Documentation
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Overview ¶
Package hyperopt implements SMBO/TPE hyper-parameter optimization for ML models
Many thanks to Masashi SHIBATA for his excellent work on goptuna I used github.com/c-bata/goptuna as a reference implementation for the paper 'Algorithms for Hyper-Parameter Optimization' https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
TPE sampler mostly derived from goptuna.
Index ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
Types ¶
type BestParams ¶
BestParams is a result of Hyper-parameters Optimization
type IntRange ¶
type IntRange [2]int
IntRange is a close integer range specified by min and max values [min,max]
type LogIntRange ¶
type LogIntRange [2]int
LogRange is a close logarithmic integer range specified by min and max values [min,max]
type LogRange ¶
type LogRange [2]float64
LogRange is a open float logarithmic range specified by min and max values (min,max)
type Range ¶
type Range [2]float64
Range is a open float range specified by min and max values (min,max)
type Space ¶
type Space struct {
Source tables.AnyData // dataset source
Features []string // dataset features
Label string // dataset label
Seed int // random seed
Kfold int // count of dataset folds
Iterations int // model fitting iterations
Metrics model.Metrics // model evaluation metrics
Score model.Score // function to calculate score of train/test metrics
// the model generation function
ModelFunc func(Params) model.HungryModel
// hyper-parameters variance
Variance Variance
}
Space is a definition of hyper-parameters optimization space
func (Space) LuckyOptimize ¶
func (ss Space) LuckyOptimize(trails int) BestParams
Source Files