Simulated Annealing Parameter Search

Uses simulated annealing to find the optimal parameters by considering a hyper cube of all possible indices to the given parameter table. Each column of the parameter table corresponds to one axis of this cube with a range corresponding to the non-masked rows of the parameter table. The radius for the annealing process assumes that all axes have unit length regardless of the number of non-masked rows. This node should be considered _experimental_ and may change in the future

Documentation

Uses simulated annealing to find the optimal parameters by considering a hyper cube of all possible indices to the given parameter table. Each column of the parameter table corresponds to one axis of this cube with a range corresponding to the non-masked rows of the parameter table. The radius for the annealing process assumes that all axes have unit length regardless of the number of non-masked rows. This node should be considered _experimental_ and may change in the future

Configuration:

• cv

  Number of fold in the default K-Fold cross validation. Ignored when cross-validation port is given

• n_iter

  Number of randomized searches done

• cooling

  Method for lowering temperature

• cooling_arg

  Argument A to cooling method. Exponential: T=A^t Linear ignores A Logarithmic: T=A/log(1+t)

Input ports:

in-modelmodel

in-model

parameter spacetable

param-space

Xtable

X

Ytable

Y

cross-validation[(table,table)]

cross-validation

Output ports:

resultstable

results

parameterstable

parameters

out-modelmodel

out-model

Definition

Input ports

in-model

model

in-model

parameter space

table

param-space

X

table

X

Y

table

Y

cross-validation

0 - 1, [(table,table)]

cross-validation

Output ports

results

table

results

parameters

table

parameters

out-model

model

out-model

class node_paramsearch.ParameterSearch_SimulatedAnnealing