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 train(self, trainFile):
print '[info] loading text'
text = loadText(trainFile)
sentences = generateSentences(text)
sentences = self.convertToIDs(sentences)
print '[info] generating N-grams', len(sentences)
total = len(sentences)
lastTime = time.time()
for i in xrange(0, total):
sentence = sentences[i]
for w in sentence:
self.gram1[w] += 1
self.totalWords += 1
for j in xrange(len(sentence) - 1):
self.gram2[(sentence[j], sentence[j+1])] += 1
for j in xrange(len(sentence) - 2):
self.gram3[(sentence[j], sentence[j+1], sentence[j+2])] += 1
if time.time() - lastTime >= 4.0:
lastTime = time.time()
print '[info] processed %.2f%%' % (100.0 * i / total)
print '[info] finished training'
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]))
def main(argv):
# default values
#path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
path = ''
outputfile = ''
startfrom = 1
numsyms = 100
generateonly = False
try:
opts, args = getopt.getopt(argv, "hi:o:s:g:x", ["ifile=", "ofile="])
except getopt.GetoptError:
print(
'program.py -i <inputfile> -o <outputfile> -g <numchars> -s <startfrom>'
)
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('-' * 50)
print('\n')
print(
'test.py -i <inputfile> -o <outputfile> -g <numchars> -s <startfrom>\n'
)
print('-i <inputfile>\t text file to train the network')
print(
'-g <numchars>\t number of characters to generate in generate-only mode'
)
print(
'-s <startfrom>\t training epoch to start from (file must exist)'
)
print('\n\n')
print('-' * 50)
sys.exit()
elif opt in ("-i", "--ifile"):
path = arg
elif opt in ("-g", "--generate"):
generateonly = True
numsyms = int(arg)
#elif opt in ("-x", "--generateonly"):
# generateonly = True
elif opt in ("-o", "--ofile"):
outputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
elif opt in ("-s", "--startepo"):
startfrom = int(arg)
print('Input file is %s' % path)
print('Output file is %s' % outputfile)
print('Starting from epoch %s' % startfrom)
print('Generative model only %s ' % generateonly)
print('Symbols to generate %d ' % numsyms)
maxlen = 5
# load data and build vocab
voc = ut.loadText(path, bychar=False, lower=True)
syms = set(voc[0]) # voc = (text, sym_indices, indices_sym)
X, y = ut.buildSkipgram(voc, maxlen=20, step=5)
#X,y = ut.buildTrainingSequences(voc, maxlen=maxlen, step=1)
print('input shape (%s, %s)' % (X.shape, y.shape))
vocsize = len(syms)
emb_dims = 128
# build the model: 2 stacked LSTM
print('Build model...')
model = Sequential()
layers = [maxlen, 100, 512, 1] # (X,y) from buildSkipgram
#layers = [maxlen, 100, 512, vocsize] # (X,y) from buildSequences
model.add(Embedding(vocsize, emb_dims))
#model.add(LSTM(input_dim=layers[0], output_dim=layers[1], return_sequences=True)) # with next lstm layer
model.add(
LSTM(input_dim=layers[0], output_dim=layers[1],
return_sequences=False)) # with no more layers
model.add(Dropout(0.5))
#model.add(LSTM(layers[2], return_sequences=False))
#model.add(Dropout(0.2))
model.add(Dense(output_dim=layers[3])) # for skipgram
model.add(Activation('sigmoid')) # for skipgram
#model.add(Activation('softmax')) # for sequences
#model.compile(loss='categorical_crossentropy', optimizer='rmsprop') # buildSequences
model.compile(loss='mse', optimizer='rmsprop') # buildSkipgram
if generateonly:
print('Loading model from epoch %d' % (startfrom))
model.load_weights('results/lstm_word_based_epo_%d' % (startfrom))
yn = 'y' # default answer
while (yn == 'y'):
#ut.generate(model, voc, numchars=numsyms)
ut.generateByWord(model, voc, numwords=numsyms)
yn = ''
while (yn not in ['y', 'n']):
print("Generate more? [Y/n]: ")
yn = str(raw_input()).lower()
else: # not generate-only mode
# train the model, output generated text after each iteration
for iteration in range(startfrom, 400, 10):
print()
print('-' * 50)
print('Starting from epoch %d' % iteration)
if iteration >= 2:
print('-' * 50)
print('Loading model from epoch %d' % (iteration - 1))
model.load_weights('results/lstm_emb_word_based_epo_%d' %
(iteration - 1))
model.fit(X, y, batch_size=128, nb_epoch=10)
model.save_weights('results/lstm_emb_word_based_epo_%d' %
(10 + iteration - 1),
overwrite=True)
print('Extracting embeddings')
emb = model.layers[0]
embeddings = emb.W.get_value()
print('embeddings shape', embeddings.shape)
print('Saving embeddings and vocabulary for t-SNE')
np.save('results/lstm_embeddings', embeddings)
#np.save('results/vocab_embeddings', voc[1])
ut.saveStuff(voc[1], 'results/vocab_embeddings')
from keras.datasets.data_utils import get_file
import numpy as np
import random
import sys
import getopt
import os.path
import utils as ut
path = 'data/alice.txt'
maxlen = 20
# load data and build vocab
voc = ut.loadText(path, bychar=True)
syms = set(voc[0])
X, y = ut.buildTrainingSet(voc, maxlen=maxlen, step=3)
# build the model: 2 stacked LSTM
print('Build model...')
