View source: R/AutoH2oMLScoring.R
AutoH2OMLScoring | R Documentation |
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.
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,
Debug = FALSE
)
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 |
A data.table of predicted values with the option to return model features as well.
Adrian Antico
Other Automated Model Scoring:
AutoCatBoostScoring()
,
AutoLightGBMScoring()
,
AutoXGBoostScoring()
## 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)
## End(Not run)
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