Autotune Action Set

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters},

name="table-name",

singlePass=TRUE | FALSE,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

casLib="string"

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

target="variable-name",

tunerOptions={

constraints={{

lowerBound=double,

metric="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "LOSSFUNCTION" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME",

upperBound=double

}, {...}},

enableLocalSearch=TRUE | FALSE,

evaluationHistory=TRUE | FALSE,

foldColumn="string",

historyTable={

appendLookupData=TRUE | FALSE

liveUpdate=TRUE | FALSE

table={

caslib="string"

compress=TRUE | FALSE

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

}

logLevel=integer,

maxBayesianModelSize=integer,

maxEvals=integer,

maxIters=integer,

maxTime=double,

maxTrainTime=64-bit-integer,

nConvergenceIterations=integer,

nCrossValFolds=integer,

nParallel=integer,

nSubsessionWorkers=integer,

objective="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME",

popSize=integer,

sampleSize=integer,

searchMethod="BAYESIAN" | "GA" | "GRID" | "LHS" | "RANDOM",

secondObjective="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "NONE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME",

seed=integer,

shuffleGridPoints=TRUE | FALSE,

targetEvent="string",

trainPartitionFraction=double,

userDefinedPartition=TRUE | FALSE,

validationPartitionFraction=double

validTable={

caslib="string",

computedOnDemand=TRUE | FALSE,

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

name="table-name",

singlePass=TRUE | FALSE,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

;

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies the data table to use for model training.
validTable	—	specifies the data table to use for model validation.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
modelNamePrefix	—	specifies the table to store the model in.
tunerOptions	historyTable (and nested parameter table)	specifies a list of parameters for adjusting how the tuner behaves.

Parameter Descriptions

inputs={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the input variables to use in the analysis.

For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	input

modelNamePrefix={casouttable}

specifies the table to store the model in.

For more information about specifying the modelNamePrefix parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

modelTypes={{tuneAllModelTypesDefinition-1} <, {tuneAllModelTypesDefinition-2}, ...>}

specifies a list of model types to be tuned concurrently.

The tuneAllModelTypesDefinition value can be one or more of the following:

modelType="ALL" | "BNET" | "DECISIONTREE" | "FACTMAC" | "FOREST" | "GLM" | "GRADBOOST" | "LOGISTIC" | "NEURALNET" | "SVM"

specifies the model type for a specified model to be tuned.

Default	ALL

ALL

specifies the tuning of parameters for all valid model types.

BNET

specifies the tuning of Bayesian network classifier parameters.

DECISIONTREE

specifies the tuning of decision tree parameters.

FACTMAC

specifies the tuning of factorization machine parameters.

FOREST

specifies the tuning of forest parameters.

GLM

specifies the tuning of linear regression model parameters.

GRADBOOST

specifies the tuning of gradient boosting tree parameters.

LOGISTIC

specifies the tuning of logistic regression parameters.

NEURALNET

specifies the tuning of neural network parameters.

SVM

specifies the tuning of support vector machine parameters.

tuningOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies the tuning options for a specified model type to be tuned.

nominals={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the nominal input variables to use in the analysis.

For more information about specifying the nominals parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	nominal

* table={castable}

specifies the data table to use for model training.

Long form	table={name="table-name"}
Shortcut form	table="table-name"

Alias	trainTable

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=TRUE | FALSE

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	FALSE

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the names of the computed variables to create. Specify an expression for each variable in the computedVarsProgram parameter. If you do not specify this parameter, then all variables from computedVarsProgram are automatically included.

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

singlePass=TRUE | FALSE

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	FALSE

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

specifies an input table that contains rows to use as a WHERE filter. If the vars parameter is not specified, then all the variable names that are common to the input table and the filtering table are used to find matching rows. If the where parameter for the input table and this parameter are specified, then this filtering table is applied first.

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

* target="variable-name"

specifies the target or response variable to use for model training.

tunerOptions={tunerOptions}

specifies a list of parameters for adjusting how the tuner behaves.

The tunerOptions value can be one or more of the following:

constraints={{autotuneConstraintDefinition-1} <, {autotuneConstraintDefinition-2}, ...>}

specifies a list of constraints and their definitions.

The autotuneConstraintDefinition value can be one or more of the following:

lowerBound=double

specifies the lower bound of the range for a tuning parameter. This parameter can be used only for numeric tuning parameters, and is ignored if a value list is specified for the tuning parameter.

Alias	LB

* metric="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "LOSSFUNCTION" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies a metric to constrain during tuning.

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

LOSSFUNCTION

specifies the Loss Function as the constraint metric.

