@Test
public void testSpeed() {
long total = 0;
UpperQuantile data = new UpperQuantile(5000);
Random gen = RandomUtils.getRandom();
for (int i = 0; i < 10000; i++) {
data.add(gen.nextDouble());
}
data.clear();
int n = 100000;
for (int i = 0; i < n; i++) {
double x = gen.nextDouble();
long t0 = System.nanoTime();
data.add(x);
long t1 = System.nanoTime();
total += t1 - t0;
}
// time per insert should be less than a micro-second. Typically this actually comes out ~300
// ns
log.debug("t = {} us", total / 1e9 / n / 1e-6);
Assert.assertTrue(total / 1e9 / n < 100e-6);
total = 0;
for (int i = 0; i < 10; i++) {
double q = gen.nextDouble() * 0.01 + 0.99;
long t0 = System.nanoTime();
double r = data.quantile(q);
long t1 = System.nanoTime();
Assert.assertEquals(String.format("q=%.3f r=%.3f i=%d", q, r, i), q, r, 0.01);
total += t1 - t0;
}
log.debug("t = {} us", total / 1e9 / 10 / 1e-6);
}
/**
* Dummy FitnessEvaluator that stores the evaluations it calculates. Uses a static storage to handle
* the evaluator duplication when passed as a Job parameter.
*/
public final class DummyEvaluator implements FitnessEvaluator<DummyCandidate> {
private final Random rng = RandomUtils.getRandom();
private static final Map<Integer, Double> evaluations = Maps.newHashMap();
public static double getFitness(Integer key) {
if (!evaluations.containsKey(key)) {
throw new IllegalArgumentException("Fitness not found");
}
return evaluations.get(key);
}
public static void clearEvaluations() {
evaluations.clear();
}
@Override
public double getFitness(DummyCandidate candidate, List<? extends DummyCandidate> population) {
if (evaluations.containsKey(candidate.getIndex())) {
throw new IllegalArgumentException("Duplicate Fitness");
}
double fitness = rng.nextDouble();
evaluations.put(candidate.getIndex(), fitness);
return fitness;
}
@Override
public boolean isNatural() {
return false;
}
}
@Test
public void testRecordReader() throws Exception {
int n = 1;
int maxNumSplits = 100;
int maxNbTrees = 1000;
Random rng = RandomUtils.getRandom();
for (int nloop = 0; nloop < n; nloop++) {
int numSplits = rng.nextInt(maxNumSplits) + 1;
int nbTrees = rng.nextInt(maxNbTrees) + 1;
Configuration conf = getConfiguration();
Builder.setNbTrees(conf, nbTrees);
InMemInputFormat inputFormat = new InMemInputFormat();
List<InputSplit> splits = inputFormat.getSplits(conf, numSplits);
for (int index = 0; index < numSplits; index++) {
InMemInputSplit split = (InMemInputSplit) splits.get(index);
InMemRecordReader reader = new InMemRecordReader(split);
reader.initialize(split, null);
for (int tree = 0; tree < split.getNbTrees(); tree++) {
// reader.next() should return true until there is no tree left
assertEquals(tree < split.getNbTrees(), reader.nextKeyValue());
assertEquals(split.getFirstId() + tree, reader.getCurrentKey().get());
}
}
}
}
private class VectIterator implements Iterator<Vector> {
private int count;
private final Random random = RandomUtils.getRandom();
@Override
public boolean hasNext() {
return count < numItems;
}
@Override
public Vector next() {
if (!hasNext()) {
throw new NoSuchElementException();
}
Vector result =
type == VectorType.SPARSE
? new RandomAccessSparseVector(numItems)
