// 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();
}
@Override
protected void compute2() {
CoxPHModel model = null;
try {
Scope.enter();
_parms.read_lock_frames(CoxPH.this);
init(true);
applyScoringFrameSideEffects();
// The model to be built
model = new CoxPHModel(dest(), _parms, new CoxPHModel.CoxPHOutput(CoxPH.this));
model.delete_and_lock(_key);
applyTrainingFrameSideEffects();
int nResponses = 1;
boolean useAllFactorLevels = false;
final DataInfo dinfo =
new DataInfo(
Key.make(),
_modelBuilderTrain,
null,
nResponses,
useAllFactorLevels,
DataInfo.TransformType.DEMEAN,
TransformType.NONE,
true,
false,
false,
false,
false,
false);
initStats(model, dinfo);
final int n_offsets =
(model._parms.offset_columns == null) ? 0 : model._parms.offset_columns.length;
final int n_coef = dinfo.fullN() - n_offsets;
final double[] step = MemoryManager.malloc8d(n_coef);
final double[] oldCoef = MemoryManager.malloc8d(n_coef);
final double[] newCoef = MemoryManager.malloc8d(n_coef);
Arrays.fill(step, Double.NaN);
Arrays.fill(oldCoef, Double.NaN);
for (int j = 0; j < n_coef; ++j) newCoef[j] = model._parms.init;
double oldLoglik = -Double.MAX_VALUE;
final int n_time = (int) (model._output.max_time - model._output.min_time + 1);
final boolean has_start_column = (model._parms.start_column != null);
final boolean has_weights_column = (model._parms.weights_column != null);
for (int i = 0; i <= model._parms.iter_max; ++i) {
model._output.iter = i;
final CoxPHTask coxMR =
new CoxPHTask(
self(),
dinfo,
newCoef,
model._output.min_time,
n_time,
n_offsets,
has_start_column,
has_weights_column)
.doAll(dinfo._adaptedFrame);
final double newLoglik = calcLoglik(model, coxMR);
if (newLoglik > oldLoglik) {
if (i == 0) calcCounts(model, coxMR);
calcModelStats(model, newCoef, newLoglik);
calcCumhaz_0(model, coxMR);
if (newLoglik == 0) model._output.lre = -Math.log10(Math.abs(oldLoglik - newLoglik));
else model._output.lre = -Math.log10(Math.abs((oldLoglik - newLoglik) / newLoglik));
if (model._output.lre >= model._parms.lre_min) break;
Arrays.fill(step, 0);
for (int j = 0; j < n_coef; ++j)
for (int k = 0; k < n_coef; ++k)
step[j] -= model._output.var_coef[j][k] * model._output.gradient[k];
for (int j = 0; j < n_coef; ++j)
if (Double.isNaN(step[j]) || Double.isInfinite(step[j])) break;
oldLoglik = newLoglik;
System.arraycopy(newCoef, 0, oldCoef, 0, oldCoef.length);
} else {
for (int j = 0; j < n_coef; ++j) step[j] /= 2;
}
for (int j = 0; j < n_coef; ++j) newCoef[j] = oldCoef[j] - step[j];
}
model.update(_key);
} 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();
_parms.read_unlock_frames(CoxPH.this);
Scope.exit();
done(); // Job done!
}
tryComplete();
}