model = Sequential()
#model.add(Embedding(len(syms), 128))
model.add(LSTM(512, return_sequences=True, input_shape=(maxlen, len(syms))))
model.add(Dropout(0.2))
model.add(LSTM(512, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(len(syms)))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
def main(argv):
# default values
#path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
path = ''
outputfile = ''
startfrom = 1
numsyms=100
generateonly = False
try:
opts, args = getopt.getopt(argv,"hi:o:s:g:x",["ifile=","ofile="])
except getopt.GetoptError:
print('program.py -i <inputfile> -o <outputfile> -g <numchars> -s <startfrom>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('-'*50)
print('\n')
print('test.py -i <inputfile> -o <outputfile> -g <numchars> -s <startfrom>\n')
print('-i <inputfile>\t text file to train the network')
print('-g <numchars>\t number of characters to generate in generate-only mode')
print('-s <startfrom>\t training epoch to start from (file must exist)')
print('\n\n')
print('-'*50)
sys.exit()
elif opt in ("-i", "--ifile"):
path = arg
elif opt in ("-g", "--generate"):
generateonly = True
numsyms = int(arg)
#elif opt in ("-x", "--generateonly"):
# generateonly = True
elif opt in ("-o", "--ofile"):
outputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
elif opt in ("-s", "--startepo"):
startfrom = int(arg)
print('Input file is %s' %path)
print('Output file is %s' %outputfile)
print('Starting from epoch %s' %startfrom)
print('Generative model only %s ' %generateonly)
print('Symbols to generate %d '%numsyms)
maxlen = 20 # alice
# load data and build vocab
voc = ut.loadText(path, bychar=True, lower=True)
syms = set(voc[0]) # voc = (text, sym_indices, indices_sym)
X,y = ut.buildTrainingSet(voc, maxlen=maxlen, step=3)
# build the model: 2 stacked LSTM
print('Build model...')
model = Sequential()
#model.add(Embedding(len(syms), 128))
model.add(LSTM(512, return_sequences=True, input_shape=(maxlen, len(syms))))
model.add(Dropout(0.2))
model.add(LSTM(512, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(len(syms)))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
if generateonly:
print('Loading model from epoch %d' %(startfrom))
#model.load_weights('results/lstm_word_based_epo_%d'%(startfrom))
model.load_weights('results/lstm_char_based/tweet_lstm_char_based_epo_%d'%(startfrom))
yn = 'y' # default answer
while(yn == 'y'):
ut.generate(model, voc, numchars=numsyms)
#ut.generateByWord(model, voc, numwords=numsyms)
yn = ''
while(yn not in ['y', 'n']):
print("Generate more? [Y/n]: ")
yn = str(raw_input()).lower()
else: # not generate-only mode
# train the model, output generated text after each iteration
for iteration in range(startfrom, 400):
print()
print('-' * 50)
print('Starting from epoch %d' %iteration)
if iteration >= 2:
print('-' * 50)
print('Loading model from epoch %d' %(iteration-1))
model.load_weights('results/lstm_char_based/tweet_lstm_char_based_epo_%d'%(iteration-1))
model.fit(X, y, batch_size=128, nb_epoch=1)
model.save_weights('results/lstm_char_based/tweet_lstm_char_based_epo_%d'%iteration, overwrite=True)
# are we learning well? let's print some
ut.generate(model, voc, numchars=42)