sile/example_report.md Secret

## example_report.md

      
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    Benchmark Result Report

Report ID: 62ed67b3c37af40a5c8c830c9383cd41ec1fb4552587c897a8245edd944d289f
Kurobako Version: 0.1.2
Number of Solvers: 4
Number of Problems: 2
Metrics Precedence: best value -> AUC -> elapsed time

Please refer to "A Strategy for Ranking Optimizers using Multiple Criteria" for the ranking strategy used in this report.
Table of Contents

Overall Results
Individual Results
Solvers
Problems
Studies

Overall Results


Solver
Borda
Firsts


RandomSampler
0
0


TPESampler
3
0


TPESampler with MedianPruner
5
1


TPESampler with SuccessiveHalvingPruner
4
1


Individual Results
(1) Problem: HPO-Bench-Protein


Ranking
Solver
Best (avg +- sd)
AUC (avg +- sd)
Elapsed (avg +- sd)


1
TPESampler with SuccessiveHalvingPruner (study)
0.228974 +- 0.006860
16.545 +- 0.579
19.918 +- 9.515


2
TPESampler with MedianPruner (study)
0.227354 +- 0.005347
16.828 +- 0.604
125.306 +- 10.155


3
TPESampler (study)
0.233030 +- 0.007815
17.797 +- 0.940
2.466 +- 0.187


4
RandomSampler (study)
0.251476 +- 0.015849
18.784 +- 1.067
0.490 +- 0.033


(2) Problem: HPO-Bench-Parkinson


Ranking
Solver
Best (avg +- sd)
AUC (avg +- sd)
Elapsed (avg +- sd)


