def build_subwords_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorfile, use_lowercase=False, max_num_words=10, upto=None):
if vectorfile == None:
return
word_to_idx, idx_to_word = build_word_vocab(trnMentions+devMentions+tstMentions) #train for characters because we only use entities names for characters
logger.info('word vocab size: %d', len(word_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_words = numpy.zeros(shape=(totals, max_num_words), dtype='int32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
words = name.split()
input_words[i] = get_ngram_seq(word_to_idx, words, max_len=max_num_words)
logger.info('shape of subwords dataset: %s', input_words.shape)
hdf5_file = dsdir + '_subwords.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset('subwords', input_words.shape, dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(word_to_idx, default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_word, default_flow_style=False)
features.attrs['vocabsize'] = len(word_to_idx)
features[...] = input_words
features.dims[0].label = 'words'
nsamples_train = len(trnMentions); nsamples_dev = len(devMentions);
split_dict = {
'train': {'subwords': (0, nsamples_train)},
'dev': {'subwords': (nsamples_train, nsamples_train + nsamples_dev)},
'test': {'subwords': (nsamples_train + nsamples_dev, totals)}}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush();f.close()
logger.info('Building subwords dataset finished. It saved in: %s', hdf5_file)
logger.info('writing subword embeddings')
idx2embeddings, vectorsize = read_embeddings_vocab(vectorfile, vocab=word_to_idx, use_lowercase=use_lowercase, num=upto)
with h5py.File(dsdir + "_subwords_embeddings.h5py", mode='w') as fp:
vectors = fp.create_dataset('vectors', compression='gzip',
data=idx2embeddings)
vectors.attrs['vectorsize'] = vectorsize
def build_letters_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorfile=None, max_len_name=30):
char_to_idx, idx_to_char = build_char_vocab(trnMentions) #train for characters because we only use entities names for characters
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_letters = numpy.zeros(shape=(totals, max_len_name), dtype='int32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
input_letters[i] = get_ngram_seq(char_to_idx, name, max_len_name)
print input_letters.shape
fuelfile = dsdir +'_letters.h5py'
f = h5py.File(fuelfile, mode='w')
features = f.create_dataset('letters', input_letters.shape, dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(char_to_idx, default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_char, default_flow_style=False)
features.attrs['vocabsize'] = len(char_to_idx)
features[...] = input_letters
features.dims[0].label = 'letters'
nsamples_train = len(trnMentions); nsamples_dev = len(devMentions);
split_dict = {
'train': {'letters': (0, nsamples_train)},
'dev': {'letters': (nsamples_train, nsamples_train + nsamples_dev)},
'test': {'letters': (nsamples_train + nsamples_dev, totals)}}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info('building letters dataset finished. It saved in: %s', fuelfile)
if vectorfile is None:
return
embeddings, vectorsize = read_embeddings_vocab(vectorfile, vocab=char_to_idx, num=-1)
logger.info('size of embedding matrix to save is: (%d, %d)', embeddings.shape[0], embeddings.shape[1])
with h5py.File(dsdir + "_letters_embeddings.h5py", mode='w') as fp:
vectors = fp.create_dataset('vectors', compression='gzip',
data=embeddings)
vectors.attrs['vectorsize'] = vectorsize
def build_letters_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorfile=None,
max_len_name=30):
char_to_idx, idx_to_char = build_char_vocab(
trnMentions
) #train for characters because we only use entities names for characters
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_letters = numpy.zeros(shape=(totals, max_len_name), dtype='int32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
input_letters[i] = get_ngram_seq(char_to_idx, name, max_len_name)
print input_letters.shape
fuelfile = dsdir + '_letters.h5py'
f = h5py.File(fuelfile, mode='w')
features = f.create_dataset('letters', input_letters.shape,
dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(char_to_idx,
default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_char,
default_flow_style=False)
features.attrs['vocabsize'] = len(char_to_idx)
features[...] = input_letters
features.dims[0].label = 'letters'
nsamples_train = len(trnMentions)
nsamples_dev = len(devMentions)
split_dict = {
'train': {
'letters': (0, nsamples_train)
},
'dev': {
'letters': (nsamples_train, nsamples_train + nsamples_dev)
},
'test': {
'letters': (nsamples_train + nsamples_dev, totals)
}
}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info('building letters dataset finished. It saved in: %s', fuelfile)
if vectorfile is None:
return
embeddings, vectorsize = read_embeddings_vocab(vectorfile,
vocab=char_to_idx,
num=-1)
logger.info('size of embedding matrix to save is: (%d, %d)',
embeddings.shape[0], embeddings.shape[1])
with h5py.File(dsdir + "_letters_embeddings.h5py", mode='w') as fp:
vectors = fp.create_dataset('vectors',
compression='gzip',
data=embeddings)
vectors.attrs['vectorsize'] = vectorsize
def build_desc_features_ds(trnMentions, devMentions, tstMentions, ent2tfidf_features_path, t2idx, dsdir, vectorfile, use_lowercase=True, upto=None):
if ent2tfidf_features_path == None:
print "Warning: ignoring tfidf features building..."
