[SPARK-11549][Docs] Replace example code in mllib-evaluation-metrics.md using include_example

examples/src/main/java/org/apache/spark/examples/mllib/JavaBinaryClassificationMetricsExample.java

@@ -0,0 +1,113 @@
+/*
+ * 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.mllib;
+
+// $example on$
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS;
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+// $example off$
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class JavaBinaryClassificationMetricsExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("Java Binary Classification Metrics Example");
+ SparkContext sc = new SparkContext(conf);
+ // $example on$
+ String path = "data/mllib/sample_binary_classification_data.txt";
+ JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+
+ // Split initial RDD into two... [60% training data, 40% testing data].
+ JavaRDD<LabeledPoint>[] splits =
+ data.randomSplit(new double[]{0.6, 0.4}, 11L);
+ JavaRDD<LabeledPoint> training = splits[0].cache();
+ JavaRDD<LabeledPoint> test = splits[1];
+
+ // Run training algorithm to build the model.
+ final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
+ .setNumClasses(2)
+ .run(training.rdd());
+
+ // Clear the prediction threshold so the model will return probabilities
+ model.clearThreshold();
+
+ // Compute raw scores on the test set.
+ JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
+ new Function<LabeledPoint, Tuple2<Object, Object>>() {
+ public Tuple2<Object, Object> call(LabeledPoint p) {
+ Double prediction = model.predict(p.features());
+ return new Tuple2<Object, Object>(prediction, p.label());
+ }
+ }
+ );
+
+ // Get evaluation metrics.
+ BinaryClassificationMetrics metrics = new BinaryClassificationMetrics(predictionAndLabels.rdd());
+
+ // Precision by threshold
+ JavaRDD<Tuple2<Object, Object>> precision = metrics.precisionByThreshold().toJavaRDD();
+ System.out.println("Precision by threshold: " + precision.toArray());
+
+ // Recall by threshold
+ JavaRDD<Tuple2<Object, Object>> recall = metrics.recallByThreshold().toJavaRDD();
+ System.out.println("Recall by threshold: " + recall.toArray());
+
+ // F Score by threshold
+ JavaRDD<Tuple2<Object, Object>> f1Score = metrics.fMeasureByThreshold().toJavaRDD();
+ System.out.println("F1 Score by threshold: " + f1Score.toArray());
+
+ JavaRDD<Tuple2<Object, Object>> f2Score = metrics.fMeasureByThreshold(2.0).toJavaRDD();
+ System.out.println("F2 Score by threshold: " + f2Score.toArray());
+
+ // Precision-recall curve
+ JavaRDD<Tuple2<Object, Object>> prc = metrics.pr().toJavaRDD();
+ System.out.println("Precision-recall curve: " + prc.toArray());
+
+ // Thresholds
+ JavaRDD<Double> thresholds = precision.map(
+ new Function<Tuple2<Object, Object>, Double>() {
+ public Double call(Tuple2<Object, Object> t) {
+ return new Double(t._1().toString());
+ }
+ }
+ );
+
+ // ROC Curve
+ JavaRDD<Tuple2<Object, Object>> roc = metrics.roc().toJavaRDD();
+ System.out.println("ROC curve: " + roc.toArray());
+
+ // AUPRC
+ System.out.println("Area under precision-recall curve = " + metrics.areaUnderPR());
+
+ // AUROC
+ System.out.println("Area under ROC = " + metrics.areaUnderROC());
+
+ // Save and load model
+ model.save(sc, "target/tmp/LogisticRegressionModel");
+ LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc,
+ "target/tmp/LogisticRegressionModel");
+ // $example off$
+ }
+}

examples/src/main/java/org/apache/spark/examples/mllib/JavaMultiLabelClassificationMetricsExample.java

