TuneHyperparameters¶

class TuneHyperparameters.TuneHyperparameters(evaluationMetric=None, models=None, numFolds=None, numRuns=None, parallelism=None, paramSpace=None, seed=0)[source]¶

Bases: mmlspark.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator

Tunes model hyperparameters

Supports distributed hyperparameter tuning through randomized grid search. In the near future will support Nelder-Mead and kernel density optimization.

Parameters:

evaluationMetric (str) – Metric to evaluate models with
models (object) – Estimators to run
numFolds (int) – Number of folds
numRuns (int) – Termination criteria for randomized search
parallelism (int) – The number of models to run in parallel
paramSpace (object) – Parameter space for generating hyperparameters
seed (long) – Random number generator seed (default: 0)

getEvaluationMetric()[source]¶

Returns:	Metric to evaluate models with
Return type:	str

static getJavaPackage()[source]¶: Returns package name String.

getModels()[source]¶

Returns:	Estimators to run
Return type:	object

getNumFolds()[source]¶

Returns:	Number of folds
Return type:	int

getNumRuns()[source]¶

Returns:	Termination criteria for randomized search
Return type:	int

getParallelism()[source]¶

Returns:	The number of models to run in parallel
Return type:	int

getParamSpace()[source]¶

Returns:	Parameter space for generating hyperparameters
Return type:	object

getSeed()[source]¶

Returns:	Random number generator seed (default: 0)
Return type:	long

classmethod read()[source]¶: Returns an MLReader instance for this class.

setEvaluationMetric(value)[source]¶

Parameters:	evaluationMetric (str) – Metric to evaluate models with

setModels(value)[source]¶

Parameters:	models (object) – Estimators to run

setNumFolds(value)[source]¶

Parameters:	numFolds (int) – Number of folds

setNumRuns(value)[source]¶

Parameters:	numRuns (int) – Termination criteria for randomized search

setParallelism(value)[source]¶

Parameters:	parallelism (int) – The number of models to run in parallel

setParamSpace(value)[source]¶

Parameters:	paramSpace (object) – Parameter space for generating hyperparameters

setParams(evaluationMetric=None, models=None, numFolds=None, numRuns=None, parallelism=None, paramSpace=None, seed=0)[source]¶

Set the (keyword only) parameters

Parameters:

evaluationMetric (str) – Metric to evaluate models with
models (object) – Estimators to run
numFolds (int) – Number of folds
numRuns (int) – Termination criteria for randomized search
parallelism (int) – The number of models to run in parallel
paramSpace (object) – Parameter space for generating hyperparameters
seed (long) – Random number generator seed (default: 0)

setSeed(value)[source]¶

Parameters:	seed (long) – Random number generator seed (default: 0)

class TuneHyperparameters.TuneHyperparametersModel(java_model=None)[source]¶

Bases: mmlspark.Utils.ComplexParamsMixin, pyspark.ml.wrapper.JavaModel, pyspark.ml.util.JavaMLWritable, pyspark.ml.util.JavaMLReadable

Model fitted by TuneHyperparameters.

This class is left empty on purpose. All necessary methods are exposed through inheritance.

static getJavaPackage()[source]¶: Returns package name String.

classmethod read()[source]¶: Returns an MLReader instance for this class.