hex.genmodel

Class GenModel

java.lang.Object

hex.genmodel.GenModel

public abstract class GenModel
extends java.lang.Object

This is a helper class to support Java generated models.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	GenModel.ModelCategory

Field Summary

Fields

Modifier and Type	Field and Description
java.lang.String[][]	_domains Categorical/factor/enum mappings, per column.
java.lang.String[]	_names Column names; last is response for supervised models

Constructor Summary

Constructors

Constructor and Description
GenModel(java.lang.String[] names, java.lang.String[][] domains)

Method Summary

Methods

Modifier and Type	Method and Description
static boolean	bitSetContains(byte[] bits, int bitoff, int num)
static void	GBM_rescale(double[] data, double[] preds)
abstract GenModel.ModelCategory	getModelCategory()
static int	getPrediction(double[] preds, double[] data) Utility function to get a best prediction from an array of class prediction distribution.
static double	GLM_identityInv(double x)
static double	GLM_inverseInv(double x)
static double	GLM_logInv(double x)
static double	GLM_logitInv(double x)
static double	GLM_tweedieInv(double x, double tweedie_link_power)
boolean	isSupervised()
static int	KMeans_closest(double[][] centers, double[] point, java.lang.String[][] domains, double[] means, double[] mults)
static double	KMeans_distance(double[] center, double[] point, java.lang.String[][] domains, double[] means, double[] mults)
static double	KMeans_distance(double[] center, float[] point, java.lang.String[][] domains, double[] means, double[] mults, double[] colSum, double[] colSumSq)
static double	log_rescale(double[] preds)
double[]	map(java.util.Map<java.lang.String,java.lang.Double> row, double[] data) Takes a HashMap mapping column names to doubles.
int	nclasses()
int	nfeatures()
abstract double[]	score0(double[] data, double[] preds) Subclasses implement the scoring logic.
double[]	score0(java.util.Map<java.lang.String,java.lang.Double> row)
double[]	score0(java.util.Map<java.lang.String,java.lang.Double> row, double[] preds)
double[]	score0(java.util.Map<java.lang.String,java.lang.Double> row, double[] data, double[] preds)
static double[]	SharedTree_clean(double[] data)

Methods inherited from class java.lang.Object

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

Field Detail

_names

public final java.lang.String[] _names

Column names; last is response for supervised models

_domains

public final java.lang.String[][] _domains

Categorical/factor/enum mappings, per column. Null for non-enum cols. Columns match the post-init cleanup columns. The last column holds the response col enums for SupervisedModels.

Constructor Detail

GenModel

public GenModel(java.lang.String[] names,
        java.lang.String[][] domains)

Method Detail

isSupervised

public boolean isSupervised()

nfeatures

public int nfeatures()

nclasses

public int nclasses()

getModelCategory

public abstract GenModel.ModelCategory getModelCategory()

map

public double[] map(java.util.Map<java.lang.String,java.lang.Double> row,
           double[] data)

Takes a HashMap mapping column names to doubles.

Looks up the column names needed by the model, and places the doubles into the data array in the order needed by the model. Missing columns use NaN.

score0

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

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.

score0

public double[] score0(java.util.Map<java.lang.String,java.lang.Double> row,
              double[] data,
              double[] preds)

score0

public double[] score0(java.util.Map<java.lang.String,java.lang.Double> row,
              double[] preds)

score0

public double[] score0(java.util.Map<java.lang.String,java.lang.Double> row)

getPrediction

public static int getPrediction(double[] preds,
                double[] data)

Utility function to get a best prediction from an array of class prediction distribution. It returns index of max value if predicted values are unique. In the case of tie, the implementation solve it in pseudo-random way.

Parameters:

preds - an array of prediction distribution. Length of arrays is equal to a number of classes+1.

Returns:

the best prediction (index of class, zero-based)

bitSetContains

public static boolean bitSetContains(byte[] bits,
                     int bitoff,
                     int num)

KMeans_closest

public static int KMeans_closest(double[][] centers,
                 double[] point,
                 java.lang.String[][] domains,
                 double[] means,
                 double[] mults)

KMeans_distance

public static double KMeans_distance(double[] center,
                     float[] point,
                     java.lang.String[][] domains,
                     double[] means,
                     double[] mults,
                     double[] colSum,
                     double[] colSumSq)

KMeans_distance

public static double KMeans_distance(double[] center,
                     double[] point,
                     java.lang.String[][] domains,
                     double[] means,
                     double[] mults)

SharedTree_clean

public static double[] SharedTree_clean(double[] data)

log_rescale

public static double log_rescale(double[] preds)

GBM_rescale

public static void GBM_rescale(double[] data,
               double[] preds)

GLM_identityInv

public static double GLM_identityInv(double x)

GLM_logitInv

public static double GLM_logitInv(double x)

GLM_logInv

public static double GLM_logInv(double x)

GLM_inverseInv

public static double GLM_inverseInv(double x)

GLM_tweedieInv

public static double GLM_tweedieInv(double x,
                    double tweedie_link_power)