@Override
protected boolean chunkInit() {
final int n_coef = _beta.length;
sumWeightedCatX = MemoryManager.malloc8d(n_coef - (_dinfo._nums - _n_offsets));
sumWeightedNumX = MemoryManager.malloc8d(_dinfo._nums);
sizeRiskSet = MemoryManager.malloc8d(_n_time);
sizeCensored = MemoryManager.malloc8d(_n_time);
sizeEvents = MemoryManager.malloc8d(_n_time);
countEvents = MemoryManager.malloc8(_n_time);
sumRiskEvents = MemoryManager.malloc8d(_n_time);
sumLogRiskEvents = MemoryManager.malloc8d(_n_time);
rcumsumRisk = MemoryManager.malloc8d(_n_time);
sumXEvents = malloc2DArray(_n_time, n_coef);
sumXRiskEvents = malloc2DArray(_n_time, n_coef);
rcumsumXRisk = malloc2DArray(_n_time, n_coef);
sumXXRiskEvents = malloc3DArray(_n_time, n_coef, n_coef);
rcumsumXXRisk = malloc3DArray(_n_time, n_coef, n_coef);
return true;
}
private static double[][][] malloc3DArray(final int d1, final int d2, final int d3) {
final double[][][] array = new double[d1][d2][];
for (int j = 0; j < d1; ++j)
for (int k = 0; k < d2; ++k) array[j][k] = MemoryManager.malloc8d(d3);
return array;
}
private static double[][] malloc2DArray(final int d1, final int d2) {
final double[][] array = new double[d1][];
for (int j = 0; j < d1; ++j) array[j] = MemoryManager.malloc8d(d2);
return array;
}
@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();
}
protected double calcLoglik(CoxPHModel model, final CoxPHTask coxMR) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
final int n_coef = o.coef.length;
final int n_time = coxMR.sizeEvents.length;
double newLoglik = 0;
for (int j = 0; j < n_coef; ++j) o.gradient[j] = 0;
for (int j = 0; j < n_coef; ++j) for (int k = 0; k < n_coef; ++k) o.hessian[j][k] = 0;
switch (p.ties) {
case efron:
final double[] newLoglik_t = MemoryManager.malloc8d(n_time);
final double[][] gradient_t = malloc2DArray(n_time, n_coef);
final double[][][] hessian_t = malloc3DArray(n_time, n_coef, n_coef);
ForkJoinTask[] fjts = new ForkJoinTask[n_time];
for (int t = n_time - 1; t >= 0; --t) {
final int _t = t;
fjts[t] =
new RecursiveAction() {
@Override
protected void compute() {
final double sizeEvents_t = coxMR.sizeEvents[_t];
if (sizeEvents_t > 0) {
final long countEvents_t = coxMR.countEvents[_t];
final double sumLogRiskEvents_t = coxMR.sumLogRiskEvents[_t];
final double sumRiskEvents_t = coxMR.sumRiskEvents[_t];
final double rcumsumRisk_t = coxMR.rcumsumRisk[_t];
final double avgSize = sizeEvents_t / countEvents_t;
newLoglik_t[_t] = sumLogRiskEvents_t;
System.arraycopy(coxMR.sumXEvents[_t], 0, gradient_t[_t], 0, n_coef);
for (long e = 0; e < countEvents_t; ++e) {
final double frac = ((double) e) / ((double) countEvents_t);
final double term = rcumsumRisk_t - frac * sumRiskEvents_t;
newLoglik_t[_t] -= avgSize * Math.log(term);
for (int j = 0; j < n_coef; ++j) {
final double djTerm =
coxMR.rcumsumXRisk[_t][j] - frac * coxMR.sumXRiskEvents[_t][j];
final double djLogTerm = djTerm / term;
gradient_t[_t][j] -= avgSize * djLogTerm;
for (int k = 0; k < n_coef; ++k) {
final double dkTerm =
coxMR.rcumsumXRisk[_t][k] - frac * coxMR.sumXRiskEvents[_t][k];
