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MIMIC_train_w2v.py
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MIMIC_train_w2v.py
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# Copyright 2020, 37.78 Tecnologia Ltda.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
## Train Word2Vec word vectors for the MIMIC-III dataset.
import argparse
import datasets
import feature_extraction as fx
def main(args):
# Load dataset
mimic = datasets.MIMIC_Dataset()
mimic.load_preprocessed()
mimic.split()
# Instantiate embedding
w2v = fx.W2V(args)
# Train
w2v.fit(mimic)
# Save embedding matrix
w2v.save_embedding(dataset_name=mimic.name)
print(f'''
Word2Vec embeddings saved!
''')
def arg_parser():
parser = argparse.ArgumentParser(description='Train Word2Vec word embeddings')
parser.add_argument('-workers', type=int, dest='workers', default=8, help='Number of CPU threads for W2V training.')
parser.add_argument('--reset_stopwords', type=bool, dest='reset_stopwords', default=0, help='True to set stopwords vectors to null. Default False.')
parser.add_argument('--train_method', type=bool, dest='sg', default=1, help='W2V train method. 0 for CBoW, 1 for Skipgram.')
return parser.parse_args()
if __name__ == '__main__':
args = arg_parser()
main(args)