public IPredictor Calibrate(IChannel ch, IDataView data, ICalibratorTrainer caliTrainer, int maxRows)
{
Host.CheckValue(ch, nameof(ch));
ch.CheckValue(data, nameof(data));
ch.CheckValue(caliTrainer, nameof(caliTrainer));
if (caliTrainer.NeedsTraining)
{
var bound = new Bound(this, new RoleMappedSchema(data.Schema));
using (var curs = data.GetRowCursor(col => true))
{
var scoreGetter = (ValueGetter <Single>)bound.CreateScoreGetter(curs, col => true, out Action disposer);
// We assume that we can use the label column of the first predictor, since if the labels are not identical
// then the whole model is garbage anyway.
var labelGetter = bound.GetLabelGetter(curs, 0, out Action disp);
disposer += disp;
var weightGetter = bound.GetWeightGetter(curs, 0, out disp);
disposer += disp;
try
{
int num = 0;
while (curs.MoveNext())
{
Single label = 0;
labelGetter(ref label);
if (!FloatUtils.IsFinite(label))
{
continue;
}
Single score = 0;
scoreGetter(ref score);
if (!FloatUtils.IsFinite(score))
{
continue;
}
Single weight = 0;
weightGetter(ref weight);
if (!FloatUtils.IsFinite(weight))
{
continue;
}
caliTrainer.ProcessTrainingExample(score, label > 0, weight);
if (maxRows > 0 && ++num >= maxRows)
{
break;
}
}
}
finally
{
disposer?.Invoke();
}
}
}
var calibrator = caliTrainer.FinishTraining(ch);
return(CalibratorUtils.CreateCalibratedPredictor(Host, this, calibrator));
}
