Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
epoch: struct<domain_cls: string, domain_kwargs: struct<lower: int64, upper: int64>, sampler_cls: string, s (... 24 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: int64, upper: int64>
child 0, lower: int64
child 1, upper: int64
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<>
k_filter: null
n_layers: null
enc_factor: null
batch_size: null
drop_p: null
latent_dim: null
beta_mi: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
beta_tc: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: int64>, sampler_cls: string, (... 37 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: int64>
child 0, lower: double
child 1, upper: int64
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
weight_decay: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
beta_dimKL: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
learning_rate: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
to
{'k_filter': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'n_layers': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'enc_factor': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'batch_size': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'learning_rate': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'drop_p': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('int64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'weight_decay': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'latent_dim': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'beta_mi': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_tc': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('int64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_dimKL': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
epoch: struct<domain_cls: string, domain_kwargs: struct<lower: int64, upper: int64>, sampler_cls: string, s (... 24 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: int64, upper: int64>
child 0, lower: int64
child 1, upper: int64
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<>
k_filter: null
n_layers: null
enc_factor: null
batch_size: null
drop_p: null
latent_dim: null
beta_mi: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
beta_tc: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: int64>, sampler_cls: string, (... 37 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: int64>
child 0, lower: double
child 1, upper: int64
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
weight_decay: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
beta_dimKL: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
learning_rate: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
child 0, domain_cls: string
child 1, domain_kwargs: struct<lower: double, upper: double>
child 0, lower: double
child 1, upper: double
child 2, sampler_cls: string
child 3, sampler_kwargs: struct<base: double>
child 0, base: double
to
{'k_filter': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'n_layers': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'enc_factor': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'batch_size': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'learning_rate': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'drop_p': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('int64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'weight_decay': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'latent_dim': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'beta_mi': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_tc': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('int64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_dimKL': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Check out the documentation for more information.
Blackbox Repository
This dataset contains hyperparameter optimization (HPO) evaluations from several paper:
- fcnet: Tabular benchmarks for joint architecture and hyperparameter optimization. Klein, A. and Hutter, F. 2019.
- icml-deepar, icml-xgboost: A quantile-based approach for hyperparameter transfer learning. Salinas, D., Shen, H., and Perrone, V. 2021.
- lcbench: Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. Lucas Zimmer, Marius Lindauer, Frank Hutter. 2020.
- nasbench201: NAS-Bench-201: Extending the scope of reproducible neural architecture search. Dong, X. and Yang, Y. 2020.
- pd1: Pre-trained Gaussian processes for Bayesian optimization. Wang, Z. and Dahl G. and Swersky K. and Lee C. and Mariet Z. and Nado Z. and Gilmer J. and Snoek J. and Ghahramani Z. 2021.
- yahpo: YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. Pfisterer F., Schneider S., Moosbauer J., Binder M., Bischl B., 2022
- tabrepo: TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. Salinas D., Erickson N., 2024.
- hpob: HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. Arango S., Jomaa H., Wistuba M., Grabocka J., 2021.
The evaluations can be accessed through Syne Tune HPO library by calling the following:
from syne_tune.blackbox_repository import load_blackbox
blackbox = load_blackbox("nasbench201")["cifar10"]
blackbox_hyperparameter = next(iter(blackbox.hyperparameters.to_dict(orient="records")))
print(f"First hyperparameter: {blackbox_hyperparameter}")
print(
f"Objectives for first hyperparameters: {blackbox(configuration=blackbox_hyperparameter, fidelity=100)}"
)
# > First hyperparameter: {'hp_x0': 'avg_pool_3x3', 'hp_x1': 'nor_conv_1x1', 'hp_x2': 'skip_connect', 'hp_x3': 'nor_conv_1x1', 'hp_x4': 'skip_connect', 'hp_x5': 'skip_connect'}
# > Objective for first hyperparameters: {'metric_valid_error': 0.4177, 'metric_train_error': 0.2246, 'metric_runtime': 15.461778, 'metric_elapsed_time': 1546.179, 'metric_latency': 0.013935976, 'metric_flops': 15.64737, 'metric_params': 0.129306}
In addition, the blackboxes can be used to simulate HPO methods such as ASHA or Bayesian Optimization very fast while keeping identical results with non-simulated tuning.
The files can also be accessed directly from here.
If you are interested in having other blackboxes feel free to create an issue on Syne Tune project, we aim to grow the set over time.
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