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

upperBound=double

specifies the upper bound of the range for a tuning parameter. This parameter can be used only for numeric tuning parameters, and is ignored if a value list is specified for the tuning parameter.

Alias	UB

enableLocalSearch=TRUE | FALSE

when set to True, local search optimization is enabled for model tuning.

Default	TRUE

evaluationHistory=TRUE | FALSE

when set to True, creates a table of every evaluation (every model configuration) and returns those tables to the client.

Default	TRUE

foldColumn="string"

specifies a column in the data table in which the cross validation fold value is indicated.

historyTable={history_table}

specifies the CAS table that is created by the autotune action that contains all evaluation history data points.

The history_table value can be one or more of the following:

appendLookupData=TRUE | FALSE

when set to True, specifies that all unused lookup data points be appended to the table specified in the historyTable parameter.

Default	FALSE

liveUpdate=TRUE | FALSE

when set to True, updates the table specified in the historyTable parameter after every evaluation. When the liveUpdate parameter is set to True, the table is promoted even if the promote parameter is not set to True, in order to allow other sessions to access the table.

Default	FALSE

* table={casouttable}

The casouttable value can be one or more of the following:

caslib="string"

specifies the name of the caslib for the output table.

compress=TRUE | FALSE

when set to True, applies data compression to the table.

Default	FALSE

indexVars={"variable-name-1" <, "variable-name-2", ...>}

specifies the list of variables to create indexes for in the output data.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default	0
Minimum value	0

maxMemSize=64-bit-integer

specifies the maximum amount of memory, in bytes, that each thread should allocate for in-memory blocks before converting to a memory-mapped file. Files are written in the directories that are specified in the CAS_DISK_CACHE environment variable.

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default	INHERIT

DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

use the default memory format that is set for the server. By default, the server uses the standard memory format. If an administrator sets the CAS_DEFAULT_MEMORY_FORMAT environment variable to DVR, then the DVR memory format is set as the default for the server.

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=TRUE | FALSE

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default	FALSE

replace=TRUE | FALSE

when set to True, overwrites an existing table that has the same name.

Default	FALSE

replication=integer

specifies the number of copies of the table to make for fault tolerance. Larger values result in slower performance and use more memory, but provide high availability for data in the event of a node failure. Data redundancy applies to distributed servers only.

Default	1
Minimum value	0

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

threadBlockSize=64-bit-integer

specifies the number of bytes to use for blocks in the output table. The blocks are read by threads. Gradually increase this value when you have a large table with millions or billions of rows and you are tuning for performance. Larger values can increase performance with indexed tables. However, if the value is too large, then you can cause thread starvation due to too few blocks for threads to work on.

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

where={"string-1" <, "string-2", ...>}

specifies one or more expressions for subsetting the output data. When multiple expressions are specified, the expressions are effectively combined using AND to form the final output filter. If an expression contains quoted values, use nested quotation marks.

logLevel=integer

specifies the level of log messages to be written: no logs (0), initialization and completion logs (1), setup and iteration results (2), or detailed evaluation results (3).

Default	2
Range	0–3

maxBayesianModelSize=integer

specifies the maximum number of points in the kriging model. This parameter is ignored unless the value "BAYESIAN" is specified in the searchMethod parameter.

Alias	maxBayesianSize
Default	500
Minimum value	10

maxEvals=integer

specifies the maximum number of objective evaluations (model configurations) to be trained and scored.

Alias	maxEvaluations
Default	0
Minimum value	0

maxIters=integer

specifies the maximum number of iterations of optimization for model tuning. At each iteration, the number of evaluated models is less than or equal to the value of the popSize parameter.

Alias	maxIterations
Default	5
Minimum value	1

maxTime=double

specifies the maximum time allowed for tuning. The actual time might exceed this value by the amount of time required to train and score a model configuration.

Default	36000
Minimum value	1

maxTrainTime=64-bit-integer

specifies the maximum time allowed for a single model training. The model training is terminated if it exceeds this time, and the objective value is set to missing.

Minimum value	0

nConvergenceIterations=integer

specifies the number of convergence iterations to use for terminating the tuning.

Alias	nConvIters
Default	4
Minimum value	1

nCrossValFolds=integer

specifies the number of folds to use for cross validation to assess model fit error as the tuning objective.

Alias	nFolds
Default	5
Minimum value	2

nParallel=integer

specifies the number of parallel subsessions to be used for parallel evaluation of model configurations.

Default	0

nSubsessionWorkers=integer

specifies the number of workers to be used by each subsession for parallel evaluation of model configurations.

Alias	nSubWorkers
Default	0
Minimum value	0

objective="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies the metric to use as the objective during tuning.

