Package ml.dmlc.xgboost4j.java

Class XGBoost

java.lang.Object
ml.dmlc.xgboost4j.java.XGBoost
public class XGBoost extends Object
trainer for xgboost
Author:
hzx

  • Field Details

    • MAXIMIZ_METRICES

      public static final String[] MAXIMIZ_METRICES

  • Constructor Details

    • XGBoost

      public XGBoost()

  • Method Details

    • loadModel

      public static Booster loadModel(String modelPath) throws XGBoostError
      load model from modelPath
      Parameters:
      modelPath - booster modelPath (model generated by booster.saveModel)
      Throws:
      XGBoostError - native error

    • loadModel

      public static Booster loadModel(InputStream in) throws XGBoostError, IOException
      Load a new Booster model from a file opened as input stream. The assumption is the input stream only contains one XGBoost Model. This can be used to load existing booster models saved by other xgboost bindings.
      Parameters:
      in - The input stream of the file, will be closed after this function call.
      Returns:
      The create boosted
      Throws:
      XGBoostError
      IOException

    • loadModel

      public static Booster loadModel(byte[] buffer) throws XGBoostError, IOException
      Load a new Booster model from a byte array buffer. The assumption is the array only contains one XGBoost Model. This can be used to load existing booster models saved by other xgboost bindings.
      Parameters:
      buffer - The byte contents of the booster.
      Returns:
      The create boosted
      Throws:
      XGBoostError
      IOException

    • train

      public static Booster train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, IObjective obj, IEvaluation eval) throws XGBoostError
      Train a booster given parameters.
      Parameters:
      dtrain - Data to be trained.
      params - Parameters.
      round - Number of boosting iterations.
      watches - a group of items to be evaluated during training, this allows user to watch performance on the validation set.
      obj - customized objective
      eval - customized evaluation
      Returns:
      The trained booster.
      Throws:
      XGBoostError

    • train

      public static Booster train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRound) throws XGBoostError
      Train a booster given parameters.
      Parameters:
      dtrain - Data to be trained.
      params - Parameters.
      round - Number of boosting iterations.
      watches - a group of items to be evaluated during training, this allows user to watch performance on the validation set.
      metrics - array containing the evaluation metrics for each matrix in watches for each iteration
      earlyStoppingRound - if non-zero, training would be stopped after a specified number of consecutive increases in any evaluation metric.
      obj - customized objective
      eval - customized evaluation
      Returns:
      The trained booster.
      Throws:
      XGBoostError

    • trainAndSaveCheckpoint

      public static Booster trainAndSaveCheckpoint(DMatrix dtrain, Map<String,Object> params, int numRounds, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRounds, Booster booster, int checkpointInterval, String checkpointPath, org.apache.hadoop.fs.FileSystem fs) throws XGBoostError, IOException
      Throws:
      XGBoostError
      IOException

    • train

      public static Booster train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRounds, Booster booster) throws XGBoostError
      Train a booster given parameters.
      Parameters:
      dtrain - Data to be trained.
      params - Parameters.
      round - Number of boosting iterations.
      watches - a group of items to be evaluated during training, this allows user to watch performance on the validation set.
      metrics - array containing the evaluation metrics for each matrix in watches for each iteration
      earlyStoppingRounds - if non-zero, training would be stopped after a specified number of consecutive goes to the unexpected direction in any evaluation metric.
      obj - customized objective
      eval - customized evaluation
      booster - train from scratch if set to null; train from an existing booster if not null.
      Returns:
      The trained booster.
      Throws:
      XGBoostError

    • isMaximizeEvaluation

      public static boolean isMaximizeEvaluation(String evalInfo, String[] evalNames, Map<String,Object> params)
      Decides whether the evaluation metrics are to be maximized or not.
      Parameters:
      evalInfo - The evaluation log string from which the metric name is inferred.
      evalNames - The names of the evaluation matrices.
      params - The parameters that contain information regarding whether the evaluation metrics are to be maximized or not.
      Returns:
      True if the evaluation metrics are to be maximized, false otherwise.

    • crossValidation

      public static String[] crossValidation(DMatrix data, Map<String,Object> params, int round, int nfold, String[] metrics, IObjective obj, IEvaluation eval) throws XGBoostError
      Cross-validation with given parameters.
      Parameters:
      data - Data to be trained.
      params - Booster params.
      round - Number of boosting iterations.
      nfold - Number of folds in CV.
      metrics - Evaluation metrics to be watched in CV.
      obj - customized objective (set to null if not used)
      eval - customized evaluation (set to null if not used)
      Returns:
      evaluation history
      Throws:
      XGBoostError - native error