def __init__(self, name, config):
self.config = config
self.name = name
self.max_len = int(config[self.name + '_max_len'])
self.dim_emb = int(config[self.name + '_emb_dim'])
self.fuelfile = config['dsdir'] + '_' + name + '.h5py'
self.nn_model = config[self.name + '_nn']
self.emb_file = self.config['dsdir'] + '_' + self.name + '_embeddings.h5py'
self.tune_tune = str_to_bool(self.config[self.name + '_tune_embedding'])
self.load_emb = str_to_bool(self.config[self.name + '_load_embedding'])
if self.load_emb == False:
assert self.tune_tune == True
self.rec = False
if 'lstm' in self.nn_model or 'rnn' in self.nn_model or 'gru' in self.nn_model:
self.rec = True
def __init__(self, name, config):
self.config = config
self.name = name
self.max_len = int(config[self.name + '_max_len'])
self.dim_emb = int(config[self.name + '_emb_dim'])
self.fuelfile = config['dsdir'] + '_' + name + '.h5py'
self.nn_model = config[self.name + '_nn']
self.emb_file = self.config[
'dsdir'] + '_' + self.name + '_embeddings.h5py'
self.tune_tune = str_to_bool(self.config[self.name +
'_tune_embedding'])
self.load_emb = str_to_bool(self.config[self.name + '_load_embedding'])
if self.load_emb == False:
assert self.tune_tune == True
self.rec = False
if 'lstm' in self.nn_model or 'rnn' in self.nn_model or 'gru' in self.nn_model:
self.rec = True
def main(args):
print 'loading config file', args[1]
config = cmn.loadConfig(args[1])
dsdir = config['dsdir']
#first generating name datasets based on the number of names for each set
if not os.path.exists(os.path.join(dsdir,'train.txt')):
generate_name_dataset(config)
trainfile = dsdir + '/train.txt'
devfile = dsdir + '/dev.txt'
testfile = dsdir + '/test.txt'
targetTypesFile=config['typefile']
vectorFile = config['ent_vectors']
vectorFile_words = config['word_vectors'] if 'word_vectors' in config else vectorFile
subword_vectorFile = config['fasttext_vecfile'] if 'fasttext_vecfile' in config else None
ent2tfidf_features_path = config['ent2tfidf_features_path'] if 'ent2tfidf_features_path' in config else None
# the_features = config['features'].split(' ') #i.e. letters entvec words tc
ngrams = [int(n) for n in config['ngrams_n'].split()] if 'ngrams_n' in config else []
ngrams_vecfiles = {ngram: config['ngrams'+str(ngram)+'_vecfile'] for ngram in ngrams}
letter_vecfile = config['letters_vecfile'] if 'letters_vecfile' in config else None
hs_ngram_path = config['hsngrampath'] if 'hsngrampath' in config else None
hs_ngram_versions = config['hsngram_vecs'].split() if hs_ngram_path else None
use_lowercase = str_to_bool(config['use_lowercase']) if 'use_lowercase' in config else False
print "uselower: ", use_lowercase
upto = -1
(t2idx, _) = cmn.loadtypes(targetTypesFile)
trnMentions = load_ent_ds(trainfile)
devMentions = load_ent_ds(devfile)
tstMentions = load_ent_ds(testfile)
logger.info("#train : %d #dev : %d #test : %d", len(trnMentions), len(devMentions), len(tstMentions))
if not os.path.exists(os.path.join(dsdir,'_targets.h5py')):
build_targets_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir)
build_entvec_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorFile, upto=-1)
build_letters_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, letter_vecfile, max_len_name=40)
build_typecosine_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorFile, upto=-1)