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

popSize=integer

specifies the maximum number of model configurations to evaluate at each iteration.

Alias	populationSize
Default	10
Minimum value	2

sampleSize=integer

specifies the number of model configurations to evaluate when the value of the searchMethod parameter is "LHS" or "RANDOM".

Default	50
Minimum value	2

searchMethod="BAYESIAN" | "GA" | "GRID" | "LHS" | "RANDOM"

specifies the search method to use during tuning.

Default	GA

BAYESIAN

uses the Bayesian search method.

GA

uses the genetic algorithm search method.

GRID

uses the grid search method.

LHS

uses the Latin hypercube sample search method.

RANDOM

uses the random search method.

secondObjective="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "NONE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies the metric to use as the second objective during tuning.

Default	NONE

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

NONE

specifies no second objective to be used for tuning.

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

seed=integer

specifies the seed to use for selecting training data and validation data for a single validation partition or fold number for each observation for cross validation.

Default	0

shuffleGridPoints=TRUE | FALSE

when set to True, shuffles the points that are generated by the grid search method before execution.

Default	TRUE

targetEvent="string"

specifies the name of the nominal target event to use for tuning.

trainPartitionFraction=double

specifies the size of a single data partition to use for model training during tuning.

Alias	trainFraction
Range	0.01–0.99

userDefinedPartition=TRUE | FALSE

when set to True, includes a user-defined partition for training and scoring.

Default	FALSE

validationPartitionFraction=double

specifies the size of a single validation partition to use in order to assess model fit error as the tuning objective.

Alias	validateFraction
Default	0.3
Range	0.01–0.99

validTable={castable}

specifies the data table to use for model validation.

Long form	validTable={name="table-name"}
Shortcut form	validTable="table-name"

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=TRUE | FALSE

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	FALSE

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

singlePass=TRUE | FALSE

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	FALSE

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

tuneAll Action

Automatically tunes hyperparameters for multiple models types concurrently..

Lua Syntax
Summary: Input and Output Tables
Parameter Descriptions

Lua Syntax

results, info = s:autotune_tuneAll{

inputs={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

modelNamePrefix={

caslib="string",

compress=true | false,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=true | false,

replace=true | false,

replication=integer,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE",

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

modelTypes={{

tuningOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

}, {...}},

nominals={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

table={

caslib="string",

computedOnDemand=true | false,

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

name="table-name",

singlePass=true | false,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

target="variable-name",

tunerOptions={

constraints={{

lowerBound=double,

upperBound=double

}, {...}},

enableLocalSearch=true | false,

evaluationHistory=true | false,

foldColumn="string",

historyTable={

appendLookupData=true | false

liveUpdate=true | false

table={

caslib="string"

compress=true | false

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=true | false

replace=true | false

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

}

logLevel=integer,

maxBayesianModelSize=integer,

maxEvals=integer,

maxIters=integer,

maxTime=double,

maxTrainTime=64-bit-integer,

nConvergenceIterations=integer,

nCrossValFolds=integer,

nParallel=integer,

nSubsessionWorkers=integer,

popSize=integer,

sampleSize=integer,

searchMethod="BAYESIAN" | "GA" | "GRID" | "LHS" | "RANDOM",

seed=integer,

shuffleGridPoints=true | false,

targetEvent="string",

trainPartitionFraction=double,

userDefinedPartition=true | false,

validationPartitionFraction=double

validTable={

caslib="string",

computedOnDemand=true | false,

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

name="table-name",

singlePass=true | false,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies the data table to use for model training.
validTable	—	specifies the data table to use for model validation.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
modelNamePrefix	—	specifies the table to store the model in.
tunerOptions	historyTable (and nested parameter table)	specifies a list of parameters for adjusting how the tuner behaves.

Parameter Descriptions

inputs={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the input variables to use in the analysis.

For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	input

modelNamePrefix={casouttable}

specifies the table to store the model in.

For more information about specifying the modelNamePrefix parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

modelTypes={{tuneAllModelTypesDefinition-1} <, {tuneAllModelTypesDefinition-2}, ...>}

specifies a list of model types to be tuned concurrently.

The tuneAllModelTypesDefinition value can be one or more of the following:

modelType="ALL" | "BNET" | "DECISIONTREE" | "FACTMAC" | "FOREST" | "GLM" | "GRADBOOST" | "LOGISTIC" | "NEURALNET" | "SVM"

specifies the model type for a specified model to be tuned.

Default	ALL

ALL

specifies the tuning of parameters for all valid model types.

BNET

specifies the tuning of Bayesian network classifier parameters.