return
ent2features = load_ent2features(ent2tfidf_features_path)
word_to_idx, idx_to_word = build_voc_from_features(ent2features)
logger.info('tfidf desc features vocab size: %d', len(word_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_features = numpy.zeros(shape=(totals, len(ent2features.values()[0])), dtype='int32')
ent_no_emb = 0
for i, men in enumerate(trnMentions + devMentions + tstMentions):
if men.entityId not in ent2features:
ent_no_emb += 1
continue
features = ent2features[men.entityId]
input_features[i] = get_ngram_seq(word_to_idx, features, max_len=input_features.shape[1])
logger.info('shape of tfidf input dataset: %s', input_features.shape)
logger.info('number of entities without embeddings: %d', ent_no_emb)
hdf5_file = dsdir + '_desc_features.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset('desc_features', input_features.shape, dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(word_to_idx, default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_word, default_flow_style=False)
features.attrs['vocabsize'] = len(word_to_idx)
features[...] = input_features
features.dims[0].label = 'description_features'
nsamples_train = len(trnMentions); nsamples_dev = len(devMentions);
split_dict = {
'train': {'desc_features': (0, nsamples_train)},
'dev': {'desc_features': (nsamples_train, nsamples_train + nsamples_dev)},
'test': {'desc_features': (nsamples_train + nsamples_dev, totals)}}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush();f.close()
logger.info('Building desc_features dataset finished. It saved in: %s', hdf5_file)
logger.info('writing word embeddings')
idx2embeddings, vectorsize = read_embeddings_vocab(vectorfile, vocab=word_to_idx, use_lowercase=use_lowercase, num=upto)
print "embeddings shape: ", idx2embeddings.shape
with h5py.File(dsdir + "_desc_features_embeddings.h5py", mode='w') as fp:
vectors = fp.create_dataset('vectors', compression='gzip',
data=idx2embeddings)
vectors.attrs['vectorsize'] = vectorsize
def build_type_words_cosine_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorfile, upto=-1, max_num_words=4):
word_to_idx, idx_to_word = build_word_vocab(trnMentions+devMentions+tstMentions) #train for characters because we only use entities names for characters
logger.info('word vocab size: %d', len(word_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
idx2embeddings, vectorsize = read_embeddings_vocab(vectorfile, vocab=word_to_idx, num=upto)
input_avg = numpy.zeros(shape=(totals, vectorsize), dtype='float32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
words = name.split()
seq_words = get_ngram_seq(word_to_idx, words, max_len=max_num_words)
avgvec = numpy.zeros(shape=(vectorsize))
for ii in seq_words:
avgvec += idx2embeddings[ii]
avgvec /= len(seq_words)
input_avg[i] = avgvec
(embeddings, voc2idx, vectorsize) = read_embeddings(vectorfile, upto)
typevecmatrix = buildtypevecmatrix(t2idx, embeddings, vectorsize, voc2idx) # a matrix with size: 102 * dim
words_types_cosin_matrix = buildcosinematrix(input_avg, typevecmatrix)
logger.info(words_types_cosin_matrix.shape)
dsdir += '_tcwords.h5py'
f = h5py.File(dsdir, mode='w')
features = f.create_dataset('tcwords', words_types_cosin_matrix.shape, dtype='float32') # @UndefinedVariable
features.attrs['vectorsize'] = words_types_cosin_matrix.shape[1]
features[...] = words_types_cosin_matrix
features.dims[0].label = 'words_types_cosine'
nsamples_train = len(trnMentions); nsamples_dev = len(devMentions);
split_dict = {
'train': {'tcwords': (0, nsamples_train)},
'dev': {'tcwords': (nsamples_train, nsamples_train + nsamples_dev)},
'test': {'tcwords': (nsamples_train + nsamples_dev, totals)}}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info('Building types-words cosine (tcwords) dataset finished. It saved in: %s', dsdir)
def build_ngram_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorfile, ngram, max_num_ngrams=98, upto=-1):
ngram_to_idx, idx_to_word, name2ngrams = build_ngram_vocab(trnMentions+devMentions+tstMentions,ngram=ngram, MIN_FREQ=5) #train for characters because we only use entities names for characters
logger.info('ngram%d vocab size: %d', ngram, len(ngram_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_words = numpy.zeros(shape=(totals, max_num_ngrams), dtype='int32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
ngrams = name2ngrams[name]
input_words[i] = get_ngram_seq(ngram_to_idx, ngrams, max_len=max_num_ngrams)
print input_words.shape
ngram_label = 'ngrams' + str(ngram)