if hs_ngram_path:
build_hsNgram_ds(config, trnMentions, devMentions, tstMentions, t2idx, dsdir, hs_ngram_path, hs_ngram_versions, vectorsize=300, upto=-1)
for ng in ngrams:
build_ngram_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, ngrams_vecfiles[ng], ng, upto=-1)
build_type_patterns(trnMentions, t2idx, dsdir, vectorFile)
save_typevecmatrix(t2idx, dsdir, vectorFile)
# build_type_words_cosine_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorFile, upto=-1)
build_subwords_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, subword_vectorFile, use_lowercase=use_lowercase, upto=-1)
build_words_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorFile_words, use_lowercase=use_lowercase, upto=-1)
build_entvec_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorFile, upto=-1)
build_desc_features_ds(trnMentions, devMentions, tstMentions, ent2tfidf_features_path, t2idx, dsdir, vectorFile_words, use_lowercase=True, upto=-1)
else:
build_desc_features_ds(trnMentions, devMentions, tstMentions, ent2tfidf_features_path, t2idx, dsdir, vectorFile_words, use_lowercase=True, upto=-1)
def build_model_new(fea2obj,
num_targets,
config,
kl_weight,
entropy_weight,
deterministic=False,
test=False):
hidden_size = config['hidden_units'].split()
use_highway = str_to_bool(
config['use_highway']) if 'use_highway' in config else False
use_gaus = str_to_bool(
config['use_gaus']) if 'use_gaus' in config else False
use_rec = str_to_bool(config['use_rec']) if 'use_rec' in config else True
n_latent_z = int(config['n_latent']) if 'use_gaus' in config else 0
use_noise = str_to_bool(
config['use_noise']) if 'use_noise' in config else False
use_vae = str_to_bool(config['use_vae']) if 'use_vae' in config else False
hu_decoder = int(
config['hu_decoder']) if 'hu_decoder' in config else hidden_size
logger.info(
'use_gaus: %s, use_rec: %s, use_noise: %s, use_vae: %s, hidden_size: %s, n_latent_z: %d, hu_decoder: %s, hu_encoder: %s',
use_gaus, use_rec, use_noise, use_vae, hidden_size, n_latent_z,
hu_decoder, hidden_size)
init_with_type = str_to_bool(
config['init_with_type']) if 'init_with_type' in config else False
y = T.matrix('targets', dtype='int32')
drop_prob = float(config['dropout']) if 'dropout' in config else 0
#build the feature vector with one model, e.g., with cnn or mean or lstm
feature_vec, feature_vec_len = build_feature_vec(fea2obj, config)
#drop out
if drop_prob > 0:
mask = T.cast(
srng.binomial(n=1, p=1 - drop_prob, size=feature_vec.shape),
'float32')
if test:
feature_vec *= (1 - drop_prob)
else:
feature_vec *= mask
#Highway network
if use_highway:
g_mlp = MLP(activations=[Rectifier()],
dims=[feature_vec_len, feature_vec_len],
name='g_mlp')
t_mlp = MLP(activations=[Logistic()],
dims=[feature_vec_len, feature_vec_len],
name='t_mlp')
initialize([g_mlp, t_mlp])
t = t_mlp.apply(feature_vec)
z = t * g_mlp.apply(feature_vec) + (1. - t) * feature_vec
feature_vec = z
#MLP(s)
logger.info('feature vec length = %s and hidden layer units = %s',
feature_vec_len, ' '.join(hidden_size))
if len(hidden_size) > 1:
#2 MLP on feature fector
mlp = MLP(
activations=[Rectifier(), Rectifier()],
dims=[feature_vec_len,
int(hidden_size[0]),
int(hidden_size[1])],
name='joint_mlp')
initialize([mlp])
before_out = mlp.apply(feature_vec)
