接着上面的回答,请告诉鲸鱼优化算法原始代码优化XGBoost模型,优化模型寻找最优参数的过程
时间: 2023-06-24 16:06:59 浏览: 56
好的,下面是鲸鱼优化算法(JADE)的原始代码优化XGBoost模型的示例:
```python
import xgboost as xgb
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from jmetal.algorithm.singleobjective import DifferentialEvolution
from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
from jmetal.problem.singleobjective import SingleObjectiveProblem
from jmetal.core.solution import Solution
from jmetal.util.termination_criterion import StoppingByEvaluations
class XGBoostHyperParameterTuning(SingleObjectiveProblem):
def __init__(self, X_train, y_train, X_test, y_test):
self.X_train = X_train
self.y_train = y_train
self.X_test = X_test
self.y_test = y_test
self.n_features = X_train.shape[1]
self.x_min = [0.1, 0.1, 1, 0, 1, 1, 0.1, 0.1, 0.1]
self.x_max = [1, 1, 10, 1, 10, 10, 1, 1, 1]
self.number_of_variables = len(self.x_min)
self.number_of_objectives = 1
self.number_of_constraints = 0
def evaluate(self, solution: Solution) -> Solution:
learning_rate = solution.variables[0]
gamma = solution.variables[1]
max_depth = int(solution.variables[2])
min_child_weight = solution.variables[3]
max_delta_step = solution.variables[4]
subsample = solution.variables[5]
colsample_bytree = solution.variables[6]
colsample_bylevel = solution.variables[7]
reg_lambda = solution.variables[8]
model = xgb.XGBClassifier(
learning_rate=learning_rate,
gamma=gamma,
max_depth=max_depth,
min_child_weight=min_child_weight,
max_delta_step=max_delta_step,
subsample=subsample,
colsample_bytree=colsample_bytree,
colsample_bylevel=colsample_bylevel,
reg_lambda=reg_lambda
)
model.fit(self.X_train, self.y_train)
y_pred = model.predict(self.X_test)
accuracy = accuracy_score(self.y_test, y_pred)
solution.objectives[0] = 1 - accuracy
return solution
if __name__ == '__main__':
data = load_digits()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42)
problem = XGBoostHyperParameterTuning(X_train, y_train, X_test, y_test)
algorithm = DifferentialEvolution(
problem=problem,
population_size=10,
crossover=DifferentialEvolutionCrossover(CR=0.5, F=0.5),
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=1000)
)
algorithm.run()
result = algorithm.get_result()
print(f'Best solution found: {result.variables}\nObjective value: {result.objectives}')
```
在这个示例中,我们使用了JMetal框架的DifferentialEvolution算法来搜索XGBoost模型的最优参数。我们首先定义了一个SingleObjectiveProblem问题类,它包含了XGBoost模型的参数范围和评估函数。然后,我们使用这个问题类来创建DifferentialEvolution算法对象,设置了一些算法参数,如种群大小、交叉和变异算子等。最后,我们运行算法并输出结果。
需要注意的是,这只是一个示例代码,实际应用中需要根据具体情况进行修改和调整。