public static void refresh() {
if (null == CONTEXT) throw new IllegalStateException("The Spark context has not been created.");
final Set<String> keepNames = new HashSet<>();
for (final RDD<?> rdd : JavaConversions.asJavaIterable(CONTEXT.persistentRdds().values())) {
if (null != rdd.name()) {
keepNames.add(rdd.name());
NAME_TO_RDD.put(rdd.name(), rdd);
}
}
// remove all stale names in the NAME_TO_RDD map
NAME_TO_RDD
.keySet()
.stream()
.filter(key -> !keepNames.contains(key))
.collect(Collectors.toList())
.forEach(NAME_TO_RDD::remove);
}
@Test
@SuppressWarnings("unchecked")
public void testJavaRDDFunctions() throws Exception {
JavaRDD<String> mockJavaRDD = mock(JavaRDD.class);
RDD<String> mockRDD = mock(RDD.class);
when(mockJavaRDD.rdd()).thenReturn(mockRDD);
GemFireJavaRDDFunctions wrapper = javaFunctions(mockJavaRDD);
assertTrue(mockRDD == wrapper.rddf.rdd());
Tuple3<SparkContext, GemFireConnectionConf, GemFireConnection> tuple3 = createCommonMocks();
when(mockRDD.sparkContext()).thenReturn(tuple3._1());
PairFunction<String, String, Integer> mockPairFunc = mock(PairFunction.class);
String regionPath = "testregion";
wrapper.saveToGemfire(regionPath, mockPairFunc, tuple3._2());
verify(mockRDD, times(1)).sparkContext();
verify(tuple3._1(), times(1)).runJob(eq(mockRDD), any(Function2.class), any(ClassTag.class));
}
public static void removeRDD(final String name) {
if (null == name) return;
Spark.refresh();
final RDD<?> rdd = NAME_TO_RDD.remove(name);
if (null != rdd) rdd.unpersist(true);
}
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 ovr = new OneVsRest().setClassifier(classifier);
String input = params.input;
RDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), input);
RDD<LabeledPoint> train;
RDD<LabeledPoint> test;
// compute the train/ test split: if testInput is not provided use part of input
String testInput = params.testInput;
if (testInput != null) {
train = inputData;
// compute the number of features in the training set.
int numFeatures = inputData.first().features().size();
test = MLUtils.loadLibSVMFile(jsc.sc(), testInput, numFeatures);
} 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 ovrModel = ovr.fit(trainingDataFrame.cache());
// score the model on test data
DataFrame testDataFrame = jsql.createDataFrame(test, LabeledPoint.class);
DataFrame predictions = ovrModel.transform(testDataFrame.cache()).select("prediction", "label");
// obtain metrics
MulticlassMetrics metrics = new MulticlassMetrics(predictions);
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");
}
Matrix confusionMatrix = metrics.confusionMatrix();
// output the Confusion Matrix
System.out.println("Confusion Matrix");
System.out.println(confusionMatrix);
System.out.println();
System.out.println(results);
jsc.stop();
}