last_hidden_size = int(hidden_size[1])
else:
hidden_size = int(hidden_size[0])
mlp = MLP(activations=[Rectifier()],
dims=[feature_vec_len, hidden_size],
name='joint_mlp')
initialize([mlp])
before_out = mlp.apply(feature_vec)
last_hidden_size = hidden_size
#compute y_hat initial guess
hidden_to_output = Linear(name='hidden_to_output',
input_dim=last_hidden_size,
output_dim=num_targets)
typemfile = None
if init_with_type:
typemfile = config['dsdir'] + '/_typematrix.npy'
#typemfile = config['dsdir'] + '/_typeCooccurrMatrix.npy'
initialize_lasthid(hidden_to_output, typemfile)
# initialize([hidden_to_output])
y_hat_init = Logistic().apply(hidden_to_output.apply(before_out))
y_hat_init.name = 'y_hat_init'
y_hat_init = debug_print(y_hat_init, 'yhat_init', False)
logpy_xz_init = cross_entropy_loss(y_hat_init, y)
logpy_xz = logpy_xz_init
y_hat_recog = y_hat_init
y_hat = y_hat_init
KLD = 0
if use_gaus:
if use_vae:
logger.info('using VAE')
vae_conditional = str_to_bool(config['vae_cond'])
if vae_conditional:
y_hat, logpy_xz, KLD, y_hat_recog = build_vae_conditoinal(
kl_weight,
entropy_weight,
y_hat_init,
feature_vec,
feature_vec_len,
config,
y,
test=test,
deterministic=deterministic,
num_targets=num_targets,
n_latent_z=n_latent_z,
hidden_size=hidden_size,
hu_decoder=hu_decoder)
else:
y_hat, logpy_xz, KLD = build_vae_basic(
kl_weight,
feature_vec,
feature_vec_len,
config,
y,
test=test,
deterministic=deterministic,
num_targets=num_targets,
n_latent_z=n_latent_z,
hidden_size=hidden_size,
hu_decoder=hu_decoder)
y_hat_recog = y_hat
else:
if use_rec:
logger.info('Not using VAE... but using recursion')
prior_in = T.concatenate([feature_vec, y_hat_init], axis=1)
mu_prior, log_sigma_prior = prior_network(
x=prior_in,
n_input=feature_vec_len + num_targets,
hu_encoder=hidden_size,
n_latent=n_latent_z)
z_prior = sampler(mu_prior,
log_sigma_prior,
deterministic=deterministic,
use_noise=use_noise)
zl = [T.concatenate([z_prior, feature_vec], axis=1)]
y_hat, logpy_xz = generation(zl,
n_latent=n_latent_z +
feature_vec_len,
hu_decoder=hu_decoder,
n_out=num_targets,
y=y)
y_hat = (y_hat + y_hat_init) / 2.
logpy_xz = (logpy_xz + logpy_xz_init) / 2.
else:
prior_in = T.concatenate([feature_vec], axis=1)
mu_prior, log_sigma_prior = prior_network(
x=prior_in,
n_input=feature_vec_len,
hu_encoder=hidden_size,
n_latent=n_latent_z)
z_prior = sampler(mu_prior,
log_sigma_prior,
deterministic=deterministic,
use_noise=use_noise)
zl = [T.concatenate([z_prior, feature_vec], axis=1)]
y_hat, logpy_xz = generation(zl,
n_latent=n_latent_z +
feature_vec_len,
hu_decoder=hu_decoder,
n_out=num_targets,
y=y)
y_hat_recog = y_hat
y_hat = debug_print(y_hat, 'y_hat', False)
pat1 = T.mean(y[T.arange(y.shape[0]), T.argmax(y_hat, axis=1)])
max_type = debug_print(T.argmax(y_hat_recog, axis=1), 'max_type', False)
pat1_recog = T.mean(y[T.arange(y.shape[0]), max_type])
mean_cross = T.mean(logpy_xz)
mean_kld = T.mean(KLD)
cost = mean_kld + mean_cross
cost.name = 'cost'
mean_kld.name = 'kld'
mean_cross.name = 'cross_entropy_loss'
pat1.name = 'p@1'
pat1_recog.name = 'p@1_recog'
misclassify_rate = MultiMisclassificationRate().apply(y, T.ge(y_hat, 0.5))
misclassify_rate.name = 'error_rate'
return cost, pat1, y_hat, mean_kld, mean_cross, pat1_recog, misclassify_rate
t2idx=t2idx)