DECISIONTREE

specifies the tuning of decision tree parameters.

FACTMAC

specifies the tuning of factorization machine parameters.

FOREST

specifies the tuning of forest parameters.

GLM

specifies the tuning of linear regression model parameters.

GRADBOOST

specifies the tuning of gradient boosting tree parameters.

LOGISTIC

specifies the tuning of logistic regression parameters.

NEURALNET

specifies the tuning of neural network parameters.

SVM

specifies the tuning of support vector machine parameters.

tuningOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies the tuning options for a specified model type to be tuned.

nominals={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the nominal input variables to use in the analysis.

For more information about specifying the nominals parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	nominal

* table={castable}

specifies the data table to use for model training.

Long form	table={name="table-name"}
Shortcut form	table="table-name"

Alias	trainTable

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=true | false

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	false

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

singlePass=true | false

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	false

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

* target="variable-name"

specifies the target or response variable to use for model training.

tunerOptions={tunerOptions}

specifies a list of parameters for adjusting how the tuner behaves.

The tunerOptions value can be one or more of the following:

constraints={{autotuneConstraintDefinition-1} <, {autotuneConstraintDefinition-2}, ...>}

specifies a list of constraints and their definitions.

The autotuneConstraintDefinition value can be one or more of the following:

lowerBound=double

specifies the lower bound of the range for a tuning parameter. This parameter can be used only for numeric tuning parameters, and is ignored if a value list is specified for the tuning parameter.

Alias	LB

* metric="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "LOSSFUNCTION" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies a metric to constrain during tuning.

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

LOSSFUNCTION

specifies the Loss Function as the constraint metric.

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

upperBound=double

specifies the upper bound of the range for a tuning parameter. This parameter can be used only for numeric tuning parameters, and is ignored if a value list is specified for the tuning parameter.

Alias	UB

enableLocalSearch=true | false

when set to True, local search optimization is enabled for model tuning.

Default	true

evaluationHistory=true | false

when set to True, creates a table of every evaluation (every model configuration) and returns those tables to the client.

Default	true

foldColumn="string"

specifies a column in the data table in which the cross validation fold value is indicated.

historyTable={history_table}

specifies the CAS table that is created by the autotune action that contains all evaluation history data points.

The history_table value can be one or more of the following:

appendLookupData=true | false

when set to True, specifies that all unused lookup data points be appended to the table specified in the historyTable parameter.

Default	false

liveUpdate=true | false

Default	false

* table={casouttable}

The casouttable value can be one or more of the following:

caslib="string"

specifies the name of the caslib for the output table.

compress=true | false

when set to True, applies data compression to the table.

Default	false

indexVars={"variable-name-1" <, "variable-name-2", ...>}

specifies the list of variables to create indexes for in the output data.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default	0
Minimum value	0

maxMemSize=64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default	INHERIT

DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=true | false

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default	false

replace=true | false

when set to True, overwrites an existing table that has the same name.

Default	false

replication=integer

Default	1
Minimum value	0

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

threadBlockSize=64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

where={"string-1" <, "string-2", ...>}

logLevel=integer

specifies the level of log messages to be written: no logs (0), initialization and completion logs (1), setup and iteration results (2), or detailed evaluation results (3).

Default	2
Range	0–3

maxBayesianModelSize=integer

specifies the maximum number of points in the kriging model. This parameter is ignored unless the value "BAYESIAN" is specified in the searchMethod parameter.

Alias	maxBayesianSize
Default	500
Minimum value	10

maxEvals=integer

specifies the maximum number of objective evaluations (model configurations) to be trained and scored.

Alias	maxEvaluations
Default	0
Minimum value	0

maxIters=integer

specifies the maximum number of iterations of optimization for model tuning. At each iteration, the number of evaluated models is less than or equal to the value of the popSize parameter.

Alias	maxIterations
Default	5
Minimum value	1

maxTime=double

specifies the maximum time allowed for tuning. The actual time might exceed this value by the amount of time required to train and score a model configuration.

Default	36000
Minimum value	1

maxTrainTime=64-bit-integer

specifies the maximum time allowed for a single model training. The model training is terminated if it exceeds this time, and the objective value is set to missing.

Minimum value	0

nConvergenceIterations=integer

specifies the number of convergence iterations to use for terminating the tuning.

Alias	nConvIters
Default	4
Minimum value	1

nCrossValFolds=integer

specifies the number of folds to use for cross validation to assess model fit error as the tuning objective.

Alias	nFolds
Default	5
Minimum value	2

nParallel=integer

specifies the number of parallel subsessions to be used for parallel evaluation of model configurations.

