ml/classification/JavaNaiveBayesSuite.java

0001 /*
0002  * Licensed to the Apache Software Foundation (ASF) under one or more
0003  * contributor license agreements.  See the NOTICE file distributed with
0004  * this work for additional information regarding copyright ownership.
0005  * The ASF licenses this file to You under the Apache License, Version 2.0
0006  * (the "License"); you may not use this file except in compliance with
0007  * the License.  You may obtain a copy of the License at
0008  *
0009  *    http://www.apache.org/licenses/LICENSE-2.0
0010  *
0011  * Unless required by applicable law or agreed to in writing, software
0012  * distributed under the License is distributed on an "AS IS" BASIS,
0013  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014  * See the License for the specific language governing permissions and
0015  * limitations under the License.
0016  */
0017
0018 package org.apache.spark.ml.classification;
0019
0020 import java.util.Arrays;
0021 import java.util.List;
0022
0023 import org.junit.Test;
0024 import static org.junit.Assert.assertEquals;
0025
0026 import org.apache.spark.SharedSparkSession;
0027 import org.apache.spark.ml.linalg.VectorUDT;
0028 import org.apache.spark.ml.linalg.Vectors;
0029 import org.apache.spark.sql.Dataset;
0030 import org.apache.spark.sql.Row;
0031 import org.apache.spark.sql.RowFactory;
0032 import org.apache.spark.sql.types.DataTypes;
0033 import org.apache.spark.sql.types.Metadata;
0034 import org.apache.spark.sql.types.StructField;
0035 import org.apache.spark.sql.types.StructType;
0036
0037 public class JavaNaiveBayesSuite extends SharedSparkSession {
0038
0039   public void validatePrediction(Dataset<Row> predictionAndLabels) {
0040     for (Row r : predictionAndLabels.collectAsList()) {
0041       double prediction = r.getAs(0);
0042       double label = r.getAs(1);
0043       assertEquals(label, prediction, 1E-5);
0044     }
0045   }
0046
0047   @Test
0048   public void naiveBayesDefaultParams() {
0049     NaiveBayes nb = new NaiveBayes();
0050     assertEquals("label", nb.getLabelCol());
0051     assertEquals("features", nb.getFeaturesCol());
0052     assertEquals("prediction", nb.getPredictionCol());
0053     assertEquals(1.0, nb.getSmoothing(), 1E-5);
0054     assertEquals("multinomial", nb.getModelType());
0055   }
0056
0057   @Test
0058   public void testNaiveBayes() {
0059     List<Row> data = Arrays.asList(
0060       RowFactory.create(0.0, Vectors.dense(1.0, 0.0, 0.0)),
0061       RowFactory.create(0.0, Vectors.dense(2.0, 0.0, 0.0)),
0062       RowFactory.create(1.0, Vectors.dense(0.0, 1.0, 0.0)),
0063       RowFactory.create(1.0, Vectors.dense(0.0, 2.0, 0.0)),
0064       RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 1.0)),
0065       RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 2.0)));
0066
0067     StructType schema = new StructType(new StructField[]{
0068       new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
0069       new StructField("features", new VectorUDT(), false, Metadata.empty())
0070     });
0071
0072     Dataset<Row> dataset = spark.createDataFrame(data, schema);
0073     NaiveBayes nb = new NaiveBayes().setSmoothing(0.5).setModelType("multinomial");
0074     NaiveBayesModel model = nb.fit(dataset);
0075
0076     Dataset<Row> predictionAndLabels = model.transform(dataset).select("prediction", "label");
0077     validatePrediction(predictionAndLabels);
0078   }
0079 }