org.apache.ignite.ml.tree

Class DecisionTreeTrainer<T extends ImpurityMeasure<T>>

java.lang.Object

org.apache.ignite.ml.trainers.DatasetTrainer<M,Double>

org.apache.ignite.ml.trainers.SingleLabelDatasetTrainer<DecisionTreeModel>

org.apache.ignite.ml.tree.DecisionTreeTrainer<T>

Type Parameters:

T - Type of impurity measure.

Direct Known Subclasses:

DecisionTreeClassificationTrainer, DecisionTreeRegressionTrainer

public abstract class DecisionTreeTrainer<T extends ImpurityMeasure<T>>
extends SingleLabelDatasetTrainer<DecisionTreeModel>

Distributed decision tree trainer that allows to fit trees using row-partitioned dataset.

Nested Class Summary

Nested classes/interfaces inherited from class org.apache.ignite.ml.trainers.DatasetTrainer

DatasetTrainer.EmptyDatasetException

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	usingIdx Use index structure instead of using sorting while learning.

Fields inherited from class org.apache.ignite.ml.trainers.DatasetTrainer

envBuilder, environment

Method Summary

Modifier and Type	Method and Description
<K,V> DecisionTreeModel	fit(Dataset<EmptyContext,DecisionTreeData> dataset)
<K,V> DecisionTreeModel	fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
protected abstract ImpurityMeasureCalculator<T>	getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset) Returns impurity measure calculator.
boolean	isUpdateable(DecisionTreeModel mdl)
static String	printTree(DecisionTreeNode node, boolean pretty) Represents DecisionTree as String.
protected <K,V> DecisionTreeModel	updateModel(DecisionTreeModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model based on dataset because there is no valid approach to update decision trees.
DecisionTreeTrainer<T>	withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder) Changes learning Environment.

Methods inherited from class org.apache.ignite.ml.trainers.DatasetTrainer

fit, fit, fit, fit, fit, fit, getLastTrainedModelOrThrowEmptyDatasetException, identityTrainer, learningEnvironment, update, update, update, update, update, withConvertedLabels

Methods inherited from class java.lang.Object

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

Field Detail

usingIdx

protected boolean usingIdx

Use index structure instead of using sorting while learning.

Method Detail

fitWithInitializedDeployingContext

public <K,V> DecisionTreeModel fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
                                                                  Preprocessor<K,V> preprocessor)

Trains model based on the specified data.

Specified by:

fitWithInitializedDeployingContext in class DatasetTrainer<DecisionTreeModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

datasetBuilder - Dataset builder.

preprocessor - Extractor of UpstreamEntry into LabeledVector.

Returns:

Model.

isUpdateable

public boolean isUpdateable(DecisionTreeModel mdl)

Specified by:

isUpdateable in class DatasetTrainer<DecisionTreeModel,Double>

Parameters:

mdl - Model.

Returns:

True if current critical for training parameters correspond to parameters from last training.

withEnvironmentBuilder

public DecisionTreeTrainer<T> withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)

Changes learning Environment.

Overrides:

withEnvironmentBuilder in class DatasetTrainer<DecisionTreeModel,Double>

Parameters:

envBuilder - Learning environment builder.

updateModel

protected <K,V> DecisionTreeModel updateModel(DecisionTreeModel mdl,
                                              DatasetBuilder<K,V> datasetBuilder,
                                              Preprocessor<K,V> preprocessor)

Trains new model based on dataset because there is no valid approach to update decision trees.

Specified by:

updateModel in class DatasetTrainer<DecisionTreeModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

mdl - Learned model.

datasetBuilder - Dataset builder.

preprocessor - Mapper from upstream entry to LabeledVector.

Returns:

New model based on new dataset.

fit

public <K,V> DecisionTreeModel fit(Dataset<EmptyContext,DecisionTreeData> dataset)

printTree

public static String printTree(DecisionTreeNode node,
                               boolean pretty)

Represents DecisionTree as String.

Parameters:

node - Decision tree.

pretty - Use pretty mode.

getImpurityMeasureCalculator

protected abstract ImpurityMeasureCalculator<T> getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset)

Returns impurity measure calculator.

Parameters:

dataset - Dataset.

Returns:

Impurity measure calculator.