def test_tabularHPObagstack():
############ Benchmark options you can set: ########################
perf_threshold = 1.1 # How much worse can performance on each dataset be vs previous performance without warning
seed_val = 10000 # random seed
subsample_size = None
hyperparameter_tune = True
stack_ensemble_levels = 2
num_bagging_folds = 2
verbosity = 2 # how much output to print
hyperparameters = None
time_limits = None
num_trials = None
fast_benchmark = True # False
# If True, run a faster benchmark (subsample training sets, less epochs, etc),
# otherwise we run full benchmark with default AutoGluon settings.
# performance_value warnings are disabled when fast_benchmark = True.
#### If fast_benchmark = True, can control model training time here. Only used if fast_benchmark=True ####
if fast_benchmark:
subsample_size = 100
nn_options = {
'num_epochs': 2,
'learning_rate': ag.Real(0.001, 0.01),
'lr_scheduler': ag.Categorical(None, 'cosine', 'step')
}
gbm_options = {
'num_boost_round': 20,
'learning_rate': ag.Real(0.01, 0.1)
}
hyperparameters = {'GBM': gbm_options, 'NN': nn_options}
time_limits = 150
num_trials = 3
fit_args = {
'num_bagging_folds': num_bagging_folds,
'stack_ensemble_levels': stack_ensemble_levels,
'hyperparameter_tune': hyperparameter_tune,
'verbosity': verbosity,
}
if hyperparameters is not None:
fit_args['hyperparameters'] = hyperparameters
if time_limits is not None:
fit_args['time_limits'] = time_limits
fit_args['num_bagging_sets'] = 2
if num_trials is not None:
fit_args['num_trials'] = num_trials
###################################################################
run_tabular_benchmarks(fast_benchmark=fast_benchmark,
subsample_size=subsample_size,
perf_threshold=perf_threshold,
seed_val=seed_val,
fit_args=fit_args)
def test_tabularHPO():
############ Benchmark options you can set: ########################
perf_threshold = 1.1 # How much worse can performance on each dataset be vs previous performance without warning
seed_val = 99 # random seed
subsample_size = None
hyperparameter_tune = True
verbosity = 2 # how much output to print
hyperparameters = None
time_limits = None
num_trials = None
fast_benchmark = True # False
# If True, run a faster benchmark (subsample training sets, less epochs, etc),
# otherwise we run full benchmark with default AutoGluon settings.
# performance_value warnings are disabled when fast_benchmark = True.
#### If fast_benchmark = True, can control model training time here. Only used if fast_benchmark=True ####
if fast_benchmark:
subsample_size = 100
nn_options = {'num_epochs': 3}
gbm_options = {'num_boost_round': 30}
hyperparameters = {'GBM': gbm_options, 'NN': nn_options}
time_limits = 60
num_trials = 3
fit_args = {
'hyperparameter_tune': hyperparameter_tune,
'verbosity': verbosity,
}
if hyperparameters is not None:
fit_args['hyperparameters'] = hyperparameters
if time_limits is not None:
fit_args['time_limits'] = time_limits
if num_trials is not None:
fit_args['num_trials'] = num_trials
###################################################################
run_tabular_benchmarks(fast_benchmark=fast_benchmark,
subsample_size=subsample_size,
perf_threshold=perf_threshold,
seed_val=seed_val,
fit_args=fit_args)