hdf5_file = dsdir + '_ngrams'+str(ngram)+'.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset(ngram_label, input_words.shape, dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(ngram_to_idx, default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_word, default_flow_style=False)
features.attrs['vocabsize'] = len(ngram_to_idx)
features[...] = input_words
features.dims[0].label = ngram_label
nsamples_train = len(trnMentions); nsamples_dev = len(devMentions);
split_dict = {
'train': {ngram_label: (0, nsamples_train)},
'dev': {ngram_label: (nsamples_train, nsamples_train + nsamples_dev)},
'test': {ngram_label: (nsamples_train + nsamples_dev, totals)}}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush();f.close()
logger.info('Building ngram%d dataset finished. It saved in: %s', ngram, hdf5_file)
if vectorfile is None or vectorfile == '':
return
logger.info('Now, writing ngram embeddings')
embeddings, vectorsize = read_embeddings_vocab(vectorfile, vocab=ngram_to_idx, num=upto)
logger.info('size of embedding matrix to save is: (%d, %d)', embeddings.shape[0], embeddings.shape[1])
with h5py.File(dsdir + "_" + ngram_label + "_embeddings.h5py", mode='w') as fp:
vectors = fp.create_dataset('vectors', compression='gzip',
data=embeddings)
vectors.attrs['vectorsize'] = vectorsize
def build_type_words_cosine_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorfile,
upto=-1,
max_num_words=4):
word_to_idx, idx_to_word = build_word_vocab(
trnMentions + devMentions + tstMentions
) #train for characters because we only use entities names for characters
logger.info('word vocab size: %d', len(word_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
idx2embeddings, vectorsize = read_embeddings_vocab(vectorfile,
vocab=word_to_idx,
num=upto)
input_avg = numpy.zeros(shape=(totals, vectorsize), dtype='float32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
words = name.split()
seq_words = get_ngram_seq(word_to_idx, words, max_len=max_num_words)
avgvec = numpy.zeros(shape=(vectorsize))
for ii in seq_words:
avgvec += idx2embeddings[ii]
avgvec /= len(seq_words)
input_avg[i] = avgvec
(embeddings, voc2idx, vectorsize) = read_embeddings(vectorfile, upto)
typevecmatrix = buildtypevecmatrix(
t2idx, embeddings, vectorsize,
voc2idx) # a matrix with size: 102 * dim
words_types_cosin_matrix = buildcosinematrix(input_avg, typevecmatrix)
logger.info(words_types_cosin_matrix.shape)
dsdir += '_tcwords.h5py'
f = h5py.File(dsdir, mode='w')
features = f.create_dataset('tcwords',
words_types_cosin_matrix.shape,
dtype='float32') # @UndefinedVariable
features.attrs['vectorsize'] = words_types_cosin_matrix.shape[1]
features[...] = words_types_cosin_matrix
features.dims[0].label = 'words_types_cosine'
nsamples_train = len(trnMentions)
nsamples_dev = len(devMentions)
split_dict = {
'train': {
'tcwords': (0, nsamples_train)
},
'dev': {
'tcwords': (nsamples_train, nsamples_train + nsamples_dev)
},
'test': {
'tcwords': (nsamples_train + nsamples_dev, totals)
}
}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info(
'Building types-words cosine (tcwords) dataset finished. It saved in: %s',
dsdir)
def build_ngram_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorfile,
ngram,
max_num_ngrams=98,
upto=-1):
ngram_to_idx, idx_to_word, name2ngrams = build_ngram_vocab(
trnMentions + devMentions + tstMentions, ngram=ngram, MIN_FREQ=5
) #train for characters because we only use entities names for characters
logger.info('ngram%d vocab size: %d', ngram, len(ngram_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_words = numpy.zeros(shape=(totals, max_num_ngrams), dtype='int32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
ngrams = name2ngrams[name]
input_words[i] = get_ngram_seq(ngram_to_idx,
ngrams,
max_len=max_num_ngrams)
print input_words.shape
ngram_label = 'ngrams' + str(ngram)
hdf5_file = dsdir + '_ngrams' + str(ngram) + '.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset(ngram_label, input_words.shape,
dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(ngram_to_idx,
default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_word,
default_flow_style=False)
features.attrs['vocabsize'] = len(ngram_to_idx)
features[...] = input_words
features.dims[0].label = ngram_label
nsamples_train = len(trnMentions)
nsamples_dev = len(devMentions)
split_dict = {
'train': {
ngram_label: (0, nsamples_train)
},
'dev': {
ngram_label: (nsamples_train, nsamples_train + nsamples_dev)
},
'test': {
ngram_label: (nsamples_train + nsamples_dev, totals)
}
}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info('Building ngram%d dataset finished. It saved in: %s', ngram,
hdf5_file)
