spark JavaLogisticRegressionSummaryExample 源码
spark JavaLogisticRegressionSummaryExample 代码
文件路径：/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java
/*
* 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.examples.ml;
// $example on$
import org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;
// $example off$
public class JavaLogisticRegressionSummaryExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaLogisticRegressionSummaryExample")
.getOrCreate();
// Load training data
Dataset<Row> training = spark.read().format("libsvm")
.load("data/mllib/sample_libsvm_data.txt");
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8);
// Fit the model
LogisticRegressionModel lrModel = lr.fit(training);
// $example on$
// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier
// example
BinaryLogisticRegressionTrainingSummary trainingSummary = lrModel.binarySummary();
// Obtain the loss per iteration.
double[] objectiveHistory = trainingSummary.objectiveHistory();
for (double lossPerIteration : objectiveHistory) {
System.out.println(lossPerIteration);
}
// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
Dataset<Row> roc = trainingSummary.roc();
roc.show();
roc.select("FPR").show();
System.out.println(trainingSummary.areaUnderROC());
// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
// this selected threshold.
Dataset<Row> fMeasure = trainingSummary.fMeasureByThreshold();
double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0);
double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure))
.select("threshold").head().getDouble(0);
lrModel.setThreshold(bestThreshold);
// $example off$
spark.stop();
}
}
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