Autotune Action Set: Syntax
Provides actions to tune machine learning algorithm hyperparameters
tuneFactMac Action
Automatically adjusts factorization machine parameters to tune a model for minimum error.
CASL Syntax
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 |
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 |
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 | 100 |
| Minimum value | 10 |
maxEvals=integer
specifies the maximum number of objective evaluations (model configurations) to be trained and scored.
| Alias | maxEvaluations |
| Default | 50 |
| Minimum value | 1 |
maxIters=integer
specifies the maximum number of iterations of local search 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 |
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"
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
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 |
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 |
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 | 100 |
| Minimum value | 10 |
maxEvals=integer
specifies the maximum number of objective evaluations (model configurations) to be trained and scored.
| Alias | maxEvaluations |
| Default | 50 |
| Minimum value | 1 |
maxIters=integer
specifies the maximum number of iterations of local search 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 |
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"
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
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 |
"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 |
"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 | 100 |
| Minimum value | 10 |
"maxEvals":integer
specifies the maximum number of objective evaluations (model configurations) to be trained and scored.
| Alias | maxEvaluations |
| Default | 50 |
| Minimum value | 1 |
"maxIters":integer
specifies the maximum number of iterations of local search 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 |
"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"
"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
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 |
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 |
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 | 100 |
| Minimum value | 10 |
maxEvals=integer
specifies the maximum number of objective evaluations (model configurations) to be trained and scored.
| Alias | maxEvaluations |
| Default | 50 |
| Minimum value | 1 |
maxIters=integer
specifies the maximum number of iterations of local search 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 |
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"
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 |