【Code】GP-UCB 算法及其实现
GP-UCB 算法
本 Notebook 主要是为 GP-UCB 算法的代码提供一个范例，这里使用了 Beale 函数作为 Benchmark ，并且后文当中有相对丰富的图像展示了 GP-UCB 算法的性能。
导入工具
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt构造函数
def beale(x: np.ndarray) -> np.ndarray:
"""
Beale function - 非凸二元函数
最小值点为 (3, 0.5)，最小值为 0
支持向量化和矩阵输入：
- 单个样本 (2,): 返回标量
- 批量样本 (n, 2): 返回向量 (n,)
Args:
x: 输入数组，形状为 (2,) 或 (n_samples, 2)
Returns:
函数值，标量或向量
"""
x = np.atleast_2d(x)
# 提取 x1 和 x2（每行是一个样本）
x1 = x[:, 0]
x2 = x[:, 1]
# 向量化计算
term1 = (1.5 - x1 + x1 * x2) ** 2
term2 = (2.25 - x1 + x1 * x2 ** 2) ** 2
term3 = (2.625 - x1 + x1 * x2 ** 3) ** 2
result = term1 + term2 + term3
# 如果输入是 1D 向量，返回标量
if x.shape[0] == 1:
return result[0]
return result下面我们为其绘制一个二维的函数图像看看。
x1 = np.linspace(-3.1, 3.1, 200) # 第一个自变量
x2 = np.linspace(-3.1, 3.1, 200) # 第二个自变量
# 生成网格数据（二维坐标矩阵）
X1, X2 = np.meshgrid(x1, x2)
# 将网格数据转换为 (n, 2) 形式进行向量化计算
X_grid = np.column_stack([X1.ravel(), X2.ravel()])
Z = beale(X_grid).reshape(X1.shape)
# ---------------------- 2. 绘制3D曲面图 ----------------------
fig = plt.figure(figsize=(12, 5))
# 子图1：3D曲面图
ax1 = fig.add_subplot(121, projection='3d')
# 绘制曲面（cmap设置颜色映射，alpha设置透明度）
surf = ax1.plot_surface(X1, X2, Z, cmap='viridis', alpha=0.8, edgecolor='none')
# 标注最小值点 (3, 0.5, 0)
ax1.scatter(3, 0.5, 0, color='red', s=100, marker='*', label='Min (3, 0.5)')
# 设置坐标轴标签和标题
ax1.set_xlabel('x1', fontsize=10)
ax1.set_ylabel('x2', fontsize=10)
ax1.set_zlabel('beale(x1, x2)', fontsize=10)
ax1.set_title('Beale Surface', fontsize=12)
ax1.legend()
# 添加颜色条
fig.colorbar(surf, ax=ax1, shrink=0.5, aspect=5)
# ---------------------- 3. 绘制等高线热力图 ----------------------
ax2 = fig.add_subplot(122)
# 绘制等高线（levels设置等高线数量，cmap设置颜色）
contour = ax2.contourf(X1, X2, Z, levels=50, cmap='viridis')
# 绘制等高线轮廓（增强对比度）
ax2.contour(X1, X2, Z, levels=50, colors='k', alpha=0.3, linewidths=0.5)
# 标注最小值点
ax2.scatter(3, 0.5, color='red', s=100, marker='*', label='Min (3, 0.5)')
# 设置坐标轴标签和标题
ax2.set_xlabel('x1', fontsize=10)
ax2.set_ylabel('x2', fontsize=10)
ax2.set_title('Beale Contour', fontsize=12)
ax2.legend()
# 添加颜色条
fig.colorbar(contour, ax=ax2, shrink=0.5, aspect=5)
# 调整布局，避免标签重叠
plt.tight_layout()
# 显示图像
plt.show()
高斯过程回归相关
核函数
def SE(x1: np.ndarray, x2: np.ndarray, h: float = 1.0, sigma: float = 1.0) -> np.ndarray:
x1 = np.atleast_2d(x1)
x2 = np.atleast_2d(x2)
diff = x1[:, np.newaxis, :] - x2[np.newaxis, :, :]
sq_dists = np.sum(diff ** 2, axis=-1)
return sigma * np.exp(-sq_dists / (2 * h ** 2))# 测试
x1 = np.array([[1.0, 0.0], [0.0, 1.0]])
x2 = np.array([[0.0, 1.0], [1.0, 0.0]])
SE(x1, x2)array([[0.36787944, 1. ],
[1. , 0.36787944]])
x1 = np.array([[1.0, 0.0]])
x2 = np.array([[0.0, 1.0]])
SE(x1, x2)array([[0.36787944]])
高斯过程回归
class GPR:
def __init__(self, kernel=SE, alpha: float = 1e-6, random_state: int = 42):
self.kernel = kernel
self.alpha = alpha
self.random_state = random_state
np.random.seed(random_state)
self.X_train_ = None
self.y_train_ = None
self.K_inv_ = None
self.K_train_ = None
def fit(self, X: np.ndarray, y: np.ndarray) -> 'GPR':
X = np.asarray(X)
y = np.asarray(y).ravel()
self.X_train_ = X
self.y_train_ = y
self.K_train_ = self.kernel(X, X)
K_reg = self.K_train_ + self.alpha * np.eye(len(X))
try:
