Autotune Action Set: Syntax
Provides actions to tune machine learning algorithm hyperparameters for individual or multiple model types
tuneFactMac Action
Automatically adjusts factorization machine parameters to tune a model for minimum error.
CASL Syntax
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
lookupTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
historyTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
Parameter Descriptions
scoreOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}
specifies a list of parameters to be used by the score action in the aStore action set; for more information, see the parameters for that action.
* trainOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}
specifies a list of parameters to be used by the factmac action in the factmac action set; for more information, see the parameters for that action.
tunerOptions={tunerOptions}
specifies a list of parameters for adjusting how the tuner behaves.
| Alias | optMinerOpts |
|---|
The tunerOptions value can be one or more of the following:
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 |
|---|
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}
For more information about specifying the table parameter, see the common casouttable parameter (Appendix A: Common Parameters).
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 |
lookupTable={lookup_table}
specifies the CAS table used by the autotune action for evaluation lookup.
The lookup_table value can be one or more of the following:
selectInitialPoint=TRUE | FALSE
when set to True, selects the best point from the lookup table and uses it as the initial (default) point.
| Alias | initPoint |
|---|---|
| Default | FALSE |
table={castable}
specifies the settings for an input table.
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 |
|---|
For more information about specifying the computedVars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
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.
| Alias | options, dataSource |
|---|
groupByMode="NOSORT" | "REDISTRIBUTE"
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.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
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 | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | hadoop-parameters | hana-parameters | impala-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sforce-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
specifies data source options.
| Alias | options, 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.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
where="where-expression"
specifies an expression for subsetting the data from the filter table.
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 threads to be used by the optimizer 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" | "F05" | "F1" | "GAMMA" | "GINI" | "KS" | "MAE" | "MCE" | "MCLL" | "MISC" | "MSE" | "MSLE" | "RASE" | "RMAE" | "RMSLE" | "TAU"
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"
secondObjective="NONE" | "SCORINGTIME" | "TRAININGTIME"
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 |
tuningParameters={{autotuneTuningParmDefinition-1} <, {autotuneTuningParmDefinition-2}, ...>}
specifies a list of custom tuning parameters and their definitions.
The autotuneTuningParmDefinition value can be one or more of the following:
exclude=TRUE | FALSE
when set to True, excludes a tuning parameter from the tuning process.
| Default | FALSE |
|---|
initValue=integer | 64-bit-integer | double | TRUE | FALSE | "string"
specifies the initial (default) value of a tuning parameter.
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 |
|---|
namePath="string"
specifies the name path of a tuning parameter. For a nested action parameter, this parameter specifies a dot-separated path that includes all its parent parameter names. For a top-level action parameter, this parameter is simply the name of the parameter.
| Alias | name |
|---|
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 |
|---|
valueList={any-list-or-data-type-1 <, any-list-or-data-type-2, ...>}
specifies the list of values to try for a tuning parameter. If this parameter is specified, the lower bound and upper bound values of the tuning parameter are ignored.
useParameters="COMBINED" | "CUSTOM" | "STANDARD"
specifies whether to use only standard tuning parameters, only custom tuning parameters, or a combination of both.
| Default | COMBINED |
|---|
tuneFactMac Action
Automatically adjusts factorization machine parameters to tune a model for minimum error.
Lua Syntax
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
lookupTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
historyTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
Parameter Descriptions
scoreOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}
specifies a list of parameters to be used by the score action in the aStore action set; for more information, see the parameters for that action.
* trainOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}
specifies a list of parameters to be used by the factmac action in the factmac action set; for more information, see the parameters for that action.
tunerOptions={tunerOptions}
specifies a list of parameters for adjusting how the tuner behaves.
| Alias | optMinerOpts |
|---|
The tunerOptions value can be one or more of the following:
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 |
|---|
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}
For more information about specifying the table parameter, see the common casouttable parameter (Appendix A: Common Parameters).
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 |
lookupTable={lookup_table}
specifies the CAS table used by the autotune action for evaluation lookup.
The lookup_table value can be one or more of the following:
selectInitialPoint=true | false
when set to True, selects the best point from the lookup table and uses it as the initial (default) point.
| Alias | initPoint |
|---|---|
| Default | false |
table={castable}
specifies the settings for an input table.
