org.apache.ignite.ml.composition.stacking

## Class StackedDatasetTrainer<IS,IA,O,AM extends IgniteModel<IA,O>,L>

- java.lang.Object
- - org.apache.ignite.ml.trainers.DatasetTrainer<StackedModel<IS,IA,O,AM>,L>
  - - org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer<IS,IA,O,AM,L>

- Type Parameters:
    `IS` - Type of submodels input.
    `IA` - Type of aggregator input.
    `O` - Type of aggregator output.
    `L` - Type of labels.
  Direct Known Subclasses:
    SimpleStackedDatasetTrainer

      public class StackedDatasetTrainer<IS,IA,O,AM extends IgniteModel<IA,O>,L>
      extends DatasetTrainer<StackedModel<IS,IA,O,AM>,L>

  `DatasetTrainer` encapsulating stacking technique for model training. Model produced by this trainer consists of two layers. First layer is a model `IS -> IA`. This layer is a "parallel" composition of several "submodels", each of them itself is a model `IS -> IA` with their outputs `[IA]` merged into single `IA`. Second layer is an aggregator model `IA -> O`. Training corresponds to this layered structure in the following way:

       1. train models of first layer;
       2. train aggregator model on dataset augmented with outputs of first layer models converted to vectors.

  During second step we can choose if we want to keep original features along with converted outputs of first layer models or use only converted results of first layer models. This choice will also affect inference. This class is a most general stacked trainer, there is a `StackedVectorDatasetTrainer`: a shortcut version of it with some types and functions specified.

- - ### Nested Class Summary

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

      `DatasetTrainer.EmptyDatasetException`

  - ### Field Summary

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

      `envBuilder, environment`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `StackedDatasetTrainer()` Constructs instance of this class.
    `StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter)` Constructs instance of this class.
    `StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter)` Create instance of this class.

  - ### Method Summary

    All Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `<M1 extends IgniteModel<IS,IA>> StackedDatasetTrainer<IS,IA,O,AM,L>` | `addTrainer(DatasetTrainer<M1,L> trainer)` Adds submodel trainer along with converters needed on training and inference stages.
    `<K,V> StackedModel<IS,IA,O,AM>` | `fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `boolean` | `isUpdateable(StackedModel<IS,IA,O,AM> mdl)` This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(M)` and `DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>)` in this class we explicitly override update method.
    `<K,V> StackedModel<IS,IA,O,AM>` | `update(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
    `protected <K,V> StackedModel<IS,IA,O,AM>` | `updateModel(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(IgniteModel)` and `DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor)` in this class we explicitly override update method.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | `withAggregatorInputMerger(IgniteBinaryOperator<IA> merger)` Specify binary operator used to merge submodels outputs to one.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | `withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)` Specify aggregator trainer.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | `withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)` Changes learning Environment.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | `withOriginalFeaturesDropped()` Drop original features during training and inference.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | `withOriginalFeaturesKept(IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter)` Keep original features during training and propagate submodels input to aggregator during inference using given function.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | `withSubmodelOutput2VectorConverter(IgniteFunction<IA,Vector> submodelOutput2VectorConverter)` Set function used for conversion of submodel output to `Vector`.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | `withVector2SubmodelInputConverter(IgniteFunction<Vector,IS> vector2SubmodelInputConverter)` Set function used for conversion of `Vector` to submodel input.

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

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

    - ### Methods inherited from class java.lang.Object

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

- - ### Constructor Detail

    - #### StackedDatasetTrainer

          public StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer,
                                       IgniteBinaryOperator<IA> aggregatingInputMerger,
                                       IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter,
                                       List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers,
                                       IgniteFunction<Vector,IS> vector2SubmodelInputConverter,
                                       IgniteFunction<IA,Vector> submodelOutput2VectorConverter)

      Create instance of this class.
      Parameters:
        `aggregatorTrainer` - Trainer of model used for aggregation of results of submodels.
        `aggregatingInputMerger` - Binary operator used to merge outputs of submodels into one output passed to aggregator model.
        `submodelInput2AggregatingInputConverter` - Function used to convert input of submodel to output of submodel this function is used if user chooses to keep original features.
        `submodelsTrainers` - List of submodel trainers.

    - #### StackedDatasetTrainer

          public StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer,
                                       IgniteBinaryOperator<IA> aggregatingInputMerger,
                                       IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter)

      Constructs instance of this class.
      Parameters:
        `aggregatorTrainer` - Trainer of model used for aggregation of results of submodels.
        `aggregatingInputMerger` - Binary operator used to merge outputs of submodels into one output passed to aggregator model.
        `submodelInput2AggregatingInputConverter` - Function used to convert input of submodel to output of submodel this function is used if user chooses to keep original features.

    - #### StackedDatasetTrainer

          public StackedDatasetTrainer()

      Constructs instance of this class.

