遗传算法优化模型超参数

问题描述：

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)

使用上述代码，但是没有指明应该安装那些包。

from evolutionary_search import EvolutionaryAlgorithmSearchCV

使用上述命令导入包，但是现在这个包只支持到python 3.4，现在已经并到sklearn-deap这个包中。

所以如果运行上述代码报错，安装sklearn-deap即可

pip install sklearn-deap