hex.glm

Class GLMModel

java.lang.Object

water.Iced

water.Lockable<Model>

water.Model

hex.glm.GLMModel

All Implemented Interfaces:

java.lang.Cloneable, java.lang.Comparable<GLMModel>, Freezable

public class GLMModel
extends Model
implements java.lang.Comparable<GLMModel>

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	GLMModel.GLMValidationTask<T extends GLMModel.GLMValidationTask<T>>
static class	GLMModel.GLMXValidationTask

Nested classes/interfaces inherited from class water.Model

Model.ModelAutobufferSerializer, Model.ModelCategory

Field Summary

Fields

Modifier and Type	Field and Description
java.lang.String[]	coefficients_names
static DocGen.FieldDoc[]	DOC_FIELDS

Fields inherited from class water.Model

_dataKey, _domains, _have_cv_results, _modelClassDist, _names, _priorClassDist, GEN_BENCHMARK_CODE, training_duration_in_ms, training_start_time, warnings

Fields inherited from class water.Lockable

_key, _lockers

Constructor Summary

Constructors

Constructor and Description
GLMModel(GLM2 job, Key selfKey, FrameTask.DataInfo dinfo, GLMParams glm, double beta_eps, double alpha, double lambda_max, double ymu, double prior)

Method Summary

Methods

Modifier and Type	Method and Description
void	addSubmodel(double lambda)
double	aic()
double	auc()
double[]	beta()
GLMModel	clone()
java.util.HashMap<java.lang.String,java.lang.Double>	coefficients() get beta coefficients in a map indexed by name
int	compareTo(GLMModel m)
double	devExplained()
void	dropSubmodel()
GLM2	get_params()
GLMParams	getParams()
int	iteration()
Request2	job()
double	lambda()
protected void	maybeComputeVariableImportances()
int	ncoefs()
double[]	norm_beta(double lambda)
int	pickBestXval()
int	rank()
int	rank(double lambda)
protected float[]	score0(double[] data, float[] preds) Subclasses implement the scoring logic.
boolean	setAndTestValidation(GLMValidation val)
void	setSubmodelIdx(int l)
void	setValidation(GLMValidation val)
void	setXValidation(double lambda, GLMValidation.GLMXValidation val)
hex.glm.GLMModel.Submodel	submodelForLambda(double lambda)
int	submodelIdForLambda(double lambda)
java.lang.String	toString()
void	updateSubmodel(double lambda, double[] beta, double[] norm_beta, int iteration, boolean sparseCoef)
GLMValidation	validation()
VarImp	varimp() Variable importance of individual input features measured by this model.

Methods inherited from class water.Model

adapt, adapt, addWarning, calcError, classNames, cm, delete_impl, errStr, getDomainMapping, getDomainMapping, getModelCategory, getModelSerializer, getUniqueId, isClassifier, missingColumnsType, mse, nclasses, nfeatures, printCrossValidationModelsHTML, responseName, score, score, score, score, score, score, score0, scoreCrossValidation, scoreImpl, setCrossValidationError, setModelClassDistribution, start_training, start_training, stop_training, testJavaScoring, toJava, toJava, toJavaDefaultMaxIters, toJavaFillPreds0, toJavaInit, toJavaInit, toJavaPredictBody, toJavaSuper, toJavaUnifyPreds

Methods inherited from class water.Lockable

delete_and_lock, delete, delete, delete, delete, is_unlocked, is_wlocked, read_lock, read_lock, unlock_all, unlock, update, write_lock

Methods inherited from class water.Iced

frozenType, init, newInstance, read, toDocField, write, writeJSON, writeJSONFields

Methods inherited from class java.lang.Object

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

Field Detail

DOC_FIELDS

public static DocGen.FieldDoc[] DOC_FIELDS

coefficients_names

@Request.API(help="column names including expanded categorical values")
public java.lang.String[] coefficients_names

Constructor Detail

GLMModel

public GLMModel(GLM2 job,
        Key selfKey,
        FrameTask.DataInfo dinfo,
        GLMParams glm,
        double beta_eps,
        double alpha,
        double lambda_max,
        double ymu,
        double prior)

Method Detail

get_params

public final GLM2 get_params()

Overrides:

get_params in class Model

job

public final Request2 job()

Overrides:

job in class Model

auc

public double auc()

aic

public double aic()

devExplained

public double devExplained()

clone

public GLMModel clone()

Overrides:

clone in class Iced

compareTo

public int compareTo(GLMModel m)

Specified by:

compareTo in interface java.lang.Comparable<GLMModel>

updateSubmodel

public void updateSubmodel(double lambda,
                  double[] beta,
                  double[] norm_beta,
                  int iteration,
                  boolean sparseCoef)

addSubmodel

public void addSubmodel(double lambda)

dropSubmodel

public void dropSubmodel()

lambda

public double lambda()

validation

public GLMValidation validation()

iteration

public int iteration()

beta

public double[] beta()

norm_beta

public double[] norm_beta(double lambda)

score0

protected float[] score0(double[] data,
             float[] preds)

Description copied from class: Model

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.

Specified by:

score0 in class Model

ncoefs

public final int ncoefs()

getParams

public GLMParams getParams()

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

rank

public int rank()

submodelIdForLambda

public int submodelIdForLambda(double lambda)

submodelForLambda

public hex.glm.GLMModel.Submodel submodelForLambda(double lambda)

rank

public int rank(double lambda)

setValidation

public void setValidation(GLMValidation val)

setAndTestValidation

public boolean setAndTestValidation(GLMValidation val)

setXValidation

public void setXValidation(double lambda,
                  GLMValidation.GLMXValidation val)

pickBestXval

public int pickBestXval()

setSubmodelIdx

public void setSubmodelIdx(int l)

coefficients

public java.util.HashMap<java.lang.String,java.lang.Double> coefficients()

get beta coefficients in a map indexed by name

Returns:

varimp

public VarImp varimp()

Description copied from class: Model

Variable importance of individual input features measured by this model.

Overrides:

varimp in class Model

maybeComputeVariableImportances

protected void maybeComputeVariableImportances()