## Uses of Interface
org.apache.ignite.ml.dataset.Dataset

- Packages that use Dataset
  Package | Description
  --- | ---
  org.apache.ignite.ml.composition.boosting.convergence | Package contains implementation of convergence checking algorithms for gradient boosting.
  org.apache.ignite.ml.composition.boosting.convergence.mean | Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.
  org.apache.ignite.ml.composition.boosting.convergence.median | Contains implementation of convergence checking computer by median of medians of errors in dataset.
  org.apache.ignite.ml.composition.boosting.convergence.simple | Contains implementation of Stub for convergence checking.
  org.apache.ignite.ml.dataset | Base package for machine learning dataset classes.
  org.apache.ignite.ml.dataset.impl.cache | Base package for cache based implementation of machine learning dataset.
  org.apache.ignite.ml.dataset.impl.local | Base package for local implementation of machine learning dataset.
  org.apache.ignite.ml.dataset.primitive | Package that contains basic primitives build on top of `Dataset`.
  org.apache.ignite.ml.knn | Contains main APIs for kNN algorithms.
  org.apache.ignite.ml.knn.classification | Contains main APIs for kNN classification algorithms.
  org.apache.ignite.ml.knn.regression | Contains helper classes for kNN regression algorithms.
  org.apache.ignite.ml.knn.utils | Contains util functionality for kNN algorithms.
  org.apache.ignite.ml.nn | Contains neural networks and related classes.
  org.apache.ignite.ml.tree | Root package for decision trees.
  org.apache.ignite.ml.tree.leaf | Root package for decision trees leaf builders.
  org.apache.ignite.ml.tree.randomforest | Contains random forest implementation classes.
  org.apache.ignite.ml.tree.randomforest.data.impurity | Contains implementation of impurity computers based on histograms.
  org.apache.ignite.ml.tree.randomforest.data.statistics | Contains implementation of statistics computers for Random Forest.

- - ### Uses of Dataset in org.apache.ignite.ml.composition.boosting.convergence

    Methods in org.apache.ignite.ml.composition.boosting.convergence with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `abstract Double` | ConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.
    `boolean` | ConvergenceChecker.`isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl)` Checks convergency on dataset.

  - ### Uses of Dataset in org.apache.ignite.ml.composition.boosting.convergence.mean

    Methods in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `Double` | MeanAbsValueConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.

  - ### Uses of Dataset in org.apache.ignite.ml.composition.boosting.convergence.median

    Methods in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `Double` | MedianOfMedianConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.

  - ### Uses of Dataset in org.apache.ignite.ml.composition.boosting.convergence.simple

    Methods in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `Double` | ConvergenceCheckerStub.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.
    `boolean` | ConvergenceCheckerStub.`isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl)` Checks convergency on dataset.

  - ### Uses of Dataset in org.apache.ignite.ml.dataset

    Methods in org.apache.ignite.ml.dataset with type parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `default <I extends Dataset<C,D>> I` | Dataset.`wrap(IgniteFunction<Dataset<C,D>,I> wrapper)` Wraps this dataset into the specified wrapper to introduce new functionality based on `compute` and `computeWithCtx` methods.

    Methods in org.apache.ignite.ml.dataset that return Dataset
    Modifier and Type | Method and Description
    --- | ---
    `<C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetBuilder.`build(LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment localLearningEnv)` Constructs a new instance of `Dataset` that includes allocation required data structures and initialization of `context` part of partitions.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment environment)` Creates a new instance of distributed dataset using the specified `partCtxBuilder` and `partDataBuilder`.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder)` Creates a new instance of distributed dataset using the specified `partCtxBuilder` and `partDataBuilder`.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(Ignite ignite, IgniteCache<K,V> upstreamCache, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment environment)` Creates a new instance of distributed dataset using the specified `partCtxBuilder` and `partDataBuilder`.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(Ignite ignite, IgniteCache<K,V> upstreamCache, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder)` Creates a new instance of distributed dataset using the specified `partCtxBuilder` and `partDataBuilder`.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(Map<K,V> upstreamMap, LearningEnvironmentBuilder envBuilder, int partitions, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment environment)` Creates a new instance of local dataset using the specified `partCtxBuilder` and `partDataBuilder`.

