Class KMeansTrainer

java.lang.Object
org.tribuo.clustering.kmeans.KMeansTrainer
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<ClusterID>
public class KMeansTrainer extends Object implements Trainer<ClusterID>
A K-Means trainer, which generates a K-means clustering of the supplied data. The model finds the centres, and then predict needs to be called to infer the centre assignments for the input data.

It's slightly contorted to fit the Tribuo Trainer and Model API, as the cluster assignments can only be retrieved from the model after training, and require re-evaluating each example.

The Trainer has a parameterised distance function, and a selectable number of threads used in the training step. The thread pool is local to an invocation of train, so there can be multiple concurrent trainings.

See: 

 J. Friedman, T. Hastie, & R. Tibshirani.
 "The Elements of Statistical Learning"
 Springer 2001. PDF

  • Constructor Details

    • KMeansTrainer

      public KMeansTrainer(int centroids, int iterations, KMeansTrainer.Distance distanceType, int numThreads, long seed)
      Constructs a K-Means trainer using the supplied parameters.
      Parameters:
      centroids - The number of centroids to use.
      iterations - The maximum number of iterations.
      distanceType - The distance function.
      numThreads - The number of threads.
      seed - The random seed.

  • Method Details

    • postConfig

      public void postConfig()
      Specified by:
      postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

    • train

      public KMeansModel train(Dataset<ClusterID> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<ClusterID>
      Parameters:
      examples - the data set containing the examples.
      runProvenance - Training run specific provenance (e.g., fold number).
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • train

      public KMeansModel train(Dataset<ClusterID> dataset)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<ClusterID>
      Parameters:
      dataset - the data set containing the examples.
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • getInvocationCount

      public int getInvocationCount()
      Description copied from interface: Trainer
      The number of times this trainer instance has had it's train method invoked.

      This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.

      Specified by:
      getInvocationCount in interface Trainer<ClusterID>
      Returns:
      The number of train invocations.

    • initialiseCentroids

      protected static DenseVector[] initialiseCentroids(int centroids, Dataset<ClusterID> examples, ImmutableFeatureMap featureMap, SplittableRandom rng)
      Initialisation method called at the start of each train call. Used to allow overriding for kmeans++, kmedoids etc.
      Parameters:
      centroids - The number of centroids to create.
      examples - The dataset to use.
      featureMap - The feature map to use for centroid sampling.
      rng - The RNG to use.
      Returns:
      A DenseVector array of centroids.

    • mStep

      protected void mStep(ForkJoinPool fjp, DenseVector[] centroidVectors, Map<Integer, List<Integer>> clusterAssignments, SparseVector[] data, double[] weights)

    • toString

      public String toString()
      Overrides:
      toString in class Object

    • getProvenance

      public TrainerProvenance getProvenance()
      Specified by:
      getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>