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[SPARK-11549][Docs] Replace example code in mllib-evaluation-metrics.md using include_example #9689

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[SPARK-11549][Docs] Replace example code in mllib-evaluation-metrics.md using include_example #9689

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377d7a9
Intial commit..added all files
Nov 9, 2015
cb9c846
Fixed scala issues
Nov 13, 2015
ed33687
fixed styling issues
Nov 13, 2015
3af5fa3
removed build files
Nov 13, 2015
1106cae
java style issues
Nov 13, 2015
ad3c01e
fixed scala style issues
Nov 13, 2015
4d18447
fixed java style issues
Nov 13, 2015
3c40a35
fixed styling issues
Nov 18, 2015
892591b
fixed mllib-evaluation-metrics.md file
Nov 18, 2015
8d2d508
fixed java issues
Nov 18, 2015
54008ce
fixed import issue
Nov 19, 2015
1c5cc8f
fixed spacing and removed few imports and re-ordered imports
Nov 19, 2015
88512e7
removed extra lines & fixed style issues
Nov 19, 2015
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java style issues
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Vikas Nelamangala authored and Vikas Nelamangala committed Nov 13, 2015
commit 1106cae4f74cf4727b870c9eb8e695dfd0c423a9
151 changes: 75 additions & 76 deletions examples/src/main/java/org/apache/spark/examples/mllib/JavaBinaryClassification.java
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Expand Up @@ -19,6 +19,7 @@
package org.apache.spark.examples.mllib;

// $example on$

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@yinxusen yinxusen Nov 13, 2015

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There should not have a blank line. Pls remove it.

import scala.Tuple2;

import org.apache.spark.api.java.*;
Expand All @@ -32,82 +33,80 @@
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;


public class JavaBinaryClassification {
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We'd better making these class names more concretely. I.e. JavaBinaryClassificatinMetricsExample. And the same for the following classes.

public static void main(String[] args) {

SparkConf conf = new SparkConf().setAppName("Binary Classification Metrics");
SparkContext sc = new SparkContext(conf);
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, "myModelPath");
LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc, "myModelPath");
}
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Binary Classification Metrics");
SparkContext sc = new SparkContext(conf);
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$example on$ here.

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)
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Fix the style error. Spark uses 2-space indent in Java and Scala, 4-indent in Python.

.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>>() {
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Fix indention. Refer to Spark Scala Guide. Check your code style with dev/scalastyle, dev/lint-python, etc.

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, "myModelPath");
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Pls change all the "myModelPath" to "target/tmp/XXXModel". Here we can use "target/tmp/LogisticRegressionModel". Change the following paths accordingly.

LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc, "myModelPath");
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$example off$ here

}
}
// $example off$
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delete this line

94 changes: 47 additions & 47 deletions examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegression.java
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Expand Up @@ -19,6 +19,7 @@
package org.apache.spark.examples.mllib;

// $example on$

import scala.Tuple2;

import org.apache.spark.api.java.*;
Expand All @@ -30,63 +31,62 @@
import org.apache.spark.mllib.evaluation.RegressionMetrics;
import org.apache.spark.SparkConf;


// Read in the ratings data
public class JavaLinearRegression {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Linear Regression Example");
JavaSparkContext sc = new JavaSparkContext(conf);
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Linear Regression Example");
JavaSparkContext sc = new JavaSparkContext(conf);

// Load and parse the data
String path = "data/mllib/sample_linear_regression_data.txt";
JavaRDD<String> data = sc.textFile(path);
JavaRDD<LabeledPoint> parsedData = data.map(
new Function<String, LabeledPoint>() {
public LabeledPoint call(String line) {
String[] parts = line.split(" ");
double[] v = new double[parts.length - 1];
for (int i = 1; i < parts.length - 1; i++)
v[i - 1] = Double.parseDouble(parts[i].split(":")[1]);
return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
}
}
);
parsedData.cache();
// Load and parse the data
String path = "data/mllib/sample_linear_regression_data.txt";
JavaRDD<String> data = sc.textFile(path);
JavaRDD<LabeledPoint> parsedData = data.map(
new Function<String, LabeledPoint>() {
public LabeledPoint call(String line) {
String[] parts = line.split(" ");
double[] v = new double[parts.length - 1];
for (int i = 1; i < parts.length - 1; i++)
v[i - 1] = Double.parseDouble(parts[i].split(":")[1]);
return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
}
}
);
parsedData.cache();

