spark JavaMulticlassLogisticRegressionWithElasticNetExample 源码
spark JavaMulticlassLogisticRegressionWithElasticNetExample 代码
文件路径:/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.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.LogisticRegression;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $example off$
public class JavaMulticlassLogisticRegressionWithElasticNetExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaMulticlassLogisticRegressionWithElasticNetExample")
.getOrCreate();
// $example on$
// Load training data
Dataset<Row> training = spark.read().format("libsvm")
.load("data/mllib/sample_multiclass_classification_data.txt");
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8);
// Fit the model
LogisticRegressionModel lrModel = lr.fit(training);
// Print the coefficients and intercept for multinomial logistic regression
System.out.println("Coefficients: \n"
+ lrModel.coefficientMatrix() + " \nIntercept: " + lrModel.interceptVector());
LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();
// Obtain the loss per iteration.
double[] objectiveHistory = trainingSummary.objectiveHistory();
for (double lossPerIteration : objectiveHistory) {
System.out.println(lossPerIteration);
}
// for multiclass, we can inspect metrics on a per-label basis
System.out.println("False positive rate by label:");
int i = 0;
double[] fprLabel = trainingSummary.falsePositiveRateByLabel();
for (double fpr : fprLabel) {
System.out.println("label " + i + ": " + fpr);
i++;
}
System.out.println("True positive rate by label:");
i = 0;
double[] tprLabel = trainingSummary.truePositiveRateByLabel();
for (double tpr : tprLabel) {
System.out.println("label " + i + ": " + tpr);
i++;
}
System.out.println("Precision by label:");
i = 0;
double[] precLabel = trainingSummary.precisionByLabel();
for (double prec : precLabel) {
System.out.println("label " + i + ": " + prec);
i++;
}
System.out.println("Recall by label:");
i = 0;
double[] recLabel = trainingSummary.recallByLabel();
for (double rec : recLabel) {
System.out.println("label " + i + ": " + rec);
i++;
}
System.out.println("F-measure by label:");
i = 0;
double[] fLabel = trainingSummary.fMeasureByLabel();
for (double f : fLabel) {
System.out.println("label " + i + ": " + f);
i++;
}
double accuracy = trainingSummary.accuracy();
double falsePositiveRate = trainingSummary.weightedFalsePositiveRate();
double truePositiveRate = trainingSummary.weightedTruePositiveRate();
double fMeasure = trainingSummary.weightedFMeasure();
double precision = trainingSummary.weightedPrecision();
double recall = trainingSummary.weightedRecall();
System.out.println("Accuracy: " + accuracy);
System.out.println("FPR: " + falsePositiveRate);
System.out.println("TPR: " + truePositiveRate);
System.out.println("F-measure: " + fMeasure);
System.out.println("Precision: " + precision);
System.out.println("Recall: " + recall);
// $example off$
spark.stop();
}
}
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