// 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();
}