return in_trn_matrix, in_tst_matrix, in_dev_matrix
print 'loading config file', sys.argv[1]
config = cmn.loadConfig(sys.argv[1])
hdf5_file = config['fuelfile']
trainfile = config['Etrain']
devfile = config['Edev']
testfile = config['Etest']
targetTypesFile = config['typefile']
max_len_name = int(config['max_name_length'])
vectorFile = config['ent_vectors']
if 'typecosine' in config:
usetypecosine = cmn.str_to_bool(config['typecosine'])
brownMappingFile = config['brownclusters']
maxngram = int(config['maxngram'])
featuresToUse = [fea for fea in config['features'].split(' ')]
npdir = config['npdir']
if not os.path.exists(npdir): os.makedirs(npdir)
upto = -1
(t2idx, idx2t) = cmn.loadtypes(targetTypesFile)
numtargets = len(t2idx)
# load entity to type datasets
(etrain2types, etrain2names, _) = cmn.load_entname_ds(trainfile, t2idx)
print "number of train examples:" + str(len(etrain2names))
(etest2types, etest2names, _) = cmn.load_entname_ds(testfile, t2idx)
print "number of test examples:" + str(len(etest2names))
(edev2types, edev2names, _) = cmn.load_entname_ds(devfile, t2idx)
parser = get_argument_parser()
args = parser.parse_args()
config = loadConfig(args.config)
brownMappingFile = config['brownclusters']
trainfile = config['Etrain']
devfile = config['Edev']
testfile = config['Etest']
batch_size = int(config['batchsize'])
targetTypesFile = config['typefile']
learning_rate = float(config['lrate'])
networkfile = config['net']
num_of_hidden_units = int(config['hidden_units'])
n_epochs = int(config['nepochs'])
maxngram = int(config['maxngram'])
MLP = str_to_bool(config['mlp'])
featuresToUse = [fea for fea in config['features'].split(' ')]
npdir = config['npdir']
if not os.path.exists(npdir): os.makedirs(npdir)
(t2idx, idx2t) = loadtypes(targetTypesFile)
numtype = len(t2idx)
(etrain2types, etrain2names, _) = load_entname_ds(trainfile,
t2idx,
use_ix=True)
print "number of train examples:" + str(len(etrain2names))
(etest2types, etest2names, _) = load_entname_ds(testfile,
t2idx,
use_ix=True)
print "number of test examples:" + str(len(etest2names))
(edev2types, edev2names, _) = load_entname_ds(devfile, t2idx, use_ix=True)
parser = get_argument_parser()
args = parser.parse_args()
config = loadConfig(args.config)
brownMappingFile=config['brownclusters']
trainfile=config['Etrain']
devfile=config['Edev']
testfile=config['Etest']
batch_size=int(config['batchsize'])
targetTypesFile=config['typefile']
learning_rate = float(config['lrate'])
networkfile = config['net']
num_of_hidden_units = int(config['hidden_units'])
n_epochs = int(config['nepochs'])
maxngram = int(config['maxngram'])
MLP=str_to_bool(config['mlp'])
featuresToUse= [fea for fea in config['features'].split(' ')]
npdir = config['npdir']
if not os.path.exists(npdir): os.makedirs(npdir)
(t2idx, idx2t) = loadtypes(targetTypesFile)
numtype = len(t2idx)
(etrain2types, etrain2names,_) = load_entname_ds(trainfile, t2idx, use_ix=True)
print "number of train examples:" + str(len(etrain2names))
(etest2types, etest2names,_) = load_entname_ds(testfile, t2idx, use_ix=True)
print "number of test examples:" + str(len(etest2names))
(edev2types, edev2names,_) = load_entname_ds(devfile, t2idx, use_ix=True)
print "number of dev examples:" + str(len(edev2names))
if args.loaddata:
