hex.genmodel.algos.targetencoder

Class TargetEncoderMojoModel

java.lang.Object

hex.genmodel.GenModel

hex.genmodel.MojoModel

hex.genmodel.algos.targetencoder.TargetEncoderMojoModel

All Implemented Interfaces:

IGenModel, java.io.Serializable, IGeneratedModel

public class TargetEncoderMojoModel
extends MojoModel

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	TargetEncoderMojoModel.SortByKeyAssociatedIndex<K extends java.lang.String,V>

Field Summary

Fields

Modifier and Type	Field and Description
double	_inflectionPoint
double	_priorMean
double	_smoothing
EncodingMaps	_targetEncodingMap
java.util.Map<java.lang.String,java.lang.Integer>	_teColumnNameToIdx
java.util.Map<java.lang.String,java.lang.Integer>	_teColumnNameToMissingValuesPresence
boolean	_withBlending

Fields inherited from class hex.genmodel.MojoModel

_algoName, _balanceClasses, _category, _defaultThreshold, _h2oVersion, _modelAttributes, _modelClassDistrib, _modelDescriptor, _mojo_version, _nclasses, _nfeatures, _priorClassDistrib, _supervised, _uuid

Fields inherited from class hex.genmodel.GenModel

_domains, _names, _offsetColumn, _responseColumn

Constructor Summary

Constructors

Constructor and Description
TargetEncoderMojoModel(java.lang.String[] columns, java.lang.String[][] domains, java.lang.String responseName)

Method Summary

Modifier and Type	Method and Description
static double	computeBlendedEncoding(double lambda, double posteriorMean, double priorMean)
static double	computeLambda(int nrows, double inflectionPoint, double smoothing)
double[]	score0(double[] row, 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, convertDouble2Float, correctProbabilities, createAuxKey, features, GBM_rescale, getCategoricalEncoding, getColIdx, getDomainValues, getDomainValues, getDomainValues, getHeader, getModelCategories, getNames, getNumClasses, getNumCols, getNumResponseClasses, getOffsetName, getOrigDomainValues, getOrigNames, getOrigNumCols, getPrediction, getPredsSize, getPredsSize, getResponseIdx, getResponseName, GLM_identityInv, GLM_inverseInv, GLM_logInv, GLM_logitInv, GLM_ologitInv, GLM_tweedieInv, img2pixels, isAutoEncoder, isClassifier, KMeans_closest, KMeans_distance, KMeans_distance, KMeans_distances, Kmeans_preprocessData, Kmeans_preprocessData, KMeans_simplex, log_rescale, mapEnum, nCatFeatures, score0, setCats, setCats, setInput, setInput

Methods inherited from class java.lang.Object

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

Field Detail

_targetEncodingMap

public EncodingMaps _targetEncodingMap

_teColumnNameToIdx

public java.util.Map<java.lang.String,java.lang.Integer> _teColumnNameToIdx

_teColumnNameToMissingValuesPresence

public java.util.Map<java.lang.String,java.lang.Integer> _teColumnNameToMissingValuesPresence

_withBlending

public boolean _withBlending

_inflectionPoint

public double _inflectionPoint

_smoothing

public double _smoothing

_priorMean

public double _priorMean

Constructor Detail

TargetEncoderMojoModel

public TargetEncoderMojoModel(java.lang.String[] columns,
                              java.lang.String[][] domains,
                              java.lang.String responseName)

Method Detail

computeLambda

public static double computeLambda(int nrows,
                                   double inflectionPoint,
                                   double smoothing)

computeBlendedEncoding

public static double computeBlendedEncoding(double lambda,
                                            double posteriorMean,
                                            double priorMean)

score0

public double[] score0(double[] row,
                       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