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package hex.schemas;
import hex.tree.CalibrationHelper;
import hex.tree.xgboost.XGBoost;
import hex.tree.xgboost.XGBoostModel.XGBoostParameters;
import water.api.API;
import water.api.schemas3.*;
public class XGBoostV3 extends ModelBuilderSchema {
public static final class XGBoostParametersV3 extends ModelParametersSchemaV3 {
static public String[] fields = new String[] {
"model_id",
"training_frame",
"validation_frame",
"nfolds",
"keep_cross_validation_models",
"keep_cross_validation_predictions",
"keep_cross_validation_fold_assignment",
"score_each_iteration",
"fold_assignment",
"fold_column",
"response_column",
"ignored_columns",
"ignore_const_cols",
"offset_column",
"weights_column",
"stopping_rounds",
"stopping_metric",
"stopping_tolerance",
"max_runtime_secs",
"seed",
"distribution",
"tweedie_power",
"categorical_encoding",
"quiet_mode",
"checkpoint",
"export_checkpoints_dir",
"custom_metric_func",
// model specific
"ntrees",
"max_depth",
"min_rows", "min_child_weight",
"learn_rate", "eta",
"sample_rate", "subsample",
"col_sample_rate", "colsample_bylevel",
"col_sample_rate_per_tree", "colsample_bytree",
"colsample_bynode",
"max_abs_leafnode_pred", "max_delta_step",
"monotone_constraints",
"interaction_constraints",
"score_tree_interval",
"min_split_improvement", "gamma",
//runtime
"nthread",
"save_matrix_directory",
"build_tree_one_node",
"parallelize_cross_validation",
//model calibration
"calibrate_model",
"calibration_frame",
"calibration_method",
//lightgbm only
"max_bins",
"max_leaves",
//dart
"sample_type",
"normalize_type",
"rate_drop",
"one_drop",
"skip_drop",
//xgboost only
"tree_method",
"grow_policy",
"booster",
"reg_lambda",
"reg_alpha",
"dmatrix_type",
"backend",
"gpu_id",
"gainslift_bins",
"auc_type",
"scale_pos_weight",
"eval_metric",
"score_eval_metric_only"
};
@API(help="(same as n_estimators) Number of trees.", gridable = true)
public int ntrees;
@API(help="Maximum tree depth (0 for unlimited).", gridable = true)
public int max_depth;
@API(help="(same as min_child_weight) Fewest allowed (weighted) observations in a leaf.", gridable = true)
public double min_rows;
@API(help="(same as min_rows) Fewest allowed (weighted) observations in a leaf.", gridable = true, level = API.Level.expert)
public double min_child_weight;
@API(help="(same as eta) Learning rate (from 0.0 to 1.0)", gridable = true, level = API.Level.expert)
public double learn_rate;
@API(help="(same as learn_rate) Learning rate (from 0.0 to 1.0)", gridable = true)
public double eta;
@API(help = "(same as subsample) Row sample rate per tree (from 0.0 to 1.0)", gridable = true)
public double sample_rate;
@API(help = "(same as sample_rate) Row sample rate per tree (from 0.0 to 1.0)", gridable = true, level = API.Level.expert)
public double subsample;
@API(help="(same as colsample_bylevel) Column sample rate (from 0.0 to 1.0)", gridable = true)
public double col_sample_rate;
@API(help="(same as col_sample_rate) Column sample rate (from 0.0 to 1.0)", gridable = true, level = API.Level.expert)
public double colsample_bylevel;
@API(help = "(same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0)", level = API.Level.secondary, gridable = true)
public double col_sample_rate_per_tree;
@API(help = "(same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0)", level = API.Level.expert, gridable = true)
public double colsample_bytree;
@API(help = "Column sample rate per tree node (from 0.0 to 1.0)", level = API.Level.secondary, gridable = true)
public double colsample_bynode;
@API(help = "A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.", level = API.Level.secondary)
public KeyValueV3[] monotone_constraints;
@API(help="(same as max_delta_step) Maximum absolute value of a leaf node prediction", level = API.Level.expert, gridable = true)
public float max_abs_leafnode_pred;
@API(help="(same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction", level = API.Level.expert, gridable = true)
public float max_delta_step;
@API(help="Score the model after every so many trees. Disabled if set to 0.", level = API.Level.secondary, gridable = false)
public int score_tree_interval;
@API(help = "Seed for pseudo random number generator (if applicable)", gridable = true)
public long seed;