(in_trn_matrix, target_trn_matrix, trnents) = load_input_matrix('train')
def build_model_new(fea2obj, num_targets, config, kl_weight, entropy_weight, deterministic=False, test=False ):
hidden_size = config['hidden_units'].split()
use_highway = str_to_bool(config['use_highway']) if 'use_highway' in config else False
use_gaus = str_to_bool(config['use_gaus']) if 'use_gaus' in config else False
use_rec = str_to_bool(config['use_rec']) if 'use_rec' in config else True
n_latent_z = int(config['n_latent']) if 'use_gaus' in config else 0
use_noise = str_to_bool(config['use_noise']) if 'use_noise' in config else False
use_vae=str_to_bool(config['use_vae']) if 'use_vae' in config else False
hu_decoder = int(config['hu_decoder']) if 'hu_decoder' in config else hidden_size
logger.info('use_gaus: %s, use_rec: %s, use_noise: %s, use_vae: %s, hidden_size: %s, n_latent_z: %d, hu_decoder: %s, hu_encoder: %s', use_gaus, use_rec, use_noise, use_vae, hidden_size, n_latent_z, hu_decoder, hidden_size)
init_with_type = str_to_bool(config['init_with_type']) if 'init_with_type' in config else False
y = T.matrix('targets', dtype='int32')
drop_prob = float(config['dropout']) if 'dropout' in config else 0
#build the feature vector with one model, e.g., with cnn or mean or lstm
feature_vec, feature_vec_len = build_feature_vec(fea2obj, config)
#drop out
if drop_prob > 0:
mask = T.cast(srng.binomial(n=1, p=1-drop_prob, size=feature_vec.shape), 'float32')
if test:
feature_vec *= (1 - drop_prob)
else:
feature_vec *= mask
#Highway network
if use_highway:
g_mlp = MLP(activations=[Rectifier()], dims=[feature_vec_len, feature_vec_len], name='g_mlp')
t_mlp = MLP(activations=[Logistic()], dims=[feature_vec_len, feature_vec_len], name='t_mlp')
initialize([g_mlp, t_mlp])
t = t_mlp.apply(feature_vec)
z = t * g_mlp.apply(feature_vec) + (1. - t) * feature_vec
feature_vec = z
#MLP(s)
logger.info('feature vec length = %s and hidden layer units = %s', feature_vec_len, ' '.join(hidden_size))
if len(hidden_size) > 1:
#2 MLP on feature fector
mlp = MLP(activations=[Rectifier(), Rectifier()], dims=[feature_vec_len, int(hidden_size[0]), int(hidden_size[1])], name='joint_mlp')
initialize([mlp])
before_out = mlp.apply(feature_vec)
last_hidden_size = int(hidden_size[1])
else:
hidden_size = int(hidden_size[0])
mlp = MLP(activations=[Rectifier()], dims=[feature_vec_len, hidden_size], name='joint_mlp')
initialize([mlp])
before_out = mlp.apply(feature_vec)
last_hidden_size = hidden_size
#compute y_hat initial guess
hidden_to_output = Linear(name='hidden_to_output', input_dim=last_hidden_size, output_dim=num_targets)
typemfile = None
if init_with_type:
typemfile = config['dsdir'] + '/_typematrix.npy'
#typemfile = config['dsdir'] + '/_typeCooccurrMatrix.npy'
initialize_lasthid(hidden_to_output, typemfile)
# initialize([hidden_to_output])
y_hat_init = Logistic().apply(hidden_to_output.apply(before_out))
y_hat_init.name='y_hat_init'
y_hat_init = debug_print(y_hat_init, 'yhat_init', False)
logpy_xz_init = cross_entropy_loss(y_hat_init, y)
logpy_xz = logpy_xz_init
y_hat_recog = y_hat_init
y_hat = y_hat_init
KLD = 0
if use_gaus:
if use_vae:
logger.info('using VAE')
vae_conditional=str_to_bool(config['vae_cond'])
if vae_conditional:
y_hat, logpy_xz, KLD, y_hat_recog = build_vae_conditoinal(kl_weight, entropy_weight, y_hat_init, feature_vec, feature_vec_len, config, y,