    Method parameters in org.apache.ignite.ml.dataset with type arguments of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `default <I extends Dataset<C,D>> I` | Dataset.`wrap(IgniteFunction<Dataset<C,D>,I> wrapper)` Wraps this dataset into the specified wrapper to introduce new functionality based on `compute` and `computeWithCtx` methods.

  - ### Uses of Dataset in org.apache.ignite.ml.dataset.impl.cache

    Classes in org.apache.ignite.ml.dataset.impl.cache that implement Dataset
    Modifier and Type | Class and Description
    --- | ---
    `class` | `CacheBasedDataset<K,V,C extends Serializable,D extends AutoCloseable>` An implementation of dataset based on Ignite Cache, which is used as `upstream` and as reliable storage for partition `context` as well.

  - ### Uses of Dataset in org.apache.ignite.ml.dataset.impl.local

    Classes in org.apache.ignite.ml.dataset.impl.local that implement Dataset
    Modifier and Type | Class and Description
    --- | ---
    `class` | `LocalDataset<C extends Serializable,D extends AutoCloseable>` An implementation of dataset based on local data structures such as `Map` and `List` and doesn't require Ignite environment.

  - ### Uses of Dataset in org.apache.ignite.ml.dataset.primitive

    Classes in org.apache.ignite.ml.dataset.primitive that implement Dataset
    Modifier and Type | Class and Description
    --- | ---
    `class` | `DatasetWrapper<C extends Serializable,D extends AutoCloseable>` A dataset wrapper that allows to introduce new functionality based on common `compute` methods.
    `class` | `SimpleDataset<C extends Serializable>` A simple dataset introduces additional methods based on a matrix of features.
    `class` | `SimpleLabeledDataset<C extends Serializable>` A simple labeled dataset introduces additional methods based on a matrix of features and labels vector.

    Fields in org.apache.ignite.ml.dataset.primitive declared as Dataset
    Modifier and Type | Field and Description
    --- | ---
    `protected Dataset<C,D>` | DatasetWrapper.`delegate` Delegate that performs `compute` actions.

    Constructors in org.apache.ignite.ml.dataset.primitive with parameters of type Dataset
    Constructor and Description
    `DatasetWrapper(Dataset<C,D> delegate)` Constructs a new instance of dataset wrapper that delegates `compute` actions to the actual delegate.
    `SimpleDataset(Dataset<C,SimpleDatasetData> delegate)` Creates a new instance of simple dataset that introduces additional methods based on a matrix of features.
    `SimpleLabeledDataset(Dataset<C,SimpleLabeledDatasetData> delegate)` Creates a new instance of simple labeled dataset that introduces additional methods based on a matrix of features and labels vector.

  - ### Uses of Dataset in org.apache.ignite.ml.knn

    Methods in org.apache.ignite.ml.knn with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `protected abstract M` | KNNTrainer.`convertDatasetIntoModel(Dataset<EmptyContext,SpatialIndex<Double>> dataset)` Convers given dataset into KNN model (classification or regression depends on implementation).

    Constructors in org.apache.ignite.ml.knn with parameters of type Dataset
    Constructor and Description
    `KNNModel(Dataset<EmptyContext,SpatialIndex<L>> dataset, DistanceMeasure distanceMeasure, int k, boolean weighted)` Constructs a new instance of KNN model.

  - ### Uses of Dataset in org.apache.ignite.ml.knn.classification

    Methods in org.apache.ignite.ml.knn.classification with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `protected KNNClassificationModel` | KNNClassificationTrainer.`convertDatasetIntoModel(Dataset<EmptyContext,SpatialIndex<Double>> dataset)` Convers given dataset into KNN model (classification or regression depends on implementation).

  - ### Uses of Dataset in org.apache.ignite.ml.knn.regression

    Methods in org.apache.ignite.ml.knn.regression with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `protected KNNRegressionModel` | KNNRegressionTrainer.`convertDatasetIntoModel(Dataset<EmptyContext,SpatialIndex<Double>> dataset)` Convers given dataset into KNN model (classification or regression depends on implementation).

