Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6115#discussion_r30350138
--- Diff:
examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
---
@@ -0,0 +1,225 @@
+/*
+ * 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;
+
+import org.apache.commons.cli.*;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegression;
+import org.apache.spark.ml.classification.OneVsRest;
+import org.apache.spark.ml.classification.OneVsRestModel;
+import org.apache.spark.ml.util.MetadataUtils;
+import org.apache.spark.mllib.evaluation.MulticlassMetrics;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.StructField;
+
+/**
+ * An example runner for Multiclass to Binary Reduction with One Vs Rest.
+ * The example uses Logistic Regression as the base classifier. All
parameters that
+ * can be specified on the base classifier can be passed in to the runner
options.
+ * Run with
+ * <pre>
+ * bin/run-example ml.JavaOneVsRestExample [options]
+ * </pre>
+ */
+public class JavaOneVsRestExample {
+
+ private static class Params {
+ String input;
+ String testInput = null;
+ Integer maxIter = 100;
+ double tol = 1E-6;
+ boolean fitIntercept = true;
+ Double regParam = null;
+ Double elasticNetParam = null;
+ double fracTest = 0.2;
+ }
+
+ public static void main(String[] args) {
+ // parse the arguments
+ Params params = parse(args);
+ SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample");
+ JavaSparkContext jsc = new JavaSparkContext(conf);
+ SQLContext jsql = new SQLContext(jsc);
+
+ // configure the base classifier
+ LogisticRegression classifier = new LogisticRegression()
+ .setMaxIter(params.maxIter)
+ .setTol(params.tol)
+ .setFitIntercept(params.fitIntercept);
+
+ if (params.regParam != null) {
+ classifier.setRegParam(params.regParam);
+ }
+ if (params.elasticNetParam != null) {
+ classifier.setElasticNetParam(params.elasticNetParam);
+ }
+
+ // instantiate the One Vs Rest Classifier
+ OneVsRest ova = new OneVsRest();
+ ova.setClassifier(classifier);
+
+ String input = params.input;
+ RDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), input);
+ RDD<LabeledPoint> train;
+ RDD<LabeledPoint> test;
+ String testInput = params.testInput;
+
+ // compute the train/test split: if testInput is not provided use part
of input.
+ if (testInput != null) {
+ train = inputData;
+ test = MLUtils.loadLibSVMFile(jsc.sc(), testInput);
+ } else {
+ double f = params.fracTest;
+ RDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{1 - f,
f}, 12345);
+ train = tmp[0];
+ test = tmp[1];
+ }
+
+ // train the multiclass model.
+ DataFrame trainingDataframe = jsql.createDataFrame(train,
LabeledPoint.class);
+ OneVsRestModel ovaModel = ova.fit(trainingDataframe.cache());
+
+ // score the model on test data.
+ DataFrame testDataframe = jsql.createDataFrame(test,
LabeledPoint.class);
+ DataFrame predictions = ovaModel.transform(testDataframe.cache())
+ .select("prediction", "label");
+
+ MulticlassMetrics metrics = new MulticlassMetrics(predictions);
+
+ // output the confusion matrix.
+ System.out.println("ConfusionMatrix");
+ System.out.println(metrics.confusionMatrix().toString());
+
+ StructField predictionColSchema =
predictions.schema().apply("prediction");
+ Integer numClasses = (Integer)
MetadataUtils.getNumClasses(predictionColSchema).get();
+
+ // compute the false positive rate per label
+ StringBuilder results = new StringBuilder();
+ results.append("label\tfpr\n");
+ for (int label = 0; label < numClasses; label++) {
+ results.append(label);
+ results.append("\t");
+ results.append(metrics.falsePositiveRate((double) label));
+ results.append("\n");
+ }
+ System.out.println(results);
+
+ jsc.stop();
+ }
+
+ private static Params parse(String[] args) {
+ String input = args[0];
--- End diff --
Could you please check for args being empty and print the help message if
it is?
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