Some confusions about nasbenchmark_201.

[zien-fan]

For different task numbers, how can I know the best results for canculating the simple or inference regret?

[Neeratyoy]

The best score is known only for the tabular benchmarks. For the nn benchmarks the following should work:

from hpobench.benchmarks.ml import TabularBenchmark

b = TabularBenchmark(model="nn", task_id=31)

b.global_minimums

[zien-fan]

HPOBench/hpobench/benchmarks/nas/nasbench_201.py

Lines 108 to 124 in 47bf141

	i) The best possible incumbents (NO AVG!) ii) The "average" incumbent
	Datastet Metric (Index of Arch, Accuracy) (Index, Loss) (Index of Arch, Accuracy) (Index, Loss)
	----------------------------------------------------------------------------------------------------------------------------------------------------------
	cifar10-valid train (258, 100.0) (2778, 0.001179278278425336) (10154, 100) (2778, 0.0013082386429297428)
	cifar10-valid x-valid (6111, 91.71999999023437) (14443, 0.3837750501537323) (6111, 91.60666665039064) (3888, 0.3894046771335602)
	cifar10-valid x-test
	cifar10-valid ori-test (14174, 91.65) (3385, 0.3850496160507202) (1459, 91.52333333333333) (3385, 0.3995230517864227)
	cifar100 train (9930, 99.948) (9930, 0.012630240231156348) (9930, 99.93733333333334) (9930, 0.012843489621082942)
	cifar100 x-valid (13714, 73.71999998779297) (13934, 1.1490126512527465) (9930, 73.4933333577474) (7361, 1.1600867895126343)
	cifar100 x-test (1459, 74.28000004882813) (15383, 1.1427113876342774) (9930, 73.51333332112631) (7337, 1.1747569534301758)
	cifar100 ori-test (9930, 73.88) (13706, 1.1610547459602356) (9930, 73.50333333333333) (7361, 1.1696554500579834)
	ImageNet16-120 train (9930, 73.2524719841793) (9930, 0.9490517352046979) (9930, 73.22918040138735) (9930, 0.9524298415108582)
	ImageNet16-120 x-valid (13778, 47.39999985758463) (10721, 2.0826991437276203) (10676, 46.73333327229818) (10721, 2.0915397168795264)
	ImageNet16-120 x-test (857, 48.03333317057292) (12887, 2.0940088628133138) (857, 47.31111100599501) (11882, 2.106453532218933)
	ImageNet16-120 ori-test (857, 47.083333353678384) (11882, 2.0950548852284747) (857, 46.8444444647895) (11882, 2.1028235816955565)

In this file, what is the mean of the prefix ori e.g. ori-test?

[Neeratyoy]

Hi,

This docstring is borrowed from the NASBench-201 paper release and thus the actual details can be found here.
This is likely to indicate the numbers on the original test set.

I shall close this issue for now as there is nothing about HPOBench here. Please feel free to reopen or ask any further queries.

[zien-fan]

HPOBench/hpobench/benchmarks/nas/nasbench_201.py

Lines 262 to 264 in 47bf141

	test_accuracies = [self.data[seed][structure_str]['eval_acc1es'][f'{valid_key}@{199}'] for seed in data_seed]
	test_losses = [self.data[seed][structure_str]['eval_losses'][f'{valid_key}@{199}'] for seed in data_seed]
	test_times = [np.sum((self.data[seed][structure_str]['eval_times'][f'{test_key}@{199}'])

For test_accuracies and test_losses, why the valid_key is used rather than the test_key?

[Neeratyoy]

Thanks for raising this.
Would you like to do a PR with this fix?

You can refer to this and this and use the local version for testing.
Once merged, we can upload a new container with this fix.