  - ### Uses of Dataset in org.apache.ignite.ml.knn.utils

    Methods in org.apache.ignite.ml.knn.utils that return Dataset
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V,C extends Serializable> @Nullable Dataset<EmptyContext,LabeledVectorSet<LabeledVector>>` | KNNUtils.`buildDataset(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer)` Builds dataset.

  - ### Uses of Dataset in org.apache.ignite.ml.nn

    Methods in org.apache.ignite.ml.nn that return types with arguments of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture>` | MLPTrainer.`getArchSupplier()` Get the multilayer perceptron architecture supplier that defines layers and activators.

    Method parameters in org.apache.ignite.ml.nn with type arguments of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `MLPTrainer<P>` | MLPTrainer.`withArchSupplier(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier)` Set up the multilayer perceptron architecture supplier that defines layers and activators.

    Constructor parameters in org.apache.ignite.ml.nn with type arguments of type Dataset
    Constructor and Description
    `MLPTrainer(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy, int maxIterations, int batchSize, int locIterations, long seed)` Constructs a new instance of multilayer perceptron trainer.

  - ### Uses of Dataset in org.apache.ignite.ml.tree

    Methods in org.apache.ignite.ml.tree with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> DecisionTreeModel` | DecisionTreeTrainer.`fit(Dataset<EmptyContext,DecisionTreeData> dataset)`
    `protected ImpurityMeasureCalculator<GiniImpurityMeasure>` | DecisionTreeClassificationTrainer.`getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset)` Returns impurity measure calculator.
    `protected abstract ImpurityMeasureCalculator<T>` | DecisionTreeTrainer.`getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset)` Returns impurity measure calculator.
    `protected ImpurityMeasureCalculator<MSEImpurityMeasure>` | DecisionTreeRegressionTrainer.`getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset)` Returns impurity measure calculator.

  - ### Uses of Dataset in org.apache.ignite.ml.tree.leaf

    Methods in org.apache.ignite.ml.tree.leaf with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `DecisionTreeLeafNode` | DecisionTreeLeafBuilder.`createLeafNode(Dataset<EmptyContext,DecisionTreeData> dataset, TreeFilter pred)` Creates new leaf node for given dataset and node predicate.
    `DecisionTreeLeafNode` | MeanDecisionTreeLeafBuilder.`createLeafNode(Dataset<EmptyContext,DecisionTreeData> dataset, TreeFilter pred)` Creates new leaf node for given dataset and node predicate.
    `DecisionTreeLeafNode` | MostCommonDecisionTreeLeafBuilder.`createLeafNode(Dataset<EmptyContext,DecisionTreeData> dataset, TreeFilter pred)` Creates new leaf node for given dataset and node predicate.

  - ### Uses of Dataset in org.apache.ignite.ml.tree.randomforest

    Methods in org.apache.ignite.ml.tree.randomforest with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `protected boolean` | RandomForestClassifierTrainer.`init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Aggregates all unique labels from dataset and assigns integer id value for each label.
    `protected boolean` | RandomForestTrainer.`init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Init-step before learning.

  - ### Uses of Dataset in org.apache.ignite.ml.tree.randomforest.data.impurity

    Methods in org.apache.ignite.ml.tree.randomforest.data.impurity with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `Map<NodeId,ImpurityHistogramsComputer.NodeImpurityHistograms<S>>` | ImpurityHistogramsComputer.`aggregateImpurityStatistics(ArrayList<RandomForestTreeModel> roots, Map<Integer,BucketMeta> histMeta, Map<NodeId,TreeNode> nodesToLearn, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Computes histograms for each feature.

  - ### Uses of Dataset in org.apache.ignite.ml.tree.randomforest.data.statistics

    Methods in org.apache.ignite.ml.tree.randomforest.data.statistics with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `List<NormalDistributionStatistics>` | NormalDistributionStatisticsComputer.`computeStatistics(List<FeatureMeta> meta, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Computes statistics of normal distribution on features in dataset.
    `void` | LeafValuesComputer.`setValuesForLeaves(ArrayList<RandomForestTreeModel> roots, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Takes a list of all built trees and in one map-reduceImpurityStatistics step collect statistics for evaluating leaf-values for each tree and sets values for leaves.

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Release Date | : September 11 2021