L = np.linalg.cholesky(K_reg)
self.K_inv_ = np.linalg.solve(L.T, np.linalg.solve(L, np.eye(len(X))))
except np.linalg.LinAlgError:
self.K_inv_ = np.linalg.pinv(K_reg)
return self
def predict(self, X: np.ndarray, return_std: bool = False):
if self.X_train_ is None or self.K_inv_ is None:
raise ValueError("模型尚未拟合，请先调用 fit() 方法")
X = np.atleast_2d(X)
# (n_train, n_test)
K_star = self.kernel(self.X_train_, X)
# 均值
mu = K_star.T @ self.K_inv_ @ self.y_train_
if return_std:
# k(x*, x*)
K_xx = self.kernel(X, X) # (n_test, n_test)
k_diag = np.diag(K_xx)
# 更稳定的方差写法
v = self.K_inv_ @ K_star # (n_train, n_test)
var = k_diag - np.sum(K_star * v, axis=0)
var = np.maximum(var, 0)
std = np.sqrt(var)
return mu, std
return mu
def sample_y(self, X: np.ndarray, n_samples: int = 1):
mu, std = self.predict(X, return_std=True)
samples = np.random.randn(n_samples, len(mu)) * std + mu
return samples.squeeze() if n_samples == 1 else samples
from sklearn.gaussian_process import GaussianProcessRegressor
gpr = GaussianProcessRegressor()
mygpr = GPR()X = 6 * np.random.random(size=(10,2)) - 3
y = beale(X)
gpr.fit(X, y)
mygpr.fit(X, y)<__main__.GPR at 0x20d66951690>
X_new = 6 * np.random.random(size=(1,2)) - 3
gpr.predict(X_new)array([104.99844737])
mygpr.predict(X_new, return_std=True)(array([104.99427546]), array([0.84634688]))
GP-UCB 算法
为了简便起见，先使用常数 $\beta_t$ 来进行，看看效果。
import numpy as np
from scipy.optimize import basinhopping, Bounds
from functools import partial
from sklearn.gaussian_process import GaussianProcessRegressor
import warnings
warnings.filterwarnings('ignore')
def noisy_neg_beale(x):
noise = 0.01 * np.random.random()
return - beale(x) + 0 * noise # 实际无噪声
np.random.seed(42)
n_init = 5
X_train = 6 * np.random.rand(n_init, 2) - 3
y_train = noisy_neg_beale(X_train)
n_iter = 150
gpr = GaussianProcessRegressor()
def ucb(x, model, beta):
"""
计算 UCB 值（标量）。输入 x 应为一维数组。
"""
x = np.atleast_2d(x) # 转为 (1, 2)
mu, std = model.predict(x, return_std=True)
return (mu + np.sqrt(beta) * std)[0] # 返回标量
best_so_far = []
for t in range(n_iter):
beta = 4 * np.log(t + 1)
# 拟合 GP
gpr.fit(X_train, y_train)
# 定义采集函数的相反数（用于最小化）
ucb_specified = partial(ucb, model=gpr, beta=beta)
def objective(x):
return -ucb_specified(x)
# 设置 basinhopping 参数
minimizer_kwargs = {
"method": "L-BFGS-B",
"bounds": [(-3.1, 3.1), (-3.1, 3.1)],
"tol": 1e-7
}
# 随机初始点（basinhopping 会在此基础上进行全局搜索）
x0 = np.random.uniform(-3.1, 3.1, size=2)
result = basinhopping(objective, x0,
minimizer_kwargs=minimizer_kwargs,
niter=100, # 全局迭代次数，可调整
stepsize=0.5, # 随机扰动步长
T=1.0, # 温度参数
seed=None) # 可固定随机种子
x_next = result.x
# 评估真实函数
y_next = noisy_neg_beale(x_next)
# 更新数据集
X_train = np.vstack([X_train, x_next])
y_train = np.append(y_train, y_next)
best_so_far.append(np.max(y_train))
print(f"Iter {t+1}: x = {x_next}, y = {y_next}, Best = {best_so_far[-1]}")
plt.figure(figsize=(10, 6))
plt.plot(range(1, n_iter+1), best_so_far, marker='o', markersize=3, linestyle='-', linewidth=1)
plt.xlabel('Iteration')
plt.ylabel('Best observed -beale(x)')
plt.title('Best so far over iterations')
plt.grid(True)
plt.savefig('best_so_far.png', dpi=150)
plt.show()Iter 1: x = [ 2.51889068 -0.30565493], y = -3.202107505033335, Best = -3.202107505033335