: new DenseVector(numItems);
result.assign(
new UnaryFunction() {
@Override
public double apply(double arg1) {
return random.nextDouble();
}
});
count++;
return result;
}
@Override
public void remove() {
throw new UnsupportedOperationException();
}
}
private static Vector randomVector(int size, double entryMean) {
Vector v = new DenseVector(size);
Random r = RandomUtils.getRandom();
for (int i = 0; i < size; ++i) {
v.setQuick(i, r.nextGaussian() * entryMean);
}
return v;
}
public void testStep0Mapper() throws Exception {
Random rng = RandomUtils.getRandom();
// create a dataset large enough to be split up
String descriptor = Utils.randomDescriptor(rng, numAttributes);
double[][] source = Utils.randomDoubles(rng, descriptor, numInstances);
String[] sData = Utils.double2String(source);
// write the data to a file
Path dataPath = Utils.writeDataToTestFile(sData);
JobConf job = new JobConf();
job.setNumMapTasks(numMaps);
FileInputFormat.setInputPaths(job, dataPath);
// retrieve the splits
TextInputFormat input = (TextInputFormat) job.getInputFormat();
InputSplit[] splits = input.getSplits(job, numMaps);
InputSplit[] sorted = Arrays.copyOf(splits, splits.length);
Builder.sortSplits(sorted);
Step0OutputCollector collector = new Step0OutputCollector(numMaps);
Reporter reporter = Reporter.NULL;
for (int p = 0; p < numMaps; p++) {
InputSplit split = sorted[p];
RecordReader<LongWritable, Text> reader = input.getRecordReader(split, job, reporter);
LongWritable key = reader.createKey();
Text value = reader.createValue();
Step0Mapper mapper = new Step0Mapper();
mapper.configure(p);
Long firstKey = null;
int size = 0;
while (reader.next(key, value)) {
if (firstKey == null) {
firstKey = key.get();
}
mapper.map(key, value, collector, reporter);
size++;
}
mapper.close();
// validate the mapper's output
assertEquals(p, collector.keys[p]);
assertEquals(firstKey.longValue(), collector.values[p].getFirstId());
assertEquals(size, collector.values[p].getSize());
}
}
public void testHashDouble() {
List<Double> original = Lists.newArrayList();
for (int k = 0; k < 10; k++) {
Random gen = RandomUtils.getRandom();
for (int i = 0; i < 10000; i++) {
double x = gen.nextDouble();
original.add(x);
}
checkCounts(original);
}
}
@Before
public void generate() {
RandomUtils.useTestSeed();
uq = new UpperQuantile(101);
data = new double[1001];
Random gen = RandomUtils.getRandom();
for (int i = 0; i < 1001; i++) {
double x = gen.nextDouble();
data[i] = x;
uq.add(x);
}
Arrays.sort(data);
}
public void testHashLong() {
List<Long> original = Lists.newArrayList();
for (int k = 0; k < 10; k++) {
Random gen = RandomUtils.getRandom();
for (int i = 0; i < 10000; i++) {
long x = gen.nextLong();
original.add(x);
}
checkCounts(original);
}
}
public void testHashFloat() {
Multiset<Integer> violations = HashMultiset.create();
for (int k = 0; k < 1000; k++) {
List<Float> original = Lists.newArrayList();
Random gen = RandomUtils.getRandom();
for (int i = 0; i < 10000; i++) {
float x = (float) gen.nextDouble();
original.add(x);
}
violations.add(checkCounts(original) <= 12 ? 0 : 1);
}
// the hashes for floats don't really have 32 bits of entropy so the test
// only succeeds at better than about 99% rate.
