sklearn-deap

Use evolutionary algorithms instead of gridsearch in scikit-learn. This allows you to exponentially reduce the time required to find the best parameters for your estimator. Instead of trying out every possible combination of parameters, evolve only the combinations that give the best results.

It's implemented using deap library: https://github.com/deap/deap

Install

To install the library just type the following on your shell:

python setup.py install

Usage examples

Example of usage:

from evolutionary_search import EvolutionaryAlgorithmSearchCV
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

grid = {
    'svc__C': [0.001, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0, 1.3, 1.5, 1.7, 10, 100, 1000],
    'svc__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
    'pca__n_components': [1, 2, 3, 4]
}
iris = datasets.load_iris()
X, y = iris.data, iris.target
pipeline = Pipeline(steps=[
    ('pca', PCA()),
    ('svc', SVC())
])
clf = EvolutionaryAlgorithmSearchCV(pipeline, grid, scoring=None, verbose=True, n_jobs=4, population_size=5)
clf.fit(X, y)

Output:

    --- Evolve in 240 possible combinations ---
    gen nevals  avg     min         max     
    0   5       0.84188 0.615919    0.966346
    1   4       0.95438 0.926282    0.972756
    2   3       0.971581    0.966346    0.973291
    3   4       0.969017    0.952457    0.973291
    4   2       0.973291    0.973291    0.973291
    5   2       0.965171    0.932692    0.973291
    6   4       0.973291    0.973291    0.973291
    7   0       0.973291    0.973291    0.973291
    8   4       0.973291    0.973291    0.973291
    9   2       0.971902    0.966346    0.973291
    10  0       0.973291    0.973291    0.973291
    Best individual is: {"pca__n_components": 3, "svc__kernel": "linear", "svc__C": 1.0}
    with fitness: (0.9732905982905983,)