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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.regression;
  
  import java.io.IOException;
  import java.util.List;
  
  import org.junit.Test;
  import static org.junit.Assert.assertEquals;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.api.java.JavaRDD;
  import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
  import org.apache.spark.ml.feature.LabeledPoint;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  
  public class JavaLinearRegressionSuite extends SharedSparkSession {
    private transient Dataset<Row> dataset;
    private transient JavaRDD<LabeledPoint> datasetRDD;
  
    @Override
    public void setUp() throws IOException {
      super.setUp();
      List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42);
      datasetRDD = jsc.parallelize(points, 2);
      dataset = spark.createDataFrame(datasetRDD, LabeledPoint.class);
      dataset.createOrReplaceTempView("dataset");
    }
  
    @Test
    public void linearRegressionDefaultParams() {
      LinearRegression lr = new LinearRegression();
      assertEquals("label", lr.getLabelCol());
      assertEquals("auto", lr.getSolver());
      LinearRegressionModel model = lr.fit(dataset);
      model.transform(dataset).createOrReplaceTempView("prediction");
      Dataset<Row> predictions = spark.sql("SELECT label, prediction FROM prediction");
      predictions.collect();
      // Check defaults
      assertEquals("features", model.getFeaturesCol());
      assertEquals("prediction", model.getPredictionCol());
    }
  
    @Test
    public void linearRegressionWithSetters() {
      // Set params, train, and check as many params as we can.
      LinearRegression lr = new LinearRegression()
        .setMaxIter(10)
        .setRegParam(1.0).setSolver("l-bfgs");
      LinearRegressionModel model = lr.fit(dataset);
      LinearRegression parent = (LinearRegression) model.parent();
      assertEquals(10, parent.getMaxIter());
      assertEquals(1.0, parent.getRegParam(), 0.0);
  
      // Call fit() with new params, and check as many params as we can.
      LinearRegressionModel model2 =
        lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred"));
      LinearRegression parent2 = (LinearRegression) model2.parent();
      assertEquals(5, parent2.getMaxIter());
      assertEquals(0.1, parent2.getRegParam(), 0.0);
      assertEquals("thePred", model2.getPredictionCol());
    }
  }
  