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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 org.apache.spark.ml;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.feature.StandardScaler;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList;
/**
* Test Pipeline construction and fitting in Java.
*/
public class JavaPipelineSuite {
private transient JavaSparkContext jsc;
private transient SQLContext jsql;
private transient DataFrame dataset;
@Before
public void setUp() {
jsc = new JavaSparkContext("local", "JavaPipelineSuite");
jsql = new SQLContext(jsc);
JavaRDD<LabeledPoint> points =
jsc.parallelize(generateLogisticInputAsList(1.0, 1.0, 100, 42), 2);
dataset = jsql.applySchema(points, LabeledPoint.class);
}
@After
public void tearDown() {
jsc.stop();
jsc = null;
}
@Test
public void pipeline() {
StandardScaler scaler = new StandardScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures");
LogisticRegression lr = new LogisticRegression()
.setFeaturesCol("scaledFeatures");
Pipeline pipeline = new Pipeline()
.setStages(new PipelineStage[] {scaler, lr});
PipelineModel model = pipeline.fit(dataset);
model.transform(dataset).registerTempTable("prediction");
DataFrame predictions = jsql.sql("SELECT label, score, prediction FROM prediction");
predictions.collectAsList();
}
}
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