protected override PkpdPredictor TrainCore(IChannel ch, RoleMappedData data, int count)
{
// Train M * (M+1) / 2 models arranged as a lower triangular matrix.
TDistPredictor[][] predictors;
predictors = new TDistPredictor[count][];
for (int i = 0; i < predictors.Length; i++)
{
predictors[i] = new TDistPredictor[i + 1];
for (int j = 0; j <= i; j++)
{
ch.Info($"Training learner ({i},{j})");
predictors[i][j] = TrainOne(ch, GetTrainer(), data, i, j);
}
}
return(new PkpdPredictor(Host, predictors));
}
private protected override PairwiseCouplingModelParameters TrainCore(IChannel ch, RoleMappedData data, int count)
{
// Train M * (M+1) / 2 models arranged as a lower triangular matrix.
var predModels = new TDistPredictor[count][];
for (int i = 0; i < predModels.Length; i++)
{
predModels[i] = new TDistPredictor[i + 1];
for (int j = 0; j <= i; j++)
{
ch.Info($"Training learner ({i},{j})");
predModels[i][j] = TrainOne(ch, Trainer, data, i, j).Model;
}
}
return(new PairwiseCouplingModelParameters(Host, predModels));
}
/// <summary>
/// Fits the data to the transformer
/// </summary>
/// <param name="input">The input data.</param>
/// <returns>The trained predictor.</returns>
public override TTransformer Fit(IDataView input)
{
string featureColumn = null;
var roles = new KeyValuePair <CR, string> [1];
roles[0] = new KeyValuePair <CR, string>(new CR(DefaultColumnNames.Label), LabelColumn.Name);
var td = new RoleMappedData(input, roles);
td.CheckMultiClassLabel(out var numClasses);
// Train M * (M+1) / 2 models arranged as a lower triangular matrix.
var predictors = new TDistPredictor[numClasses][];
using (var ch = Host.Start("Fitting"))
{
for (int i = 0; i < predictors.Length; i++)
{
predictors[i] = new TDistPredictor[i + 1];
for (int j = 0; j <= i; j++)
{
ch.Info($"Training learner ({i},{j})");
// need to capture the featureColum, and it is the same for all the transformers
if (i == 0 && j == 0)
{
var transformer = TrainOne(ch, Trainer, td, i, j);
featureColumn = transformer.FeatureColumn;
}
predictors[i][j] = TrainOne(ch, Trainer, td, i, j).Model;
}
}
}
return(new MulticlassPredictionTransformer <PairwiseCouplingModelParameters>(Host, new PairwiseCouplingModelParameters(Host, predictors), input.Schema, featureColumn, LabelColumn.Name));
}
