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| Package | Description |
|---|---|
| org.apache.ignite.ml.composition.boosting |
Contains Gradient Boosting regression and classification abstract classes
allowing regressor type selecting in child classes.
|
| org.apache.ignite.ml.dataset |
Base package for machine learning dataset classes.
|
| org.apache.ignite.ml.dataset.impl.bootstrapping |
Base package for bootstrapped implementation of machine learning dataset.
|
| org.apache.ignite.ml.dataset.impl.cache |
Base package for cache based implementation of machine learning dataset.
|
| org.apache.ignite.ml.dataset.impl.cache.util |
Contains util classes used in cache based implementation of 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.dataset.primitive.builder.context |
Contains partition
context builders. |
| org.apache.ignite.ml.dataset.primitive.builder.data |
Contains partition
data builders. |
| org.apache.ignite.ml.environment |
Package contains environment utils for ML algorithms.
|
| org.apache.ignite.ml.knn |
Contains main APIs for kNN algorithms.
|
| org.apache.ignite.ml.math.isolve.lsqr |
Contains LSQR algorithm implementation.
|
| org.apache.ignite.ml.preprocessing |
Base package for machine learning preprocessing classes.
|
| org.apache.ignite.ml.recommendation |
Contains recommendation system framework.
|
| org.apache.ignite.ml.recommendation.util |
Contains util classes used in recommendation system framework.
|
| org.apache.ignite.ml.selection.cv |
Root package for cross-validation algorithms.
|
| org.apache.ignite.ml.structures.partition |
Contains internal APIs for dataset partitioned labeled datasets.
|
| org.apache.ignite.ml.trainers |
Contains model trainers.
|
| org.apache.ignite.ml.tree.data |
Contains data and data builder required for decision tree trainers built on top of partition based dataset.
|
| org.apache.ignite.ml.util.genetic |
Contains some genetic algorithms for discrete optimization task in ML module locally.
|
| Modifier and Type | Field and Description |
|---|---|
protected LearningEnvironment |
GDBLearningStrategy.trainerEnvironment
Learning environment used for trainer.
|
| Modifier and Type | Method and Description |
|---|---|
UpstreamTransformer |
UpstreamTransformerBuilder.build(LearningEnvironment env)
Create
UpstreamTransformer based on learning environment. |
<C extends Serializable,D extends AutoCloseable> |
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. |
C |
PartitionContextBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize)
Builds a new partition
context from an upstream data. |
D |
PartitionDataBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
default C |
PartitionContextBuilder.build(LearningEnvironment env,
Stream<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize)
Builds a new partition
context from an upstream data. |
default D |
PartitionDataBuilder.build(LearningEnvironment env,
Stream<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
static <K,V,C extends Serializable,D extends AutoCloseable> |
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> |
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> |
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. |
| Modifier and Type | Method and Description |
|---|---|
default void |
Dataset.compute(IgniteBiConsumer<D,LearningEnvironment> map)
|
default <R> R |
Dataset.compute(IgniteBiFunction<D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce)
Applies the specified
map function to every partition data and LearningEnvironment
in the dataset and then reduces map results to final result by using the reduce function. |
<R> R |
Dataset.compute(IgniteBiFunction<D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data and LearningEnvironment
in the dataset and then reduces map results to final result by using the reduce function. |
default void |
Dataset.computeWithCtx(IgniteTriConsumer<C,D,LearningEnvironment> map)
Applies the specified
map function to every partition data, context and
LearningEnvironment in the dataset. |
default <R> R |
Dataset.computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce)
Applies the specified
map function to every partition data, context and
LearningEnvironment in the dataset and then reduces map results to final
result by using the reduce function. |
<R> R |
Dataset.computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data, context and partition
index in the dataset and then reduces map results to final result by using the reduce function. |
| Modifier and Type | Method and Description |
|---|---|
BootstrappedDatasetPartition |
BootstrappedDatasetBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
EmptyContext ctx)
Builds a new partition
data from a partition upstream data and partition context. |
| Modifier and Type | Method and Description |
|---|---|
<C extends Serializable,D extends AutoCloseable> |
CacheBasedDatasetBuilder.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. |
| Modifier and Type | Method and Description |
|---|---|
<R> R |
CacheBasedDataset.compute(IgniteBiFunction<D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data and LearningEnvironment
in the dataset and then reduces map results to final result by using the reduce function. |
<R> R |
CacheBasedDataset.computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data, context and partition
index in the dataset and then reduces map results to final result by using the reduce function. |
| Constructor and Description |
|---|
CacheBasedDataset(Ignite ignite,
IgniteCache<K,V> upstreamCache,
IgniteBiPredicate<K,V> filter,
UpstreamTransformerBuilder upstreamTransformerBuilder,
IgniteCache<Integer,C> datasetCache,
LearningEnvironmentBuilder envBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
UUID datasetId,
boolean upstreamKeepBinary,
LearningEnvironment localLearningEnv,
int retriesCnt)
Constructs a new instance of dataset based on Ignite Cache, which is used as
upstream and as reliable storage for
partition context as well. |
| Modifier and Type | Method and Description |
|---|---|
static LearningEnvironment |
ComputeUtils.getLearningEnvironment(Ignite ignite,
UUID datasetId,
int part,
LearningEnvironmentBuilder envBuilder)
Gets learning environment for given partition.
