hex.maxrglm

Class MaxRGLMUtils

java.lang.Object

hex.maxrglm.MaxRGLMUtils

public class MaxRGLMUtils
extends java.lang.Object

Constructor Summary

Constructors

Constructor and Description
MaxRGLMUtils()

Method Summary

Modifier and Type	Method and Description
static GLM[]	buildGLMBuilders(GLMModel.GLMParameters[] trainingParams)
static int	calculateModelNumber(int numPredictors, int maxPredictorNumber) Given the number of predictors in the training frame and the maximum predictor number, we are going to calculate the number of models that we need to build in order to find: - best model with 1 predictor; - best model with 2 predictors; ...
static GLMModel	findBestModel(GLM[] glmResults) Given GLM run results of a fixed number of predictors, find the model with the best R2 value.
static GLMModel.GLMParameters[]	generateGLMParameters(water.fvec.Frame[] trainingFrames, MaxRGLMModel.MaxRGLMParameters parms, int nfolds, java.lang.String foldColumn, hex.Model.Parameters.FoldAssignmentScheme foldAssignment)
static water.fvec.Frame	generateOneFrame(int[] predIndices, MaxRGLMModel.MaxRGLMParameters parms, java.lang.String[] predNames, java.lang.String foldColumn) Given a predictor indices set, this function will generate a training frame containing the predictors with indices in predIndices.
static java.lang.String[]	generatePredictorNames(MaxRGLMModel.MaxRGLMParameters parms) Given parms.train(), generate an String array that contains names of elligible predictors.
static water.fvec.Frame[]	generateTrainingFrames(MaxRGLMModel.MaxRGLMParameters parms, int predNum, java.lang.String[] predNames, int numModels, java.lang.String foldColumn)
static void	removeTrainingFrames(water.fvec.Frame[] trainingFrames)
static void	setParamField(hex.Model.Parameters params, GLMModel.GLMParameters glmParam, boolean superClassParams, java.lang.reflect.Field[] paramFields, java.util.List<java.lang.String> excludeList)
static void	updateLaterIndices(int[] currentPredIndices, int indexUpdated, int lastPredInd) Give 5 predictors and say we want the combo of 3 predictors, this function will properly reset the prediction combination indices say from [0, 1, 4] -> [0, 2, 3] or [0, 3, 4] -> [1, 2, 3].
static void	updatePredIndices(int[] currentPredIndices, int[] indicesBounds) Given predictor indices stored in currentPredIndices, we need to find the next combination of predictor indices to use to generate the next combination.

Methods inherited from class java.lang.Object

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

Constructor Detail

MaxRGLMUtils

public MaxRGLMUtils()

Method Detail

calculateModelNumber

public static int calculateModelNumber(int numPredictors,
                                       int maxPredictorNumber)

Given the number of predictors in the training frame and the maximum predictor number, we are going to calculate the number of models that we need to build in order to find: - best model with 1 predictor; - best model with 2 predictors; ... - best model with naxPredictorNumber. This basically boils down to calculating the following: combination(numPredictors, 1) + combination(numPredictors, 2) + ... + combination(numPredictors, maxPredictorNumber)

Parameters:

numPredictors: - number of predictors in the training frame

maxPredictorNumber: - maximum number of predictors of interest

Returns:

an integer that is the number of models that are going to be built

generateTrainingFrames

public static water.fvec.Frame[] generateTrainingFrames(MaxRGLMModel.MaxRGLMParameters parms,
                                                        int predNum,
                                                        java.lang.String[] predNames,
                                                        int numModels,
                                                        java.lang.String foldColumn)

updatePredIndices

public static void updatePredIndices(int[] currentPredIndices,
                                     int[] indicesBounds)

Given predictor indices stored in currentPredIndices, we need to find the next combination of predictor indices to use to generate the next combination. For example, if we have 4 predictors and we are looking to take two predictors, predictor indices can change in the following sequence [0,1]->[0,2]->[0,3]->[1,2]->[1,2]->[2,3].

Parameters:

currentPredIndices -

indicesBounds -

updateLaterIndices

public static void updateLaterIndices(int[] currentPredIndices,
                                      int indexUpdated,
                                      int lastPredInd)

Give 5 predictors and say we want the combo of 3 predictors, this function will properly reset the prediction combination indices say from [0, 1, 4] -> [0, 2, 3] or [0, 3, 4] -> [1, 2, 3]. Given an index that was just updated, it will update the indices that come later in the list correctly.

Parameters:

currentPredIndices -

indexUpdated -

lastPredInd -

generateOneFrame

public static water.fvec.Frame generateOneFrame(int[] predIndices,
                                                MaxRGLMModel.MaxRGLMParameters parms,
                                                java.lang.String[] predNames,
                                                java.lang.String foldColumn)

Given a predictor indices set, this function will generate a training frame containing the predictors with indices in predIndices.

Parameters:

predIndices -

parms -

predNames -

Returns:

generateGLMParameters

public static GLMModel.GLMParameters[] generateGLMParameters(water.fvec.Frame[] trainingFrames,
                                                             MaxRGLMModel.MaxRGLMParameters parms,
                                                             int nfolds,
                                                             java.lang.String foldColumn,
                                                             hex.Model.Parameters.FoldAssignmentScheme foldAssignment)

setParamField

public static void setParamField(hex.Model.Parameters params,
                                 GLMModel.GLMParameters glmParam,
                                 boolean superClassParams,
                                 java.lang.reflect.Field[] paramFields,
                                 java.util.List<java.lang.String> excludeList)

generatePredictorNames

public static java.lang.String[] generatePredictorNames(MaxRGLMModel.MaxRGLMParameters parms)

Given parms.train(), generate an String array that contains names of elligible predictors.

Parameters:

parms -

Returns:

buildGLMBuilders

public static GLM[] buildGLMBuilders(GLMModel.GLMParameters[] trainingParams)

removeTrainingFrames

public static void removeTrainingFrames(water.fvec.Frame[] trainingFrames)

findBestModel

public static GLMModel findBestModel(GLM[] glmResults)

Given GLM run results of a fixed number of predictors, find the model with the best R2 value.

Parameters:

glmResults -