// Building the model
int numIterations = 100;
final LinearRegressionModel model =
LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations);
// Building the model
int numIterations = 100;
final LinearRegressionModel model =
LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations);

// Evaluate model on training examples and compute training error
JavaRDD<Tuple2<Object, Object>> valuesAndPreds = parsedData.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
public Tuple2<Object, Object> call(LabeledPoint point) {
double prediction = model.predict(point.features());
return new Tuple2<Object, Object>(prediction, point.label());
}
}
);
// Evaluate model on training examples and compute training error
JavaRDD<Tuple2<Object, Object>> valuesAndPreds = parsedData.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
public Tuple2<Object, Object> call(LabeledPoint point) {
double prediction = model.predict(point.features());
return new Tuple2<Object, Object>(prediction, point.label());
}
}
);

// Instantiate metrics object
RegressionMetrics metrics = new RegressionMetrics(valuesAndPreds.rdd());
// Instantiate metrics object
RegressionMetrics metrics = new RegressionMetrics(valuesAndPreds.rdd());

// Squared error
System.out.format("MSE = %f\n", metrics.meanSquaredError());
System.out.format("RMSE = %f\n", metrics.rootMeanSquaredError());
// Squared error
System.out.format("MSE = %f\n", metrics.meanSquaredError());
System.out.format("RMSE = %f\n", metrics.rootMeanSquaredError());

// R-squared
System.out.format("R Squared = %f\n", metrics.r2());
// R-squared
System.out.format("R Squared = %f\n", metrics.r2());

// Mean absolute error
System.out.format("MAE = %f\n", metrics.meanAbsoluteError());
// Mean absolute error
System.out.format("MAE = %f\n", metrics.meanAbsoluteError());

// Explained variance
System.out.format("Explained Variance = %f\n", metrics.explainedVariance());
// Explained variance
System.out.format("Explained Variance = %f\n", metrics.explainedVariance());

// Save and load model
model.save(sc.sc(), "myModelPath");
LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(), "myModelPath");
}
// Save and load model
model.save(sc.sc(), "myModelPath");
LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(), "myModelPath");
}
}
// $example off$
73 changes: 38 additions & 35 deletions examples/src/main/java/org/apache/spark/examples/mllib/JavaMultiLabelClassification.java
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Expand Up @@ -19,60 +19,63 @@
package org.apache.spark.examples.mllib;

// $example on$

import scala.Tuple2;

import org.apache.spark.api.java.*;
import org.apache.spark.rdd.RDD;
import org.apache.spark.mllib.evaluation.MultilabelMetrics;
import org.apache.spark.SparkConf;

import java.util.Arrays;
import java.util.List;
// $example off$
import org.apache.spark.SparkContext;

// $example on$
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We do not place $example on$ in the beginning of a class.

public class JavaMultiLabelClassification {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Multilabel Classification Metrics");
JavaSparkContext sc = new JavaSparkContext(conf);
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Multilabel Classification Metrics");
JavaSparkContext sc = new JavaSparkContext(conf);

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$example on$ here.

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);
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());
// 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());
// 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]));
}
// 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]));
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We use 100 chars per line. Wrap the line if it exceeds 100 chars.

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());
// 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());
// Hamming loss
System.out.format("Hamming loss = %f\n", metrics.hammingLoss());

// Subset accuracy
System.out.format("Subset accuracy = %f\n", metrics.subsetAccuracy());
// Subset accuracy
System.out.format("Subset accuracy = %f\n", metrics.subsetAccuracy());

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substitute the blank line with a $example off$.

}
}
}
// $example off$
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remove it.

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