Iter 2: x = [3.1 3.1], y = -9343.143085410005, Best = -3.202107505033335
Iter 3: x = [ 3.1 -3.1], y = -9580.116075410002, Best = -3.202107505033335
Iter 4: x = [ 1.78670372 -0.03995438], y = -1.0480727279111917, Best = -1.0480727279111917
Iter 5: x = [0.68048489 0.63280797], y = -9.437331230537993, Best = -1.0480727279111917
Iter 6: x = [ 2.76506357 -0.0123833 ], y = -1.9726644946439815, Best = -1.0480727279111917
Iter 7: x = [ 2.35622003 -0.10048311], y = -1.2723640165163654, Best = -1.0480727279111917
Iter 8: x = [1.11070608 0.54827861], y = -6.04783480837626, Best = -1.0480727279111917
Iter 9: x = [-0.45992392 0.6111082 ], y = -18.141009905154306, Best = -1.0480727279111917
Iter 10: x = [0.1327098 0.93649357], y = -13.980809615902743, Best = -1.0480727279111917
Iter 11: x = [-0.25652403 0.00803157], y = -17.663902784644705, Best = -1.0480727279111917
Iter 12: x = [0.90999182 0.84643817], y = -10.956944860902093, Best = -1.0480727279111917
Iter 13: x = [0.01085158 0.37120643], y = -14.08673484804645, Best = -1.0480727279111917
Iter 14: x = [-3.1 -3.1], y = -10418.404125410003, Best = -1.0480727279111917
Iter 15: x = [-1.51085043 -1.50315827], y = -113.90079946045758, Best = -1.0480727279111917
Iter 16: x = [ 0.66120115 -0.5643021 ], y = -6.858486707706051, Best = -1.0480727279111917
Iter 17: x = [-1.85133991 -1.65628164], y = -208.24594194209166, Best = -1.0480727279111917
Iter 18: x = [-1.0804457 -2.42377449], y = -400.5538385202729, Best = -1.0480727279111917
Iter 19: x = [ 3.1 -0.21288432], y = -5.865670287053692, Best = -1.0480727279111917
Iter 20: x = [-1.49681218 -1.9645376 ], y = -278.7116308509419, Best = -1.0480727279111917
Iter 21: x = [-0.91073624 -0.8228715 ], y = -32.80613736787235, Best = -1.0480727279111917
Iter 22: x = [-0.14126564 -3.1 ], y = -54.038152943143956, Best = -1.0480727279111917
Iter 23: x = [ 1.29079114 -0.57921006], y = -3.4019933468936565, Best = -1.0480727279111917
Iter 24: x = [-1.00765132 0.7883688 ], y = -19.712100249285427, Best = -1.0480727279111917
Iter 25: x = [ 0.7749 -0.28313589], y = -5.9768757106181205, Best = -1.0480727279111917
Iter 26: x = [ 2.85179384 -0.13885171], y = -3.4086607712072468, Best = -1.0480727279111917
Iter 27: x = [-0.44953204 -3.1 ], y = -284.94909332548605, Best = -1.0480727279111917
Iter 28: x = [ 0.24107065 -2.38675154], y = -12.70536086319614, Best = -1.0480727279111917
Iter 29: x = [0.0924205 1.51102521], y = -16.13479208964737, Best = -1.0480727279111917
Iter 30: x = [ 1.95451633 -0.45957323], y = -2.5627745449205395, Best = -1.0480727279111917
Iter 31: x = [3.1 0.15250957], y = -2.091048016802226, Best = -1.0480727279111917
Iter 32: x = [-1.24341497 -0.93230889], y = -44.82728479553856, Best = -1.0480727279111917
Iter 33: x = [-0.93602351 -1.19088586], y = -42.50091873900034, Best = -1.0480727279111917
Iter 34: x = [ 0.32872193 -3.07329022], y = -77.78802985134172, Best = -1.0480727279111917
Iter 35: x = [1.92531161 0.31598122], y = -0.8789667643428183, Best = -0.8789667643428183
Iter 36: x = [ 1.88076885 -0.30713728], y = -1.6931413926102414, Best = -0.8789667643428183
Iter 37: x = [1.66864602 0.29633471], y = -1.635535113866493, Best = -0.8789667643428183