1
TPESampler with MedianPruner (study)
0.010032 +- 0.004297
0.906 +- 0.295
126.191 +- 13.636


2
TPESampler (study)
0.010139 +- 0.002943
1.197 +- 0.390
2.427 +- 0.169


3
TPESampler with SuccessiveHalvingPruner (study)
0.012239 +- 0.003359
0.964 +- 0.282
21.887 +- 7.314


4
RandomSampler (study)
0.015099 +- 0.004315
1.445 +- 0.463
0.486 +- 0.028


Solvers
ID: 5bbf512911e2fb8399759136a96da61083895cf701f4eb4b02b82b685e4f9823
recipe:
{
  "name": "RandomSampler",
  "optuna": {
    "sampler": "random",
    "pruner": "nop"
  }
}
specification:
{
  "name": "RandomSampler",
  "attrs": {
    "github": "https://github.com/optuna/optuna",
    "paper": "Akiba, Takuya, et al. \"Optuna: A next-generation hyperparameter optimization framework.\" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019.",
    "version": "optuna=0.19.0, kurobako-py=0.1.0"
  },
  "capabilities": [
    "UNIFORM_CONTINUOUS",
    "UNIFORM_DISCRETE",
    "LOG_UNIFORM_CONTINUOUS",
    "CATEGORICAL",
    "CONDITIONAL",
    "CONCURRENT"
  ]
}
ID: 6bead68572afd302421695fee7a215caff657b35b3500167f8f72d11e7a06331
recipe:
{
  "name": "TPESampler",
  "optuna": {
    "pruner": "nop"
  }
}
specification:
{
  "name": "TPESampler",
  "attrs": {
    "github": "https://github.com/optuna/optuna",
    "paper": "Akiba, Takuya, et al. \"Optuna: A next-generation hyperparameter optimization framework.\" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019.",
    "version": "optuna=0.19.0, kurobako-py=0.1.0"
  },
  "capabilities": [
    "UNIFORM_CONTINUOUS",
    "UNIFORM_DISCRETE",
    "LOG_UNIFORM_CONTINUOUS",
    "CATEGORICAL",
    "CONDITIONAL",
    "CONCURRENT"
  ]
}
ID: 9618c6b63a9af6753f480723eb758dcd11dfe253c08042b7b7c5c4f853004062
recipe:
{
  "name": "TPESampler with MedianPruner",
  "optuna": {
    "median_warmup_steps": 4
  }
}
specification:
{
  "name": "TPESampler with MedianPruner",
  "attrs": {
    "github": "https://github.com/optuna/optuna",
    "paper": "Akiba, Takuya, et al. \"Optuna: A next-generation hyperparameter optimization framework.\" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019.",
    "version": "optuna=0.19.0, kurobako-py=0.1.0"
  },
  "capabilities": [
    "UNIFORM_CONTINUOUS",
    "UNIFORM_DISCRETE",
    "LOG_UNIFORM_CONTINUOUS",
    "CATEGORICAL",
    "CONDITIONAL",
    "CONCURRENT"
  ]
}
ID: 3a6863792684248c1e7a56d7e27b644c8ac09cc1025a375bd2a0687f7db9af04
recipe:
{
  "name": "TPESampler with SuccessiveHalvingPruner",
  "optuna": {
    "pruner": "asha",
    "asha_min_resource": 4
  }
}
specification:
{
  "name": "TPESampler with SuccessiveHalvingPruner",
  "attrs": {
    "github": "https://github.com/optuna/optuna",
    "paper": "Akiba, Takuya, et al. \"Optuna: A next-generation hyperparameter optimization framework.\" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019.",
    "version": "optuna=0.19.0, kurobako-py=0.1.0"
  },
  "capabilities": [
    "UNIFORM_CONTINUOUS",
    "UNIFORM_DISCRETE",
    "LOG_UNIFORM_CONTINUOUS",
    "CATEGORICAL",
    "CONDITIONAL",
    "CONCURRENT"
  ]
}
Problems
ID: 1d1cf159c9a05ae1ab2d37ee01e2d2b7e0e75c344f118b985afd4fb8215157e8
recipe:
{
  "hpobench": {
    "dataset": "fcnet_parkinsons_telemonitoring_data.hdf5"
  }
}
specification:
{
  "name": "HPO-Bench-Parkinson",
  "attrs": {
    "github": "https://github.com/automl/nas_benchmarks",
    "paper": "Klein, Aaron, and Frank Hutter. \"Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization.\" arXiv preprint arXiv:1905.04970 (2019).",
    "version": "kurobako_problems=0.1.2"
  },
  "params_domain": [
    {
      "name": "activation_fn_1",
      "range": {
        "type": "CATEGORICAL",
        "choices": [
          "tanh",
          "relu"
        ]
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "activation_fn_2",
      "range": {
        "type": "CATEGORICAL",
        "choices": [
          "tanh",
          "relu"
        ]
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "batch_size",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 4
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "dropout_1",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 3
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "dropout_2",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 3
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "init_lr",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 6
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "lr_schedule",
      "range": {
        "type": "CATEGORICAL",
        "choices": [
          "cosine",
          "const"
        ]
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "n_units_1",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 6
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "n_units_2",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 6
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Validation MSE",
      "range": {
        "type": "CONTINUOUS",
        "low": 0.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "steps": 100
}
ID: 007a182ddea33a47c9e27e1edb07ff9232478e0b863d85edaf9e499be5e770d8
recipe:
{
  "hpobench": {
    "dataset": "fcnet_protein_structure_data.hdf5"
  }
}
specification:
{
  "name": "HPO-Bench-Protein",
  "attrs": {
    "github": "https://github.com/automl/nas_benchmarks",
    "paper": "Klein, Aaron, and Frank Hutter. \"Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization.\" arXiv preprint arXiv:1905.04970 (2019).",
    "version": "kurobako_problems=0.1.2"
  },
  "params_domain": [
    {
      "name": "activation_fn_1",
      "range": {
        "type": "CATEGORICAL",
        "choices": [
          "tanh",
          "relu"
        ]
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "activation_fn_2",
      "range": {
        "type": "CATEGORICAL",
        "choices": [
          "tanh",
          "relu"
        ]
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "batch_size",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 4
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "dropout_1",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 3
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "dropout_2",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 3
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "init_lr",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 6
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "lr_schedule",
      "range": {
        "type": "CATEGORICAL",
        "choices": [
          "cosine",
          "const"
        ]
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "n_units_1",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 6
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "n_units_2",
      "range": {
        "type": "DISCRETE",
        "low": 0,
        "high": 6
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Validation MSE",
      "range": {
        "type": "CONTINUOUS",
        "low": 0.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "steps": 100
}
Studies
ID: ca02d174780afb82db130e9b2feae8b8671552b42a82dbdba2a6b7122e838c4f