Default	0

nSubsessionWorkers=integer

specifies the number of workers to be used by each subsession for parallel evaluation of model configurations.

Alias	nSubWorkers
Default	0
Minimum value	0

objective="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies the metric to use as the objective during tuning.

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

popSize=integer

specifies the maximum number of model configurations to evaluate at each iteration.

Alias	populationSize
Default	10
Minimum value	2

sampleSize=integer

specifies the number of model configurations to evaluate when the value of the searchMethod parameter is "LHS" or "RANDOM".

Default	50
Minimum value	2

searchMethod="BAYESIAN" | "GA" | "GRID" | "LHS" | "RANDOM"

specifies the search method to use during tuning.

Default	GA

BAYESIAN

uses the Bayesian search method.

GA

uses the genetic algorithm search method.

GRID

uses the grid search method.

LHS

uses the Latin hypercube sample search method.

RANDOM

uses the random search method.

secondObjective="ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "NONE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies the metric to use as the second objective during tuning.

Default	NONE

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

NONE

specifies no second objective to be used for tuning.

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

seed=integer

specifies the seed to use for selecting training data and validation data for a single validation partition or fold number for each observation for cross validation.

Default	0

shuffleGridPoints=true | false

when set to True, shuffles the points that are generated by the grid search method before execution.

Default	true

targetEvent="string"

specifies the name of the nominal target event to use for tuning.

trainPartitionFraction=double

specifies the size of a single data partition to use for model training during tuning.

Alias	trainFraction
Range	0.01–0.99

userDefinedPartition=true | false

when set to True, includes a user-defined partition for training and scoring.

Default	false

validationPartitionFraction=double

specifies the size of a single validation partition to use in order to assess model fit error as the tuning objective.

Alias	validateFraction
Default	0.3
Range	0.01–0.99

validTable={castable}

specifies the data table to use for model validation.

Long form	validTable={name="table-name"}
Shortcut form	validTable="table-name"

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=true | false

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	false

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

singlePass=true | false

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	false

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

tuneAll Action

Automatically tunes hyperparameters for multiple models types concurrently..

Python Syntax
Summary: Input and Output Tables
Parameter Descriptions

Python Syntax

results=s.autotune.tuneAll(

inputs=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

modelNamePrefix={

"caslib":"string",

"compress":True | False,

"indexVars":["variable-name-1" <, "variable-name-2", ...>],

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"promote":True | False,

"replace":True | False,

"replication":integer,

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE",

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

"where":["string-1" <, "string-2", ...>]

modelTypes=[{

"tuningOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

}<, {...}>],

nominals=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

table={

"caslib":"string",

"computedOnDemand":True | False,

"computedVars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"computedVarsProgram":"string",

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>},

"groupByMode":"NOSORT" | "REDISTRIBUTE",

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters},

"name":"table-name",

"singlePass":True | False,

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"where":"where-expression",

"whereTable":{

"casLib":"string"

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

target="variable-name",

tunerOptions={

"constraints":[{

"lowerBound":double,

"metric":"ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "LOSSFUNCTION" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME",

"upperBound":double

}<, {...}>],

"enableLocalSearch":True | False,

"evaluationHistory":True | False,

"foldColumn":"string",

"historyTable":{

"appendLookupData":True | False

"liveUpdate":True | False

"table":{

"caslib":"string"

"compress":True | False

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

"name":"table-name"

"promote":True | False

"replace":True | False

"replication":integer

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

"threadBlockSize":64-bit-integer

"timeStamp":"string"

"where":["string-1" <, "string-2", ...>]

}

"logLevel":integer,

"maxBayesianModelSize":integer,

"maxEvals":integer,

"maxIters":integer,

"maxTime":double,

"maxTrainTime":64-bit-integer,

"nConvergenceIterations":integer,

"nCrossValFolds":integer,

"nParallel":integer,

"nSubsessionWorkers":integer,

"objective":"ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME",

"popSize":integer,

"sampleSize":integer,

"searchMethod":"BAYESIAN" | "GA" | "GRID" | "LHS" | "RANDOM",

"secondObjective":"ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "NONE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME",

"seed":integer,

"shuffleGridPoints":True | False,

"targetEvent":"string",

"trainPartitionFraction":double,

"userDefinedPartition":True | False,

"validationPartitionFraction":double

validTable={

"caslib":"string",

"computedOnDemand":True | False,

"computedVars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"computedVarsProgram":"string",

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>},

"groupByMode":"NOSORT" | "REDISTRIBUTE",

"name":"table-name",

"singlePass":True | False,

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"where":"where-expression",

"whereTable":{

"casLib":"string"

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

)

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies the data table to use for model training.
validTable	—	specifies the data table to use for model validation.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
modelNamePrefix	—	specifies the table to store the model in.
tunerOptions	historyTable (and nested parameter table)	specifies a list of parameters for adjusting how the tuner behaves.

