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APPENDLOOKUP
specifies that the rows from the table specified in the LOOKUPTABLE= option be appended to the output table that is created using the HISTORYTABLE= option.
This option is ignored if you do not specify both the HISTORYTABLE= and LOOKUPTABLE= options.
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EVALHISTORY=ALL |LOG |NONE |TABLE
specifies how to report the evaluation history of the tuner.
You can specify one of the following values:
- ALL
reports each evaluation in the log and creates the EvaluationHistory ODS table.
- LOG
prints the following information to the log for each evaluation: evaluation number, objective value, best objective value up to that point, evaluation time, and elapsed time since the beginning of the tuning process.
- NONE
suppresses reporting of evaluations in the log and does not create the EvaluationHistory ODS table.
- TABLE
creates the EvaluationHistory ODS table, which contains all evaluated points. The table contains columns for the evaluation number, all tuning parameters, and the objective function value.
By default, EVALHISTORY=TABLE.
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FRACTION=number
specifies the fraction of all data to be used for validation, where number must be between 0.01 and 0.99, inclusive. If you specify this option, the tuner uses a single-partition validation for finding the objective value (validation error estimate). Using this option might not be advisable for small or unbalanced data tables, where the random assignment of the validation subset might not provide a good estimate of error. For large, balanced data tables, a single-partition validation is usually sufficient for estimating error; a single partition is more efficient than cross validation in terms of the total execution time.
If a PARTITION statement is specified, the validation partition defined in that statement is used, and this option is ignored. You cannot specify this option in combination with the KFOLD= option.
If the TRAINFRACTION= option is used, then by default the value of the FRACTION= option is one minus the value of the TRAINFRACTION= option. Otherwise, by default, FRACTION=0.3.
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HISTORYTABLE=CAS-libref.data-table
specifies the data table created by the tuning process that contains the evaluation history. CAS-libref.data-table is a two-level name, where CAS-libref refers to the caslib and session identifier, and data-table specifies the name of the output data table. For more information about this two-level name, see the section Using CAS Sessions and CAS Engine Librefs.
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KFOLD=number
specifies the number of folds (partitions) in the cross validation process, where number must be between 2 and 20, inclusive. If you specify this option, the tuner uses cross validation to find the objective value. In cross validation, each model evaluation requires number of training executions (on number–1 data folds) and number of scoring executions (on one hold-out fold). Thus, the evaluation time is increased by approximately a factor of number. For small to medium data tables or for unbalanced data tables, cross validation provides on average a better representation of error across the entire data table (a better generalization error).
If you do not specify either this option or the FRACTION= option, then the default of FRACTION=0.3 is used. If a PARTITION statement is specified, the validation partition defined in that statement is used, and this option is ignored. You cannot specify this option in combination with the FRACTION= option.
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LIVEUPDATE
specifies that the table specified in the HISTORYTABLE= option be updated at every evaluation. This option forces the table to be a promoted CAS table so that it can be seen by other sessions.
This option is ignored if you do not specify the HISTORYTABLE= option.
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LOOKUPTABLE=CAS-libref.data-table
specifies the data table previously created by the tuning process that you want to use as the lookup table. CAS-libref.data-table is a two-level name, where CAS-libref refers to the caslib and session identifier, and data-table specifies the name of the input data table. For more information about this two-level name, see the section Using CAS Sessions and CAS Engine Librefs.
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MAXBAYES=number
specifies the maximum number of points in the Kriging model. This option is honored only when SEARCHMETHOD=BAYESIAN and when number has a minimum value of 10.
By default, MAXBAYES=100.
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MAXEVALS=number
specifies the maximum number of configuration evaluations allowed for the tuner, where number must be an integer greater than or equal to 3. When the number of evaluations is reached, the tuner terminates the search and returns the results. To produce a single objective function value (validation error estimate), each configuration evaluation requires either a single model training and scoring execution on a validation partition, or a number of training and scoring executions equal to the value of the KFOLD= option for cross validation. The MAXEVALS= option might lead to termination before the value of the MAXITER= option or the MAXTIME= option is reached.
