public class RecommendationBinaryDatasetDataBuilder extends Object implements PartitionDataBuilder<Object,BinaryObject,EmptyContext,RecommendationDatasetData<Serializable,Serializable>>
Constructor and Description |
---|
RecommendationBinaryDatasetDataBuilder(String objFieldName,
String subjFieldName,
String ratingFieldName)
Constructs a new instance of recommendation binary dataset data builder.
|
Modifier and Type | Method and Description |
---|---|
RecommendationDatasetData<Serializable,Serializable> |
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 . |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
andThen, build
public RecommendationBinaryDatasetDataBuilder(String objFieldName, String subjFieldName, String ratingFieldName)
objFieldName
- Object field name.subjFieldName
- Subject field name.ratingFieldName
- Rating field name.public RecommendationDatasetData<Serializable,Serializable> build(LearningEnvironment env, Iterator<UpstreamEntry<Object,BinaryObject>> upstreamData, long upstreamDataSize, EmptyContext ctx)
data
from a partition upstream
data and partition context
.
Important: there is no guarantee that there will be no more than one UpstreamEntry with given key,
UpstreamEntry should be thought rather as a container saving all data from upstream, but omitting uniqueness
constraint. This constraint is omitted to allow upstream data transformers in DatasetBuilder
replicating
entries. For example it can be useful for bootstrapping.build
in interface PartitionDataBuilder<Object,BinaryObject,EmptyContext,RecommendationDatasetData<Serializable,Serializable>>
env
- Learning environment.upstreamData
- Partition upstream
data.upstreamDataSize
- Partition upstream
data size.ctx
- Partition context
.data
.
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024