请提供一段python代码：基于GA的遗传算法，用于优化20个one class svm单类分类器模型的20组参数

以下是一段基于GA的遗传算法，用于优化20个one class svm单类分类器模型的20组参数的Python代码：

import numpy as np
from sklearn import svm
from sklearn.metrics import roc_auc_score
from genetic_algorithm import GeneticAlgorithm

# define the parameter space
param_space = {
    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
    'gamma': [0.001, 0.01, 0.1, 1, 10, 100],
    'nu': np.linspace(0.01, 0.99, 99),
    'degree': range(1, 6),
    'coef0': np.linspace(0, 1, 101)
}

# define the fitness function
def fitness_func(params):
    clf = svm.OneClassSVM(**params)
    y_pred = clf.fit_predict(X_train)
    auc = roc_auc_score(y_train, y_pred)
    return auc

# define the genetic algorithm
ga = GeneticAlgorithm(param_space, fitness_func)

# define the training data
X_train = np.random.randn(100, 20)
y_train = np.ones(100)

# run the genetic algorithm
ga.evolve()

# get the best parameters
best_params = ga.best_individual()[0]

# train the models with the best parameters
models = []
for i in range(20):
    model = svm.OneClassSVM(**best_params)
    model.fit(X_train)
    models.append(model)