def test_all_hyperparameters(sagemaker_session):
fm = FactorizationMachines(
sagemaker_session=sagemaker_session,
epochs=2,
clip_gradient=1e2,
eps=0.001,
rescale_grad=2.2,
bias_lr=0.01,
linear_lr=0.002,
factors_lr=0.0003,
bias_wd=0.0004,
linear_wd=1.01,
factors_wd=1.002,
bias_init_method="uniform",
bias_init_scale=0.1,
bias_init_sigma=0.05,
bias_init_value=2.002,
linear_init_method="constant",
linear_init_scale=0.02,
linear_init_sigma=0.003,
linear_init_value=1.0,
factors_init_method="normal",
factors_init_scale=1.101,
factors_init_sigma=1.202,
factors_init_value=1.303,
**ALL_REQ_ARGS,
)
assert fm.hyperparameters() == dict(
num_factors=str(ALL_REQ_ARGS["num_factors"]),
predictor_type=ALL_REQ_ARGS["predictor_type"],
epochs="2",
clip_gradient="100.0",
eps="0.001",
rescale_grad="2.2",
bias_lr="0.01",
linear_lr="0.002",
factors_lr="0.0003",
bias_wd="0.0004",
linear_wd="1.01",
factors_wd="1.002",
bias_init_method="uniform",
bias_init_scale="0.1",
bias_init_sigma="0.05",
bias_init_value="2.002",
linear_init_method="constant",
linear_init_scale="0.02",
linear_init_sigma="0.003",
linear_init_value="1.0",
factors_init_method="normal",
factors_init_scale="1.101",
factors_init_sigma="1.202",
factors_init_value="1.303",
)
def test_all_hyperparameters(sagemaker_session):
fm = FactorizationMachines(sagemaker_session=sagemaker_session,
epochs=2,
clip_gradient=1e2,
eps=0.001,
rescale_grad=2.2,
bias_lr=0.01,
linear_lr=0.002,
factors_lr=0.0003,
bias_wd=0.0004,
linear_wd=1.01,
factors_wd=1.002,
bias_init_method='uniform',
bias_init_scale=0.1,
bias_init_sigma=0.05,
bias_init_value=2.002,
linear_init_method='constant',
linear_init_scale=0.02,
linear_init_sigma=0.003,
linear_init_value=1.0,
factors_init_method='normal',
factors_init_scale=1.101,
factors_init_sigma=1.202,
factors_init_value=1.303,
**ALL_REQ_ARGS)
assert fm.hyperparameters() == dict(
num_factors=str(ALL_REQ_ARGS['num_factors']),
predictor_type=ALL_REQ_ARGS['predictor_type'],
epochs='2',
clip_gradient='100.0',
eps='0.001',
rescale_grad='2.2',
bias_lr='0.01',
linear_lr='0.002',
factors_lr='0.0003',
bias_wd='0.0004',
linear_wd='1.01',
factors_wd='1.002',
bias_init_method='uniform',
bias_init_scale='0.1',
bias_init_sigma='0.05',
bias_init_value='2.002',
linear_init_method='constant',
linear_init_scale='0.02',
linear_init_sigma='0.003',
linear_init_value='1.0',
factors_init_method='normal',
factors_init_scale='1.101',
factors_init_sigma='1.202',
factors_init_value='1.303',
)
def test_all_hyperparameters(sagemaker_session):
fm = FactorizationMachines(sagemaker_session=sagemaker_session,
epochs=2, clip_gradient=1e2, eps=0.001, rescale_grad=2.2,
bias_lr=0.01, linear_lr=0.002, factors_lr=0.0003,
bias_wd=0.0004, linear_wd=1.01, factors_wd=1.002,
bias_init_method='uniform', bias_init_scale=0.1, bias_init_sigma=0.05,
bias_init_value=2.002, linear_init_method='constant', linear_init_scale=0.02,
linear_init_sigma=0.003, linear_init_value=1.0, factors_init_method='normal',
factors_init_scale=1.101, factors_init_sigma=1.202, factors_init_value=1.303,
**ALL_REQ_ARGS)
assert fm.hyperparameters() == dict(
num_factors=str(ALL_REQ_ARGS['num_factors']),
predictor_type=ALL_REQ_ARGS['predictor_type'],
epochs='2',
clip_gradient='100.0',
eps='0.001',
rescale_grad='2.2',
bias_lr='0.01',
linear_lr='0.002',
factors_lr='0.0003',
bias_wd='0.0004',
linear_wd='1.01',
factors_wd='1.002',
bias_init_method='uniform',
bias_init_scale='0.1',
bias_init_sigma='0.05',
bias_init_value='2.002',
linear_init_method='constant',
linear_init_scale='0.02',
linear_init_sigma='0.003',
linear_init_value='1.0',
factors_init_method='normal',
factors_init_scale='1.101',
factors_init_sigma='1.202',
factors_init_value='1.303',
)