hex.tree.gbm.GBMModel Maven / Gradle / Ivy
package hex.tree.gbm;
import hex.Model;
import hex.schemas.GBMModelV2;
import hex.tree.*;
import java.util.Arrays;
import water.H2O;
import water.Key;
import water.api.ModelSchema;
import water.fvec.Chunk;
import water.fvec.Frame;
import water.util.ArrayUtils;
public class GBMModel extends SharedTreeModel {
public static class GBMParameters extends SharedTreeModel.SharedTreeParameters {
/** Distribution functions. Note: AUTO will select gaussian for
* continuous, and multinomial for categorical response
*
* TODO: Replace with drop-down that displays different distributions
* depending on cont/cat response
*/
public enum Family { AUTO, bernoulli }
public Family _loss = Family.AUTO;
public float _learn_rate=0.1f; // Learning rate from 0.0 to 1.0
}
public static class GBMOutput extends SharedTreeModel.SharedTreeOutput {
public GBMOutput( GBM b ) { super(b); }
}
public GBMModel(Key selfKey, GBMParameters parms, GBMOutput output ) { super(selfKey,parms,output); }
// Default publically visible Schema is V2
@Override public ModelSchema schema() { return new GBMModelV2(); }
/** Bulk scoring API for one row. Chunks are all compatible with the model,
* and expect the last Chunks are for the final distribution and prediction.
* Default method is to just load the data into the tmp array, then call
* subclass scoring logic. */
@Override protected float[] score0( Chunk chks[], int row_in_chunk, double[] tmp, float[] preds ) {
assert chks.length>=tmp.length;
for( int i=0; i1 ) { // classification
// Because we call Math.exp, we have to be numerically stable or else
// we get Infinities, and then shortly NaN's. Rescale the data so the
// largest value is +/-1 and the other values are smaller.
// See notes here: http://www.hongliangjie.com/2011/01/07/logsum/
float maxval=Float.NEGATIVE_INFINITY;
float dsum=0;
if( _output.nclasses()==2 ) p[2] = - p[1];
// Find a max
for( int k=1; k