By default, MAXEVALS=50.
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MAXITER=number
specifies the maximum number of iterations of the optimization tuner, where number must be greater than or equal to 1. Each iteration normally involves a number of objective evaluations up to the value of the POPSIZE= option. The MAXITER= option is used only when SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN; it is ignored when SEARCHMETHOD=RANDOM or SEARCHMETHOD=LHS. The MAXITER= option might lead to termination before the value of the MAXEVALS= option or the MAXTIME= option is reached.
By default, MAXITER=5.
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MAXTIME=number
specifies the maximum time (in seconds) allowed for the tuner, where number must be greater than or equal to 1. When this value is reached, the tuner terminates the search and returns results. The actual run time for optimization might be longer because it includes the remaining time needed to finish the current evaluation. For long-running model training (large data tables), the actual run time might significantly exceed number. The MAXTIME= option might lead to termination before the value of the MAXEVALS= option or the MAXITER= option is reached.
By default, MAXTIME=36000.
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MAXTRAINTIME=number
specifies the maximum time allowed for a single model train. The model train is terminated if it exceeds this time, and the objective value is set to missing.
By default, there is no maximum time allowed for a single train.
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NCONVITER=number
specifies the number of convergence iterations after which tuning is terminated.
By default, NCONVITER=4.
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NOGRIDSHUFFLE
requests that the points that are generated by the grid search method not be shuffled before execution.
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NOLOCALSEARCH
disables local search optimization for the tuning process.
By default, local search optimization is enabled.
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NPARALLEL=number
specifies the number of evaluations to be performed in parallel, where number must be greater than or equal to 0.
By default, NPARALLEL=0, which indicates that the value of number is determined as follows:
If SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN, then the number of parallel evaluations is equal to the value of the POPSIZE= option minus 1.
If SEARCHMETHOD=LHS or SEARCHMETHOD=RANDOM, then the number of parallel evaluations is equal to the value of the SAMPLESIZE= option, with a maximum value of 32.
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NSUBSESSIONWORKERS=number
specifies the number of workers to use in parallel subsessions. When alternative configurations are evaluated in parallel, a number of subsessions is created by the tuner, with each using multiple workers. By default, the number of workers that are used in a parallel subsession is determined as described in the section Determining the Number of Parallel Evaluations.
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OBJECTIVE=function
specifies which measure of model performance the tuner uses as the objective function.
You can specify one of the following values for function. Some values can be specified only when the target variable is of a particular type, as shown in parentheses.
- ASE
uses average squared error as the objective function.
- AUC
uses area under the curve as the objective function (nominal type only).
- F05
uses the F0.5 coefficient as the objective function (nominal type only).
- F1
uses the F1 coefficient as the objective function (nominal type only).
- GAMMA
uses the gamma coefficient as the objective function (nominal type only).
- GINI
uses the Gini coefficient as the objective function (nominal type only).
- KS
uses the Kolmogorov-Smirnov coefficient as the objective function (nominal type only).
- MAE
uses the mean absolute error as the objective function (interval type only).
- MCE
uses the misclassification rate as the objective function (nominal type only).
- MCLL
uses the multiclass log loss as the objective function (nominal type only).
- MISC
uses the misclassification error percentage as the objective function (nominal type only).
- MSE
uses the mean squared error as the objective function (interval type only).
- MSLE
uses the mean squared logarithmic error as the objective function (interval type only).
- RASE
uses the root average squared error as the objective function.
- RMAE
uses the root mean absolute error as the objective function (interval type only).
- RMSLE
uses the root mean squared logarithmic error as the objective function (interval type only).
- TAU
uses the tau coefficient as the objective function (nominal type only).
By default, OBJECTIVE=MISC for nominal targets, and OBJECTIVE=MSE for interval targets.