assertTrue(violations.count(0) >= 985);
}
@Test
public void testSplits() throws Exception {
int n = 1;
int maxNumSplits = 100;
int maxNbTrees = 1000;
Random rng = RandomUtils.getRandom();
for (int nloop = 0; nloop < n; nloop++) {
int numSplits = rng.nextInt(maxNumSplits) + 1;
int nbTrees = rng.nextInt(maxNbTrees) + 1;
Configuration conf = getConfiguration();
Builder.setNbTrees(conf, nbTrees);
InMemInputFormat inputFormat = new InMemInputFormat();
List<InputSplit> splits = inputFormat.getSplits(conf, numSplits);
assertEquals(numSplits, splits.size());
int nbTreesPerSplit = nbTrees / numSplits;
int totalTrees = 0;
int expectedId = 0;
for (int index = 0; index < numSplits; index++) {
assertTrue(splits.get(index) instanceof InMemInputSplit);
InMemInputSplit split = (InMemInputSplit) splits.get(index);
assertEquals(expectedId, split.getFirstId());
if (index < numSplits - 1) {
assertEquals(nbTreesPerSplit, split.getNbTrees());
} else {
assertEquals(nbTrees - totalTrees, split.getNbTrees());
}
totalTrees += split.getNbTrees();
expectedId += split.getNbTrees();
}
}
}
@Test
public void testProcessOutput() throws Exception {
Configuration conf = getConfiguration();
conf.setInt("mapred.map.tasks", NUM_MAPS);
Random rng = RandomUtils.getRandom();
// prepare the output
TreeID[] keys = new TreeID[NUM_TREES];
MapredOutput[] values = new MapredOutput[NUM_TREES];
int[] firstIds = new int[NUM_MAPS];
randomKeyValues(rng, keys, values, firstIds);
// store the output in a sequence file
Path base = getTestTempDirPath("testdata");
FileSystem fs = base.getFileSystem(conf);
Path outputFile = new Path(base, "PartialBuilderTest.seq");
Writer writer =
SequenceFile.createWriter(fs, conf, outputFile, TreeID.class, MapredOutput.class);
try {
for (int index = 0; index < NUM_TREES; index++) {
writer.append(keys[index], values[index]);
}
} finally {
Closeables.close(writer, false);
}
// load the output and make sure its valid
TreeID[] newKeys = new TreeID[NUM_TREES];
Node[] newTrees = new Node[NUM_TREES];
PartialBuilder.processOutput(new Job(conf), base, newKeys, newTrees);
// check the forest
for (int tree = 0; tree < NUM_TREES; tree++) {
assertEquals(values[tree].getTree(), newTrees[tree]);
}
assertTrue("keys not equal", Arrays.deepEquals(keys, newKeys));
}
@Test
public void testTreeID() {
Random rng = RandomUtils.getRandom();
for (int nloop = 0; nloop < 1000000; nloop++) {
int partition = Math.abs(rng.nextInt());
int treeId = rng.nextInt(TreeID.MAX_TREEID);
TreeID t1 = new TreeID(partition, treeId);
assertEquals(partition, t1.partition());
assertEquals(treeId, t1.treeId());
TreeID t2 = new TreeID();
t2.set(partition, treeId);
assertEquals(partition, t2.partition());
assertEquals(treeId, t2.treeId());
}
}
private void initializeModel() {
TopicModel topicModel =
new TopicModel(
numTopics,
numTerms,
eta,
alpha,
RandomUtils.getRandom(),
terms,
numUpdatingThreads,
initialModelCorpusFraction == 0 ? 1 : initialModelCorpusFraction * totalCorpusWeight);
topicModel.setConf(getConf());
TopicModel updatedModel =
initialModelCorpusFraction == 0
? new TopicModel(numTopics, numTerms, eta, alpha, null, terms, numUpdatingThreads, 1)
: topicModel;
updatedModel.setConf(getConf());
docTopicCounts = new DenseMatrix(numDocuments, numTopics);
docTopicCounts.assign(1.0 / numTopics);
modelTrainer =
new ModelTrainer(topicModel, updatedModel, numTrainingThreads, numTopics, numTerms);
}
@Test
public void testTopItemsRandom() throws Exception {
long[] ids = new long[100];
for (int i = 0; i < 100; i++) {
ids[i] = i;
}
LongPrimitiveIterator possibleItemIds = new LongPrimitiveArrayIterator(ids);
final Random random = RandomUtils.getRandom();
TopItems.Estimator<Long> estimator =
new TopItems.Estimator<Long>() {
@Override
public double estimate(Long thing) {
return random.nextDouble();
}
};
List<RecommendedItem> topItems = TopItems.getTopItems(10, possibleItemIds, null, estimator);
assertEquals(10, topItems.size());
double last = 2.0;
for (RecommendedItem topItem : topItems) {
assertTrue(topItem.getValue() <= last);
last = topItem.getItemID();
}
}
public void testProcessOutput() throws Exception {
Random rng = RandomUtils.getRandom();
// create a dataset large enough to be split up
String descriptor = Utils.randomDescriptor(rng, numAttributes);
double[][] source = Utils.randomDoubles(rng, descriptor, numInstances);
