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Apache Ignite® is a Distributed Database For High-Performance Computing With In-Memory Speed.
There is a newer version: 2.15.0
/*
 * 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.pipeline;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.ml.Model;
import org.apache.ignite.ml.dataset.DatasetBuilder;
import org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder;
import org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder;
import org.apache.ignite.ml.math.functions.IgniteBiFunction;
import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.preprocessing.PreprocessingTrainer;
import org.apache.ignite.ml.trainers.DatasetTrainer;

/**
 * A simple pipeline, which acts as a global trainer which produce a Pipeline Model.
 * A Pipeline consists of a sequence of stages, each of which is either a Preprocessing Stage or a Trainer.
 * When {@code fit()} method is called, the stages are executed in order.
 *
 * @param  Type of a key in {@code upstream} data.
 * @param  Type of a value in {@code upstream} data.
 * @param  Type of a result in {@code upstream} feature extractor.
 */
public class Pipeline {
    /** Feature extractor. */
    private IgniteBiFunction finalFeatureExtractor;

    /** Label extractor. */
    private IgniteBiFunction lbExtractor;

    /** Prerpocessor stages. */
    private List preprocessors = new ArrayList<>();

    /** Final trainer stage. */
    private DatasetTrainer finalStage;

    /**
     * Adds feature extractor as a zero stage.
     *
     * @param featureExtractor The parameter value.
     * @return The updated Pipeline.
     */
    public Pipeline addFeatureExtractor(IgniteBiFunction featureExtractor) {
        this.finalFeatureExtractor = featureExtractor;
        return this;
    }

    /**
     * Adds a label extractor for the produced model.
     *
     * @param lbExtractor The parameter value.
     * @return The updated Pipeline.
     */
    public Pipeline addLabelExtractor(IgniteBiFunction lbExtractor) {
        this.lbExtractor = lbExtractor;
        return this;
    }

    /**
     * Adds a preprocessor.
     *
     * @param preprocessor The parameter value.
     * @return The updated Pipeline.
     */
    public Pipeline addPreprocessor(PreprocessingTrainer preprocessor) {
        preprocessors.add(preprocessor);
        return this;
    }

    /**
     * Adds a trainer.
     *
     * @param trainer The parameter value.
     * @return The updated Pipeline.
     */
    public Pipeline addTrainer(DatasetTrainer trainer) {
        this.finalStage = trainer;
        return this;
    }

    /**
     * Fits the pipeline to the input cache.
     *
     * @param ignite Ignite instance.
     * @param cache Ignite cache with {@code upstream} data.
     * @return The fitted model based on chain of preprocessors and final trainer.
     */
    public PipelineMdl fit(Ignite ignite, IgniteCache cache) {
        DatasetBuilder datasetBuilder = new CacheBasedDatasetBuilder<>(ignite, cache);
        return fit(datasetBuilder);
    }

    /**
     * Fits the pipeline to the input mock data.
     *
     * @param data Data.
     * @param parts Number of partitions.
     * @return The fitted model based on chain of preprocessors and final trainer.
     */
    public PipelineMdl fit(Map data, int parts) {
        DatasetBuilder datasetBuilder = new LocalDatasetBuilder<>(data, parts);
        return fit(datasetBuilder);
    }

    /** Fits the pipeline to the input dataset builder. */
    private PipelineMdl fit(DatasetBuilder datasetBuilder) {
        assert lbExtractor != null;
        assert finalFeatureExtractor != null;

        if (finalStage == null)
            throw new IllegalStateException("The Pipeline should be finished with the Training Stage.");

        preprocessors.forEach(e -> {

            finalFeatureExtractor = e.fit(
                datasetBuilder,
                finalFeatureExtractor
            );
        });

        Model internalMdl = finalStage
            .fit(
                datasetBuilder,
                finalFeatureExtractor,
                lbExtractor
            );

        return new PipelineMdl()
            .withFeatureExtractor(finalFeatureExtractor)
            .withLabelExtractor(lbExtractor)
            .withInternalMdl(internalMdl);
    }
}