def run(self, datapath, pipetype):
print('Loading training data from {0}...'.format(datapath))
with open(datapath, 'r') as f:
training_data = json.load(f)
docs = ['{0} {1}'.format(d['title'], d['text']) for d in training_data]
# These train to the PIPELINE_PATH specified in argos.conf.app
if pipetype == 'bow':
vector.train(docs)
if pipetype in ['stanford', 'spotlight', 'keyword']:
concept.train(docs, pipetype=pipetype)
def train(pipetype, datapath):
"""
Train the feature pipelines.
"""
with open(datapath, 'r') as f:
training_data = json.load(f)
docs = ['{0} {1}'.format(d['title'], d['text']) for d in training_data]
if pipetype == 'bow':
vector.train(docs)
if pipetype in ['stanford', 'spotlight', 'keyword']:
concept.train(docs, pipetype=pipetype)
def train(pipetype, datapath):
"""
Train the feature pipelines.
"""
with open(datapath, 'r') as f:
training_data = json.load(f)
docs = ['{0} {1}'.format(d['title'], d['text']) for d in training_data]
if pipetype == 'bow':
vector.train(docs)
if pipetype in ['stanford', 'spotlight', 'keyword']:
concept.train(docs, pipetype=pipetype)
def run(self, datapath, pipetype):
print('Loading training data from {0}...'.format(datapath))
with open(datapath, 'r') as f:
training_data = json.load(f)
docs = [
'{0} {1}'.format(d['title'], d['text']) for d in training_data
]
# These train to the PIPELINE_PATH specified in argos.conf.app
if pipetype == 'bow':
vector.train(docs)
if pipetype in ['stanford', 'spotlight', 'keyword']:
concept.train(docs, pipetype=pipetype)
