %.4f, spearman = %.4f' %
(dataset, results[dataset]['pearson'][0],
results[dataset]['spearman'][0]))
weights = [results[dset]['nsamples'] for dset in results.keys()]
list_prs = np.array([results[dset]['pearson'][0] for
dset in results.keys()])
list_spr = np.array([results[dset]['spearman'][0] for
dset in results.keys()])
avg_pearson = np.average(list_prs)
avg_spearman = np.average(list_spr)
wavg_pearson = np.average(list_prs, weights=weights)
wavg_spearman = np.average(list_spr, weights=weights)
all_pearson = pearsonr(all_sys_scores, all_gs_scores)
all_spearman = spearmanr(all_sys_scores, all_gs_scores)
results['all'] = {'pearson': {'all': all_pearson[0],
'mean': avg_pearson,
'wmean': wavg_pearson},
'spearman': {'all': all_spearman[0],
'mean': avg_spearman,
'wmean': wavg_spearman}}
logging.debug('ALL : Pearson = %.4f, \
Spearman = %.4f' % (all_pearson[0], all_spearman[0]))
logging.debug('ALL (weighted average) : Pearson = %.4f, \
Spearman = %.4f' % (wavg_pearson, wavg_spearman))
logging.debug('ALL (average) : Pearson = %.4f, \
Spearman = %.4f\n' % (avg_pearson, avg_spearman))
return results
class STS12Eval(STSEval):
def __init__(self, taskpath, seed=1111):
logging.debug('***** Transfer task : STS12 *****\n\n')
self.seed = seed
self.datasets = ['MSRpar', 'MSRvid', 'SMTeuroparl',
'surprise.OnWN', 'surprise.SMTnews']
self.loadFile(taskpath)
class STS13Eval(STSEval):
# STS13 here does not contain the "SMT" subtask due to LICENSE issue
def __init__(self, taskpath, seed=1111):
logging.debug('***** Transfer task : STS13 (-SMT) *****\n\n')
self.seed = seed
self.datasets = ['FNWN', 'headlines', 'OnWN']
self.loadFile(taskpath)
class STS14Eval(STSEval):
def __init__(self, taskpath, seed=1111):
logging.debug('***** Transfer task : STS14 *****\n\n')
self.seed = seed
self.datasets = ['deft-forum', 'deft-news', 'headlines',
'images', 'OnWN', 'tweet-news']
self.loadFile(taskpath)
class STS15Eval(STSEval):
def __init__(self, taskpath, seed=1111):
logging.debug('***** Transfer task : STS15 *****\n\n')
self.seed = seed
self.datasets = ['answers-forums', 'answers-students',
'belief', 'headlines', 'images']
self.loadFile(taskpath)
class STS16Eval(STSEval):
def __init__(self, taskpath, seed=1111):
logging.debug('***** Transfer task : STS16 *****\n\n')
self.seed = seed
self.datasets = ['answer-answer', 'headlines', 'plagiarism',
'postediting', 'question-question']
self.loadFile(taskpath)
class STSBenchmarkEval(STSEval):
def __init__(self, task_path, seed=1111):
logging.debug('\n\n***** Transfer task : STSBenchmark*****\n\n')
self.seed = seed
self.samples = []
train = self.loadFile(os.path.join(task_path, 'sts-train.csv'))
dev = self.loadFile(os.path.join(task_path, 'sts-dev.csv'))
test = self.loadFile(os.path.join(task_path, 'sts-test.csv'))
self.datasets = ['train', 'dev', 'test']
self.data = {'train': train, 'dev': dev, 'test': test}
def loadFile(self, fpath):
sick_data = {'X_A': [], 'X_B': [], 'y': []}
with io.open(fpath, 'r', encoding='utf-8') as f:
for line in f:
text = line.strip().split('\t')
sick_data['X_A'].append(text[5].split())
sick_data['X_B'].append(text[6].split())
sick_data['y'].append(text[4])
sick_data['y'] = [float(s) for s in sick_data['y']]
self.samples += sick_data['X_A'] + sick_data["X_B"]
return (sick_data['X_A'], sick_data["X_B"], sick_data['y'])
class STSBenchmarkFinetune(SICKEval):
def __init__(self, task_path, seed=1111):
logging.debug('\n\n***** Transfer task : STSBenchmark*****\n\n')
self.seed = seed
train = self.loadFile(os.path.join(task_path, 'sts-train.csv'))
dev = self.loadFile(os.path.join(task_path, 'sts-dev.csv'))
test = self.loadFile(os.path.join(task_path, 'sts-test.csv'))
self.sick_data = {'train': train, 'dev': dev, 'test': test}
def loadFile(self, fpath):
sick_data = {'X_A': [], 'X_B': [], 'y': []}
with io.open(fpath, 'r', encoding='utf-8') as f:
for line in f:
text = line.strip().split('\t')
sick_data['X_A'].append(text[5].split())
sick_data['X_B'].append(text[6].split())
sick_data['y'].append(text[4])
sick_data['y'] = [float(s) for s in sick_data['y']]
return sick_data
class SICKRelatednessEval(STSEval):
def __init__(self, task_path, seed=1111):
logging.debug('\n\n***** Transfer task : SICKRelatedness*****\n\n')
self.seed = seed
self.samples = []
train = self.loadFile(os.path.join(task_path, 'SICK_train.txt'))
dev = self.loadFile(os.path.join(task_path, 'SICK_trial.txt'))
test = self.loadFile(os.path.join(task_path, 'SICK_test_annotated.txt'))
self.datasets = ['train', 'dev', 'test']
self.data = {'train': train, 'dev': dev, 'test': test}
def loadFile(self, fpath):
skipFirstLine = True
sick_data = {'X_A': [], 'X_B': [], 'y': []}
with io.open(fpath, 'r', encoding='utf-8') as f:
for line in f:
if skipFirstLine:
skipFirstLine = False
else:
text = line.strip().split('\t')
sick_data['X_A'].append(text[1].split())
sick_data['X_B'].append(text[2].split())
sick_data['y'].append(text[3])
sick_data['y'] = [float(s) for s in sick_data['y']]
self.samples += sick_data['X_A'] + sick_data["X_B"]
return (sick_data['X_A'], sick_data["X_B"], sick_data['y'])
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