AutoH2OMLScoring: AutoH2OMLScoring is an automated scoring function that compliments the AutoH2o model training functions.

Description

AutoH2OMLScoring is an automated scoring function that compliments the AutoH2oGBM__() and AutoH2oDRF__() models training functions. This function requires you to supply features for scoring. It will run ModelDataPrep()to prepare your features for H2O data conversion and scoring.

Usage

AutoH2OMLScoring(
  ScoringData = NULL,
  ModelObject = NULL,
  ModelType = "mojo",
  H2OShutdown = TRUE,
  H2OStartUp = TRUE,
  MaxMem = {     gc()    
    paste0(as.character(floor(as.numeric(system("awk '/MemFree/ {print $2}' /proc/meminfo",
    intern = TRUE))/1e+06)), "G") },
  NThreads = max(1, parallel::detectCores() - 2),
  JavaOptions = "-Xmx1g -XX:ReservedCodeCacheSize=256m",
  ModelPath = NULL,
  ModelID = NULL,
  ReturnFeatures = TRUE,
  TransformNumeric = FALSE,
  BackTransNumeric = FALSE,
  TargetColumnName = NULL,
  TransformationObject = NULL,
  TransID = NULL,
  TransPath = NULL,
  MDP_Impute = TRUE,
  MDP_CharToFactor = TRUE,
  MDP_RemoveDates = TRUE,
  MDP_MissFactor = "0",
  MDP_MissNum = -1
)

Arguments

ScoringData

This is your data.table of features for scoring. Can be a single row or batch.

ModelObject

Supply a model object from AutoH2oDRF__()

ModelType

Set to either "mojo" or "standard" depending on which version you saved

H2OShutdown

Set to TRUE to shutdown H2O inside the function.

H2OStartUp

Defaults to TRUE which means H2O will be started inside the function

MaxMem

Set to you dedicated amount of memory. E.g. "28G"

NThreads

Default set to max(1, parallel::detectCores()-2)

JavaOptions

Change the default to your machines specification if needed. Default is '-Xmx1g -XX:ReservedCodeCacheSize=256m',

ModelPath

Supply your path file used in the AutoH2o__() function

ModelID

Supply the model ID used in the AutoH2o__() function

ReturnFeatures

Set to TRUE to return your features with the predicted values.

TransformNumeric

Set to TRUE if you have features that were transformed automatically from an Auto__Regression() model AND you haven't already transformed them.

BackTransNumeric

Set to TRUE to generate back-transformed predicted values. Also, if you return features, those will also be back-transformed.

TargetColumnName

Input your target column name used in training if you are utilizing the transformation service

TransformationObject

Set to NULL if you didn't use transformations or if you want the function to pull from the file output from the Auto__Regression() function. You can also supply the transformation data.table object with the transformation details versus having it pulled from file.

TransID

Set to the ID used for saving the transformation data.table object or set it to the ModelID if you are pulling from file from a build with Auto__Regression().

TransPath

Set the path file to the folder where your transformation data.table detail object is stored. If you used the Auto__Regression() to build, set it to the same path as ModelPath.

MDP_Impute

Set to TRUE if you did so for modeling and didn't do so before supplying ScoringData in this function

MDP_CharToFactor

Set to TRUE to turn your character columns to factors if you didn't do so to your ScoringData that you are supplying to this function

MDP_RemoveDates

Set to TRUE if you have date of timestamp columns in your ScoringData

MDP_MissFactor

If you set MDP_Impute to TRUE, supply the character values to replace missing values with

MDP_MissNum

If you set MDP_Impute to TRUE, supply a numeric value to replace missing values with

Value

A data.table of predicted values with the option to return model features as well.

Examples

Run this code

# NOT RUN {
Preds <- AutoH2OMLScoring(
  ScoringData = data,
  ModelObject = NULL,
  ModelType = "mojo",
  H2OShutdown = TRUE,
  H2OStartUp = TRUE,
  MaxMem = {gc();paste0(as.character(floor(as.numeric(system("awk '/MemFree/ {print $2}' /proc/meminfo", intern=TRUE)) / 1000000)),"G")},
  NThreads = max(1, parallel::detectCores()-2),
  JavaOptions = '-Xmx1g -XX:ReservedCodeCacheSize=256m',
  ModelPath = normalizePath("./"),
  ModelID = "ModelTest",
  ReturnFeatures = TRUE,
  TransformNumeric = FALSE,
  BackTransNumeric = FALSE,
  TargetColumnName = NULL,
  TransformationObject = NULL,
  TransID = NULL,
  TransPath = NULL,
  MDP_Impute = TRUE,
  MDP_CharToFactor = TRUE,
  MDP_RemoveDates = TRUE,
  MDP_MissFactor = "0",
  MDP_MissNum = -1)
# }

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