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POPSIZE=number
specifies the maximum population size, where population is the number of configuration evaluations in one iteration (population). The number must be greater than or equal to 1. In some cases, the tuner algorithm might generate a number of new configurations that is smaller than number. The POPSIZE= option is used only when SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN; it is ignored when SEARCHMETHOD=RANDOM or SEARCHMETHOD=LHS.
By default, POPSIZE=10.
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SAMPLESIZE=number
specifies the total number of evaluations, where number must be greater than or equal to 1. This option is only used when SEARCHMETHOD=RANDOM or SEARCHMETHOD=LHS; it is ignored when SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN.
By default, SAMPLESIZE=50.
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SEARCHMETHOD=BAYESIAN |GA |GRID |LHS |RANDOM
specifies the search method to use for tuning. You can specify the following values:
- BAYESIAN
builds a kriging surrogate model to approximate the objective value and uses this surrogate model to generate new alternative configurations at each iteration. The kriging model is continuously updated during the search process.
- GA
uses an initial Latin hypercube sample that seeds a genetic algorithm to generate a new population of alternative configurations at each iteration.
- GRID
uses all combinations of selected values of the hyperparameters.
- LHS
uses a Latin hypercube to generate a single sample of configurations that is uniform in each tuning parameter but random in combinations.
- RANDOM
generates a single sample of purely random configurations.
By default, SEARCHMETHOD=GA.
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SECONDOBJECTIVE=NONE |SCORETIME |TRAINTIME
specifies the type of the second objective to use for tuning. You can specify the following values:
- NONE
does not use a second objective for tuning.
- SCORETIME
specifies the model scoring time as the second objective for tuning.
- TRAINTIME
specifies the model training time as the second objective for tuning.
By default, SECONDOBJECTIVE=NONE.
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SELECTINITPOINT
specifies that the tuner select the best evaluation from the table specified in the LOOKUPTABLE= option as the initial point for the tuning process.
This option is ignored if you do not specify the LOOKUPTABLE= option.
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TARGETEVENT=string
specifies the target event to use for calculating the selected objective function. This option is ignored when the value of the OBJECTIVE= option is not AUC, F1, F05, GINI, GAMMA, TAU, or KS.
If you do not specify the TARGETEVENT= option, the tuner selects one of the target levels and uses it for calculating the specified objective function.
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TRAINFRACTION=number
specifies the fraction of all data to be used for training, where number must be between 0.01 and 0.99, inclusive. If you specify this option, the tuner uses a single-partition validation to find the objective value (validation error estimate). Using this option might not be advisable for small or unbalanced data tables, where the random assignment of the validation subset might not provide a good estimate of error. For large, balanced data tables, a single-partition validation is usually sufficient for estimating error; a single partition is more efficient than cross validation in terms of the total execution time.
If a PARTITION statement is specified, the validation partition defined in that statement is used, and this option is ignored. You cannot specify this option in combination with the KFOLD= option.
If the FRACTION= option is used, then by default the value of the TRAINFRACTION= option is one minus the value of the FRACTION= option. Otherwise, by default, TRAINFRACTION=0.7.
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TUNINGPARAMETERS=(suboption |…|<suboption>)
TUNEPARMS=(suboption |…|<suboption>)
specifies which parameters to tune and which ranges to tune over. If USEPARAMETERS=STANDARD, this option is ignored.
For more information about which tuning suboptions are available, see the specific procedure chapters.
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USEPARAMETERS=COMBINED |CUSTOM |STANDARD
specifies which set of parameters to tune.
You can specify the following values:
- COMBINED
tunes the parameters that are specified in the TUNINGPARAMETERS= option and uses default bounds and initial values to tune all other parameters.
- CUSTOM
tunes only the parameters that are specified in the TUNINGPARAMETERS= option.
- STANDARD
tunes all parameters by using their default bounds and initial values.
By default, USEPARAMETERS=COMBINED.