org.apache.ignite.ml.composition.boosting.convergence.simple

Class ConvergenceCheckerStub<K,V>

java.lang.Object

org.apache.ignite.ml.composition.boosting.convergence.ConvergenceChecker<K,V>

org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub<K,V>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

All Implemented Interfaces:

Serializable

public class ConvergenceCheckerStub<K,V>
extends ConvergenceChecker<K,V>

This strategy skip estimating error on dataset step. According to this strategy, training will stop after reaching the maximum number of iterations.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ConvergenceCheckerStub(long sampleSize, IgniteFunction externalLbToInternalMapping, Loss loss, DatasetBuilder datasetBuilder, Preprocessor<K,V> preprocessor, double precision) Creates an instance of ConvergenceCheckerStub.

Method Summary

Modifier and Type	Method and Description
Double	computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl) Compute error for given model on learning dataset.
boolean	isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl) Checks convergency on dataset.
boolean	isConverged(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl) Checks convergency on dataset.

Methods inherited from class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceChecker

computeError

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ConvergenceCheckerStub

public ConvergenceCheckerStub(long sampleSize,
                              IgniteFunction externalLbToInternalMapping,
                              Loss loss,
                              DatasetBuilder datasetBuilder,
                              Preprocessor<K,V> preprocessor,
                              double precision)

Creates an instance of ConvergenceCheckerStub.

Parameters:

sampleSize - Sample size.

externalLbToInternalMapping - External label to internal mapping.

loss - Loss function.

datasetBuilder - Dataset builder.

preprocessor - Preprocessor.

precision - Precision.

Method Detail

isConverged

public boolean isConverged(LearningEnvironmentBuilder envBuilder,
                           DatasetBuilder<K,V> datasetBuilder,
                           ModelsComposition currMdl)

Checks convergency on dataset.

Overrides:

isConverged in class ConvergenceChecker<K,V>

Parameters:

envBuilder - Learning environment builder.

currMdl - Current model.

Returns:

True if GDB is converged.

isConverged

public boolean isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset,
                           ModelsComposition currMdl)

Checks convergency on dataset.

Overrides:

isConverged in class ConvergenceChecker<K,V>

Parameters:

dataset - Dataset.

currMdl - Current model.

Returns:

True if GDB is converged.

computeMeanErrorOnDataset

public Double computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset,
                                        ModelsComposition mdl)

Compute error for given model on learning dataset.

Specified by:

computeMeanErrorOnDataset in class ConvergenceChecker<K,V>

Parameters:

dataset - Learning dataset.

mdl - Model.

Returns:

Error mean value.