test=test, deterministic=deterministic, num_targets=num_targets, n_latent_z=n_latent_z, hidden_size=hidden_size, hu_decoder=hu_decoder)
else:
y_hat, logpy_xz, KLD = build_vae_basic(kl_weight, feature_vec, feature_vec_len, config, y,
test=test, deterministic=deterministic, num_targets=num_targets, n_latent_z=n_latent_z, hidden_size=hidden_size, hu_decoder=hu_decoder)
y_hat_recog = y_hat
else:
if use_rec:
logger.info('Not using VAE... but using recursion')
prior_in = T.concatenate([feature_vec, y_hat_init], axis=1)
mu_prior, log_sigma_prior = prior_network(x=prior_in, n_input=feature_vec_len+num_targets, hu_encoder=hidden_size, n_latent=n_latent_z)
z_prior = sampler(mu_prior, log_sigma_prior, deterministic=deterministic, use_noise=use_noise)
zl = [T.concatenate([z_prior, feature_vec], axis=1)]
y_hat, logpy_xz = generation(zl, n_latent=n_latent_z+feature_vec_len, hu_decoder=hu_decoder, n_out=num_targets, y=y)
y_hat = (y_hat + y_hat_init) / 2.
logpy_xz = (logpy_xz + logpy_xz_init) / 2.
else:
prior_in = T.concatenate([feature_vec], axis=1)
mu_prior, log_sigma_prior = prior_network(x=prior_in, n_input=feature_vec_len, hu_encoder=hidden_size, n_latent=n_latent_z)
z_prior = sampler(mu_prior, log_sigma_prior, deterministic=deterministic, use_noise=use_noise)
zl = [T.concatenate([z_prior, feature_vec], axis=1)]
y_hat, logpy_xz = generation(zl, n_latent=n_latent_z+feature_vec_len, hu_decoder=hu_decoder, n_out=num_targets, y=y)
y_hat_recog = y_hat
y_hat = debug_print(y_hat, 'y_hat', False)
pat1 = T.mean(y[T.arange(y.shape[0]), T.argmax(y_hat, axis=1)])
max_type = debug_print(T.argmax(y_hat_recog, axis=1), 'max_type', False)
pat1_recog = T.mean(y[T.arange(y.shape[0]), max_type])
mean_cross = T.mean(logpy_xz)
mean_kld = T.mean(KLD)
cost = mean_kld + mean_cross
cost.name = 'cost'; mean_kld.name = 'kld'; mean_cross.name = 'cross_entropy_loss'; pat1.name = 'p@1'; pat1_recog.name = 'p@1_recog'
misclassify_rate = MultiMisclassificationRate().apply(y, T.ge(y_hat, 0.5))
misclassify_rate.name = 'error_rate'
return cost, pat1, y_hat, mean_kld, mean_cross, pat1_recog, misclassify_rate
(in_tst_matrix, _, _) = make_input_matrix(etest2rawFea, etest2types, fea2id, 'test', npdir=npdir, t2idx=t2idx)
(in_dev_matrix, _, _) = make_input_matrix(edev2rawFea, edev2types, fea2id, 'dev', npdir=npdir, t2idx=t2idx)
return in_trn_matrix, in_tst_matrix, in_dev_matrix
print 'loading config file', sys.argv[1]
config = cmn.loadConfig(sys.argv[1])
hdf5_file = config['fuelfile']
trainfile=config['Etrain']
devfile=config['Edev']
testfile=config['Etest']
targetTypesFile=config['typefile']
max_len_name= int(config['max_name_length'])
vectorFile=config['ent_vectors']
if 'typecosine' in config:
usetypecosine = cmn.str_to_bool(config['typecosine'])
brownMappingFile=config['brownclusters']
maxngram = int(config['maxngram'])
featuresToUse= [fea for fea in config['features'].split(' ')]
npdir = config['npdir']
if not os.path.exists(npdir): os.makedirs(npdir)
upto = -1
(t2idx, idx2t) = cmn.loadtypes(targetTypesFile)
numtargets = len(t2idx)
# load entity to type datasets
(etrain2types, etrain2names,_) = cmn.load_entname_ds(trainfile, t2idx)
print "number of train examples:" + str(len(etrain2names))
(etest2types, etest2names,_) = cmn.load_entname_ds(testfile, t2idx)
