private protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups)
{
Options["objective"] = "regression";
// Add default metric.
if (!Options.ContainsKey("metric"))
{
Options["metric"] = "l2";
}
}
private protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups)
{
Host.AssertValue(ch);
Options["objective"] = "lambdarank";
ch.CheckValue(groups, nameof(groups));
// Add default metric.
if (!Options.ContainsKey("metric"))
{
Options["metric"] = "ndcg";
}
// Only output one ndcg score.
Options["eval_at"] = "5";
}
private protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups)
{
Host.AssertValue(ch);
ch.Assert(PredictionKind == PredictionKind.MulticlassClassification);
ch.Assert(_numClass > 1);
Options["num_class"] = _numClass;
bool useSoftmax = false;
if (LightGbmTrainerOptions.UseSoftmax.HasValue)
{
useSoftmax = LightGbmTrainerOptions.UseSoftmax.Value;
}
else
{
if (labels.Length >= _minDataToUseSoftmax)
{
useSoftmax = true;
}
ch.Info("Auto-tuning parameters: " + nameof(LightGbmTrainerOptions.UseSoftmax) + " = " + useSoftmax);
}
if (useSoftmax)
{
Options["objective"] = "multiclass";
}
else
{
Options["objective"] = "multiclassova";
}
// Add default metric.
if (!Options.ContainsKey("metric"))
{
Options["metric"] = "multi_error";
}
}