Iter 38: x = [ 1.00376823 -0.82583938], y = -4.953617511134677, Best = -0.8789667643428183
Iter 39: x = [2.13025161 0.15416198], y = -0.3726997257416546, Best = -0.3726997257416546
Iter 40: x = [3.1 0.02096274], y = -3.3020564635054357, Best = -0.3726997257416546
Iter 41: x = [2.4625061 0.24497689], y = -0.172729528162955, Best = -0.172729528162955
Iter 42: x = [-0.98076929 1.12337397], y = -10.781819090487444, Best = -0.172729528162955
Iter 43: x = [2.37417138 0.21523708], y = -0.20743900056352005, Best = -0.172729528162955
Iter 44: x = [2.69614849 0.21564749], y = -0.48273134857059946, Best = -0.172729528162955
Iter 45: x = [ 0.30523912 -0.15255789], y = -10.504390447363642, Best = -0.172729528162955
Iter 46: x = [2.46135515 0.23102445], y = -0.19826213528257905, Best = -0.172729528162955
Iter 47: x = [2.33176238 0.28897077], y = -0.1598589214472119, Best = -0.1598589214472119
Iter 48: x = [2.36790433 0.26581466], y = -0.15025788023021555, Best = -0.15025788023021555
Iter 49: x = [-0.0563219 -2.61281718], y = -20.181460445614768, Best = -0.15025788023021555
Iter 50: x = [ 0.09032754 -3.1 ], y = -10.4676363448958, Best = -0.15025788023021555
Iter 51: x = [2.38406308 0.27106787], y = -0.141435603331058, Best = -0.141435603331058
Iter 52: x = [0.13107018 2.43627822], y = -30.507763629744545, Best = -0.141435603331058
Iter 53: x = [2.39179695 0.27234952], y = -0.1383104852731489, Best = -0.1383104852731489
Iter 54: x = [-1.24161559 1.09922353], y = -10.778588589509756, Best = -0.1383104852731489
Iter 55: x = [ 0.68926825 -1.93860237], y = -27.031441997446578, Best = -0.1383104852731489
Iter 56: x = [2.39551829 0.27425958], y = -0.13588085611279554, Best = -0.13588085611279554
Iter 57: x = [2.40299328 0.27754461], y = -0.13149084605208594, Best = -0.13149084605208594
Iter 58: x = [2.41671298 0.28486056], y = -0.12275384929533942, Best = -0.12275384929533942
Iter 59: x = [2.45304493 0.30097213], y = -0.10352468236961365, Best = -0.10352468236961365
Iter 60: x = [3.1 0.39305593], y = -0.36548493510803415, Best = -0.10352468236961365
Iter 61: x = [2.5413036 0.3293202], y = -0.07246237489325107, Best = -0.07246237489325107
Iter 62: x = [2.7754598 0.36353012], y = -0.09649012474042937, Best = -0.07246237489325107
Iter 63: x = [2.60476396 0.3445751 ], y = -0.061091740111244325, Best = -0.061091740111244325
Iter 64: x = [2.61693018 0.34733529], y = -0.05973798388546668, Best = -0.05973798388546668
Iter 65: x = [2.61945004 0.34800198], y = -0.059383093946907065, Best = -0.059383093946907065
Iter 66: x = [2.62306206 0.3489114 ], y = -0.05893657633088271, Best = -0.05893657633088271
Iter 67: x = [-1.09500856 -1.05372232], y = -43.59873572682462, Best = -0.05893657633088271
Iter 68: x = [-0.04430565 -1.79008379], y = -15.815221902188396, Best = -0.05893657633088271
Iter 69: x = [-1.86516307 0.74101611], y = -27.408839355844293, Best = -0.05893657633088271
Iter 70: x = [-0.00702215 -0.81358554], y = -14.309018343952836, Best = -0.05893657633088271
Iter 71: x = [2.63849543 0.34575334], y = -0.06565071463776927, Best = -0.05893657633088271
Iter 72: x = [-0.24656807 -2.06574099], y = -32.628368373776524, Best = -0.05893657633088271
