content: /*
   * 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 java.io.IOException;
  
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.ml.classification.LogisticRegression;
  import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
  import org.apache.spark.ml.feature.LabeledPoint;
  import org.apache.spark.ml.feature.StandardScaler;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  
  /**
   * Test Pipeline construction and fitting in Java.
   */
  public class JavaPipelineSuite extends SharedSparkSession {
  
    private transient Dataset<Row> dataset;
  
    @Override
    public void setUp() throws IOException {
      super.setUp();
      JavaRDD<LabeledPoint> points =
        jsc.parallelize(generateLogisticInputAsList(1.0, 1.0, 100, 42), 2);
      dataset = spark.createDataFrame(points, LabeledPoint.class);
    }
  
    @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).createOrReplaceTempView("prediction");
      Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction");
      predictions.collectAsList();
    }
  }
  