org.apache.spark.examples.ml.JavaFMClassifierExample Maven / Gradle / Ivy

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 * 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
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 *    http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.examples.ml;

// $example on$
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.FMClassificationModel;
import org.apache.spark.ml.classification.FMClassifier;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.ml.feature.*;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $example off$

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

    // $example on$
    // Load and parse the data file, converting it to a DataFrame.
    Dataset data = spark
        .read()
        .format("libsvm")
        .load("data/mllib/sample_libsvm_data.txt");

    // Index labels, adding metadata to the label column.
    // Fit on whole dataset to include all labels in index.
    StringIndexerModel labelIndexer = new StringIndexer()
        .setInputCol("label")
        .setOutputCol("indexedLabel")
        .fit(data);
    // Scale features.
    MinMaxScalerModel featureScaler = new MinMaxScaler()
        .setInputCol("features")
        .setOutputCol("scaledFeatures")
        .fit(data);

    // Split the data into training and test sets (30% held out for testing)
    Dataset[] splits = data.randomSplit(new double[] {0.7, 0.3});
    Dataset trainingData = splits[0];
    Dataset testData = splits[1];

    // Train a FM model.
    FMClassifier fm = new FMClassifier()
        .setLabelCol("indexedLabel")
        .setFeaturesCol("scaledFeatures")
        .setStepSize(0.001);

    // Convert indexed labels back to original labels.
    IndexToString labelConverter = new IndexToString()
        .setInputCol("prediction")
        .setOutputCol("predictedLabel")
        .setLabels(labelIndexer.labelsArray()[0]);

    // Create a Pipeline.
    Pipeline pipeline = new Pipeline()
        .setStages(new PipelineStage[] {labelIndexer, featureScaler, fm, labelConverter});

    // Train model.
    PipelineModel model = pipeline.fit(trainingData);

    // Make predictions.
    Dataset predictions = model.transform(testData);

    // Select example rows to display.
    predictions.select("predictedLabel", "label", "features").show(5);

    // Select (prediction, true label) and compute test accuracy.
    MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
        .setLabelCol("indexedLabel")
        .setPredictionCol("prediction")
        .setMetricName("accuracy");
    double accuracy = evaluator.evaluate(predictions);
    System.out.println("Test Accuracy = " + accuracy);

    FMClassificationModel fmModel = (FMClassificationModel)(model.stages()[2]);
    System.out.println("Factors: " + fmModel.factors());
    System.out.println("Linear: " + fmModel.linear());
    System.out.println("Intercept: " + fmModel.intercept());
    // $example off$

    spark.stop();
  }
}