|
| Modifier and Type | Method and Description |
|---|---|
static <K,V,C extends Serializable,D extends AutoCloseable> |
ComputeUtils.getData(Ignite ignite,
String upstreamCacheName,
IgniteBiPredicate<K,V> filter,
UpstreamTransformerBuilder transformerBuilder,
String datasetCacheName,
UUID datasetId,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
LearningEnvironment env,
boolean isKeepBinary)
Extracts partition
data from the local storage, if it's not found in local storage recovers this data from a partition upstream and context. |
| Modifier and Type | Method and Description |
|---|---|
<C extends Serializable,D extends AutoCloseable> |
LocalDatasetBuilder.build(LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
LearningEnvironment learningEnvironment)
Constructs a new instance of
Dataset that includes allocation required data structures and
initialization of context part of partitions. |
| Modifier and Type | Method and Description |
|---|---|
<R> R |
LocalDataset.compute(IgniteBiFunction<D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data and LearningEnvironment
in the dataset and then reduces map results to final result by using the reduce function. |
<R> R |
LocalDataset.computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data, context and partition
index in the dataset and then reduces map results to final result by using the reduce function. |
| Modifier and Type | Method and Description |
|---|---|
FeatureMatrixWithLabelsOnHeapData |
FeatureMatrixWithLabelsOnHeapDataBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
| Modifier and Type | Method and Description |
|---|---|
<R> R |
DatasetWrapper.compute(IgniteBiFunction<D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data and LearningEnvironment
in the dataset and then reduces map results to final result by using the reduce function. |
<R> R |
DatasetWrapper.computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data, context and partition
index in the dataset and then reduces map results to final result by using the reduce function. |
| Modifier and Type | Method and Description |
|---|---|
EmptyContext |
EmptyContextBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize)
Builds a new partition
context from an upstream data. |
| Modifier and Type | Method and Description |
|---|---|
SimpleDatasetData |
SimpleDatasetDataBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
SimpleLabeledDatasetData |
SimpleLabeledDatasetDataBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
| Modifier and Type | Field and Description |
|---|---|
static LearningEnvironment |
LearningEnvironment.DEFAULT_TRAINER_ENV
Default environment
|
| Modifier and Type | Method and Description |
|---|---|
default LearningEnvironment |
LearningEnvironmentBuilder.buildForTrainer()
Builds learning environment for trainer.
|
LearningEnvironment |
DefaultLearningEnvironmentBuilder.buildForWorker(int part)
Builds
LearningEnvironment for worker on given partition. |
LearningEnvironment |
LearningEnvironmentBuilder.buildForWorker(int part)
Builds
LearningEnvironment for worker on given partition. |
| Modifier and Type | Method and Description |
|---|---|
SpatialIndex<Double> |
KNNPartitionDataBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
EmptyContext ctx)
Builds a new partition
data from a partition upstream data and partition context. |
| Constructor and Description |
|---|
LSQROnHeap(DatasetBuilder<K,V> datasetBuilder,
LearningEnvironmentBuilder envBuilder,
PartitionDataBuilder<K,V,LSQRPartitionContext,SimpleLabeledDatasetData> partDataBuilder,
LearningEnvironment localLearningEnv)
Constructs a new instance of OnHeap LSQR algorithm implementation.
|
| Modifier and Type | Method and Description |
|---|---|
default <K,V> LearningEnvironment |
PreprocessingTrainer.learningEnvironment(Preprocessor<K,V> basePreprocessor)
Returns local learning environment with initialized deploying context by base preptocessor.
|
| Modifier and Type | Method and Description |
|---|---|
RecommendationTrainer |
RecommendationTrainer.withTrainerEnvironment(LearningEnvironment trainerEnvironment)
Set up trainer learning environment.
|
| Modifier and Type | Method and Description |
|---|---|
RecommendationDatasetData<O,S> |
RecommendationDatasetDataBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,Z>> upstreamData,
long upstreamDataSize,
EmptyContext ctx)
Builds a new partition
data from a partition upstream data and partition context. |
RecommendationDatasetData<Serializable,Serializable> |
RecommendationBinaryDatasetDataBuilder.build(LearningEnvironment env,
Iterator<UpstreamEntry<Object,BinaryObject>> upstreamData,
long upstreamDataSize,
EmptyContext ctx)
Builds a new partition
data from a partition upstream data and partition context. |
| Modifier and Type | Field and Description |
|---|---|
protected LearningEnvironment |
AbstractCrossValidation.environment
Learning Environment.
|
| Modifier and Type | Method and Description |
|---|---|
LabeledVectorSet<LabeledVector> |
LabeledDatasetPartitionDataBuilderOnHeap.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
LabelPartitionDataOnHeap |
LabelPartitionDataBuilderOnHeap.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
| Modifier and Type | Field and Description |
|---|---|
protected LearningEnvironment |
DatasetTrainer.environment
Learning Environment.
|
| Modifier and Type | Method and Description |
|---|---|
LearningEnvironment |
DatasetTrainer.learningEnvironment()
Get learning environment.
|
| Modifier and Type | Method and Description |
|---|---|
<K,V> M |
DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
LearningEnvironment learningEnvironment)
Trains model based on the specified data.
|
| Modifier and Type | Method and Description |
|---|---|
DecisionTreeData |
DecisionTreeDataBuilder.build(LearningEnvironment envBuilder,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context. |
| Modifier and Type | Method and Description |
|---|---|
void |
GeneticAlgorithm.runParallel(LearningEnvironment environment)
The main method for genetic algorithm.
|
GridGain In-Memory Computing Platform : ver. 8.9.15 Release Date : December 3 2024