  - ### Method Detail

    - #### withOriginalFeaturesKept

          public StackedDatasetTrainer<IS,IA,O,AM,L> withOriginalFeaturesKept(IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter)

      Keep original features during training and propagate submodels input to aggregator during inference using given function. Note that if this object is on, training will be done on vector obtaining from concatenating features passed to submodels trainers and outputs of submodels converted to vectors, this can, for example influence aggregator model input vector dimension (if `IS = Vector`), or, more generally, some `IS` parameters which are not reflected just by its type. So converter should be written accordingly.
      Parameters:
        `submodelInput2AggregatingInputConverter` - Function used to propagate submodels input to aggregator.
      Returns:
        This object.

    - #### withOriginalFeaturesDropped

          public StackedDatasetTrainer<IS,IA,O,AM,L> withOriginalFeaturesDropped()

      Drop original features during training and inference.
      Returns:
        This object.

    - #### withSubmodelOutput2VectorConverter

          public StackedDatasetTrainer<IS,IA,O,AM,L> withSubmodelOutput2VectorConverter(IgniteFunction<IA,Vector> submodelOutput2VectorConverter)

      Set function used for conversion of submodel output to `Vector`. This function is used during building of dataset for training aggregator model. This dataset is augmented with results of submodels converted to `Vector`.
      Parameters:
        `submodelOutput2VectorConverter` - Function used for conversion of submodel output to `Vector`.
      Returns:
        This object.

    - #### withVector2SubmodelInputConverter

          public StackedDatasetTrainer<IS,IA,O,AM,L> withVector2SubmodelInputConverter(IgniteFunction<Vector,IS> vector2SubmodelInputConverter)

      Set function used for conversion of `Vector` to submodel input. This function is used during building of dataset for training aggregator model. This dataset is augmented with results of submodels applied to `Vector`s in original dataset.
      Parameters:
        `vector2SubmodelInputConverter` - Function used for conversion of `Vector` to submodel input.
      Returns:
        This object.

    - #### withAggregatorTrainer

          public StackedDatasetTrainer<IS,IA,O,AM,L> withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)

      Specify aggregator trainer.
      Parameters:
        `aggregatorTrainer` - Aggregator trainer.
      Returns:
        This object.

    - #### withAggregatorInputMerger

          public StackedDatasetTrainer<IS,IA,O,AM,L> withAggregatorInputMerger(IgniteBinaryOperator<IA> merger)

      Specify binary operator used to merge submodels outputs to one.
      Parameters:
        `merger` - Binary operator used to merge submodels outputs to one.
      Returns:
        This object.

    - #### addTrainer

          public <M1 extends IgniteModel<IS,IA>> StackedDatasetTrainer<IS,IA,O,AM,L> addTrainer(DatasetTrainer<M1,L> trainer)

      Adds submodel trainer along with converters needed on training and inference stages.
      Parameters:
        `trainer` - Submodel trainer.
      Returns:
        This object.

    - #### fitWithInitializedDeployingContext

          public <K,V> StackedModel<IS,IA,O,AM> fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
                                                                                   Preprocessor<K,V> preprocessor)

      Trains model based on the specified data.
      Specified by:
        `fitWithInitializedDeployingContext` in class `DatasetTrainer<StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>>,L>`
      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.

    - #### update

          public <K,V> StackedModel<IS,IA,O,AM> update(StackedModel<IS,IA,O,AM> mdl,
                                                       DatasetBuilder<K,V> datasetBuilder,
                                                       Preprocessor<K,V> preprocessor)

      Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model. In other case trains new model.
      Overrides:
        `update` in class `DatasetTrainer<StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>>,L>`
      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` - Extractor of `UpstreamEntry` into `LabeledVector`.
      Returns:
        Updated model.

    - #### withEnvironmentBuilder

          public StackedDatasetTrainer<IS,IA,O,AM,L> withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)

      Changes learning Environment.
      Overrides:
        `withEnvironmentBuilder` in class `DatasetTrainer<StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>>,L>`
      Parameters:
        `envBuilder` - Learning environment builder.

    - #### updateModel

          protected <K,V> StackedModel<IS,IA,O,AM> updateModel(StackedModel<IS,IA,O,AM> mdl,
                                                               DatasetBuilder<K,V> datasetBuilder,
                                                               Preprocessor<K,V> preprocessor)

      This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(IgniteModel)` and `DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor)` in this class we explicitly override update method.
      Specified by:
        `updateModel` in class `DatasetTrainer<StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>>,L>`
      Type Parameters:
        `K` - Type of a key in `upstream` data.
        `V` - Type of a value in `upstream` data.
      Parameters:
        `mdl` - Model.
        `datasetBuilder` - Dataset builder.
        `preprocessor` - Extractor of `UpstreamEntry` into `LabeledVector`.
      Returns:
        Updated model.

    - #### isUpdateable

          public boolean isUpdateable(StackedModel<IS,IA,O,AM> mdl)

      This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(M)` and `DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>)` in this class we explicitly override update method.
      Specified by:
        `isUpdateable` in class `DatasetTrainer<StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>>,L>`
      Parameters:
        `mdl` - Model.
      Returns:
        True if current critical for training parameters correspond to parameters from last training.

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Release Date | : April 20 2022