dmitrievanthony commented on a change in pull request #5767: [ML] IGNITE-10573: Consistent API for Ensemble training URL: https://github.com/apache/ignite/pull/5767#discussion_r247536556
########## File path: modules/ml/src/main/java/org/apache/ignite/ml/composition/combinators/sequential/TrainersSequentialComposition.java ########## @@ -0,0 +1,125 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.ignite.ml.composition.combinators.sequential; + +import org.apache.ignite.ml.IgniteModel; +import org.apache.ignite.ml.composition.CompositionUtils; +import org.apache.ignite.ml.composition.DatasetMapping; +import org.apache.ignite.ml.dataset.DatasetBuilder; +import org.apache.ignite.ml.math.functions.IgniteBiFunction; +import org.apache.ignite.ml.math.functions.IgniteFunction; +import org.apache.ignite.ml.math.primitives.vector.Vector; +import org.apache.ignite.ml.math.primitives.vector.VectorUtils; +import org.apache.ignite.ml.trainers.DatasetTrainer; +import sun.reflect.generics.reflectiveObjects.NotImplementedException; + +/** + * Sequential composition of trainers. + * Sequential composition of trainers is itself trainer which produces {@link ModelsSequentialComposition}. + * Training is done in following fashion: + * <pre> + * 1. First trainer is trained and `mdl1` is produced. + * 2. From `mdl1` {@link DatasetMapping} is constructed. This mapping `dsM` encapsulates dependency between first + * training result and second trainer. + * 3. Second trainer is trained using dataset aquired from application `dsM` to original dataset; `mdl2` is produced. + * 4. `mdl1` and `mdl2` are composed into {@link ModelsSequentialComposition}. + * </pre> + * + * @param <I> Type of input of model produced by first trainer. + * @param <O1> Type of output of model produced by first trainer. + * @param <O2> Type of output of model produced by second trainer. + * @param <L> Type of labels. + */ +public class TrainersSequentialComposition<I, O1, O2, L> extends DatasetTrainer<ModelsSequentialComposition<I, O1, O2>, L> { + /** First trainer. */ + private DatasetTrainer<IgniteModel<I, O1>, L> tr1; + + /** Second trainer. */ + private DatasetTrainer<IgniteModel<O1, O2>, L> tr2; + + /** Dataset mapping. */ + private IgniteFunction<? super IgniteModel<I, O1>, DatasetMapping<L, L>> datasetMapping; + + /** + * Construct sequential composition of given two trainers. + * + * @param tr1 First trainer. + * @param tr2 Second trainer. + * @param datasetMapping Dataset mapping. + */ + public TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I, O1>, L> tr1, + DatasetTrainer<? extends IgniteModel<O1, O2>, L> tr2, + IgniteFunction<? super IgniteModel<I, O1>, DatasetMapping<L, L>> datasetMapping) { + this.tr1 = CompositionUtils.unsafeCoerce(tr1); + this.tr2 = CompositionUtils.unsafeCoerce(tr2); + this.datasetMapping = datasetMapping; + } + + /** {@inheritDoc} */ + @Override public <K, V> ModelsSequentialComposition<I, O1, O2> fit(DatasetBuilder<K, V> datasetBuilder, + IgniteBiFunction<K, V, Vector> featureExtractor, IgniteBiFunction<K, V, L> lbExtractor) { + + IgniteModel<I, O1> mdl1 = tr1.fit(datasetBuilder, featureExtractor, lbExtractor); + DatasetMapping<L, L> mapping = datasetMapping.apply(mdl1); + + IgniteModel<O1, O2> mdl2 = tr2.fit(datasetBuilder, + featureExtractor.andThen(mapping::mapFeatures), + lbExtractor.andThen(mapping::mapLabels)); + + return new ModelsSequentialComposition<>(mdl1, mdl2); + } + + /** {@inheritDoc} */ + @Override public <K, V> ModelsSequentialComposition<I, O1, O2> update( + ModelsSequentialComposition<I, O1, O2> mdl, DatasetBuilder<K, V> datasetBuilder, + IgniteBiFunction<K, V, Vector> featureExtractor, IgniteBiFunction<K, V, L> lbExtractor) { + + IgniteModel<I, O1> firstUpdated = tr1.update(mdl.firstModel(), datasetBuilder, featureExtractor, lbExtractor); + DatasetMapping<L, L> mapping = datasetMapping.apply(firstUpdated); + + IgniteModel<O1, O2> secondUpdated = tr2.update(mdl.secondModel(), + datasetBuilder, + featureExtractor.andThen(mapping::mapFeatures), + lbExtractor.andThen(mapping::mapLabels)); + + return new ModelsSequentialComposition<>(firstUpdated, secondUpdated); + } + + /** {@inheritDoc} */ + @Override protected boolean checkState(ModelsSequentialComposition<I, O1, O2> mdl) { + // Never called. + throw new IllegalStateException(); + } + + /** {@inheritDoc} */ + @Override protected <K, V> ModelsSequentialComposition<I, O1, O2> updateModel( Review comment: Update javadoc to reflect the fact that this method shell not be called. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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