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bgt_evaluate_mod.py
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bgt_evaluate_mod.py
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import sentencepiece as spm
import os
import io
import numpy as np
import logging
from sacremoses import MosesTokenizer
from scipy.stats import spearmanr, pearsonr
#python -u evaluate.py training/fr/data-joint-bin/ -s en -t fr --path checkpoints/bgt5-fr-65536-25-0-sample-0.5-1.0/checkpoint20.pt --sentencepiece training/fr/fr-en.1m.tok.all.sp.20k.model --cpu --model-overrides "{'cpu': 1}"
def cosine(u, v):
return np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))
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):
self.similarity = lambda s1, s2: np.nan_to_num(cosine(np.nan_to_num(s1), np.nan_to_num(s2)))
def run(self, params, batcher):
results = {}
for dataset in self.datasets:
sys_scores = []
input1, input2, gs_scores = self.data[dataset]
lang_1 = "en"
lang_2 = "en"
if dataset == "STS.input.track1.ar-ar.txt":
lang_1 = "ar"
lang_2 = "ar"
elif dataset == "STS.input.track2.ar-en.txt":
lang_1 = "en"
lang_2 = "ar"
elif dataset == "STS.input.track3.es-es.txt":
lang_1 = "es"
lang_2 = "es"
elif dataset == "STS.input.track4a.es-en.txt":
lang_1 = "es"
lang_2 = "en"
elif dataset == "STS.input.track6.tr-en.txt":
lang_1 = "en"
lang_2 = "tr"
elif dataset == "STS.input.extended.de-en.txt":
lang_1 = "en"
lang_2 = "de"
elif dataset == "STS.input.extended.fr-en.txt":
lang_1 = "fr"
lang_2 = "en"
elif dataset == "STS.input.extended.it-en.txt":
lang_1 = "it"
lang_2 = "en"
elif dataset == "STS.input.extended.nl-en.txt":
lang_1 = "nl"
lang_2 = "en"
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, lang_1)
enc2 = batcher(params, batch2, lang_2)
for kk in range(enc2.shape[0]):
sys_score = self.similarity(enc1[kk], enc2[kk])
sys_scores.append(sys_score)
results[self.name + "." + 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[self.name + "." + dataset]['pearson'][0],
results[self.name + "." + 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)
results[self.name + "." + 'all'] = {'pearson': {'mean': avg_pearson,
'wmean': wavg_pearson},
'spearman': {'mean': avg_spearman,
'wmean': wavg_spearman}}
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)
self.name = "STS12"
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', 'SMT']
self.loadFile(taskpath)
self.name = "STS13"
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)
self.name = "STS14"
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)
self.name = "STS15"
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)
self.name = "STS16"
class STSBenchmarkEval(STSEval):
def __init__(self, task_path, seed=1111):
logging.debug('\n\n***** Transfer task : STSBenchmark*****\n\n')
self.seed = seed
dev = self.loadFile(os.path.join(task_path, 'sts-dev.csv'))
test = self.loadFile(os.path.join(task_path, 'sts-test.csv'))
self.data = {}
self.data['dev'] = dev
self.data['test'] = test
self.datasets = ["dev", "test"]
self.name = "Benchmark"
def loadFile(self, fpath):
gs_scores = []
sent1 = []
sent2 = []
with io.open(fpath, 'r', encoding='utf-8') as f:
for line in f:
text = line.strip().split('\t')
sent1.append(text[5].split())
sent2.append(text[6].split())
gs_scores.append(float(text[4]))
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))
return sent1, sent2, gs_scores
class STSHard(STSEval):
def __init__(self, task_path, seed=1111):
logging.debug('\n\n***** Transfer task : STSHard*****\n\n')
self.seed = seed
hard_pos = self.loadFile(os.path.join(task_path, 'hard-pos.txt'))
hard_neg = self.loadFile(os.path.join(task_path, 'hard-neg.txt'))