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 |
|---|
For more information about specifying the computedVars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
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.
| Alias | options, dataSource |
|---|
groupByMode="NOSORT" | "REDISTRIBUTE"
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.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
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 | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | hadoop-parameters | hana-parameters | impala-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sforce-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
specifies data source options.
| Alias | options, 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.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
where="where-expression"
specifies an expression for subsetting the data from the filter table.
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 threads to be used by the optimizer 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" | "F05" | "F1" | "GAMMA" | "GINI" | "KS" | "MAE" | "MCE" | "MCLL" | "MISC" | "MSE" | "MSLE" | "RASE" | "RMAE" | "RMSLE" | "TAU"
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"
secondObjective="NONE" | "SCORINGTIME" | "TRAININGTIME"
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 |
tuningParameters={{autotuneTuningParmDefinition-1} <, {autotuneTuningParmDefinition-2}, ...>}
specifies a list of custom tuning parameters and their definitions.
The autotuneTuningParmDefinition value can be one or more of the following:
exclude=true | false
when set to True, excludes a tuning parameter from the tuning process.
| Default | false |
|---|
initValue=integer | 64-bit-integer | double | true | false | "string"
specifies the initial (default) value of a tuning parameter.
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 |
|---|
namePath="string"
specifies the name path of a tuning parameter. For a nested action parameter, this parameter specifies a dot-separated path that includes all its parent parameter names. For a top-level action parameter, this parameter is simply the name of the parameter.
| Alias | name |
|---|
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 |
|---|
valueList={any-list-or-data-type-1 <, any-list-or-data-type-2, ...>}
specifies the list of values to try for a tuning parameter. If this parameter is specified, the lower bound and upper bound values of the tuning parameter are ignored.
useParameters="COMBINED" | "CUSTOM" | "STANDARD"
specifies whether to use only standard tuning parameters, only custom tuning parameters, or a combination of both.
| Default | COMBINED |
|---|
tuneFactMac Action
Automatically adjusts factorization machine parameters to tune a model for minimum error.
Python Syntax
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
lookupTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
historyTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
Parameter Descriptions
scoreOptions={"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}
specifies a list of parameters to be used by the score action in the aStore action set; for more information, see the parameters for that action.
* trainOptions={"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}
specifies a list of parameters to be used by the factmac action in the factmac action set; for more information, see the parameters for that action.
tunerOptions={tunerOptions}
specifies a list of parameters for adjusting how the tuner behaves.
| Alias | optMinerOpts |
|---|
The tunerOptions value can be one or more of the following:
"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 |
|---|
"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}
For more information about specifying the table parameter, see the common casouttable parameter (Appendix A: Common Parameters).
"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 |
"lookupTable":{lookup_table}
specifies the CAS table used by the autotune action for evaluation lookup.
The lookup_table value can be one or more of the following:
"selectInitialPoint":True | False
when set to True, selects the best point from the lookup table and uses it as the initial (default) point.
| Alias | initPoint |
|---|---|
| Default | False |
"table":{castable}
specifies the settings for an input table.
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 |
|---|
For more information about specifying the computedVars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
"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.
| Alias | options, dataSource |
|---|
"groupByMode":"NOSORT" | "REDISTRIBUTE"
"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.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
"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 | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | hadoop-parameters | hana-parameters | impala-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sforce-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
specifies data source options.
| Alias | options, 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.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
"where":"where-expression"
specifies an expression for subsetting the data from the filter table.
"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 threads to be used by the optimizer 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" | "F05" | "F1" | "GAMMA" | "GINI" | "KS" | "MAE" | "MCE" | "MCLL" | "MISC" | "MSE" | "MSLE" | "RASE" | "RMAE" | "RMSLE" | "TAU"
"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"
"secondObjective":"NONE" | "SCORINGTIME" | "TRAININGTIME"
"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 |
tuningParameters=[{autotuneTuningParmDefinition-1} <, {autotuneTuningParmDefinition-2}, ...>]
specifies a list of custom tuning parameters and their definitions.
The autotuneTuningParmDefinition value can be one or more of the following:
"exclude":True | False
when set to True, excludes a tuning parameter from the tuning process.
| Default | False |
|---|
"initValue":integer | 64-bit-integer | double | True | False | "string"
specifies the initial (default) value of a tuning parameter.
