def run(args, setup): loc = args['<location>'] print '>>> changing location to %s' % loc os.chdir(loc) print '>>> loading data' pars = load_module(os.path.join('cfg.py')).pars print pars data = setup.load_data(pars) trainer = make_trainer(pars, setup, data) trainer.fit() print '>>> making report' last_pars = trainer.switch_pars(trainer.best_pars) report = setup.make_report(pars, trainer, data) trainer.switch_pars(last_pars) print '>>> saving to checkpoint' idx = contrib.to_checkpoint('.', trainer) fn = 'report-last.json' if trainer.stopped else 'report-%i.json' % idx with open(fn, 'w') as fp: json.dump(report, fp, cls=JsonForgivingEncoder) return 0 if trainer.stopped else 9
def run(args, mod): loc = args['<location>'] print '>>> changing location to %s' % loc os.chdir(loc) print '>>> loading data' pars = load_module(os.path.join('./cfg.py')).pars data = mod.load_data(pars) trainer = make_trainer(pars, mod, data) train_data, val_data = data if isinstance(trainer.model, UnsupervisedBrezeWrapperBase): print '>>> Fitting unsupervised model' trainer.fit(*train_data) else: print '>>> Fitting supervised model' trainer.fit(*train_data) print '>>> making report' report = mod.make_report(pars, trainer, data) fn = 'report-last.json' if trainer.stopped else 'report-%i.json' % idx with open(fn, 'w') as fp: json.dump(report, fp, cls=JsonForgivingEncoder) print '>>> saving to checkpoint' idx = contrib.to_checkpoint('.', trainer) return 0 if trainer.stopped else 9
def run(args, mod): loc = args['<location>'] print '>>> changing location to %s' % loc os.chdir(loc) print '>>> loading data' pars = load_module(os.path.join('./cfg.py')).pars data = mod.load_data(pars) trainer = make_trainer(pars, mod, data) # TODO: this will only work with supervised models! Fix this! NOW!!!!! print '>>> Fitting model' trainer.fit(data[0], data[1]) print '>>> saving to checkpoint' idx = contrib.to_checkpoint('.', trainer) print '>>> making report' report = mod.make_report(pars, trainer, data) fn = 'report-last.json' if trainer.stopped else 'report-%i.json' % idx with open(fn, 'w') as fp: json.dump(report, fp, cls=JsonForgivingEncoder) return 0 if trainer.stopped else 9