// each instance label is its index in the dataset
int labelId = Utils.findLabel(descriptor);
for (int index = 0; index < numInstances; index++) {
source[index][labelId] = index;
}
String[] sData = Utils.double2String(source);
// write the data to a file
Path dataPath = Utils.writeDataToTestFile(sData);
// prepare a data converter
Dataset dataset = DataLoader.generateDataset(descriptor, sData);
DataConverter converter = new DataConverter(dataset);
JobConf job = new JobConf();
job.setNumMapTasks(numMaps);
FileInputFormat.setInputPaths(job, dataPath);
// retrieve the splits
TextInputFormat input = (TextInputFormat) job.getInputFormat();
InputSplit[] splits = input.getSplits(job, numMaps);
InputSplit[] sorted = Arrays.copyOf(splits, splits.length);
Builder.sortSplits(sorted);
Reporter reporter = Reporter.NULL;
int[] keys = new int[numMaps];
Step0Output[] values = new Step0Output[numMaps];
int[] expectedIds = new int[numMaps];
for (int p = 0; p < numMaps; p++) {
InputSplit split = sorted[p];
RecordReader<LongWritable, Text> reader = input.getRecordReader(split, job, reporter);
LongWritable key = reader.createKey();
Text value = reader.createValue();
Long firstKey = null;
int size = 0;
while (reader.next(key, value)) {
if (firstKey == null) {
firstKey = key.get();
expectedIds[p] = converter.convert(0, value.toString()).label;
}
size++;
}
keys[p] = p;
values[p] = new Step0Output(firstKey, size);
}
Step0Output[] partitions = Step0Job.processOutput(keys, values);
int[] actualIds = Step0Output.extractFirstIds(partitions);
assertTrue(
"Expected: " + Arrays.toString(expectedIds) + " But was: " + Arrays.toString(actualIds),
Arrays.equals(expectedIds, actualIds));
}
public class Uniform extends AbstractContinousDistribution {
private double min;
private double max;
// The uniform random number generated shared by all <b>static</b> methods.
protected static final Uniform shared = new Uniform(RandomUtils.getRandom());
/**
* Constructs a uniform distribution with the given minimum and maximum, using a {@link
* org.apache.mahout.math.jet.random.engine.MersenneTwister} seeded with the given seed.
*/
public Uniform(double min, double max, int seed) {
this(min, max, RandomUtils.getRandom(seed));
}
/** Constructs a uniform distribution with the given minimum and maximum. */
public Uniform(double min, double max, Random randomGenerator) {
setRandomGenerator(randomGenerator);
setState(min, max);
}
/** Constructs a uniform distribution with <tt>min=0.0</tt> and <tt>max=1.0</tt>. */
public Uniform(Random randomGenerator) {
this(0, 1, randomGenerator);
}
/** Returns the cumulative distribution function (assuming a continous uniform distribution). */
@Override
public double cdf(double x) {
if (x <= min) {
return 0.0;
}
if (x >= max) {
return 1.0;
}
return (x - min) / (max - min);
}
/** Returns a uniformly distributed random <tt>boolean</tt>. */
public boolean nextBoolean() {
return randomGenerator.nextDouble() > 0.5;
}
/**
* Returns a uniformly distributed random number in the open interval <tt>(min,max)</tt>
* (excluding <tt>min</tt> and <tt>max</tt>).
*/
@Override
public double nextDouble() {
return min + (max - min) * randomGenerator.nextDouble();
}
/**
* Returns a uniformly distributed random number in the open interval <tt>(from,to)</tt>
* (excluding <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public double nextDoubleFromTo(double from, double to) {
return from + (to - from) * randomGenerator.nextDouble();
}
/**
* Returns a uniformly distributed random number in the open interval <tt>(from,to)</tt>
* (excluding <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public float nextFloatFromTo(float from, float to) {
return (float) nextDoubleFromTo(from, to);
}
/**
* Returns a uniformly distributed random number in the closed interval <tt>[from,to]</tt>
* (including <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public int nextIntFromTo(int from, int to) {
return (int)
((long) from + (long) ((1L + (long) to - (long) from) * randomGenerator.nextDouble()));
}
/**
* Returns a uniformly distributed random number in the closed interval <tt>[from,to]</tt>
* (including <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public long nextLongFromTo(long from, long to) {
/* Doing the thing turns out to be more tricky than expected.
