it.unipd.dei.dm1617.examples.JavaWord2VecExample.java Source code

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/*
 * 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 it.unipd.dei.dm1617.examples;

// $example on$
import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.Word2Vec;
import org.apache.spark.ml.feature.Word2VecModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
// $example off$

public class JavaWord2VecExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession.builder().appName("JavaWord2VecExample").getOrCreate();

        // $example on$
        // Input data: Each row is a bag of words from a sentence or document.
        List<Row> data = Arrays.asList(RowFactory.create(Arrays.asList("Hi I heard about Spark heard".split(" "))),
                RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
                RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" "))));
        StructType schema = new StructType(new StructField[] {
                new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty()) });
        Dataset<Row> documentDF = spark.createDataFrame(data, schema);

        // Learn a mapping from words to Vectors.
        Word2Vec word2Vec = new Word2Vec().setInputCol("text").setOutputCol("result").setVectorSize(4)
                .setMinCount(0);

        Word2VecModel model = word2Vec.fit(documentDF);
        Dataset<Row> result = model.transform(documentDF);

        for (Row row : result.collectAsList()) {
            List<String> text = row.getList(0);
            Vector vector = (Vector) row.get(1);
            System.out.println("Text: " + text + " => \nVector: " + vector + "\n");
        }
        // $example off$

        spark.stop();
    }
}