Parameter Descriptions

inputs=[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the input variables to use in the analysis.

For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	input

modelNamePrefix={casouttable}

specifies the table to store the model in.

For more information about specifying the modelNamePrefix parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

modelTypes=[{tuneAllModelTypesDefinition-1} <, {tuneAllModelTypesDefinition-2}, ...>]

specifies a list of model types to be tuned concurrently.

The tuneAllModelTypesDefinition value can be one or more of the following:

"modelType":"ALL" | "BNET" | "DECISIONTREE" | "FACTMAC" | "FOREST" | "GLM" | "GRADBOOST" | "LOGISTIC" | "NEURALNET" | "SVM"

specifies the model type for a specified model to be tuned.

Default	ALL

ALL

specifies the tuning of parameters for all valid model types.

BNET

specifies the tuning of Bayesian network classifier parameters.

DECISIONTREE

specifies the tuning of decision tree parameters.

FACTMAC

specifies the tuning of factorization machine parameters.

FOREST

specifies the tuning of forest parameters.

GLM

specifies the tuning of linear regression model parameters.

GRADBOOST

specifies the tuning of gradient boosting tree parameters.

LOGISTIC

specifies the tuning of logistic regression parameters.

NEURALNET

specifies the tuning of neural network parameters.

SVM

specifies the tuning of support vector machine parameters.

"tuningOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

specifies the tuning options for a specified model type to be tuned.

nominals=[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the nominal input variables to use in the analysis.

For more information about specifying the nominals parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	nominal

* table={castable}

specifies the data table to use for model training.

Long form	table={"name":"table-name"}
Shortcut form	table="table-name"

Alias	trainTable

The castable value can be one or more of the following:

"caslib":"string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

"computedOnDemand":True | False

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	False

"computedVars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

Alias	compVars

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"computedVarsProgram":"string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

"groupByMode":"NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the input table.

"singlePass":True | False

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	False

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the input data.

"whereTable":{groupbytable}

The groupbytable value can be one or more of the following:

"casLib":"string"

specifies the caslib for the filter table. By default, the active caslib is used.

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the filter table.

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the data from the filter table.

* target="variable-name"

specifies the target or response variable to use for model training.

tunerOptions={tunerOptions}

specifies a list of parameters for adjusting how the tuner behaves.

The tunerOptions value can be one or more of the following:

"constraints":[{autotuneConstraintDefinition-1} <, {autotuneConstraintDefinition-2}, ...>]

specifies a list of constraints and their definitions.

The autotuneConstraintDefinition value can be one or more of the following:

"lowerBound":double

specifies the lower bound of the range for a tuning parameter. This parameter can be used only for numeric tuning parameters, and is ignored if a value list is specified for the tuning parameter.

Alias	LB

* "metric":"ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "LOSSFUNCTION" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies a metric to constrain during tuning.

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

LOSSFUNCTION

specifies the Loss Function as the constraint metric.

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

"upperBound":double

specifies the upper bound of the range for a tuning parameter. This parameter can be used only for numeric tuning parameters, and is ignored if a value list is specified for the tuning parameter.

Alias	UB

"enableLocalSearch":True | False

when set to True, local search optimization is enabled for model tuning.

Default	True

"evaluationHistory":True | False

when set to True, creates a table of every evaluation (every model configuration) and returns those tables to the client.

Default	True

"foldColumn":"string"

specifies a column in the data table in which the cross validation fold value is indicated.

"historyTable":{history_table}

specifies the CAS table that is created by the autotune action that contains all evaluation history data points.

The history_table value can be one or more of the following:

"appendLookupData":True | False

when set to True, specifies that all unused lookup data points be appended to the table specified in the historyTable parameter.

Default	False

"liveUpdate":True | False

Default	False

* "table":{casouttable}

The casouttable value can be one or more of the following:

"caslib":"string"

specifies the name of the caslib for the output table.

"compress":True | False

when set to True, applies data compression to the table.

Default	False

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

specifies the list of variables to create indexes for in the output data.

"label":"string"

specifies the descriptive label to associate with the table.

"lifetime":64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default	0
Minimum value	0

"maxMemSize":64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default	INHERIT

DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

STANDARD

use the standard memory format.

"name":"table-name"

specifies the name for the output table.

"promote":True | False

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default	False

"replace":True | False

when set to True, overwrites an existing table that has the same name.

