Package org.tribuo.common.xgboost

Class XGBoostModel<T extends Output<T>>

java.lang.Object
org.tribuo.Model<T>
org.tribuo.common.xgboost.XGBoostModel<T>
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable
public final class XGBoostModel<T extends Output<T>> extends Model<T>
A Model which wraps around a XGBoost.Booster.

XGBoost is a fast implementation of gradient boosted decision trees.

Throws IllegalStateException if the XGBoost C++ library fails to load or throws an exception.

See: 

 Chen T, Guestrin C.
 "XGBoost: A Scalable Tree Boosting System"
 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
and for the original algorithm: 
 Friedman JH.
 "Greedy Function Approximation: a Gradient Boosting Machine"
 Annals of statistics, 2001.

N.B.: XGBoost4J wraps the native C implementation of xgboost that links to various C libraries, including libgomp and glibc (on Linux). If you're running on Alpine, which does not natively use glibc, you'll need to install glibc into the container. On the macOS binary on Maven Central is compiled without OpenMP support, meaning that XGBoost is single threaded on macOS. You can recompile the macOS binary with OpenMP support after installing libomp from homebrew if necessary.

See Also:

  • Field Details

    • models

      protected transient List<ml.dmlc.xgboost4j.java.Booster> models
      The XGBoost4J Boosters.

  • Method Details

    • getInnerModels

      public List<ml.dmlc.xgboost4j.java.Booster> getInnerModels()
      Returns an unmodifiable list containing a copy of each model.

      As XGBoost4J models don't expose a copy constructor this requires serializing each model to a byte array and rebuilding it, and is thus quite expensive.

      Returns:
      A copy of all of the models.

    • setNumThreads

      public void setNumThreads(int threads)
      Sets the number of threads to use at prediction time.

      If set to 0 sets nthreads = num hardware threads.

      Parameters:
      threads - The new number of threads.

    • predict

      public List<Prediction<T>> predict(Dataset<T> examples)
      Uses the model to predict the labels for multiple examples contained in a data set.
      Overrides:
      predict in class Model<T extends Output<T>>
      Parameters:
      examples - the data set containing the examples to predict.
      Returns:
      the results of the predictions, in the same order as the data set generates the example.

    • predict

      public List<Prediction<T>> predict(Iterable<Example<T>> examples)
      Uses the model to predict the label for multiple examples.
      Overrides:
      predict in class Model<T extends Output<T>>
      Parameters:
      examples - the examples to predict.
      Returns:
      the results of the prediction, in the same order as the examples.

    • predict

      public Prediction<T> predict(Example<T> example)
      Description copied from class: Model
      Uses the model to predict the output for a single example.

      predict does not mutate the example.

      Throws IllegalArgumentException if the example has no features or no feature overlap with the model.

      Specified by:
      predict in class Model<T extends Output<T>>
      Parameters:
      example - the example to predict.
      Returns:
      the result of the prediction.

    • getFeatureImportance

      public List<XGBoostFeatureImportance> getFeatureImportance()
      Creates objects to report feature importance metrics for XGBoost. See the documentation of XGBoostFeatureImportance for more information on what those metrics mean. Typically this list will contain a single instance for the entire model. For multidimensional regression the list will have one entry per dimension, in dimension order.
      Returns:
      The feature importance object(s).

    • getTopFeatures

      public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)
      Description copied from class: Model
      Gets the top n features associated with this model.

      If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.

      If the model cannot describe it's top features then it returns Collections.emptyMap().

      Specified by:
      getTopFeatures in class Model<T extends Output<T>>
      Parameters:
      n - the number of features to return. If this value is less than 0, all features should be returned for each class, unless the model cannot score it's features.
      Returns:
      a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model

    • getModelDump

      public List<String[]> getModelDump()
      Returns the string model dumps from each Booster.
      Returns:
      The model dumps.

    • getExcuse

      public Optional<Excuse<T>> getExcuse(Example<T> example)
      Description copied from class: Model
      Generates an excuse for an example.

      This attempts to explain a classification result. Generating an excuse may be quite an expensive operation.

      This excuse either contains per class information or an entry with key Model.ALL_OUTPUTS.

      The optional is empty if the model does not provide excuses.

      Specified by:
      getExcuse in class Model<T extends Output<T>>
      Parameters:
      example - The input example.
      Returns:
      An optional excuse object. The optional is empty if this model does not provide excuses.

    • copy

      protected Model<T> copy(String newName, ModelProvenance newProvenance)
      Description copied from class: Model
      Copies a model, replacing its provenance and name with the supplied values.

      Used to provide the provenance removal functionality.

      Specified by:
      copy in class Model<T extends Output<T>>
      Parameters:
      newName - The new name.
      newProvenance - The new provenance.
      Returns:
      A copy of the model.