avoids overflows and underflows.
treats cases like from=-1, to=1 and the like right.
the following code would NOT solve the problem: return (long) (Doubles.randomFromTo(from,to));
rounding avoids the unsymmetric behaviour of casts from double to long: (long) -0.7 = 0, (long) 0.7 = 0.
checking for overflows and underflows is also necessary.
*/
// first the most likely and also the fastest case.
if (from >= 0 && to < Long.MAX_VALUE) {
return from + (long) (nextDoubleFromTo(0.0, to - from + 1));
}
// would we get a numeric overflow?
// if not, we can still handle the case rather efficient.
double diff = ((double) to) - (double) from + 1.0;
if (diff <= Long.MAX_VALUE) {
return from + (long) (nextDoubleFromTo(0.0, diff));
}
// now the pathologic boundary cases.
// they are handled rather slow.
long random;
if (from == Long.MIN_VALUE) {
if (to == Long.MAX_VALUE) {
// return Math.round(nextDoubleFromTo(from,to));
int i1 = nextIntFromTo(Integer.MIN_VALUE, Integer.MAX_VALUE);
int i2 = nextIntFromTo(Integer.MIN_VALUE, Integer.MAX_VALUE);
return ((i1 & 0xFFFFFFFFL) << 32) | (i2 & 0xFFFFFFFFL);
}
random = Math.round(nextDoubleFromTo(from, to + 1));
if (random > to) {
random = from;
}
} else {
random = Math.round(nextDoubleFromTo(from - 1, to));
if (random < from) {
random = to;
}
}
return random;
}
/** Returns the probability distribution function (assuming a continous uniform distribution). */
@Override
public double pdf(double x) {
if (x <= min || x >= max) {
return 0.0;
}
return 1.0 / (max - min);
}
/** Sets the internal state. */
public void setState(double min, double max) {
if (max < min) {
setState(max, min);
return;
}
this.min = min;
this.max = max;
}
/** Returns a uniformly distributed random <tt>boolean</tt>. */
public static boolean staticNextBoolean() {
synchronized (shared) {
return shared.nextBoolean();
}
}
/**
* Returns a uniformly distributed random number in the open interval <tt>(0,1)</tt> (excluding
* <tt>0</tt> and <tt>1</tt>).
*/
public static double staticNextDouble() {
synchronized (shared) {
return shared.nextDouble();
}
}
/**
* Returns a uniformly distributed random number in the open interval <tt>(from,to)</tt>
* (excluding <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public static double staticNextDoubleFromTo(double from, double to) {
synchronized (shared) {
return shared.nextDoubleFromTo(from, to);
}
}
/**
* Returns a uniformly distributed random number in the open interval <tt>(from,to)</tt>
* (excluding <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public static float staticNextFloatFromTo(float from, float to) {
synchronized (shared) {
return shared.nextFloatFromTo(from, to);
}
}
/**
* Returns a uniformly distributed random number in the closed interval <tt>[from,to]</tt>
* (including <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public static int staticNextIntFromTo(int from, int to) {
synchronized (shared) {
return shared.nextIntFromTo(from, to);
}
}
/**
* Returns a uniformly distributed random number in the closed interval <tt>[from,to]</tt>
* (including <tt>from</tt> and <tt>to</tt>). Pre conditions: <tt>from <= to</tt>.
*/
public static long staticNextLongFromTo(long from, long to) {
synchronized (shared) {
return shared.nextLongFromTo(from, to);
}
}
/** Returns a String representation of the receiver. */
public String toString() {
return this.getClass().getName() + '(' + min + ',' + max + ')';
}
}
protected AbstractDifferenceRecommenderEvaluatorCrossDomain() {
random = RandomUtils.getRandom();
maxPreference = Float.NaN;
minPreference = Float.NaN;
}
/**
* Constructs a uniform distribution with the given minimum and maximum, using a {@link
* org.apache.mahout.math.jet.random.engine.MersenneTwister} seeded with the given seed.
