examples/src/main/java/org/apache/ignite/examples/ml/preprocessing/ImputingExample.java

/*
 * 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
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 * 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
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package org.apache.ignite.examples.ml.preprocessing;

import java.io.Serializable;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
import org.apache.ignite.configuration.CacheConfiguration;
import org.apache.ignite.examples.ml.util.DatasetHelper;
import org.apache.ignite.ml.dataset.DatasetFactory;
import org.apache.ignite.ml.dataset.feature.extractor.Vectorizer;
import org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer;
import org.apache.ignite.ml.dataset.primitive.SimpleDataset;
import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
import org.apache.ignite.ml.preprocessing.Preprocessor;
import org.apache.ignite.ml.preprocessing.imputing.ImputerTrainer;

/**
 * Example that shows how to use <a href="https://en.wikipedia.org/wiki/Imputation_(statistics)">Imputing</a>
 * preprocessor to impute the missing value in the given data.
 * <p>
 * Code in this example launches Ignite grid and fills the cache with simple test data.</p>
 * <p>
 * After that it defines preprocessors that extract features from an upstream data and impute missing values.</p>
 * <p>
 * Finally, it creates the dataset based on the processed data and uses Dataset API to find and output various
 * statistical metrics of the data.</p>
 * <p>
 * You can change the test data used in this example and re-run it to explore this functionality further.</p>
 */
public class ImputingExample {
    /**
     * Run example.
     */
    public static void main(String[] args) throws Exception {
        try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
            System.out.println(">>> Imputing example started.");

            IgniteCache<Integer, Vector> data = null;
            try {
                data = createCache(ignite);

                Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(1, 2);

                // Defines second preprocessor that imputing features.
                Preprocessor<Integer, Vector> preprocessor = new ImputerTrainer<Integer, Vector>()
                    .fit(ignite, data, vectorizer);

                // Creates a cache based simple dataset containing features and providing standard dataset API.
                try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(ignite, data, preprocessor)) {
                    new DatasetHelper(dataset).describe();
                }

                System.out.println(">>> Imputing example completed.");
            }
            finally {
                data.destroy();
            }
        }
        finally {
            System.out.flush();
        }
    }

    /**
     *
     */
    private static IgniteCache<Integer, Vector> createCache(Ignite ignite) {
        CacheConfiguration<Integer, Vector> cacheConfiguration = new CacheConfiguration<>();

        cacheConfiguration.setName("PERSONS");
        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 2));

        IgniteCache<Integer, Vector> persons = ignite.createCache(cacheConfiguration);

        persons.put(1, new DenseVector(new Serializable[] {"Mike", 10, 1}));
        persons.put(1, new DenseVector(new Serializable[] {"John", 20, 2}));
        persons.put(1, new DenseVector(new Serializable[] {"George", 15, 1}));
        persons.put(1, new DenseVector(new Serializable[] {"Piter", 25, Double.NaN}));
        persons.put(1, new DenseVector(new Serializable[] {"Karl", Double.NaN, 1}));
        persons.put(1, new DenseVector(new Serializable[] {"Gustaw", 20, 2}));
        persons.put(1, new DenseVector(new Serializable[] {"Alex", 20, 2}));

        return persons;
    }
}