public static void main(String[] args) {
if (!parseArgs(args)) throw new IllegalArgumentException("Parse arguments failed.");
SparkConf conf = new SparkConf().setAppName("Logistic Regression with SGD");
SparkContext sc = new SparkContext(conf);
JavaRDD<String> data = sc.textFile(inputFile, 1).toJavaRDD();
JavaRDD<LabeledPoint> training =
data.map(
new Function<String, LabeledPoint>() {
public LabeledPoint call(String line) {
String[] splits = line.split(",");
double[] features = new double[3];
try {
features[0] = Double.valueOf(splits[1]);
features[1] = Double.valueOf(splits[2]);
features[2] = Double.valueOf(splits[3]);
return new LabeledPoint(Double.valueOf(splits[3]), Vectors.dense(features));
} catch (NumberFormatException e) {
return null; // Nothing to do..
}
}
})
.filter(
new Function<LabeledPoint, Boolean>() {
public Boolean call(LabeledPoint p) {
return p != null;
}
})
.cache();
LogisticRegressionModel model = lrs.run(training.rdd());
model.save(sc, outputFile);
sc.stop();
}
public static void create(final Configuration configuration) {
final SparkConf sparkConf = new SparkConf();
configuration
.getKeys()
.forEachRemaining(key -> sparkConf.set(key, configuration.getProperty(key).toString()));
sparkConf.setAppName("Apache TinkerPop's Spark-Gremlin");
CONTEXT = SparkContext.getOrCreate(sparkConf);
}
public static void main(String[] args) throws FileNotFoundException {
// Set up contexts.
SparkContext sc = SparkUtils.getSparkContext();
SQLContext sqlContext = SparkUtils.getSqlContext(sc);
String workingDirectory = args[0];
// read positive data set, cases where the PDB ID occurs in the sentence of the primary citation
String positivesIFileName = workingDirectory + "/" + "PositivesI.parquet";
DataFrame positivesI = sqlContext.read().parquet(positivesIFileName);
sqlContext.registerDataFrameAsTable(positivesI, "positivesI");
DataFrame positives =
sqlContext.sql(
"SELECT pdb_id, match_type, deposition_year, pmc_id, pm_id, publication_year, CAST(primary_citation AS double) AS label, sentence, blinded_sentence FROM positivesI");
positives.show(10);
String positivesIIFileName = workingDirectory + "/" + "PositivesII.parquet";
DataFrame positivesII = sqlContext.read().parquet(positivesIIFileName);
// System.out.println("Sampling 16% of positivesII");
// positivesII = positivesII.sample(false, 0.16, 1);
sqlContext.registerDataFrameAsTable(positivesII, "positivesII");
DataFrame negatives =
sqlContext.sql(
"SELECT pdb_id, match_type, deposition_year, pmc_id, pm_id, publication_year, CAST(primary_citation AS double) AS label, sentence, blinded_sentence FROM positivesII WHERE sentence NOT LIKE '%deposited%' AND sentence NOT LIKE '%submitted%'");
negatives.show(10);
long start = System.nanoTime();
String metricFileName = workingDirectory + "/" + "PdbPrimaryCitationMetrics.txt";
PrintWriter writer = new PrintWriter(metricFileName);
writer.println("PDB Primary Citation Classification: Logistic Regression Results");
String modelFileName = workingDirectory + "/" + "PdbPrimaryCitationModel.ser";
writer.println(train(sqlContext, positives, negatives, modelFileName));
writer.close();
long end = System.nanoTime();
System.out.println("Time: " + (end - start) / 1E9 + " sec.");
sc.stop();
}
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);
}
/** NewHadoopRDD */
@Override
public RDD<Tuple> convert(List<RDD<Tuple>> predecessorRdds, POLoad poLoad) throws IOException {
if (predecessorRdds.size() != 0) {
throw new RuntimeException(
"Should not have predecessorRdds for Load. Got : " + predecessorRdds);
}
JobConf loadJobConf = SparkUtil.newJobConf(pigContext);
configureLoader(physicalPlan, poLoad, loadJobConf);
RDD<Tuple2<Text, Tuple>> hadoopRDD =
sparkContext.newAPIHadoopFile(
poLoad.getLFile().getFileName(),
PigInputFormat.class, // InputFormat class
Text.class, // K class
Tuple.class, // V class
loadJobConf);
// map to get just RDD<Tuple>
return hadoopRDD.map(TO_TUPLE_FUNCTION, ScalaUtil.getClassTag(Tuple.class));
}
public static void create(final String master) {
final SparkConf sparkConf = new SparkConf();
sparkConf.setAppName("Apache TinkerPop's Spark-Gremlin");
sparkConf.setMaster(master);
CONTEXT = SparkContext.getOrCreate(sparkConf);
}
public static void close() {
NAME_TO_RDD.clear();
if (null != CONTEXT) CONTEXT.stop();
CONTEXT = null;
}
@BeforeClass
public static void setUp() {
SparkConf sparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkIT");
javaSparkContext = JavaSparkContext.fromSparkContext(SparkContext.getOrCreate(sparkConf));
}
