# Hyper Parameter Optimization Comparison¶

Posted by Anonymous Author

Comparison of Hyperparameter Optimization (HPO) algorithms on several problems.

Hyperparameter Optimization algorithms are listed in Tuner: Tuning Algorithms.

All algorithms run in NNI local environment.

Machine Environment:

```OS: Linux Ubuntu 16.04 LTS
CPU: Intel(R) Xeon(R) CPU E5-2690 v3 @ 2.60GHz 2600 MHz
Memory: 112 GB
NNI Version: v0.7
NNI Mode(local|pai|remote): local
Python version: 3.6
Is conda or virtualenv used?: Conda
is running in docker?: no
```

## AutoGBDT Example¶

### Problem Description¶

Nonconvex problem on the hyper-parameter search of AutoGBDT example.

### Search Space¶

```{
"num_leaves": {
"_type": "choice",
"_value": [10, 12, 14, 16, 18, 20, 22, 24, 28, 32, 48, 64, 96, 128]
},
"learning_rate": {
"_type": "choice",
"_value": [0.00001, 0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.5]
},
"max_depth": {
"_type": "choice",
"_value": [-1, 2, 3, 4, 5, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 28, 32, 48, 64, 96, 128]
},
"feature_fraction": {
"_type": "choice",
"_value": [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2]
},
"bagging_fraction": {
"_type": "choice",
"_value": [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2]
},
"bagging_freq": {
"_type": "choice",
"_value": [1, 2, 4, 8, 10, 12, 14, 16]
}
}
```

The total search space is 1,204,224, we set the number of maximum trial to 1000. The time limitation is 48 hours.

### Results¶

Algorithm

Best loss

Average of Best 5 Losses

Average of Best 10 Losses

Random Search

0.418854

0.420352

0.421553

Random Search

0.417364

0.420024

0.420997

Random Search

0.417861

0.419744

0.420642

Grid Search

0.498166

0.498166

0.498166

Evolution

0.409887

0.409887

0.409887

Evolution

0.413620

0.413875

0.414067

Evolution

0.409887

0.409887

0.409887

Anneal

0.414877

0.417289

0.418281

Anneal

0.409887

0.409887

0.410118

Anneal

0.413683

0.416949

0.417537

Metis

0.416273

0.420411

0.422380

Metis

0.420262

0.423175

0.424816

Metis

0.421027

0.424172

0.425714

TPE

0.414478

0.414478

0.414478

TPE

0.415077

0.417986

0.418797

TPE

0.415077

0.417009

0.418053

SMAC

0.408386

0.408386

0.408386

SMAC

0.414012

0.414012

0.414012

SMAC

0.408386

0.408386

0.408386

BOHB

0.410464

0.415319

0.417755

BOHB

0.418995

0.420268

0.422604

BOHB

0.415149

0.418072

0.418932

HyperBand

0.414065

0.415222

0.417628

HyperBand

0.416807

0.417549

0.418828

HyperBand

0.415550

0.415977

0.417186

GP

0.414353

0.418563

0.420263

GP

0.414395

0.418006

0.420431

GP

0.412943

0.416566

0.418443

In this example, all the algorithms are used with default parameters. For Metis, there are about 300 trials because it runs slowly due to its high time complexity O(n^3) in Gaussian Process.

## RocksDB Benchmark ‘fillrandom’ and ‘readrandom’¶

### Problem Description¶

DB_Bench is the main tool that is used to benchmark RocksDB‘s performance. It has so many hapermeter to tune.

The performance of `DB_Bench` is associated with the machine configuration and installation method. We run the `DB_Bench`in the Linux machine and install the Rock in shared library.

### Machine configuration¶

```RocksDB:    version 6.1
CPU:        6 * Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz
CPUCache:   35840 KB
Keys:       16 bytes each
Values:     100 bytes each (50 bytes after compression)
Entries:    1000000
```

### Storage performance¶

Latency: each IO request will take some time to complete, this is called the average latency. There are several factors that would affect this time including network connection quality and hard disk IO performance.

IOPS: IO operations per second, which means the amount of read or write operations that could be done in one seconds time.

IO size: the size of each IO request. Depending on the operating system and the application/service that needs disk access it will issue a request to read or write a certain amount of data at the same time.

Throughput (in MB/s) = Average IO size x IOPS

IOPS is related to online processing ability and we use the IOPS as the metric in my experiment.

### Search Space¶

```{
"max_background_compactions": {
"_type": "quniform",
"_value": [1, 256, 1]
},
"block_size": {
"_type": "quniform",
"_value": [1, 500000, 1]
},
"write_buffer_size": {
"_type": "quniform",
"_value": [1, 130000000, 1]
},
"max_write_buffer_number": {
"_type": "quniform",
"_value": [1, 128, 1]
},
"min_write_buffer_number_to_merge": {
"_type": "quniform",
"_value": [1, 32, 1]
},
"level0_file_num_compaction_trigger": {
"_type": "quniform",
"_value": [1, 256, 1]
},
"level0_slowdown_writes_trigger": {
"_type": "quniform",
"_value": [1, 1024, 1]
},
"level0_stop_writes_trigger": {
"_type": "quniform",
"_value": [1, 1024, 1]
},
"cache_size": {
"_type": "quniform",
"_value": [1, 30000000, 1]
},
"_type": "quniform",
"_value": [1, 30000000, 1]
},
"_type": "randint",
"_value": [1]
}
}
```

The search space is enormous (about 10^40) and we set the maximum number of trial to 100 to limit the computation resource.

### fillrandom’ Benchmark¶

Model

Best IOPS (Repeat 1)

Best IOPS (Repeat 2)

Best IOPS (Repeat 3)

Random

449901

427620

477174

Anneal

461896

467150

437528

Evolution

436755

389956

389790

TPE

378346

482316

468989

SMAC

491067

490472

491136

Metis

444920

457060

454438

Figure:

Model

Best IOPS (Repeat 1)

Best IOPS (Repeat 2)

Best IOPS (Repeat 3)

Random

2276157

2285301

2275142

Anneal

2286330

2282229

2284012

Evolution

2286524

2283673

2283558

TPE

2287366

2282865

2281891

SMAC

2270874

2284904

2282266

Metis

2287696

2283496

2277701

Figure: