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 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 int	findMinAbs(java.util.List<java.lang.Double> zList)
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 java.lang.String	joinDouble(double[] val)
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 double[][]	shrinkDoubleArray(double[][] array, int numModels)
static water.Key[]	shrinkKeyArray(water.Key[] array, int numModels)
static java.lang.String[][]	shrinkStringArray(java.lang.String[][] array, int numModels)
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

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:

findMinAbs

public static int findMinAbs(java.util.List<java.lang.Double> zList)

shrinkStringArray

public static java.lang.String[][] shrinkStringArray(java.lang.String[][] array,
                                                     int numModels)

shrinkDoubleArray

public static double[][] shrinkDoubleArray(double[][] array,
                                           int numModels)

shrinkKeyArray

public static water.Key[] shrinkKeyArray(water.Key[] array,
                                         int numModels)

joinDouble

public static java.lang.String joinDouble(double[] val)

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)