The SEMISUPLEARN Procedure

AUTOTUNE Statement

AUTOTUNE <options>;

The AUTOTUNE statement searches for the best combination of values of the GAMMA= parameter and the number of nearest-neighbor parameters (when you specify the KNN option in the KERNEL statement) by minimizing the loss function in the PROC SEMISUPLEARN statement.

Table 2 summarizes the options that you can specify in the AUTOTUNE statement. For more information about all options except the TUNINGPARAMETERS= option, see the option’s description in the section AUTOTUNE Statement in Chapter 2, Shared Concepts. The TUNINGPARAMETERS= option is described following Table 2.

Table 2: AUTOTUNE Statement Options

Option	Description
APPENDLOOKUP	Specifies that the table specified in the HISTORYTABLE= option contain the rows from the table specified in the LOOKUPTABLE= option
EVALHISTORY=	Specifies how to report the evaluation history of the tuner
HISTORYTABLE=	Specifies the CAS table that contains the evaluation history
LIVEUPDATE	Specifies that the table specified in the HISTORYTABLE= option be updated at every evaluation
LOOKUPTABLE=	Specifies the CAS table to use for evaluation lookup
MAXBAYES=	Specifies the maximum number of points in the kriging model
MAXEVALS=	Specifies the maximum number of evaluations
MAXITER=	Specifies the maximum number of iterations when SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN
MAXTIME=	Specifies the maximum time for all iterations
MAXTRAINTIME=	Specifies the maximum time for a model training
NCONVITER=	Specifies the number of convergence iterations
NOGRIDSHUFFLE	Requests that the grid points not be shuffled
NOLOCALSEARCH	Disables local search optimization
NPARALLEL=	Specifies the number of parallel sessions
NSUBSESSIONWORKERS=	Specifies the number of workers in parallel sessions
POPSIZE=	Specifies the population size when SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN
SAMPLESIZE=	Specifies the sample size when SEARCHMETHOD=LHS or SEARCHMETHOD=RANDOM
SEARCHMETHOD=	Specifies the search method that the optimizer uses
SELECTINITPOINT	Specifies that the tuner select the best evaluation from the lookup table
TARGETEVENT=	Specifies the target event for ROC-based calculations
TUNINGPARAMETERS=	Specifies the custom tuning parameters
USEPARAMETERS=	Specifies how to handle the TUNINGPARAMETERS= option

TUNINGPARAMETERS=(suboption |…|<suboption>) TUNEPARMS=(suboption |…|<suboption>)

specifies which parameters to tune and which ranges to tune over. If USEPARAMETERS=STANDARD, this option is ignored.

You can specify the following suboptions:

GAMMA (LB=number UB=number VALUES=value-list INIT=number EXCLUDE)

specifies information about the number of gamma values to use for tuning the semisupervised learning model. For more information, see the GAMMA= option in the PROC SEMISUPLEARN statement.

You can specify the following additional suboptions:

LB=number

specifies the minimum number of gamma values to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption.

By default, LB=0.01.

UB=number

specifies the maximum number of gamma values to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption.

By default, UB=100000.

VALUES=value-list

specifies a list of values to consider for the number of gamma values during tuning, where value-list is a space-separated list of double numbers greater than 0. If you specify this suboption, you cannot specify either the LB= or UB= suboption.

INIT=number

specifies the initial number of gamma values for the tuner to use.

By default, INIT=20.

EXCLUDE

excludes the number of gamma values from the tuning process. If you specify this suboption, the LB=, UB=, VALUES=, and INIT= suboptions are ignored.

K (LB=number UB=number VALUES=value-list INIT=number EXCLUDE)

specifies information about the number of K values (that is, the number of nearest neighbors in the KNN kernel) to use for tuning the semisupervised learning model. For more information, see the K= option in the KERNEL statement.

You can specify the following additional suboptions:

LB=number

specifies the minimum number of K values to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption.

By default, LB=2.

UB=number

specifies the maximum number of K values to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption.

By default, UB=200.

VALUES=value-list

specifies a list of values to consider for the number of K values during tuning, where value-list is a space-separated list of integer numbers greater than or equal to 1. If you specify this suboption, you cannot specify either the LB= or UB= suboption.

INIT=number

specifies the initial number of K values for the tuner to use.

By default, INIT=2.

EXCLUDE

excludes the number of K values from the tuning process. If you specify this suboption, the LB=, UB=, VALUES=, and INIT= suboptions are ignored.

Last updated: November 11, 2020