Parallel processing

Relying on the Deap capability to parallelize the evaluation of the fitness function, we can use the multiprocessing module to parallelize the evaluation of the fitness function. This is done passing the use_parallel parameter as True to initialize the Optimizer object. This parameter is set to False by default.

An example of the speedup that can be achieved using parallel processing is shown below.

Note

In the example below, the seed is set to 25 to ensure the result using parallel processing is the same as the one without parallel processing.

Warning

Parallel processing is not supported for the XGBClassifier and KerasClassifier classifiers.

from mloptimizer.core import Optimizer
from mloptimizer.hyperparams import HyperparameterSpace
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
import time

# Load the dataset and get the features and target
X, y = load_iris(return_X_y=True)

# Define the hyperparameter space (a default space is provided for some algorithms)
hyperparameter_space = HyperparameterSpace.get_default_hyperparameter_space(DecisionTreeClassifier)

# Set the seed to ensure the result using parallel processing is the same as the one without parallel processing
my_seed = 25
population = 50
generations = 4

opt_with_parallel = Optimizer(estimator_class=DecisionTreeClassifier, features=X, labels=y,
                              hyperparam_space=hyperparameter_space, seed=my_seed, use_parallel=True)

start_time_parallel = time.time()
clf_with_parallel = opt_with_parallel.optimize_clf(population, generations)
end_time_parallel = time.time()

opt = Optimizer(estimator_class=DecisionTreeClassifier, features=X, labels=y,
                hyperparam_space=hyperparameter_space, seed=my_seed, use_parallel=False)
start_time = time.time()
clf = opt.optimize_clf(population, generations)
end_time = time.time()

elapsed_time_parallel = end_time_parallel - start_time_parallel
elapsed_time = end_time - start_time
speedup = round(((elapsed_time_parallel / elapsed_time) - 1) * 100, 2)

print(f"Elapsed time with parallel: {elapsed_time_parallel}")
print(f"Elapsed time without parallel: {elapsed_time}")
print(f"Speedup: {speedup}%")