public virtual RoleMappedData GetTestData(Subset subset, Batch batch)
{
Host.CheckValueOrNull(subset);
Host.CheckValue(batch.TestInstances, nameof(batch), "Batch does not have test data");
if (subset == null || subset.SelectedFeatures == null)
{
return(batch.TestInstances);
}
return(EnsembleUtils.SelectFeatures(Host, batch.TestInstances, subset.SelectedFeatures));
}
public Subset SelectFeatures(RoleMappedData data, Random rand)
{
_host.CheckValue(data, nameof(data));
data.CheckFeatureFloatVector();
var type = data.Schema.Feature.Value.Type;
int len = type.GetVectorSize();
var features = new BitArray(len);
for (int j = 0; j < len; j++)
{
features[j] = rand.NextDouble() < _args.FeaturesSelectionProportion;
}
var dataNew = EnsembleUtils.SelectFeatures(_host, data, features);
return(new Subset(dataNew, features));
}
public void Train(List <FeatureSubsetModel <IPredictorProducing <TOutput> > > models, RoleMappedData data, IHostEnvironment env)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register(Stacking.LoadName);
host.CheckValue(models, nameof(models));
host.CheckValue(data, nameof(data));
using (var ch = host.Start("Training stacked model"))
{
ch.Check(Meta == null, "Train called multiple times");
ch.Check(BasePredictorType != null);
var maps = new ValueMapper <VBuffer <Single>, TOutput> [models.Count];
for (int i = 0; i < maps.Length; i++)
{
Contracts.Assert(models[i].Predictor is IValueMapper);
var m = (IValueMapper)models[i].Predictor;
maps[i] = m.GetMapper <VBuffer <Single>, TOutput>();
}
// REVIEW: Should implement this better....
var labels = new Single[100];
var features = new VBuffer <Single> [100];
int count = 0;
// REVIEW: Should this include bad values or filter them?
using (var cursor = new FloatLabelCursor(data, CursOpt.AllFeatures | CursOpt.AllLabels))
{
TOutput[] predictions = new TOutput[maps.Length];
var vBuffers = new VBuffer <Single> [maps.Length];
while (cursor.MoveNext())
{
Parallel.For(0, maps.Length, i =>
{
var model = models[i];
if (model.SelectedFeatures != null)
{
EnsembleUtils.SelectFeatures(ref cursor.Features, model.SelectedFeatures, model.Cardinality, ref vBuffers[i]);
maps[i](ref vBuffers[i], ref predictions[i]);
}
else
{
maps[i](ref cursor.Features, ref predictions[i]);
}
});
Utils.EnsureSize(ref labels, count + 1);
Utils.EnsureSize(ref features, count + 1);
labels[count] = cursor.Label;
FillFeatureBuffer(predictions, ref features[count]);
count++;
}
}
ch.Info("The number of instances used for stacking trainer is {0}", count);
var bldr = new ArrayDataViewBuilder(host);
Array.Resize(ref labels, count);
Array.Resize(ref features, count);
bldr.AddColumn(DefaultColumnNames.Label, NumberType.Float, labels);
bldr.AddColumn(DefaultColumnNames.Features, NumberType.Float, features);
var view = bldr.GetDataView();
var rmd = new RoleMappedData(view, DefaultColumnNames.Label, DefaultColumnNames.Features);
var trainer = BasePredictorType.CreateComponent(host);
if (trainer.Info.NeedNormalization)
{
ch.Warning("The trainer specified for stacking wants normalization, but we do not currently allow this.");
}
Meta = trainer.Train(rmd);
CheckMeta();
}
}
