def evaluateJamspell(modelFile, testText, alphabetFile, maxWords=50000):
utils.loadAlphabet(alphabetFile)
corrector = JamspellCorrector(modelFile)
random.seed(42)
originalText = loadText(testText)
erroredText = generateTypos(originalText)
assert len(originalText) == len(erroredText)
originalSentences = generateSentences(originalText)
erroredSentences = generateSentences(erroredText)
errorsRate, fixRate, broken, topNerr, topNfix, execTime = \
evaluateCorrector('jamspell', corrector, originalSentences, erroredSentences, maxWords)
return errorsRate, fixRate, broken, topNerr, topNfix
def main():
parser = argparse.ArgumentParser(
description='spelling correctors evaluation')
parser.add_argument('file',
type=str,
help='text file to use for evaluation')
parser.add_argument('-hs',
'--hunspell',
type=str,
help='path to hunspell model')
parser.add_argument('-ns',
'--norvig',
type=str,
help='path to train file for Norvig spell corrector')
parser.add_argument('-cs',
'--context',
type=str,
help='path to context spell model')
parser.add_argument('-csp',
'--context_prototype',
type=str,
help='path to context spell prototype model')
parser.add_argument('-jsp',
'--jamspell',
type=str,
help='path to jamspell model file')
parser.add_argument('-t', '--test', action="store_true")
parser.add_argument('-mx',
'--max_words',
type=int,
help='max words to evaluate')
parser.add_argument('-a', '--alphabet', type=str, help='alphabet file')
args = parser.parse_args()
if args.alphabet:
utils.loadAlphabet(args.alphabet)
correctors = {
'dummy': DummyCorrector(),
}
# corrector = correctors['dummy']
maxWords = args.max_words
print('[info] loading models')
if args.hunspell:
corrector = correctors['hunspell'] = HunspellCorrector(args.hunspell)
if args.norvig:
corrector = correctors['norvig'] = NorvigCorrector(args.norvig)
if args.context:
corrector = correctors['context'] = ContextCorrector(args.context)
if args.context_prototype:
corrector = correctors['prototype'] = ContextPrototypeCorrector(
args.context_prototype)
if args.jamspell:
corrector = correctors['jamspell'] = JamspellCorrector(args.jamspell)
if args.test:
return testMode(corrector)
random.seed(42)
print('[info] loading text')
originalText = loadText(args.file)
originalTextLen = len(list(originalText))
print('[info] generating typos')
#将原始的词随机修改，并以单个词的集合-列表返回
erroredText = generateTypos(originalText)
erroredTextLen = len(list(erroredText))
assert originalTextLen == erroredTextLen
#将原始文本分割成句子（去掉其中的非法符号和非句号）（不包含句号）
originalSentences = generateSentences(originalText)
erroredSentences = generateSentences(erroredText)
assert len(originalSentences) == len(erroredSentences)
# for s in originalSentences[:50]:
# print ' '.join(s) + '.'
print('[info] total words: %d' % len(originalText))
print('[info] evaluating')
results = {}
for correctorName, corrector in correctors.items():
errorsRate, fixRate, broken, topNerr, topNfix, execTime = \
evaluateCorrector(correctorName, corrector, originalSentences, erroredSentences, maxWords)
results[
correctorName] = errorsRate, fixRate, broken, topNerr, topNfix, execTime
print('')
print('[info] %12s %8s %8s %8s %8s %8s %8s' %
('', 'errRate', 'fixRate', 'broken', 'topNerr', 'topNfix', 'time'))
# 将多个打分器的结果 resultsfixRate从大到小排序打印出来
# 匿名函数 ~ 将x替换为results.items()即就是results.items[i][1]
for k, _ in sorted(results.items(), key=lambda x: x[1]):
print('[info] %10s %8.2f%% %8.2f%% %8.2f%% %8.2f%% %8.2f%% %8.2fs' % \
(k,
100.0 * results[k][0],
100.0 * results[k][1],
100.0 * results[k][2],
100.0 * results[k][3],
100.0 * results[k][4],
results[k][5]))