self.data = {}
self.data['hard-pos'] = hard_pos
self.data['hard-neg'] = hard_neg
self.datasets = ["hard-pos", "hard-neg"]
self.name = "Hard"
def loadFile(self, fpath):
gs_scores = []
sent1 = []
sent2 = []
with io.open(fpath, 'r', encoding='utf-8') as f:
for line in f:
text = line.strip().split('\t')
sent1.append(text[0].split())
sent2.append(text[1].split())
gs_scores.append(float(text[2]))
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))
return sent1, sent2, gs_scores
class SemEval17(STSEval):
def __init__(self, task_path, seed=1111):
logging.debug('\n\n***** Transfer task : SemEval17*****\n\n')
self.seed = seed
self.data = {}
self.datasets = ["STS.input.track1.ar-ar.txt",
"STS.input.track2.ar-en.txt",
"STS.input.track3.es-es.txt",
"STS.input.track4a.es-en.txt",
"STS.input.track5.en-en.txt",
"STS.input.track6.tr-en.txt",
"STS.input.extended.de-en.txt",
"STS.input.extended.fr-en.txt",
"STS.input.extended.it-en.txt",
"STS.input.extended.nl-en.txt"]
for i in self.datasets:
self.data[i] = self.loadFile(os.path.join(task_path, i))
self.name = "SemEval17"
def loadFile(self, fpath):
gs_scores = []
sent1 = []
sent2 = []
with io.open(fpath, 'r', encoding='utf-8') as f:
for line in f:
text = line.strip().split('\t')
if len(text) != 3:
continue
sent1.append(text[0].split())
sent2.append(text[1].split())
gs_scores.append(float(text[2]))
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))
return sent1, sent2, gs_scores
def batcher(params, batch, lang="en"):
batch = [" ".join(s) for s in batch]
new_batch = []
for p in batch:
if params.tokenize:
tok = params.entok.tokenize(p, escape=False)
p = " ".join(tok)
p = p.lower()
p = params.sp.EncodeAsPieces(p)
if len(p) > 1023:
p = p[:1023]
p = " ".join(p)
new_batch.append(p)
vecs = params.embedder.embed(new_batch, params.encoder)
return vecs
def evaluate(embedder, args):
sp = spm.SentencePieceProcessor()
sp.Load(args.sentencepiece)
entok = MosesTokenizer(lang='en')
from argparse import Namespace
args = Namespace(batch_size=32, entok=entok, sp=sp, embedder=embedder,
encoder=args.eval_encoder, tokenize=args.tokenize)
s = STS12Eval('STS/STS12-en-test')
s.do_prepare()
results = s.run(args, batcher)
s = STS13Eval('STS/STS13-en-test')
s.do_prepare()
results.update(s.run(args, batcher))
s = STS14Eval('STS/STS14-en-test')
s.do_prepare()
results.update(s.run(args, batcher))
s = STS15Eval('STS/STS15-en-test')
s.do_prepare()
results.update(s.run(args, batcher))
s = STS16Eval('STS/STS16-en-test')
s.do_prepare()
results.update(s.run(args, batcher))
s = SemEval17('STS/STS17-test')
s.do_prepare()
results.update(s.run(args, batcher))
s = STSBenchmarkEval('STS/STSBenchmark')
s.do_prepare()
results.update(s.run(args, batcher))
s = STSHard('STS/STSHard')
s.do_prepare()
results.update(s.run(args, batcher))
for i in results:
print(i, results[i])
total = []
all = []
cross = []
foreign = []
for i in results:
if "STS" in i and "all" not in i and "SemEval17" not in i:
total.append(results[i]["pearson"][0])
if "STS" in i and "all" in i:
all.append(results[i]["pearson"]["mean"])
if i == "SemEval17.STS.input.track2.ar-en.txt" or i == "SemEval17.STS.input.track4a.es-en.txt" \
or i == "SemEval17.STS.input.track6.tr-en.txt":
cross.append(results[i]["pearson"][0])
if i == "SemEval17.STS.input.track1.ar-ar.txt" or i == "SemEval17.STS.input.track3.es-es.txt":
foreign.append(results[i]["pearson"][0])
print("Average (cross): {0}".format(np.mean(cross)))
print("Average (foreign): {0}".format(np.mean(foreign)))
print("Average (datasets): {0}".format(np.mean(total)))
print("Average (comps): {0}".format(np.mean(all)), flush=True)
return np.mean(all)