Default	False

"replication":integer

Default	1
Minimum value	0

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

"threadBlockSize":64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

"timeStamp":"string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

"where":["string-1" <, "string-2", ...>]

"logLevel":integer

specifies the level of log messages to be written: no logs (0), initialization and completion logs (1), setup and iteration results (2), or detailed evaluation results (3).

Default	2
Range	0–3

"maxBayesianModelSize":integer

specifies the maximum number of points in the kriging model. This parameter is ignored unless the value "BAYESIAN" is specified in the searchMethod parameter.

Alias	maxBayesianSize
Default	500
Minimum value	10

"maxEvals":integer

specifies the maximum number of objective evaluations (model configurations) to be trained and scored.

Alias	maxEvaluations
Default	0
Minimum value	0

"maxIters":integer

specifies the maximum number of iterations of optimization for model tuning. At each iteration, the number of evaluated models is less than or equal to the value of the popSize parameter.

Alias	maxIterations
Default	5
Minimum value	1

"maxTime":double

specifies the maximum time allowed for tuning. The actual time might exceed this value by the amount of time required to train and score a model configuration.

Default	36000
Minimum value	1

"maxTrainTime":64-bit-integer

specifies the maximum time allowed for a single model training. The model training is terminated if it exceeds this time, and the objective value is set to missing.

Minimum value	0

"nConvergenceIterations":integer

specifies the number of convergence iterations to use for terminating the tuning.

Alias	nConvIters
Default	4
Minimum value	1

"nCrossValFolds":integer

specifies the number of folds to use for cross validation to assess model fit error as the tuning objective.

Alias	nFolds
Default	5
Minimum value	2

"nParallel":integer

specifies the number of parallel subsessions to be used for parallel evaluation of model configurations.

Default	0

"nSubsessionWorkers":integer

specifies the number of workers to be used by each subsession for parallel evaluation of model configurations.

Alias	nSubWorkers
Default	0
Minimum value	0

"objective":"ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies the metric to use as the objective during tuning.

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

"popSize":integer

specifies the maximum number of model configurations to evaluate at each iteration.

Alias	populationSize
Default	10
Minimum value	2

"sampleSize":integer

specifies the number of model configurations to evaluate when the value of the searchMethod parameter is "LHS" or "RANDOM".

Default	50
Minimum value	2

"searchMethod":"BAYESIAN" | "GA" | "GRID" | "LHS" | "RANDOM"

specifies the search method to use during tuning.

Default	GA

BAYESIAN

uses the Bayesian search method.

GA

uses the genetic algorithm search method.

GRID

uses the grid search method.

LHS

uses the Latin hypercube sample search method.

RANDOM

uses the random search method.

"secondObjective":"ASE" | "AUC" | "CUMULATIVEGAIN" | "F05" | "F1" | "FNR" | "FPR" | "GAMMA" | "GINI" | "HITRATE" | "KS" | "MAE" | "MCE" | "MCLL" | "MEANAVEPRECISION" | "MEANRECIPRANK" | "MISC" | "MSE" | "MSLE" | "NONE" | "RASE" | "RECALL" | "RMAE" | "RMSLE" | "SCORINGTIME" | "TAU" | "TNR" | "TPR" | "TRAININGTIME"

specifies the metric to use as the second objective during tuning.

Default	NONE

ASE

specifies the average square error as the objective function or the constraint metric.

AUC

specifies area under the curve as the objective function or the constraint metric (nominal targets only).

CUMULATIVEGAIN

specifies weighted normalized discounted cumulative gain as the objective function or the constraint metric (recommender model types only).

F05

specifies the F0.5 coefficient as the objective function or the constraint metric (nominal targets only).

F1

specifies the F1 coefficient as the objective function or the constraint metric (nominal targets only).

FNR

specifies false negative rate as the objective function or the constraint metric (nominal targets only).

FPR

specifies false positive rate as the objective function or the constraint metric (nominal targets only).

GAMMA

specifies the gamma coefficient as the objective function or the constraint metric (nominal targets only).

GINI

specifies the Gini coefficient as the objective function or the constraint metric (nominal targets only).

HITRATE

specifies hit rate as the objective function or the constraint metric (recommender model types only).

KS

specifies the Kolmogorov-Smirnov coefficient as the objective function or the constraint metric (nominal targets only).

MAE

specifies the mean absolute error as the objective function or the constraint metric (interval targets only).

MCE

specifies the misclassification rate as the objective function or the constraint metric (nominal targets only).

MCLL

specifies the multiclass log loss as the objective function or the constraint metric (nominal targets only).

MEANAVEPRECISION

specifies mean average precision as the objective function or the constraint metric (recommender model types only).

