Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
package hex.genmodel.algos.gbm;
import hex.genmodel.GenModel;
import hex.genmodel.PredictContributions;
import hex.genmodel.algos.tree.*;
import hex.genmodel.utils.DistributionFamily;
import hex.genmodel.utils.LinkFunctionType;
import static hex.genmodel.utils.DistributionFamily.*;
/**
* "Gradient Boosting Machine" MojoModel
*/
public final class GbmMojoModel extends SharedTreeMojoModelWithContributions implements SharedTreeGraphConverter {
public DistributionFamily _family;
public LinkFunctionType _link_function;
public double _init_f;
public GbmMojoModel(String[] columns, String[][] domains, String responseColumn) {
super(columns, domains, responseColumn);
}
@Override
protected PredictContributions getContributionsPredictor(TreeSHAPPredictor treeSHAPPredictor) {
return new SharedTreeContributionsPredictor(this, treeSHAPPredictor);
}
@Override
public double getInitF() {
return _init_f;
}
/**
* Corresponds to `hex.tree.gbm.GbmMojoModel.score0()`
*/
@Override
public final double[] score0(double[] row, double offset, double[] preds) {
super.scoreAllTrees(row, preds);
return unifyPreds(row, offset, preds);
}
@Override
public final double[] unifyPreds(double[] row, double offset, double[] preds) {
if (_family == bernoulli || _family == quasibinomial || _family == modified_huber) {
double f = preds[1] + _init_f + offset;
preds[2] = linkInv(_link_function, f);
preds[1] = 1.0 - preds[2];
} else if (_family == multinomial) {
if (_nclasses == 2) { // 1-tree optimization for binomial
preds[1] += _init_f + offset; //offset is not yet allowed, but added here to be future-proof
preds[2] = -preds[1];
}
GenModel.GBM_rescale(preds);
} else { // Regression
double f = preds[0] + _init_f + offset;
preds[0] = linkInv(_link_function, f);
return preds;
}
if (_balanceClasses)
GenModel.correctProbabilities(preds, _priorClassDistrib, _modelClassDistrib);
preds[0] = GenModel.getPrediction(preds, _priorClassDistrib, row, _defaultThreshold);
return preds;
}
/**
* Calculate inverse link depends on distribution type - every distribution has own link function
* Be careful if you are changing code here - you have to change it in hex.LinkFunction too
* @param linkFunction link function to compute link inversion
* @param f raw prediction
* @return calculated inverse link value
*/
private double linkInv(LinkFunctionType linkFunction, double f){
switch (linkFunction) {
case log:
return exp(f);
case logit:
case ologit:
return 1 / (1 + exp(-f));
case ologlog:
return 1 - exp(-1 * exp(f));
case oprobit:
return 0;
case inverse:
double xx = f < 0 ? Math.min(-1e-5, f) : Math.max(-1e-5, f);
return 1.0/xx;
case identity:
default:
return f;
}
}
/**
* Sanitized exponential function - helper function.
* Be careful if you are changing code here - you have to change it in hex.LogExpUtils too
*
* @param x value to be transform
* @return result of exp function
*/
public static double exp(double x) { return Math.min(1e19, Math.exp(x)); }
/**
* Sanitized log function - helper function
* Be careful if you are changing code here - you have to change it in hex.LogExpUtils too
*
* @param x value to be transform
* @return result of log function
*/
public static double log(double x) {
x = Math.max(0, x);
return x == 0 ? -19 : Math.max(-19, Math.log(x));
}
@Override
public double[] score0(double[] row, double[] preds) {
return score0(row, 0.0, preds);
}
public String[] leaf_node_assignment(double[] row) {
return getDecisionPath(row);
}
@Override
public String[] getOutputNames() {
if (_family == quasibinomial && getDomainValues(getResponseIdx()) == null) {
return new String[]{"predict", "pVal0", "pVal1"};
}
return super.getOutputNames();
}
}
