hex.modelselection

Class ModelSelectionUtils

java.lang.Object

hex.modelselection.ModelSelectionUtils

public class ModelSelectionUtils
extends java.lang.Object

Constructor Summary

Constructors

Constructor and Description
ModelSelectionUtils()

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 java.lang.String[]	extractPredictorNames(ModelSelectionModel.ModelSelectionParameters parms, DataInfo dinfo, java.lang.String foldColumn)
static java.util.List<java.lang.String>	extraModelColumnNames(java.util.List<java.lang.String> coefNames, GLMModel bestModel)
static GLMModel	findBestModel(GLM[] glmResults) Given GLM run results of a fixed number of predictors, find the model with the best R2 value.
static int	findBestR2Model(double lastBestR2, GLMModel[] bestR2Models) Given an array GLMModel built, find the one with the highest R2 value that exceeds lastBestR2.
static GLMModel.GLMParameters[]	generateGLMParameters(water.fvec.Frame[] trainingFrames, ModelSelectionModel.ModelSelectionParameters parms, int nfolds, java.lang.String foldColumn, hex.Model.Parameters.FoldAssignmentScheme foldAssignment)
static water.fvec.Frame[]	generateMaxRTrainingFrames(ModelSelectionModel.ModelSelectionParameters parms, java.lang.String[] predictorNames, java.lang.String foldColumn, java.util.List<java.lang.Integer> currSubsetIndices, int newPredPos, java.util.List<java.lang.Integer> validSubsets) Give a predictor subset with indices stored in currSubsetIndices, an array of training frames are generated by adding one predictor from predictorNames with predictors not already included in currSubsetIndices.
static water.fvec.Frame	generateOneFrame(int[] predIndices, ModelSelectionModel.ModelSelectionParameters 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 water.fvec.Frame[]	generateTrainingFrames(ModelSelectionModel.ModelSelectionParameters 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.
static void	updateValidSubset(java.util.List<java.lang.Integer> validSubset, java.util.List<java.lang.Integer> originalSubset, java.util.List<java.lang.Integer> currSubsetIndices)

Methods inherited from class java.lang.Object

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

Constructor Detail

ModelSelectionUtils

public ModelSelectionUtils()

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(ModelSelectionModel.ModelSelectionParameters 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,
                                                ModelSelectionModel.ModelSelectionParameters 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:

generateMaxRTrainingFrames

public static water.fvec.Frame[] generateMaxRTrainingFrames(ModelSelectionModel.ModelSelectionParameters parms,
                                                            java.lang.String[] predictorNames,
                                                            java.lang.String foldColumn,
                                                            java.util.List<java.lang.Integer> currSubsetIndices,
                                                            int newPredPos,
                                                            java.util.List<java.lang.Integer> validSubsets)

Give a predictor subset with indices stored in currSubsetIndices, an array of training frames are generated by adding one predictor from predictorNames with predictors not already included in currSubsetIndices.

Parameters:

parms -

predictorNames -

foldColumn -

currSubsetIndices -

validSubsets - Lists containing only valid predictor indices to choose from

Returns:

findBestR2Model

public static int findBestR2Model(double lastBestR2,
                                  GLMModel[] bestR2Models)

Given an array GLMModel built, find the one with the highest R2 value that exceeds lastBestR2. If found, return the index where the best model is. Else return -1

Parameters:

lastBestR2 -

bestR2Models -

Returns:

generateGLMParameters

public static GLMModel.GLMParameters[] generateGLMParameters(water.fvec.Frame[] trainingFrames,
                                                             ModelSelectionModel.ModelSelectionParameters 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)

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 -

extractPredictorNames

public static java.lang.String[] extractPredictorNames(ModelSelectionModel.ModelSelectionParameters parms,
                                                       DataInfo dinfo,
                                                       java.lang.String foldColumn)

extraModelColumnNames

public static java.util.List<java.lang.String> extraModelColumnNames(java.util.List<java.lang.String> coefNames,
                                                                     GLMModel bestModel)

updateValidSubset

public static void updateValidSubset(java.util.List<java.lang.Integer> validSubset,
                                     java.util.List<java.lang.Integer> originalSubset,
                                     java.util.List<java.lang.Integer> currSubsetIndices)