MEANRECIPRANK

specifies mean reciprocal rank as the objective function or the constraint metric (recommender model types only).

MISC

specifies the misclassification error percentage as the objective function or the constraint metric (nominal targets only).

MSE

specifies the mean square error as the objective function or the constraint metric (interval targets only).

MSLE

specifies the mean square logarithmic error as the objective function or the constraint metric (interval targets only).

NONE

specifies no second objective to be used for tuning.

RASE

specifies the root average square error as the objective function or the constraint metric.

RECALL

specifies recall value as the objective function or the constraint metric (recommender model types only).

RMAE

specifies the root mean absolute error as the objective function or the constraint metric (interval targets only).

RMSLE

specifies the root mean square logarithmic error as the objective function or the constraint metric (interval targets only).

SCORINGTIME

specifies model scoring time as the objective for tuning.

TAU

specifies the tau coefficient as the objective function or the constraint metric (nominal targets only).

TNR

specifies true negative rate as the objective function or the constraint metric (nominal targets only).

TPR

specifies true positive rate as the objective function or the constraint metric (nominal targets only).

TRAININGTIME

specifies model training time as the objective for tuning.

"seed":integer

specifies the seed to use for selecting training data and validation data for a single validation partition or fold number for each observation for cross validation.

Default	0

"shuffleGridPoints":True | False

when set to True, shuffles the points that are generated by the grid search method before execution.

Default	True

"targetEvent":"string"

specifies the name of the nominal target event to use for tuning.

"trainPartitionFraction":double

specifies the size of a single data partition to use for model training during tuning.

Alias	trainFraction
Range	0.01–0.99

"userDefinedPartition":True | False

when set to True, includes a user-defined partition for training and scoring.

Default	False

"validationPartitionFraction":double

specifies the size of a single validation partition to use in order to assess model fit error as the tuning objective.

Alias	validateFraction
Default	0.3
Range	0.01–0.99

validTable={castable}

specifies the data table to use for model validation.

Long form	validTable={"name":"table-name"}
Shortcut form	validTable="table-name"

The castable value can be one or more of the following:

"caslib":"string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

"computedOnDemand":True | False

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	False

"computedVars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

Alias	compVars

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"computedVarsProgram":"string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

"groupByMode":"NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the input table.

"singlePass":True | False

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	False

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the input data.

"whereTable":{groupbytable}

The groupbytable value can be one or more of the following:

"casLib":"string"

specifies the caslib for the filter table. By default, the active caslib is used.

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the filter table.

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the data from the filter table.

tuneAll Action

Automatically tunes hyperparameters for multiple models types concurrently..

R Syntax
Summary: Input and Output Tables
Parameter Descriptions

R Syntax

results <– cas.autotune.tuneAll(s,

inputs=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

modelNamePrefix=list(

caslib="string",

compress=TRUE | FALSE,

indexVars=list("variable-name-1" <, "variable-name-2", ...>),

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE",

threadBlockSize=64-bit-integer,

timeStamp="string",

where=list("string-1" <, "string-2", ...>)

modelTypes=list( list(

tuningOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)

) <, list(...)>),

nominals=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

table=list(

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

computedVarsProgram="string",

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>),

name="table-name",

singlePass=TRUE | FALSE,

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

where="where-expression",

whereTable=list(

casLib="string"

name="table-name"

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

)

target="variable-name",

tunerOptions=list(

constraints=list( list(

lowerBound=double,

upperBound=double

) <, list(...)>),

enableLocalSearch=TRUE | FALSE,

evaluationHistory=TRUE | FALSE,

foldColumn="string",

historyTable=list(

appendLookupData=TRUE | FALSE

liveUpdate=TRUE | FALSE

table=list(

caslib="string"

compress=TRUE | FALSE

indexVars=list("variable-name-1" <, "variable-name-2", ...>)

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where=list("string-1" <, "string-2", ...>)

)

logLevel=integer,

maxBayesianModelSize=integer,

maxEvals=integer,

maxIters=integer,

maxTime=double,

maxTrainTime=64-bit-integer,

nConvergenceIterations=integer,

nCrossValFolds=integer,

nParallel=integer,

nSubsessionWorkers=integer,

popSize=integer,

sampleSize=integer,

searchMethod="BAYESIAN" | "GA" | "GRID" | "LHS" | "RANDOM",

seed=integer,

shuffleGridPoints=TRUE | FALSE,

targetEvent="string",

trainPartitionFraction=double,

userDefinedPartition=TRUE | FALSE,

validationPartitionFraction=double

validTable=list(

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

computedVarsProgram="string",

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>),