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))
23 Dec 08, 2022
2k Jan 01, 2023
124 Sep 16, 2022
32 Dec 11, 2022
33 Feb 21, 2022
3 Aug 13, 2022
1 Jan 11, 2022
7 Nov 24, 2022
118 Dec 29, 2022
71 Dec 27, 2022
114 Dec 18, 2022
42 Dec 07, 2022
87 Dec 28, 2022
1 Mar 16, 2022
49 Nov 10, 2022
40 Dec 09, 2022
6.2k Jan 01, 2023
0 Dec 16, 2021
4 May 26, 2022
165 Nov 22, 2022