"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 |
|---|
"namePath":"string"
specifies the name path of a tuning parameter. For a nested action parameter, this parameter specifies a dot-separated path that includes all its parent parameter names. For a top-level action parameter, this parameter is simply the name of the parameter.
| Alias | name |
|---|
"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 |
|---|
"valueList":{any-list-or-data-type-1 <, any-list-or-data-type-2, ...>}
specifies the list of values to try for a tuning parameter. If this parameter is specified, the lower bound and upper bound values of the tuning parameter are ignored.
useParameters="COMBINED" | "CUSTOM" | "STANDARD"
specifies whether to use only standard tuning parameters, only custom tuning parameters, or a combination of both.
| Default | COMBINED |
|---|
tuneFactMac Action
Automatically adjusts factorization machine parameters to tune a model for minimum error.
R Syntax
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
lookupTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
historyTable (and nested parameter table) |
specifies a list of parameters for adjusting how the tuner behaves. |
Parameter Descriptions
scoreOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)
specifies a list of parameters to be used by the score action in the aStore action set; for more information, see the parameters for that action.
* trainOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)
specifies a list of parameters to be used by the factmac action in the factmac action set; for more information, see the parameters for that action.
tunerOptions=list(tunerOptions)
specifies a list of parameters for adjusting how the tuner behaves.
| Alias | optMinerOpts |
|---|
The tunerOptions value can be one or more of the following:
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 |
|---|
historyTable=list(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=list(casouttable)
For more information about specifying the table parameter, see the common casouttable parameter (Appendix A: Common Parameters).
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 |
lookupTable=list(lookup_table)
specifies the CAS table used by the autotune action for evaluation lookup.
The lookup_table value can be one or more of the following:
selectInitialPoint=TRUE | FALSE
when set to True, selects the best point from the lookup table and uses it as the initial (default) point.
| Alias | initPoint |
|---|---|
| Default | FALSE |
table=list(castable)
specifies the settings for an input table.
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=list( list(casinvardesc-1) <, list(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 |
|---|
For more information about specifying the computedVars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
computedVarsProgram="string"
specifies an expression for each computed variable that you include in the computedVars parameter.
| Alias | compPgm |
|---|
dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)
specifies data source options.
| Alias | options, dataSource |
|---|
groupByMode="NOSORT" | "REDISTRIBUTE"
importOptions=list(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=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)
specifies the variables to use in the action.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
where="where-expression"
specifies an expression for subsetting the input data.
whereTable=list(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=list(adls_noreq-parameters | bigquery-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | hadoop-parameters | hana-parameters | impala-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sforce-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters)
specifies data source options.
| Alias | options, dataSource |
|---|
For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).
importOptions=list(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=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)
specifies the variable names to use from the filter table.
For more information about specifying the vars parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).
where="where-expression"
specifies an expression for subsetting the data from the filter table.
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 threads to be used by the optimizer 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" | "F05" | "F1" | "GAMMA" | "GINI" | "KS" | "MAE" | "MCE" | "MCLL" | "MISC" | "MSE" | "MSLE" | "RASE" | "RMAE" | "RMSLE" | "TAU"
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"
secondObjective="NONE" | "SCORINGTIME" | "TRAININGTIME"
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 |
tuningParameters=list( list(autotuneTuningParmDefinition-1) <, list(autotuneTuningParmDefinition-2), ...>)
specifies a list of custom tuning parameters and their definitions.
The autotuneTuningParmDefinition value can be one or more of the following:
exclude=TRUE | FALSE
when set to True, excludes a tuning parameter from the tuning process.
| Default | FALSE |
|---|
initValue=integer | 64-bit-integer | double | TRUE | FALSE | "string"
specifies the initial (default) value of a tuning parameter.
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 |
|---|
namePath="string"
specifies the name path of a tuning parameter. For a nested action parameter, this parameter specifies a dot-separated path that includes all its parent parameter names. For a top-level action parameter, this parameter is simply the name of the parameter.
| Alias | name |
|---|
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 |
|---|
valueList=list(any-list-or-data-type-1 <, any-list-or-data-type-2, ...>)
specifies the list of values to try for a tuning parameter. If this parameter is specified, the lower bound and upper bound values of the tuning parameter are ignored.
useParameters="COMBINED" | "CUSTOM" | "STANDARD"
specifies whether to use only standard tuning parameters, only custom tuning parameters, or a combination of both.
| Default | COMBINED |
|---|