@API(help="(same as gamma) Minimum relative improvement in squared error reduction for a split to happen", level = API.Level.secondary, gridable = true)
public float min_split_improvement;
@API(help="(same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen", level = API.Level.expert, gridable = true)
public float gamma;
@API(help = "Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available", level = API.Level.expert)
public int nthread;
@API(help="Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.", level = API.Level.expert, gridable = false)
public boolean build_tree_one_node;
@API(help = "Directory where to save matrices passed to XGBoost library. Useful for debugging.", level = API.Level.expert)
public String save_matrix_directory;
@API(help="Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.", level = API.Level.expert)
public boolean calibrate_model;
@API(help="Data for model calibration", level = API.Level.expert, direction = API.Direction.INOUT)
public KeyV3.FrameKeyV3 calibration_frame;
@API(help="Calibration method to use", values = {"AUTO", "PlattScaling", "IsotonicRegression"}, level = API.Level.expert, direction = API.Direction.INOUT)
public CalibrationHelper.CalibrationMethod calibration_method;
@API(help = "For tree_method=hist only: maximum number of bins", level = API.Level.expert, gridable = true)
public int max_bins;
@API(help = "For tree_method=hist only: maximum number of leaves", level = API.Level.secondary, gridable = true)
public int max_leaves;
@API(help="Tree method", values = { "auto", "exact", "approx", "hist"}, level = API.Level.secondary, gridable = true)
public XGBoostParameters.TreeMethod tree_method;
@API(help="Grow policy - depthwise is standard GBM, lossguide is LightGBM", values = { "depthwise", "lossguide"}, level = API.Level.secondary, gridable = true)
public XGBoostParameters.GrowPolicy grow_policy;
@API(help="Booster type", values = { "gbtree", "gblinear", "dart"}, level = API.Level.expert, gridable = true)
public XGBoostParameters.Booster booster;
@API(help = "L2 regularization", level = API.Level.expert, gridable = true)
public float reg_lambda;
@API(help = "L1 regularization", level = API.Level.expert, gridable = true)
public float reg_alpha;
@API(help="Enable quiet mode", level = API.Level.expert, gridable = false)
public boolean quiet_mode;
@API(help="For booster=dart only: sample_type", values = { "uniform", "weighted"}, level = API.Level.expert, gridable = true)
public XGBoostParameters.DartSampleType sample_type;
@API(help="For booster=dart only: normalize_type", values = { "tree", "forest"}, level = API.Level.expert, gridable = true)
public XGBoostParameters.DartNormalizeType normalize_type;
@API(help="For booster=dart only: rate_drop (0..1)", level = API.Level.expert, gridable = true)
public float rate_drop;
@API(help="For booster=dart only: one_drop", level = API.Level.expert, gridable = true)
public boolean one_drop;
@API(help="For booster=dart only: skip_drop (0..1)", level = API.Level.expert, gridable = true)
public float skip_drop;
@API(help="Type of DMatrix. For sparse, NAs and 0 are treated equally.", values = { "auto", "dense", "sparse" }, level = API.Level.secondary, gridable = true)
public XGBoostParameters.DMatrixType dmatrix_type;
@API(help="Backend. By default (auto), a GPU is used if available.", values = { "auto", "gpu", "cpu" }, level = API.Level.expert, gridable = true)
public XGBoostParameters.Backend backend;
@API(help="Which GPU(s) to use. ", level = API.Level.expert, gridable = false)
public int[] gpu_id;
@API(help="A set of allowed column interactions.", level= API.Level.expert)
public String[][] interaction_constraints;
@API(help="Controls the effect of observations with positive labels in relation to the observations with negative labels on gradient calculation. Useful for imbalanced problems.", level= API.Level.expert, gridable = true)
public float scale_pos_weight;
@API(help="Specification of evaluation metric that will be passed to the native XGBoost backend.", level= API.Level.expert, gridable = true)
public String eval_metric;
@API(help="If enabled, score only the evaluation metric. This can make model training faster if scoring is frequent (eg. each iteration).", level= API.Level.expert, gridable = true)
public boolean score_eval_metric_only;
}
}