print "number of test examples:" + str(len(etest2names))
(edev2types, edev2names,_) = cmn.load_entname_ds(devfile, t2idx)
def main(args):
print 'loading config file', args[1]
config = cmn.loadConfig(args[1])
dsdir = config['dsdir']
#first generating name datasets based on the number of names for each set
if not os.path.exists(os.path.join(dsdir, 'train.txt')):
generate_name_dataset(config)
trainfile = dsdir + '/train.txt'
devfile = dsdir + '/dev.txt'
testfile = dsdir + '/test.txt'
targetTypesFile = config['typefile']
vectorFile = config['ent_vectors']
vectorFile_words = config[
'word_vectors'] if 'word_vectors' in config else vectorFile
subword_vectorFile = config[
'fasttext_vecfile'] if 'fasttext_vecfile' in config else None
ent2tfidf_features_path = config[
'ent2tfidf_features_path'] if 'ent2tfidf_features_path' in config else None
# the_features = config['features'].split(' ') #i.e. letters entvec words tc
ngrams = [int(n) for n in config['ngrams_n'].split()
] if 'ngrams_n' in config else []
ngrams_vecfiles = {
ngram: config['ngrams' + str(ngram) + '_vecfile']
for ngram in ngrams
}
letter_vecfile = config[
'letters_vecfile'] if 'letters_vecfile' in config else None
hs_ngram_path = config['hsngrampath'] if 'hsngrampath' in config else None
hs_ngram_versions = config['hsngram_vecs'].split(
) if hs_ngram_path else None
use_lowercase = str_to_bool(
config['use_lowercase']) if 'use_lowercase' in config else False
print "uselower: ", use_lowercase
upto = -1
(t2idx, _) = cmn.loadtypes(targetTypesFile)
trnMentions = load_ent_ds(trainfile)
devMentions = load_ent_ds(devfile)
tstMentions = load_ent_ds(testfile)
logger.info("#train : %d #dev : %d #test : %d", len(trnMentions),
len(devMentions), len(tstMentions))
if not os.path.exists(os.path.join(dsdir, '_targets.h5py')):
build_targets_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir)
build_entvec_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorFile,
upto=-1)
build_letters_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
letter_vecfile,
max_len_name=40)
build_typecosine_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorFile,
upto=-1)
if hs_ngram_path:
build_hsNgram_ds(config,
trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
hs_ngram_path,
hs_ngram_versions,
vectorsize=300,
upto=-1)
for ng in ngrams:
build_ngram_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
ngrams_vecfiles[ng],
ng,
upto=-1)
build_type_patterns(trnMentions, t2idx, dsdir, vectorFile)
save_typevecmatrix(t2idx, dsdir, vectorFile)
# build_type_words_cosine_ds(trnMentions, devMentions, tstMentions, t2idx, dsdir, vectorFile, upto=-1)
build_subwords_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
subword_vectorFile,
use_lowercase=use_lowercase,
upto=-1)
build_words_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorFile_words,
use_lowercase=use_lowercase,
upto=-1)
build_entvec_ds(trnMentions,
devMentions,
tstMentions,
t2idx,
dsdir,
vectorFile,
upto=-1)
build_desc_features_ds(trnMentions,
devMentions,
tstMentions,
ent2tfidf_features_path,
t2idx,
dsdir,
vectorFile_words,
use_lowercase=True,
upto=-1)
else:
build_desc_features_ds(trnMentions,
devMentions,
tstMentions,
ent2tfidf_features_path,
t2idx,
dsdir,
vectorFile_words,
use_lowercase=True,
upto=-1)
