pip install gradient-free-optimizers[progress]  # 带有 tqdm 的进度条
pip install gradient-free-optimizers[sklearn]   # 用于代理模型的 scikit-learn
pip install gradient-free-optimizers[full]      # 所有可选依赖

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_wine
import numpy as np

from gradient_free_optimizers import BayesianOptimizer

X, y = load_wine(return_X_y=True)

def objective(params):
    model = GradientBoostingClassifier(
        n_estimators=params["n_estimators"],
        max_depth=params["max_depth"],
        learning_rate=params["learning_rate"],
    )
    return cross_val_score(model, X, y, cv=5).mean()

search_space = {
    "n_estimators": np.arange(50, 300, 10),
    "max_depth": np.arange(2, 10),
    "learning_rate": np.logspace(-3, 0, 20),
}

opt = BayesianOptimizer(search_space)
opt.search(objective, n_iter=50)

import numpy as np
from gradient_free_optimizers import BayesianOptimizer

def ackley(params):
    x, y = params["x"], params["y"]
    return -(
        -20 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2)))
        - np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
        + np.e + 20
    )

search_space = {
    "x": np.arange(-5, 5, 0.01),
    "y": np.arange(-5, 5, 0.01),
}

opt = BayesianOptimizer(search_space)
opt.search(ackley, n_iter=100)

import numpy as np
from gradient_free_optimizers import ParticleSwarmOptimizer

def rastrigin(params):
    A = 10
    values = [params[f"x{i}"] for i in range(5)]
    return -sum(v**2 - A * np.cos(2 * np.pi * v) + A for v in values)

search_space = {f"x{i}": np.arange(-5.12, 5.12, 0.1) for i in range(5)}

opt = ParticleSwarmOptimizer(search_space, population=20)
opt.search(rastrigin, n_iter=500)

import numpy as np
from gradient_free_optimizers import SimulatedAnnealingOptimizer

def sphere(params):
    return -(params["x"]**2 + params["y"]**2)

search_space = {
    "x": np.arange(-10, 10, 0.1),
    "y": np.arange(-10, 10, 0.1),
}

opt = SimulatedAnnealingOptimizer(
    search_space,
    start_temp=1.2,
    annealing_rate=0.99,
)
opt.search(sphere, n_iter=1000)

import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer

def objective(params):
    return params["x"] + params["y"]

def constraint(params):
    # 只有 x + y < 5 的位置才是有效的
    return params["x"] + params["y"] < 5

search_space = {
    "x": np.arange(0, 10, 0.1),
    "y": np.arange(0, 10, 0.1),
}

opt = RandomSearchOptimizer(search_space, constraints=[constraint])
opt.search(objective, n_iter=1000)

import numpy as np
from gradient_free_optimizers import HillClimbingOptimizer

def expensive_function(params):
    # 模拟昂贵的计算
    return -(params["x"]**2 + params["y"]**2)

search_space = {
    "x": np.arange(-10, 10, 0.1),
    "y": np.arange(-10, 10, 0.1),
}

# 第一次搜索
opt1 = HillClimbingOptimizer(search_space)
opt1.search(expensive_function, n_iter=100, memory=True)

# 使用之前的搜索数据进行热启动继续搜索
opt2 = HillClimbingOptimizer(search_space)
opt2.search(expensive_function, n_iter=100, memory_warm_start=opt1.search_data)

import numpy as np
from gradient_free_optimizers import BayesianOptimizer

def objective(params):
    return -(params["x"]**2 + params["y"]**2)

search_space = {
    "x": np.arange(-10, 10, 0.1),
    "y": np.arange(-10, 10, 0.1),
}

opt = BayesianOptimizer(search_space)
opt.search(
    objective,
    n_iter=1000,
    max_time=60,           # 60秒后停止
    max_score=-0.01,       # 得分达到 -0.01 时停止
    early_stopping={       # 如果连续50次迭代没有改进则停止
        "n_iter_no_change": 50,
    },
)