Practical bayesian optimization using Goptuna

  • Tuning Video Streaming Algorithms
  • Tuning Machine Learning Models
  • AR/VR Hardware Design
  • Tuning Just-In-Time compiler (for HHVM)

Introduction of Bayesian Optimization

Investigating existing Bayesian Optimization library for Go

Optimization example for a quadratic function using go-bayesopt.
$ go run _examples/main.go
2019/07/25 15:23:23 x: map[bayesopt.Param]float64{bayesopt.UniformParam{Name:"X1", Max:10, Min:-10}:1.0221672603083967, bayesopt.UniformParam{Name:"X2", Max:10, Min:-10}:1.8540991095989217}
2019/07/25 15:23:23 y: 0.021778
Values of X1 searched by go-bayesopt.
Values of x2 searched by go-bayesopt

TPE bayesian optimization using Goptuna

An example of Goptuna
See the paper of Optuna at KDD 2019 for more details about Define-by-Run interface. Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19), August 4–8, 2019.
$ go run _examples/simple_random_search/main.go
- best value 2.5904889254208054
- best param map[x1:0.4405659427118902 x2:0.7883530201915825]
$ go run _examples/simple_tpe/main.go
- best value 0.6195459685895217
- best param map[x1:0.7770961438621743 x2:1.2451093857329996]
Goptuna (TPE) works extremely fast!





Creator of go-prompt and kube-prompt. Optuna committer. Kubeflow/Katib reviewer. GitHub: c-bata

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Creator of go-prompt and kube-prompt. Optuna committer. Kubeflow/Katib reviewer. GitHub: c-bata

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