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', )