// Main worker thread
@Override
protected void compute2() {
KMeansModel model = null;
try {
init(true);
// Do lock even before checking the errors, since this block is finalized by unlock
// (not the best solution, but the code is more readable)
_parms.read_lock_frames(KMeans.this); // Fetch & read-lock input frames
// Something goes wrong
if (error_count() > 0)
throw H2OModelBuilderIllegalArgumentException.makeFromBuilder(KMeans.this);
// The model to be built
model = new KMeansModel(dest(), _parms, new KMeansModel.KMeansOutput(KMeans.this));
model.delete_and_lock(_key);
//
final Vec vecs[] = _train.vecs();
// mults & means for standardization
final double[] means = _train.means(); // means are used to impute NAs
final double[] mults = _parms._standardize ? _train.mults() : null;
final int[] impute_cat = new int[vecs.length];
for (int i = 0; i < vecs.length; i++)
impute_cat[i] = vecs[i].isNumeric() ? -1 : DataInfo.imputeCat(vecs[i]);
model._output._normSub = means;
model._output._normMul = mults;
// Initialize cluster centers and standardize if requested
double[][] centers = initial_centers(model, vecs, means, mults, impute_cat);
if (centers == null) return; // Stopped/cancelled during center-finding
double[][] oldCenters = null;
// ---
// Run the main KMeans Clustering loop
// Stop after enough iterations or average_change < TOLERANCE
model._output._iterations =
0; // Loop ends only when iterations > max_iterations with strict inequality
while (!isDone(model, centers, oldCenters)) {
Lloyds task =
new Lloyds(centers, means, mults, impute_cat, _isCats, _parms._k, hasWeightCol())
.doAll(vecs);
// Pick the max categorical level for cluster center
max_cats(task._cMeans, task._cats, _isCats);
// Handle the case where some centers go dry. Rescue only 1 cluster
// per iteration ('cause we only tracked the 1 worst row)
if (cleanupBadClusters(task, vecs, centers, means, mults, impute_cat)) continue;
// Compute model stats; update standardized cluster centers
oldCenters = centers;
centers = computeStatsFillModel(task, model, vecs, means, mults, impute_cat);
model.update(_key); // Update model in K/V store
update(1); // One unit of work
if (model._parms._score_each_iteration) Log.info(model._output._model_summary);
}
Log.info(model._output._model_summary);
// Log.info(model._output._scoring_history);
//
// Log.info(((ModelMetricsClustering)model._output._training_metrics).createCentroidStatsTable().toString());
// At the end: validation scoring (no need to gather scoring history)
if (_valid != null) {
model.score(_parms.valid()).delete(); // this appends a ModelMetrics on the validation set
model._output._validation_metrics = ModelMetrics.getFromDKV(model, _parms.valid());
model.update(_key); // Update model in K/V store
}
done(); // Job done!
} catch (Throwable t) {
Job thisJob = DKV.getGet(_key);
if (thisJob._state == JobState.CANCELLED) {
Log.info("Job cancelled by user.");
} else {
t.printStackTrace();
failed(t);
throw t;
}
} finally {
updateModelOutput();
if (model != null) model.unlock(_key);
_parms.read_unlock_frames(KMeans.this);
}
tryComplete();
}
// Initialize cluster centers
double[][] initial_centers(
KMeansModel model,
final Vec[] vecs,
final double[] means,
final double[] mults,
final int[] modes) {
// Categoricals use a different distance metric than numeric columns.
model._output._categorical_column_count = 0;
_isCats = new String[vecs.length][];
for (int v = 0; v < vecs.length; v++) {
_isCats[v] = vecs[v].isCategorical() ? new String[0] : null;
if (_isCats[v] != null) model._output._categorical_column_count++;
}
Random rand = water.util.RandomUtils.getRNG(_parms._seed - 1);
double centers[][]; // Cluster centers
if (null != _parms._user_points) { // User-specified starting points
Frame user_points = _parms._user_points.get();
int numCenters = (int) user_points.numRows();
int numCols = model._output.nfeatures();
centers = new double[numCenters][numCols];
Vec[] centersVecs = user_points.vecs();
// Get the centers and standardize them if requested
for (int r = 0; r < numCenters; r++) {
for (int c = 0; c < numCols; c++) {
centers[r][c] = centersVecs[c].at(r);
centers[r][c] = data(centers[r][c], c, means, mults, modes);
}
}
} else { // Random, Furthest, or PlusPlus initialization
if (_parms._init == Initialization.Random) {
// Initialize all cluster centers to random rows
centers = new double[_parms._k][model._output.nfeatures()];
for (double[] center : centers) randomRow(vecs, rand, center, means, mults, modes);
} else {
centers = new double[1][model._output.nfeatures()];
// Initialize first cluster center to random row
randomRow(vecs, rand, centers[0], means, mults, modes);
model._output._iterations = 0;
while (model._output._iterations < 5) {
// Sum squares distances to cluster center
SumSqr sqr = new SumSqr(centers, means, mults, modes, _isCats).doAll(vecs);
// Sample with probability inverse to square distance
Sampler sampler =
new Sampler(
centers,
means,
mults,
modes,
_isCats,
sqr._sqr,
_parms._k * 3,
_parms._seed,
hasWeightCol())
.doAll(vecs);
centers = ArrayUtils.append(centers, sampler._sampled);
// Fill in sample centers into the model
if (!isRunning()) return null; // Stopped/cancelled
model._output._centers_raw = destandardize(centers, _isCats, means, mults);
model._output._tot_withinss = sqr._sqr / _train.numRows();
model._output._iterations++; // One iteration done
model.update(
_key); // Make early version of model visible, but don't update progress using
// update(1)
}
// Recluster down to k cluster centers
centers = recluster(centers, rand, _parms._k, _parms._init, _isCats);
model._output._iterations = 0; // Reset iteration count
}
}
return centers;
}