def __init__(self, *args, **kwargs):
Algo.__init__(self, *args, **kwargs)
Optimizable.__init__(self, *args, **kwargs)
TensorboardExtention.__init__(self, *args, **kwargs)
self.logger = log.get_logger('MockAlgo')
option = ALSOption().get_default_option()
optimize_option = ALSOption().get_default_optimize_option()
optimize_option.start_with_default_parameters = False
option.optimize = optimize_option
option.model_path = 'hello.world.bin'
self.opt = option
self._optimize_loss = {'loss': 987654321.0}
def test4_optimize(self):
set_log_level(2)
opt = ALSOption().get_default_option()
opt.d = 5
opt.num_workers = 2
opt.model_path = 'als.bin'
opt.validation = aux.Option({'topk': 10})
optimize_option = aux.Option({
'loss': 'val_rmse',
'max_trials': 10,
'deployment': True,
'start_with_default_parameters': True,
'space': {
'd': ['randint', ['d', 10, 20]],
'reg_u': ['uniform', ['reg_u', 0.1, 0.3]],
'reg_i': ['uniform', ['reg_i', 0.1, 0.3]],
'alpha': ['randint', ['alpha', 8, 10]]
}
})
opt.optimize = optimize_option
opt.evaluation_period = 1
opt.tensorboard = aux.Option({'root': './tb',
'name': 'als'})
data_opt = MatrixMarketOptions().get_default_option()
data_opt.input.main = self.ml_100k + 'main'
data_opt.input.uid = self.ml_100k + 'uid'
data_opt.input.iid = self.ml_100k + 'iid'
data_opt.data.value_prepro = aux.Option({'name': 'OneBased'})
als = ALS(opt, data_opt=data_opt)
als.init_factors()
als.train()
default_result = als.get_validation_results()
als.optimize()
base_loss = default_result['rmse'] # val_rmse
optimize_loss = als.get_optimization_data()['best']['val_rmse']
self.assertTrue(base_loss > optimize_loss)
als.load('als.bin')
loss = als.get_validation_results()
self.assertAlmostEqual(loss['rmse'], optimize_loss)
os.remove('als.bin')
