hex.schemas.SharedTreeV3 Maven / Gradle / Ivy
package hex.schemas;
import hex.tree.SharedTree;
import hex.tree.SharedTreeModel.SharedTreeParameters;
import water.api.*;
import water.api.FrameV3.ColSpecifierV3;
public class SharedTreeV3, P extends SharedTreeV3.SharedTreeParametersV3> extends ModelBuilderSchema {
public static class SharedTreeParametersV3> extends ModelParametersSchema {
static public String[] own_fields = new String[] {
"response_column",
"balance_classes",
"class_sampling_factors",
"max_after_balance_size",
"max_confusion_matrix_size",
"max_hit_ratio_k",
"ntrees", "max_depth", "min_rows", "nbins", "nbins_cats", "r2_stopping", "seed"
};
// supervised Schema
// TODO: pass these as a new helper class that contains frame and vec; right now we have no automagic way to
// know which frame a Vec name corresponds to, so there's hardwired logic in the adaptor which knows that these
// column names are related to training_frame.
@API(help = "Response column", is_member_of_frames = {"training_frame", "validation_frame"}, is_mutually_exclusive_with = {"ignored_columns"}, direction = API.Direction.INOUT)
public ColSpecifierV3 response_column;
/*Imbalanced Classes*/
/**
* For imbalanced data, balance training data class counts via
* over/under-sampling. This can result in improved predictive accuracy.
*/
@API(help = "Balance training data class counts via over/under-sampling (for imbalanced data).", level = API.Level.secondary, direction = API.Direction.INOUT)
public boolean balance_classes;
/**
* Desired over/under-sampling ratios per class (lexicographic order).
* Only when balance_classes is enabled.
* If not specified, they will be automatically computed to obtain class balance during training.
*/
@API(help = "Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.", level = API.Level.expert, direction = API.Direction.INOUT)
public float[] class_sampling_factors;
/**
* When classes are balanced, limit the resulting dataset size to the
* specified multiple of the original dataset size.
*/
@API(help = "Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.", /* dmin=1e-3, */ level = API.Level.expert, direction = API.Direction.INOUT)
public float max_after_balance_size;
/** For classification models, the maximum size (in terms of classes) of
* the confusion matrix for it to be printed. This option is meant to
* avoid printing extremely large confusion matrices. */
@API(help = "Maximum size (# classes) for confusion matrices to be printed in the Logs", level = API.Level.secondary, direction = API.Direction.INOUT)
public int max_confusion_matrix_size;
/**
* The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
*/
@API(help = "Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)", level = API.Level.secondary, direction=API.Direction.INOUT)
public int max_hit_ratio_k;
//
@API(help="Number of trees.", gridable = true)
public int ntrees;
@API(help="Maximum tree depth.", gridable = true)
public int max_depth;
@API(help="Fewest allowed observations in a leaf (in R called 'nodesize').", gridable = true)
public int min_rows;
@API(help="For numerical columns (real/int), build a histogram of this many bins, then split at the best point", gridable = true)
public int nbins;
@API(help="For categorical columns (enum), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.", gridable = true)
public int nbins_cats;
@API(help="Stop making trees when the R^2 metric equals or exceeds this", level = API.Level.secondary)
public double r2_stopping;
@API(help = "Seed for pseudo random number generator (if applicable)")
public long seed;
}
}