@@ -0,0 +1,80 @@
+/*
+ * 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.mllib;
+
+// $example on$
+import java.util.Arrays;
+import java.util.List;
+
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.mllib.evaluation.MultilabelMetrics;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.SparkConf;
+// $example off$
+import org.apache.spark.SparkContext;
+
+public class JavaMultiLabelClassificationMetricsExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("Multilabel Classification Metrics Example");
+ JavaSparkContext sc = new JavaSparkContext(conf);
+ // $example on$
+ List<Tuple2<double[], double[]>> data = Arrays.asList(
+ new Tuple2<double[], double[]>(new double[]{0.0, 1.0}, new double[]{0.0, 2.0}),
+ new Tuple2<double[], double[]>(new double[]{0.0, 2.0}, new double[]{0.0, 1.0}),
+ new Tuple2<double[], double[]>(new double[]{}, new double[]{0.0}),
+ new Tuple2<double[], double[]>(new double[]{2.0}, new double[]{2.0}),
+ new Tuple2<double[], double[]>(new double[]{2.0, 0.0}, new double[]{2.0, 0.0}),
+ new Tuple2<double[], double[]>(new double[]{0.0, 1.0, 2.0}, new double[]{0.0, 1.0}),
+ new Tuple2<double[], double[]>(new double[]{1.0}, new double[]{1.0, 2.0})
+ );
+ JavaRDD<Tuple2<double[], double[]>> scoreAndLabels = sc.parallelize(data);
+
+ // Instantiate metrics object
+ MultilabelMetrics metrics = new MultilabelMetrics(scoreAndLabels.rdd());
+
+ // Summary stats
+ System.out.format("Recall = %f\n", metrics.recall());
+ System.out.format("Precision = %f\n", metrics.precision());
+ System.out.format("F1 measure = %f\n", metrics.f1Measure());
+ System.out.format("Accuracy = %f\n", metrics.accuracy());
+
+ // Stats by labels
+ for (int i = 0; i < metrics.labels().length - 1; i++) {
+ System.out.format("Class %1.1f precision = %f\n", metrics.labels()[i], metrics.precision
+ (metrics.labels()[i]));
+ System.out.format("Class %1.1f recall = %f\n", metrics.labels()[i], metrics.recall(metrics
+ .labels()[i]));
+ System.out.format("Class %1.1f F1 score = %f\n", metrics.labels()[i], metrics.f1Measure
+ (metrics.labels()[i]));
+ }
+
+ // Micro stats
+ System.out.format("Micro recall = %f\n", metrics.microRecall());
+ System.out.format("Micro precision = %f\n", metrics.microPrecision());
+ System.out.format("Micro F1 measure = %f\n", metrics.microF1Measure());
+
+ // Hamming loss
+ System.out.format("Hamming loss = %f\n", metrics.hammingLoss());
+
+ // Subset accuracy
+ System.out.format("Subset accuracy = %f\n", metrics.subsetAccuracy());
+ // $example off$
+ }
+}

examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java

@@ -0,0 +1,97 @@
+/*
+ * 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.mllib;
+
+// $example on$
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS;
+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.mllib.linalg.Matrix;
+// $example off$
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class JavaMulticlassClassificationMetricsExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("Multi class Classification Metrics Example");
+ SparkContext sc = new SparkContext(conf);
+ // $example on$
+ String path = "data/mllib/sample_multiclass_classification_data.txt";
+ JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+
+ // Split initial RDD into two... [60% training data, 40% testing data].
+ JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.6, 0.4}, 11L);
+ JavaRDD<LabeledPoint> training = splits[0].cache();
+ JavaRDD<LabeledPoint> test = splits[1];
+
+ // Run training algorithm to build the model.
+ final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
+ .setNumClasses(3)
+ .run(training.rdd());
+
+ // Compute raw scores on the test set.
+ JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
+ new Function<LabeledPoint, Tuple2<Object, Object>>() {
+ public Tuple2<Object, Object> call(LabeledPoint p) {
+ Double prediction = model.predict(p.features());
+ return new Tuple2<Object, Object>(prediction, p.label());
+ }
+ }
+ );
+
+ // Get evaluation metrics.
+ MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd());
+
+ // Confusion matrix
+ Matrix confusion = metrics.confusionMatrix();
+ System.out.println("Confusion matrix: \n" + confusion);
+
+ // Overall statistics
+ System.out.println("Precision = " + metrics.precision());
+ System.out.println("Recall = " + metrics.recall());
+ System.out.println("F1 Score = " + metrics.fMeasure());
+
+ // Stats by labels
+ for (int i = 0; i < metrics.labels().length; i++) {
+ System.out.format("Class %f precision = %f\n", metrics.labels()[i],metrics.precision
+ (metrics.labels()[i]));
+ System.out.format("Class %f recall = %f\n", metrics.labels()[i], metrics.recall(metrics
+ .labels()[i]));
+ System.out.format("Class %f F1 score = %f\n", metrics.labels()[i], metrics.fMeasure
+ (metrics.labels()[i]));
+ }
+
+ //Weighted stats
+ System.out.format("Weighted precision = %f\n", metrics.weightedPrecision());
+ System.out.format("Weighted recall = %f\n", metrics.weightedRecall());
+ System.out.format("Weighted F1 score = %f\n", metrics.weightedFMeasure());
+ System.out.format("Weighted false positive rate = %f\n", metrics.weightedFalsePositiveRate());
+
+ // Save and load model
+ model.save(sc, "target/tmp/LogisticRegressionModel");
+ LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc,
+ "target/tmp/LogisticRegressionModel");
+ // $example off$
+ }
+}