class RandomHyperOptimizer extends HyperOptimizer
Search for hypers by randomly sampling the search space
This optimizer can be evaluated multiple times and will persist the best results across those calls. Created by maxhutch on 12/7/16.
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- new RandomHyperOptimizer(rng: Random = Random)
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- def addHyperGrid(name: String, values: Seq[Any]): RandomHyperOptimizer.this.type
Add a 1D hyper range to the space searched by this optimizer
Add a 1D hyper range to the space searched by this optimizer
- name
of the hyper
- values
it takes, enumerated as a seq
- returns
calling instance
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- var best: Map[String, Any]
Keep track of the best hypers outside of the optimize call so it persists across calls
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- var hyperGrids: Map[String, Seq[Any]]
The search space
The search space
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- var loss: Double
Likewise with the lowest loss
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- def optimize(trainingData: Seq[(Vector[Any], Any)], numIterations: Int, builder: (Map[String, Any]) => Learner): (Map[String, Any], Double)
Search over numIterations random draws for the search space
Search over numIterations random draws for the search space
- trainingData
the data to train/test on
- numIterations
number of draws to take
- returns
the best hyper map found in give iterations and the corresponding loss
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- RandomHyperOptimizer → HyperOptimizer
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