hex.genmodel.algos.glm

Class GlmMojoModel

java.lang.Object

hex.genmodel.GenModel

hex.genmodel.MojoModel

hex.genmodel.algos.glm.GlmMojoModel

All Implemented Interfaces:

IGenModel, java.io.Serializable, IGeneratedModel

public class GlmMojoModel
extends MojoModel

See Also:

Serialized Form

Field Summary

Fields inherited from class hex.genmodel.MojoModel

_balanceClasses, _category, _defaultThreshold, _modelClassDistrib, _nclasses, _nfeatures, _priorClassDistrib, _supervised, _uuid

Fields inherited from class hex.genmodel.GenModel

_domains, _names, _offsetColumn

Method Summary

Methods

Modifier and Type	Method and Description
double[]	applyCoefficients(double[] data, double[] output, int destPos) Applies GLM coefficients to a given row of data to calculate feature contributions.
double	getIntercept()
double[]	score0(double[] data, double[] preds) Subclasses implement the scoring logic.

Methods inherited from class hex.genmodel.MojoModel

getModelCategory, getUUID, isSupervised, load, load, nclasses, nfeatures

Methods inherited from class hex.genmodel.GenModel

bitSetContains, bitSetIsInRange, calibrateClassProbabilities, correctProbabilities, createAuxKey, GBM_rescale, getColIdx, getDomainValues, getDomainValues, getDomainValues, getHeader, getModelCategories, getNames, getNumClasses, getNumCols, getNumResponseClasses, getPrediction, getPredsSize, getPredsSize, getResponseIdx, getResponseName, GLM_identityInv, GLM_inverseInv, GLM_logInv, GLM_logitInv, GLM_tweedieInv, img2pixels, isAutoEncoder, isClassifier, KMeans_closest, KMeans_distance, KMeans_distance, Kmeans_preprocessData, Kmeans_preprocessData, KMeans_simplex, log_rescale, mapEnum, score0, setInput

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

applyCoefficients

public double[] applyCoefficients(double[] data,
                         double[] output,
                         int destPos)

Applies GLM coefficients to a given row of data to calculate feature contributions. Note: for internal purposes only (k-LIME)

Parameters:

data - input row of data (same input as to glmScore0)

output - target output array

destPos - index to the output array where the result should start

Returns:

feature contributions, prediction = linkFunction(sum(output) + intercept)

getIntercept

public double getIntercept()

score0

public final double[] score0(double[] data,
              double[] preds)

Description copied from class: GenModel

Subclasses implement the scoring logic. The data is pre-loaded into a re-used temp array, in the order the model expects. The predictions are loaded into the re-used temp array, which is also returned. This call exactly matches the hex.Model.score0, but uses the light-weight GenModel class.

Specified by:

score0 in class GenModel