level=logging.INFO, format= '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', datefmt='%a, %d %b %Y %H:%M:%S', ) if len(sys.argv) >= 2: dataIdx = '{:0>2}'.format(sys.argv[1]) else: dataIdx = 'CoNLL' trainPath = 'data/normal/en_train_{}.txt'.format(dataIdx) testPath = 'data/normal/en_test_CoNLL.txt' data = Data(inputPathList=[trainPath], testPath=testPath) return_data = data.loadCoNLL(trainPath, loadFeatures=True) split_data = train_test_split(*return_data, test_size=0.1, random_state=0) X_train = split_data[:-2:2] X_val = split_data[1:-2:2] y_train, y_val = split_data[-2:] modelWrapper = BiLSTMCRF(data) model = modelWrapper.buildModel(feature2idx=data.feature2idx) history = metricHistory(X_val, y_val, saveDir=dataIdx) history.set_model(model) model.fit(X_train, y_train, epochs=50, batch_size=64,
import sys import logging import re from neuralnets.BiLSTMCRF import load_model from util.data import Data logging.basicConfig( level=logging.INFO, format= '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', datefmt='%a, %d %b %Y %H:%M:%S', ) if len(sys.argv) >= 3: dataIdx = '{:0>2}'.format(sys.argv[1]) else: dataIdx = 'CoNLL' trainPath = 'data/normal/en_train_{}.txt'.format(dataIdx) testPath = 'data/normal/en_test_CoNLL.txt' data = Data(inputPathList=[trainPath], testPath=testPath) X_test = data.loadCoNLL(testPath, loadFeatures=True, mode='test') model = load_model('h5/' + dataIdx + '/' + sys.argv[-1]) acc = float(re.search(r'acc([\d.]+)\d', sys.argv[-1]).group(1)) data.predictWithFeature(model, X_test, '%s_enrich_%.5f.txt' % (dataIdx, acc))