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mloptimizer.core.base

mloptimizer.core.base#

Module Contents#

Classes#

Optimizer

Base class for the optimization of a classifier
class mloptimizer.core.base.Optimizer(estimator_class, features: numpy.array, labels: numpy.array, folder=os.curdir, log_file='mloptimizer.log', hyperparam_space: mloptimizer.hyperparams.HyperparameterSpace = None, eval_function=train_score, fitness_score='accuracy', metrics=None, seed=random.randint(0, 1000000), use_parallel=False, use_mlflow=False)[source]#

Base class for the optimization of a classifier

estimator_class#

class of the classifier

Type:

class

features#

np.array with the features

Type:

np.array

labels#

np.array with the labels

Type:

np.array

hyperparam_space#

object with the hyperparameter space: fixed and evolvable hyperparams

Type:

HyperparameterSpace

evaluator#

object to evaluate the classifier

Type:

Evaluator

eval_dict#

dictionary with the evaluation of the individuals

Type:

dict

populations#

list of populations

Type:

list

logbook#

list of logbook

Type:

list

seed#

seed for the random functions

Type:

int

use_parallel#

flag to use parallel processing

Type:

bool

use_mlflow#

flag to use mlflow

Type:

bool

set_mlopt_seed(seed)[source]#

Method to set the seed for the random functions

Parameters:

seed (int) – seed for the random functions

static get_subclasses(my_class)[source]#

Method to get all the subclasses of a class (in this case use to get all the classifiers that can be optimized).

Parameters:

my_class (class) – class to get the subclasses

Returns:

list of subclasses

Return type:

list

get_clf(individual)[source]#
optimize_clf(population_size: int = 10, generations: int = 3, cxpb=0.5, mutpb=0.5, tournsize=4, indpb=0.5, n_elites=10, checkpoint: str = None, opt_run_folder_name: str = None) → object[source]#

Method to optimize the classifier. It uses the custom_ea_simple method to optimize the classifier.

Parameters:

  • population_size (int, optional (default=10)) – number of individuals in each generation

  • generations (int, optional (default=3)) – number of generations

  • cxpb (float, optional (default=0.5)) – crossover probability

  • mutpb (float, optional (default=0.5)) – mutation probability

  • tournsize (int, optional (default=4)) – number of individuals to select in the tournament

  • indpb (float, optional (default=0.5)) – independent probability for each attribute to be mutated

  • n_elites (int, optional (default=10)) – number of elites to keep in the next generation

  • checkpoint (str, optional (default=None)) – path to the checkpoint file

  • opt_run_folder_name (str, optional (default=None)) – name of the folder where the execution will be saved

Returns:

clf – classifier with the best hyperparams

Return type:

classifier

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