avplatonov 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_r247813164
 
 

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 File path: 
modules/ml/src/main/java/org/apache/ignite/ml/composition/bagging/BaggedTrainer.java
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 @@ -0,0 +1,200 @@
+/*
+ * 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.bagging;
+
+import java.util.Collections;
+import java.util.List;
+import java.util.Random;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+import java.util.stream.Stream;
+import org.apache.ignite.ml.IgniteModel;
+import org.apache.ignite.ml.composition.CompositionUtils;
+import 
org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition;
+import 
org.apache.ignite.ml.composition.predictionsaggregator.PredictionsAggregator;
+import org.apache.ignite.ml.dataset.DatasetBuilder;
+import org.apache.ignite.ml.environment.LearningEnvironmentBuilder;
+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.AdaptableDatasetTrainer;
+import org.apache.ignite.ml.trainers.DatasetTrainer;
+import org.apache.ignite.ml.trainers.transformers.BaggingUpstreamTransformer;
+import org.apache.ignite.ml.util.Utils;
+
+/**
+ * Trainer encapsulating logic of bootstrap aggregating (bagging).
+ * This trainer accepts some other trainer and returns bagged version of it.
+ * Resulting model consists of submodels results of which are aggregated by a 
specified aggregator.
+ * <p>Bagging is done
+ * on both samples and features (<a 
href="https://en.wikipedia.org/wiki/Bootstrap_aggregating";></a>Samples 
bagging</a>,
+ * <a href="https://en.wikipedia.org/wiki/Random_subspace_method";></a>Features 
bagging</a>).</p>
+ *
+ * @param <M> Type of model produced by trainer for which bagged version is 
created.
+ * @param <L> Type of labels.
+ * @param <T> Type of trainer for which bagged version is created.
+ */
+public class BaggedTrainer<M extends IgniteModel<Vector, Double>, L, T extends 
DatasetTrainer<M, L>> extends
+    DatasetTrainer<BaggedModel, L> {
+    /** Trainer for which bagged version is created. */
+    private final DatasetTrainer<M, L> tr;
+
+    /** Aggregator of submodels results. */
+    private final PredictionsAggregator aggregator;
+
+    /** Count of submodels in the ensemble. */
+    private final int ensembleSize;
+
+    /** Ratio determining which part of dataset will be taken as subsample for 
each submodel training. */
+    private final double subsampleRatio;
+
+    /** Dimensionality of feature vectors. */
+    private final int featuresVectorSize;
+
+    /** Dimension of subspace on which all samples from subsample are 
projected. */
+    private final int featureSubspaceDim;
+
+    /**
+     * Construct instance of this class with given parameters.
+     *
+     * @param tr Trainer for making bagged.
+     * @param aggregator Aggregator of models.
+     * @param ensembleSize Size of ensemble.
+     * @param subsampleRatio Ratio (subsample size) / (initial dataset size).
+     * @param featuresVectorSize Dimensionality of feature vector.
+     * @param featureSubspaceDim Dimensionality of feature subspace.
+     */
+    public BaggedTrainer(DatasetTrainer<M, L> tr,
+        PredictionsAggregator aggregator, int ensembleSize, double 
subsampleRatio, int featuresVectorSize,
+        int featureSubspaceDim) {
+        this.tr = tr;
+        this.aggregator = aggregator;
+        this.ensembleSize = ensembleSize;
+        this.subsampleRatio = subsampleRatio;
+        this.featuresVectorSize = featuresVectorSize;
+        this.featureSubspaceDim = featureSubspaceDim;
+    }
+
+    /**
+     * Create trainer bagged trainer.
+     *
+     * @return Bagged trainer.
+     */
+    private DatasetTrainer<IgniteModel<Vector, Double>, L> getTrainer() {
+        List<int[]> mappings = (featuresVectorSize > 0 && featureSubspaceDim 
!= featuresVectorSize) ?
+            IntStream.range(0, ensembleSize).mapToObj(
+                modelIdx -> getMapping(
+                    featuresVectorSize,
+                    featureSubspaceDim,
+                    environment.randomNumbersGenerator().nextLong() + 
modelIdx))
 
 Review comment:
   You can delete "+ modelIdx" because  mapToObj + nextLong() sequentially 
change state of generator as I think

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