def main(self):
self.data = self.parser.parse_args()
logging.basicConfig(level=self.data.verbose)
logger = logging.getLogger('b4msa')
logger.setLevel(self.data.verbose)
params_fname = self.data.params_fname
param_list = load_json(params_fname)
best = param_list[0]
svc = SVC.fit_from_file(self.data.training_set, best)
with open(self.get_output(), 'wb') as fpt:
pickle.dump(svc, fpt)
def main(self):
self.data = self.parser.parse_args()
params_fname = self.data.params_fname
if params_fname is not None:
best = load_json(params_fname)
if isinstance(best, list):
best = best[0]
else:
best = dict()
best = clean_params(best)
kw = json.loads(self.data.kwargs) if self.data.kwargs is not None else dict()
best.update(kw)
svc = SVC.fit_from_file(self.data.training_set, best)
save_model(svc, self.get_output())
def main(self):
self.data = self.parser.parse_args()
logging.basicConfig(level=self.data.verbose)
params_fname = self.data.params_fname
if params_fname.endswith('.gz'):
with gzip.open(params_fname) as fpt:
cdn = fpt.read()
param_list = json.loads(str(cdn, encoding='utf-8'))
else:
with open(params_fname) as fpt:
param_list = json.loads(fpt.read())
best = param_list[0]
svc = SVC.fit_from_file(self.data.training_set, best)
with open(self.get_output(), 'wb') as fpt:
pickle.dump(svc, fpt)