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 main(args):
logger.info('loading config file: %s', args.config)
exp_dir, _ = os.path.split(os.path.abspath(args.config))
config = cmn.loadConfig(args.config)
config['exp_dir'] = exp_dir
config['net'] = os.path.join(exp_dir, config['net'])
batch_size = int(config['batchsize'])
features = config['features'].split(' ') #i.e. letters words entvec
if batch_size == 0: batch_size = None
inp_srcs = []
for fea in features:
if 'ngrams' in fea:
inp_srcs.extend(['ngrams' + ng for ng in config['ngrams_n'].split()])
else:
inp_srcs.append(fea)
our_sources = inp_srcs + ['targets']
fea2obj = build_input_objs(our_sources, config)
typer = EntityTypingGlobal(config)
if args.train:
import shutil
#typer.training(fea2obj, batch_size, learning_rate=float(config['lrate']), steprule=config['steprule'], wait_epochs=10, kl_weight_init=1, klw_ep=100, klw_inc_rate=0, num_epochs=50)
typer.training(fea2obj, batch_size, learning_rate=float(config['lrate']), steprule=config['steprule'], wait_epochs=3, num_epochs=30)
shutil.copyfile(config['net']+'.best.pkl', config['net']+'.toload.pkl')
shutil.copyfile(config['net']+'.best.pkl', config['net']+'.best1.pkl')
# logger.info('One more epoch training...')
# typer.training(fea2obj, batch_size, learning_rate=float(config['lrate'])/2, steprule=config['steprule'], wait_epochs=2, klw_ep=10, kl_weight_init=0.008, num_epochs=20)
# shutil.copyfile(config['net']+'.best.pkl', config['net']+'.toload.pkl')
# shutil.copyfile(config['net']+'.best.pkl', config['net']+'.best2.pkl')
#logger.info('One more epoch training...')
#typer.training(fea2obj, batch_size, learning_rate=float(config['lrate'])/2, steprule=config['steprule'], wait_epochs=2, klw_ep=10, kl_weight_init=0.02, num_epochs=10)
shutil.copyfile(config['net']+'.best.pkl', config['net']+'.toload.pkl')
logger.info('One more epoch training...')
typer.training(fea2obj, batch_size=100, learning_rate=0.005, steprule='adagrad', wait_epochs=2, klw_ep=10, kl_weight_init=None, num_epochs=10)
if args.test:
typer.testing(fea2obj)
if args.eval:
typer.evaluate(args.config)
(etest2types, etest2names, _) = cmn.load_entname_ds(testfile, t2idx)
logger.info("number of test examples: %d",len(etest2names))
(edev2types, edev2names, _) = cmn.load_entname_ds(devfile, t2idx)
logger.info("number of dev examples: %d", len(edev2names))
logger.info('number of names for each entity in trn,dev,test: %s', max_names)
logger.info('generating new datasets based on entity names')
# dsdir = dsdir + 'maxname' + ','.join([str(n) for n in max_names])
# if not os.path.exists(dsdir): os.makedirs(dsdir)
gen_new_ds(etrain2names, etrain2types, max_names[0], outfile = dsdir + '/train.txt')
gen_new_ds(edev2names, edev2types, max_names[1], outfile = dsdir + '/dev.txt')
gen_new_ds(etest2names, etest2types, max_names[2], outfile = dsdir + '/test.txt')
if __name__ == '__main__':
print 'loading config file', sys.argv[1]
config = cmn.loadConfig(sys.argv[1])
trainfile = config['Etrain']
devfile = config['Edev']
testfile = config['Etest']
targetTypesFile=config['typefile']
max_names = [int(n) for n in config['name_num'].split()]
dsdir = config['dsdir']
upto = -1
(t2idx, idx2t) = cmn.loadtypes(targetTypesFile)
numtargets = len(t2idx)
(etrain2types, etrain2names, _) = cmn.load_entname_ds(trainfile, t2idx)
logger.info("number of train examples: %d",len(etrain2names))
(etest2types, etest2names, _) = cmn.load_entname_ds(testfile, t2idx)
logger.info("number of test examples: %d",len(etest2names))
(edev2types, edev2names, _) = cmn.load_entname_ds(devfile, t2idx)
type=bool,
help="Training the model on the test data, or not")
parser.add_argument("--loaddata",
"-lo",
type=bool,
help="To load the feature matrices or not?")
return parser
if __name__ == '__main__':
UPTO = -1
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)
parser.add_argument(
"--test", "-t", type=bool, help="Applying the model on the test data, or not")
parser.add_argument(
"--train", "-tr", type=bool, help="Training the model on the test data, or not")
parser.add_argument(
"--loaddata", "-lo", type=bool, help="To load the feature matrices or not?")
return parser
if __name__ == '__main__':
UPTO = -1
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)
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)