problem: HPO-Bench-Parkinson
solver: RandomSampler
budget: 80
repeats: 30
concurrency: 1

ID: 53740ce3b9507abbc84d38b9f4ced0689e5ad009d54a0ee07c735964f0e09f26

problem: HPO-Bench-Parkinson
solver: TPESampler
budget: 80
repeats: 30
concurrency: 1

ID: 4b5aaab282e7ad9b30d1ad0ccee7dc1a54733a481d3051d97fbce47e14e947e5

problem: HPO-Bench-Parkinson
solver: TPESampler with MedianPruner
budget: 80
repeats: 30
concurrency: 1

ID: 5003170772452078f4cf0c207fc6601f18bc8cc8395e5a338a74157a244085d5

problem: HPO-Bench-Parkinson
solver: TPESampler with SuccessiveHalvingPruner
budget: 80
repeats: 30
concurrency: 1

ID: a24912e3c978e0f49b782a04ac42183d5b011324791d6fd2407777483ba1573e

problem: HPO-Bench-Protein
solver: RandomSampler
budget: 80
repeats: 30
concurrency: 1

ID: 072a57f416255d80ce490df266672d5fac8d200158df1f2d969a2db7526ca94f

problem: HPO-Bench-Protein
solver: TPESampler
budget: 80
repeats: 30
concurrency: 1

ID: 05106a894aed96547cbc1dbd5cc01dc898f3589ce55d4100af1016977112e1c8

problem: HPO-Bench-Protein
solver: TPESampler with MedianPruner
budget: 80
repeats: 30
concurrency: 1

ID: 54f1ecaeccb35d4e218fef1e194bd626b983ddf414231960020e65de283ab71b

problem: HPO-Bench-Protein
solver: TPESampler with SuccessiveHalvingPruner
budget: 80
repeats: 30
concurrency: 1
Solver	Borda	Firsts
RandomSampler	0	0
TPESampler	3	0
TPESampler with MedianPruner	5	1
TPESampler with SuccessiveHalvingPruner	4	1
Ranking	Solver	Best (avg +- sd)	AUC (avg +- sd)	Elapsed (avg +- sd)
1	TPESampler with SuccessiveHalvingPruner (study)	0.228974 +- 0.006860	16.545 +- 0.579	19.918 +- 9.515
2	TPESampler with MedianPruner (study)	0.227354 +- 0.005347	16.828 +- 0.604	125.306 +- 10.155
3	TPESampler (study)	0.233030 +- 0.007815	17.797 +- 0.940	2.466 +- 0.187
4	RandomSampler (study)	0.251476 +- 0.015849	18.784 +- 1.067	0.490 +- 0.033
Ranking	Solver	Best (avg +- sd)	AUC (avg +- sd)	Elapsed (avg +- sd)
1	TPESampler with MedianPruner (study)	0.010032 +- 0.004297	0.906 +- 0.295	126.191 +- 13.636
2	TPESampler (study)	0.010139 +- 0.002943	1.197 +- 0.390	2.427 +- 0.169
3	TPESampler with SuccessiveHalvingPruner (study)	0.012239 +- 0.003359	0.964 +- 0.282	21.887 +- 7.314
4	RandomSampler (study)	0.015099 +- 0.004315	1.445 +- 0.463	0.486 +- 0.028