*/
public Uniform(double min, double max, int seed) {
this(min, max, RandomUtils.getRandom(seed));
}
@Test
public void testSSVDSolver() throws Exception {
Configuration conf = new Configuration();
conf.set("mapred.job.tracker", "local");
conf.set("fs.default.name", "file:///");
// conf.set("mapred.job.tracker","localhost:11011");
// conf.set("fs.default.name","hdfs://localhost:11010/");
Deque<Closeable> closeables = new LinkedList<Closeable>();
Random rnd = RandomUtils.getRandom();
File tmpDir = getTestTempDir("svdtmp");
conf.set("hadoop.tmp.dir", tmpDir.getAbsolutePath());
Path aLocPath = new Path(getTestTempDirPath("svdtmp/A"), "A.seq");
// create distributed row matrix-like struct
SequenceFile.Writer w =
SequenceFile.createWriter(
FileSystem.getLocal(conf),
conf,
aLocPath,
IntWritable.class,
VectorWritable.class,
CompressionType.BLOCK,
new DefaultCodec());
closeables.addFirst(w);
int n = 100;
Vector dv;
VectorWritable vw = new VectorWritable();
IntWritable roww = new IntWritable();
double muAmplitude = 50.0;
int m = 1000;
for (int i = 0; i < m; i++) {
dv = new SequentialAccessSparseVector(n);
for (int j = 0; j < n / 5; j++) {
dv.setQuick(rnd.nextInt(n), muAmplitude * (rnd.nextDouble() - 0.5));
}
roww.set(i);
vw.set(dv);
w.append(roww, vw);
}
closeables.remove(w);
w.close();
FileSystem fs = FileSystem.get(conf);
Path tempDirPath = getTestTempDirPath("svd-proc");
Path aPath = new Path(tempDirPath, "A/A.seq");
fs.copyFromLocalFile(aLocPath, aPath);
Path svdOutPath = new Path(tempDirPath, "SSVD-out");
// make sure we wipe out previous test results, just a convenience
fs.delete(svdOutPath, true);
int ablockRows = 251;
int p = 60;
int k = 40;
SSVDSolver ssvd = new SSVDSolver(conf, new Path[] {aPath}, svdOutPath, ablockRows, k, p, 3);
// ssvd.setcUHalfSigma(true);
// ssvd.setcVHalfSigma(true);
ssvd.setOverwrite(true);
ssvd.run();
double[] stochasticSValues = ssvd.getSingularValues();
System.out.println("--SSVD solver singular values:");
dumpSv(stochasticSValues);
System.out.println("--Colt SVD solver singular values:");
// try to run the same thing without stochastic algo
double[][] a = SSVDSolver.loadDistributedRowMatrix(fs, aPath, conf);
// SingularValueDecompositionImpl svd=new SingularValueDecompositionImpl(new
// Array2DRowRealMatrix(a));
SingularValueDecomposition svd2 = new SingularValueDecomposition(new DenseMatrix(a));
a = null;
double[] svalues2 = svd2.getSingularValues();
dumpSv(svalues2);
for (int i = 0; i < k + p; i++) {
Assert.assertTrue(Math.abs(svalues2[i] - stochasticSValues[i]) <= s_epsilon);
}
double[][] q =
SSVDSolver.loadDistributedRowMatrix(
fs, new Path(svdOutPath, "Bt-job/" + BtJob.OUTPUT_Q + "-*"), conf);
SSVDPrototypeTest.assertOrthonormality(new DenseMatrix(q), false, s_epsilon);
double[][] u = SSVDSolver.loadDistributedRowMatrix(fs, new Path(svdOutPath, "U/[^_]*"), conf);
SSVDPrototypeTest.assertOrthonormality(new DenseMatrix(u), false, s_epsilon);
double[][] v = SSVDSolver.loadDistributedRowMatrix(fs, new Path(svdOutPath, "V/[^_]*"), conf);
SSVDPrototypeTest.assertOrthonormality(new DenseMatrix(v), false, s_epsilon);
}