sklearn-deap

Use evolutionary algorithms instead of gridsearch in scikit-learn. This allows you to 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.

Here is an ipython notebook comparing EvolutionaryAlgorithmSearchCV against GridSearchCV and RandomizedSearchCV.

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

Install

To install the library use pip:

pip install sklearn-deap

or clone the repo and just type the following on your shell:

python setup.py install

Usage examples

Example of usage:

import sklearn.datasets
import numpy as np
import random

data = sklearn.datasets.load_digits()
X = data["data"]
y = data["target"]

from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold

paramgrid = {"kernel": ["rbf"],
             "C"     : np.logspace(-9, 9, num=25, base=10),
             "gamma" : np.logspace(-9, 9, num=25, base=10)}

random.seed(1)

from evolutionary_search import EvolutionaryAlgorithmSearchCV
cv = EvolutionaryAlgorithmSearchCV(estimator=SVC(),
                                   params=paramgrid,
                                   scoring="accuracy",
                                   cv=StratifiedKFold(n_splits=4),
                                   verbose=1,
                                   population_size=50,
                                   gene_mutation_prob=0.10,
                                   gene_crossover_prob=0.5,
                                   tournament_size=3,
                                   generations_number=5,
                                   n_jobs=4)
cv.fit(X, y)

Output:

    Types [1, 2, 2] and maxint [0, 24, 24] detected
    --- Evolve in 625 possible combinations ---
    gen	nevals	avg     	min    	max
    0  	50    	0.202404	0.10128	0.962716
    1  	26    	0.383083	0.10128	0.962716
    2  	31    	0.575214	0.155259	0.962716
    3  	29    	0.758308	0.105732	0.976071
    4  	22    	0.938086	0.158041	0.976071
    5  	26    	0.934201	0.155259	0.976071
    Best individual is: {'kernel': 'rbf', 'C': 31622.776601683792, 'gamma': 0.001}
    with fitness: 0.976071229827

Example for maximizing just some function:

from evolutionary_search import maximize

def func(x, y, m=1., z=False):
    return m * (np.exp(-(x**2 + y**2)) + float(z))

param_grid = {'x': [-1., 0., 1.], 'y': [-1., 0., 1.], 'z': [True, False]}
args = {'m': 1.}
best_params, best_score, score_results, _, _ = maximize(func, param_grid, args, verbose=False)

Output:

best_params = {'x': 0.0, 'y': 0.0, 'z': True}
best_score  = 2.0
score_results = (({'x': 1.0, 'y': -1.0, 'z': True}, 1.1353352832366128),
 ({'x': -1.0, 'y': 1.0, 'z': True}, 1.3678794411714423),
 ({'x': 0.0, 'y': 1.0, 'z': True}, 1.3678794411714423),
 ({'x': -1.0, 'y': 0.0, 'z': True}, 1.3678794411714423),
 ({'x': 1.0, 'y': 1.0, 'z': True}, 1.1353352832366128),
 ({'x': 0.0, 'y': 0.0, 'z': False}, 2.0),
 ({'x': -1.0, 'y': -1.0, 'z': False}, 0.36787944117144233),
 ({'x': 1.0, 'y': 0.0, 'z': True}, 1.3678794411714423),
 ({'x': -1.0, 'y': -1.0, 'z': True}, 1.3678794411714423),
 ({'x': 0.0, 'y': -1.0, 'z': False}, 1.3678794411714423),
 ({'x': 1.0, 'y': -1.0, 'z': False}, 1.1353352832366128),
 ({'x': 0.0, 'y': 0.0, 'z': True}, 2.0),
 ({'x': 0.0, 'y': -1.0, 'z': True}, 2.0))