logger.info('Sampling training data...')
# FIXME: pick_positive_name ignores those whose gold standard length is not one (multiple or nil)
# positives = [sp_training.pick_positive_name(config,corpus_train,concept,i) for i in range(len(corpus_train.names))]
# positives = [*zip(positives,corpus_train.names)]
# positives_dev_truncated = [sp_training.pick_positive_name(config,corpus_dev_truncated,concept,i) for i in range(len(corpus_dev_truncated.names))]
# positives_dev_truncated = [*zip(positives_dev_truncated,corpus_dev_truncated.names)]
# with open('gitig_positive_indices.pickle','wb') as f:
# pickle.dump([positives,positives_dev_truncated],f)
from sample import prepare_positives, examples
positives_training, positives_dev, positives_dev_truncated = pickle.load(
open(os.path.join(directory, 'gitig_positive_indices.pickle'), 'rb'))
positives_training = prepare_positives(positives_training, nltk.word_tokenize,
vocabulary)
positives_dev = prepare_positives(positives_dev, nltk.word_tokenize,
vocabulary)
del positives_dev_truncated
# sampling
def examples(config,
concept,
positives,
vocab,
neg_count=config.getint('sample', 'neg_count')):
"""
Builds positive and negative examples.
"""
while True:
logger.info('Using truncated development corpus for evaluation.')
#corpus_dev = sample.NewDataSet('dev corpus')
[real_val_data, concept_order, corpus_dev] = pickle.load(
open(
os.path.join(directory, 'gitig_real_val_data_truncated_d50_p5.pickle'),
'rb'))
real_val_data.y = np.array(real_val_data.y)
concept = concept_obj(config, dictionary, order=concept_order)
from sample import prepare_positives, examples
positives_training, positives_dev, positives_dev_truncated = pickle.load(
open(os.path.join(directory, 'gitig_positive_indices.pickle'), 'rb'))
# positives_dev = prepare_positives(positives_dev,nltk.word_tokenize,vocabulary)
positives_dev_truncated = prepare_positives(positives_dev_truncated,
nltk.word_tokenize, vocabulary)
del positives_dev, positives_training
# corpus
# corpus_train = sample.NewDataSet('training corpus')
# corpus_train.objects = load.load(os.path.normpath(config['corpus']['training_file']),'NCBI')
corpus_dev = sample.NewDataSet('dev corpus')
corpus_dev.objects = load.load(config['corpus']['development_file'], 'NCBI')
for corpus in [corpus_dev]:
corpus.ids = [] # list of all ids (gold standard for each mention)
corpus.names = [] # list of all names
corpus.all = [
] # list of tuples (mention_text,gold,context,(start,end,docid))
