Gaussian Process ¶
The Gaussian process regression represents the first step of our Gaussian process implementation and is done using the
sklearn.gaussian_process.GaussianProcessRegressor
class from the scikit-learn package.

Main Optimization Method ¶
- hyppo.surrogate.gp_opt. gp ( config , log_dir , step , rank , xlow , xup , mult , names , loops = 1000 , istop = None , nhof = 10 , ngen = 100 , mu = 100 , cxpb = 0.75 , indpb = 0.1 , default = {} , ** kwargs ) [source] ¶
-
Gaussian Process based surrogate optimization algorithm.
- Parameters :
-
-
config
dict
-
Configuration dictionary object to be used when evaluating subsequent sample sets.
-
log_dir
str
-
Relative path where log files are stored
-
loops
int
-
Number of iterations to do surrogate modeling over.
-
step
int
-
Current SLURM step
-
rank
int
-
Current processor rank
-
xlow
list
[int
] -
Lower boundaries of hyperparameters to evaluate.
-
xup
list
[int
] -
Upper boundaries of hyperparameters to evaluate.
-
mult
list
[int
] -
Scaling factor for each hyperparameter
-
names
list
[str
] -
Name of each hyperparameter
-
nbof
int
-
Number of best individuals to keep in the hall of fame
-
ngen
int
-
Number of generations to run during genetic process
-
mu
int
-
Number of individuals in the genetic population
-
cxpb
float
-
Cross-over probability
-
indpb
float
-
Independent probability for each individual to be exchanged
-
default
dict
-
Dictionary of default hyperparameter values
-
config
Expected Improvement ¶
Generational Process ¶
Selection of Individuals ¶
Crossover Operator ¶
Crossover is an operator used to vary chromosomes from one generator to the next
Mutation Operator ¶
The mutation operator is used to maintain genetic diversity from one generation of a population to the next.