/// <summary>
/// REVIEW: consider adding an overload that returns <see cref="VBuffer{DvText}"/>
/// Loads optionally feature names from the repository directory.
/// Returns false iff no stream was found for feature names, iff result is set to null.
/// </summary>
public static bool TryLoadFeatureNames(out FeatureNameCollection featureNames, RepositoryReader rep)
{
Contracts.CheckValue(rep, nameof(rep));
using (var ent = rep.OpenEntryOrNull(ModelFileUtils.DirTrainingInfo, "FeatureNames.bin"))
{
if (ent != null)
{
using (var ctx = new ModelLoadContext(rep, ent, ModelFileUtils.DirTrainingInfo))
{
featureNames = FeatureNameCollection.Create(ctx);
return(true);
}
}
}
featureNames = null;
return(false);
}
public void IntrospectiveTraining()
{
using (var env = new LocalEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename)));
var words = WordBagTransform.Create(env, new WordBagTransform.Arguments()
{
NgramLength = 1,
Column = new[] { new WordBagTransform.Column()
{
Name = "Tokenize", Source = new[] { "SentimentText" }
} }
}, loader);
var lda = new LdaTransform(env, new LdaTransform.Arguments()
{
NumTopic = 10,
NumIterations = 3,
NumThreads = 1,
Column = new[] { new LdaTransform.Column {
Source = "Tokenize", Name = "Features"
} }
}, words);
var trainData = lda;
var cachedTrain = new CacheDataView(env, trainData, prefetch: null);
// Train the first predictor.
var linearTrainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
});
var trainRoles = new RoleMappedData(cachedTrain, label: "Label", feature: "Features");
var linearPredictor = linearTrainer.Train(new Runtime.TrainContext(trainRoles));
VBuffer <float> weights = default;
linearPredictor.GetFeatureWeights(ref weights);
var topicSummary = lda.GetTopicSummary();
var treeTrainer = new FastTreeBinaryClassificationTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features, numTrees: 2);
var ftPredictor = treeTrainer.Train(new Runtime.TrainContext(trainRoles));
FastTreeBinaryPredictor treePredictor;
if (ftPredictor is CalibratedPredictorBase calibrator)
{
treePredictor = (FastTreeBinaryPredictor)calibrator.SubPredictor;
}
else
{
treePredictor = (FastTreeBinaryPredictor)ftPredictor;
}
var featureNameCollection = FeatureNameCollection.Create(trainRoles.Schema);
foreach (var tree in treePredictor.GetTrees())
{
var lteChild = tree.LteChild;
var gteChild = tree.GtChild;
// Get nodes.
for (var i = 0; i < tree.NumNodes; i++)
{
var node = tree.GetNode(i, false, featureNameCollection);
var gainValue = GetValue <double>(node.KeyValues, "GainValue");
var splitGain = GetValue <double>(node.KeyValues, "SplitGain");
var featureName = GetValue <string>(node.KeyValues, "SplitName");
var previousLeafValue = GetValue <double>(node.KeyValues, "PreviousLeafValue");
var threshold = GetValue <string>(node.KeyValues, "Threshold").Split(new[] { ' ' }, 2)[1];
var nodeIndex = i;
}
// Get leaves.
for (var i = 0; i < tree.NumLeaves; i++)
{
var node = tree.GetNode(i, true, featureNameCollection);
var leafValue = GetValue <double>(node.KeyValues, "LeafValue");
var extras = GetValue <string>(node.KeyValues, "Extras");
var nodeIndex = ~i;
}
}
}
}