if vectorfile is None or vectorfile == '':
return
logger.info('Now, writing ngram embeddings')
embeddings, vectorsize = read_embeddings_vocab(vectorfile,
vocab=ngram_to_idx,
num=upto)
logger.info('size of embedding matrix to save is: (%d, %d)',
embeddings.shape[0], embeddings.shape[1])
with h5py.File(dsdir + "_" + ngram_label + "_embeddings.h5py",
mode='w') as fp:
vectors = fp.create_dataset('vectors',
compression='gzip',
data=embeddings)
vectors.attrs['vectorsize'] = vectorsize
def build_subwords_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorfile,
use_lowercase=False,
max_num_words=10,
upto=None):
if vectorfile == None:
return
word_to_idx, idx_to_word = build_word_vocab(
trnMentions + devMentions + tstMentions
) #train for characters because we only use entities names for characters
logger.info('word vocab size: %d', len(word_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_words = numpy.zeros(shape=(totals, max_num_words), dtype='int32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
name = men.name
words = name.split()
input_words[i] = get_ngram_seq(word_to_idx,
words,
max_len=max_num_words)
logger.info('shape of subwords dataset: %s', input_words.shape)
hdf5_file = dsdir + '_subwords.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset('subwords', input_words.shape,
dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(word_to_idx,
default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_word,
default_flow_style=False)
features.attrs['vocabsize'] = len(word_to_idx)
features[...] = input_words
features.dims[0].label = 'words'
nsamples_train = len(trnMentions)
nsamples_dev = len(devMentions)
split_dict = {
'train': {
'subwords': (0, nsamples_train)
},
'dev': {
'subwords': (nsamples_train, nsamples_train + nsamples_dev)
},
'test': {
'subwords': (nsamples_train + nsamples_dev, totals)
}
}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info('Building subwords dataset finished. It saved in: %s',
hdf5_file)
logger.info('writing subword embeddings')
idx2embeddings, vectorsize = read_embeddings_vocab(
vectorfile, vocab=word_to_idx, use_lowercase=use_lowercase, num=upto)
with h5py.File(dsdir + "_subwords_embeddings.h5py", mode='w') as fp:
vectors = fp.create_dataset('vectors',
compression='gzip',
data=idx2embeddings)
vectors.attrs['vectorsize'] = vectorsize
def build_desc_features_ds(trnMentions,
devMentions,
tstMentions,
ent2tfidf_features_path,
t2idx,
dsdir,
vectorfile,
use_lowercase=True,
upto=None):
if ent2tfidf_features_path == None:
print "Warning: ignoring tfidf features building..."
return
ent2features = load_ent2features(ent2tfidf_features_path)
word_to_idx, idx_to_word = build_voc_from_features(ent2features)
logger.info('tfidf desc features vocab size: %d', len(word_to_idx))
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_features = numpy.zeros(shape=(totals, len(ent2features.values()[0])),
dtype='int32')
ent_no_emb = 0
for i, men in enumerate(trnMentions + devMentions + tstMentions):
if men.entityId not in ent2features:
ent_no_emb += 1
continue
features = ent2features[men.entityId]
input_features[i] = get_ngram_seq(word_to_idx,
features,
max_len=input_features.shape[1])
logger.info('shape of tfidf input dataset: %s', input_features.shape)
logger.info('number of entities without embeddings: %d', ent_no_emb)
hdf5_file = dsdir + '_desc_features.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset('desc_features',
input_features.shape,
dtype='int32') # @UndefinedVariable
features.attrs['voc2idx'] = yaml.dump(word_to_idx,
default_flow_style=False)
features.attrs['idx2voc'] = yaml.dump(idx_to_word,
default_flow_style=False)
features.attrs['vocabsize'] = len(word_to_idx)
features[...] = input_features
features.dims[0].label = 'description_features'
nsamples_train = len(trnMentions)
nsamples_dev = len(devMentions)
split_dict = {
'train': {
'desc_features': (0, nsamples_train)
},
'dev': {
'desc_features': (nsamples_train, nsamples_train + nsamples_dev)
},
'test': {
'desc_features': (nsamples_train + nsamples_dev, totals)
}
}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info('Building desc_features dataset finished. It saved in: %s',
hdf5_file)
logger.info('writing word embeddings')
idx2embeddings, vectorsize = read_embeddings_vocab(
vectorfile, vocab=word_to_idx, use_lowercase=use_lowercase, num=upto)
print "embeddings shape: ", idx2embeddings.shape
with h5py.File(dsdir + "_desc_features_embeddings.h5py", mode='w') as fp:
vectors = fp.create_dataset('vectors',
compression='gzip',
data=idx2embeddings)
vectors.attrs['vectorsize'] = vectorsize