final double djkTerm =
coxMR.rcumsumXXRisk[_t][j][k]
- frac * coxMR.sumXXRiskEvents[_t][j][k];
hessian_t[_t][j][k] -=
avgSize * (djkTerm / term - (djLogTerm * (dkTerm / term)));
}
}
}
}
}
};
}
ForkJoinTask.invokeAll(fjts);
for (int t = 0; t < n_time; ++t) newLoglik += newLoglik_t[t];
for (int t = 0; t < n_time; ++t)
for (int j = 0; j < n_coef; ++j) o.gradient[j] += gradient_t[t][j];
for (int t = 0; t < n_time; ++t)
for (int j = 0; j < n_coef; ++j)
for (int k = 0; k < n_coef; ++k) o.hessian[j][k] += hessian_t[t][j][k];
break;
case breslow:
for (int t = n_time - 1; t >= 0; --t) {
final double sizeEvents_t = coxMR.sizeEvents[t];
if (sizeEvents_t > 0) {
final double sumLogRiskEvents_t = coxMR.sumLogRiskEvents[t];
final double rcumsumRisk_t = coxMR.rcumsumRisk[t];
newLoglik += sumLogRiskEvents_t;
newLoglik -= sizeEvents_t * Math.log(rcumsumRisk_t);
for (int j = 0; j < n_coef; ++j) {
final double dlogTerm = coxMR.rcumsumXRisk[t][j] / rcumsumRisk_t;
o.gradient[j] += coxMR.sumXEvents[t][j];
o.gradient[j] -= sizeEvents_t * dlogTerm;
for (int k = 0; k < n_coef; ++k)
o.hessian[j][k] -=
sizeEvents_t
* (((coxMR.rcumsumXXRisk[t][j][k] / rcumsumRisk_t)
- (dlogTerm * (coxMR.rcumsumXRisk[t][k] / rcumsumRisk_t))));
}
}
}
break;
default:
throw new IllegalArgumentException("ties method must be either efron or breslow");
}
return newLoglik;
}
protected void initStats(final CoxPHModel model, final DataInfo dinfo) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
o.n = p.stop_column.length();
o.data_info = dinfo;
final int n_offsets = (p.offset_columns == null) ? 0 : p.offset_columns.length;
final int n_coef = o.data_info.fullN() - n_offsets;
final String[] coefNames = o.data_info.coefNames();
o.coef_names = new String[n_coef];
System.arraycopy(coefNames, 0, o.coef_names, 0, n_coef);
o.coef = MemoryManager.malloc8d(n_coef);
o.exp_coef = MemoryManager.malloc8d(n_coef);
o.exp_neg_coef = MemoryManager.malloc8d(n_coef);
o.se_coef = MemoryManager.malloc8d(n_coef);
o.z_coef = MemoryManager.malloc8d(n_coef);
o.gradient = MemoryManager.malloc8d(n_coef);
o.hessian = malloc2DArray(n_coef, n_coef);
o.var_coef = malloc2DArray(n_coef, n_coef);
o.x_mean_cat = MemoryManager.malloc8d(n_coef - (o.data_info._nums - n_offsets));
o.x_mean_num = MemoryManager.malloc8d(o.data_info._nums - n_offsets);
o.mean_offset = MemoryManager.malloc8d(n_offsets);
o.offset_names = new String[n_offsets];
System.arraycopy(coefNames, n_coef, o.offset_names, 0, n_offsets);
final Vec start_column = p.start_column;
final Vec stop_column = p.stop_column;
o.min_time =
p.start_column == null ? (long) stop_column.min() : (long) start_column.min() + 1;
o.max_time = (long) stop_column.max();
final int n_time = new Vec.CollectDomain().doAll(stop_column).domain().length;
o.time = MemoryManager.malloc8(n_time);
o.n_risk = MemoryManager.malloc8d(n_time);
o.n_event = MemoryManager.malloc8d(n_time);
o.n_censor = MemoryManager.malloc8d(n_time);
o.cumhaz_0 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_1 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_2 = malloc2DArray(n_time, n_coef);
}
