def build_typecosine_ds(trnMentions, devMentions, tstMentions, t2idx, hdf5_file, vectorfile, upto=-1):
(embeddings, voc2idx, vectorsize) = read_embeddings(vectorfile, upto)
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_entvec = numpy.zeros(shape=(totals, vectorsize), dtype='float32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
mye = men.entityId
entvec = numpy.zeros(vectorsize)
if mye in voc2idx:
entvec = embeddings[voc2idx[mye]]
input_entvec[i] = entvec
typevecmatrix = buildtypevecmatrix(t2idx, embeddings, vectorsize, voc2idx) # a matrix with size: 102 * dim
ent_types_cosin_matrix = buildcosinematrix(input_entvec, typevecmatrix)
logger.info(ent_types_cosin_matrix.shape)
hdf5_file += '_tc.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset('tc', ent_types_cosin_matrix.shape, dtype='float32') # @UndefinedVariable
features.attrs['vectorsize'] = ent_types_cosin_matrix.shape[1]
features[...] = ent_types_cosin_matrix
features.dims[0].label = 'types_ent_cosine'
nsamples_train = len(trnMentions); nsamples_dev = len(devMentions);
split_dict = {
'train': {'tc': (0, nsamples_train)},
'dev': {'tc': (nsamples_train, nsamples_train + nsamples_dev)},
'test': {'tc': (nsamples_train + nsamples_dev, totals)}}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info('Building types-ent cosine (tc) dataset finished. It saved in: %s', hdf5_file)
def save_typevecmatrix(t2idx, dsdir, vectorfile, upto=-1):
(embeddings, voc2idx, vectorsize) = read_embeddings(vectorfile, upto)
typevecmatrix = buildtypevecmatrix(
t2idx, embeddings, vectorsize,
voc2idx) # a matrix with size: 102 * dim
dsdir += '_typematrix.npy'
numpy.save(dsdir, numpy.transpose(typevecmatrix))
def build_typecosine_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
hdf5_file,
vectorfile,
upto=-1):
(embeddings, voc2idx, vectorsize) = read_embeddings(vectorfile, upto)
totals = len(trnMentions) + len(devMentions) + len(tstMentions)
input_entvec = numpy.zeros(shape=(totals, vectorsize), dtype='float32')
for i, men in enumerate(trnMentions + devMentions + tstMentions):
mye = men.entityId
entvec = numpy.zeros(vectorsize)
if mye in voc2idx:
entvec = embeddings[voc2idx[mye]]
input_entvec[i] = entvec
typevecmatrix = buildtypevecmatrix(
t2idx, embeddings, vectorsize,
voc2idx) # a matrix with size: 102 * dim
ent_types_cosin_matrix = buildcosinematrix(input_entvec, typevecmatrix)
logger.info(ent_types_cosin_matrix.shape)
hdf5_file += '_tc.h5py'
f = h5py.File(hdf5_file, mode='w')
features = f.create_dataset('tc',
ent_types_cosin_matrix.shape,
dtype='float32') # @UndefinedVariable
features.attrs['vectorsize'] = ent_types_cosin_matrix.shape[1]
features[...] = ent_types_cosin_matrix
features.dims[0].label = 'types_ent_cosine'
nsamples_train = len(trnMentions)
nsamples_dev = len(devMentions)
split_dict = {
'train': {
'tc': (0, nsamples_train)
},
'dev': {
'tc': (nsamples_train, nsamples_train + nsamples_dev)
},
'test': {
'tc': (nsamples_train + nsamples_dev, totals)
}
}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
logger.info(
'Building types-ent cosine (tc) dataset finished. It saved in: %s',
hdf5_file)
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 save_typevecmatrix(t2idx, dsdir, vectorfile, upto=-1):
(embeddings, voc2idx, vectorsize) = read_embeddings(vectorfile, upto)
typevecmatrix = buildtypevecmatrix(t2idx, embeddings, vectorsize, voc2idx) # a matrix with size: 102 * dim
dsdir += '_typematrix.npy'
numpy.save(dsdir, numpy.transpose(typevecmatrix))
testfile=sys.argv[2]
outf=sys.argv[3]
use_tanh_out = False
outputtype = config['outtype'] #hinge or softmax
usetypecosine = False
if 'typecosine' in config:
usetypecosine = utils.str_to_bool(config['typecosine'])
(t2ind, n_targets, wordvectors, vectorsize, typefreq_traindev) = utils.loadTypesAndVectors(targetTypesFile, vectorFile)
(rvt, input_matrix_test, iet,resvectstnall, ntrn) = utils.fillOnlyEntityData(testfile,vectorsize, wordvectors, t2ind, n_targets, upto=-1, ds='test', binoutvec=True)
# train network
rng = numpy.random.RandomState(23455)
if usetypecosine:
print 'using cosine(e,t) as another input feature'
typevecmatrix = utils.buildtypevecmatrix(t2ind, wordvectors, vectorsize) # a matrix with size: 102 * dim
e2simmatrix_test = utils.buildcosinematrix(input_matrix_test, typevecmatrix)
input_matrix_test = utils.extend_in_matrix(input_matrix_test, e2simmatrix_test)
dt = theano.config.floatX # @UndefinedVariable
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.imatrix('y') # the labels are presented as 1D vector of
# [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
rng = numpy.random.RandomState(23455)
layer1 = layers.HiddenLayer(rng, input=x, n_in=input_matrix_test.shape[1],n_out=num_of_hidden_units, activation=T.tanh)
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)
typefreq_traindev) = utils.loadTypesAndVectors(targetTypesFile, vectorFile)
(rvt, input_matrix_test, iet, resvectstnall,
ntrn) = utils.fillOnlyEntityData(testfile,
vectorsize,
wordvectors,
t2ind,
n_targets,
upto=-1,
ds='test',
binoutvec=True)
# train network
rng = numpy.random.RandomState(23455)
if usetypecosine:
print 'using cosine(e,t) as another input feature'
typevecmatrix = utils.buildtypevecmatrix(
t2ind, wordvectors, vectorsize) # a matrix with size: 102 * dim
e2simmatrix_test = utils.buildcosinematrix(input_matrix_test,
typevecmatrix)
input_matrix_test = utils.extend_in_matrix(input_matrix_test,
e2simmatrix_test)
dt = theano.config.floatX # @UndefinedVariable
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.imatrix('y') # the labels are presented as 1D vector of
# [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'