Iter 73: x = [2.62641027 0.3467363 ], y = -0.06190166131381526, Best = -0.05893657633088271
Iter 74: x = [-0.66520166 1.53519297], y = -3.904688794883871, Best = -0.05893657633088271
Iter 75: x = [ 0.47412293 -1.64966188], y = -9.461168949034604, Best = -0.05893657633088271
Iter 76: x = [2.62080874 0.34813742], y = -0.059433407448007444, Best = -0.05893657633088271
Iter 77: x = [2.62909866 0.3513693 ], y = -0.057214602372593196, Best = -0.057214602372593196
Iter 78: x = [2.6232904 0.3514298], y = -0.05634402412126846, Best = -0.05634402412126846
Iter 79: x = [2.61543769 0.35348223], y = -0.05359103026850126, Best = -0.05359103026850126
Iter 80: x = [-1.12088044 -0.04214693], y = -32.50053997133553, Best = -0.05359103026850126
Iter 81: x = [2.6198486 0.35750273], y = -0.05039393018735008, Best = -0.05039393018735008
Iter 82: x = [-1.72686683 1.03179213], y = -12.688347526152736, Best = -0.05039393018735008
Iter 83: x = [-0.19065789 2.06090645], y = -5.658899030570903, Best = -0.05039393018735008
Iter 84: x = [2.61753407 0.36549974], y = -0.04448259008228815, Best = -0.04448259008228815
Iter 85: x = [2.59414884 0.37403653], y = -0.043445321255764495, Best = -0.043445321255764495
Iter 86: x = [2.61126778 0.37486812], y = -0.040447386681891406, Best = -0.040447386681891406
Iter 87: x = [2.61033787 0.37755334], y = -0.03978308979145451, Best = -0.03978308979145451
Iter 88: x = [2.61639899 0.37989641], y = -0.03823607990977361, Best = -0.03823607990977361
Iter 89: x = [2.61974895 0.3811214 ], y = -0.03741503978328788, Best = -0.03741503978328788
Iter 90: x = [2.61310099 0.38207311], y = -0.03834622072439612, Best = -0.03741503978328788
Iter 91: x = [2.61647506 0.38267906], y = -0.03762354637445014, Best = -0.03741503978328788
Iter 92: x = [2.6137543 0.38335983], y = -0.038026338157956335, Best = -0.03741503978328788
Iter 93: x = [2.62853757 0.38229462], y = -0.03586566056291591, Best = -0.03586566056291591
Iter 94: x = [2.62833764 0.38413184], y = -0.035393156146423285, Best = -0.035393156146423285
Iter 95: x = [2.6313213 0.38518116], y = -0.034706652260212675, Best = -0.034706652260212675
Iter 96: x = [2.63673661 0.38732672], y = -0.03342448614539346, Best = -0.03342448614539346
Iter 97: x = [2.64545041 0.3889291 ], y = -0.031883181347474886, Best = -0.031883181347474886
Iter 98: x = [2.6613657 0.39260384], y = -0.02912120260307533, Best = -0.02912120260307533
Iter 99: x = [2.67690545 0.40016066], y = -0.02548798613873108, Best = -0.02548798613873108
Iter 100: x = [2.71157391 0.4055991 ], y = -0.021633928244593894, Best = -0.021633928244593894
Iter 101: x = [2.73893436 0.41331611], y = -0.01818075587574117, Best = -0.01818075587574117
Iter 102: x = [2.76928389 0.42146499], y = -0.015153295314412292, Best = -0.015153295314412292
Iter 103: x = [2.79111615 0.42659497], y = -0.013746112685090068, Best = -0.013746112685090068
Iter 104: x = [2.80449805 0.43123934], y = -0.012189971538547858, Best = -0.012189971538547858
Iter 105: x = [2.81626074 0.43235531], y = -0.012635525917586495, Best = -0.012189971538547858
Iter 106: x = [2.81206262 0.43313055], y = -0.01175766925941466, Best = -0.01175766925941466
Iter 107: x = [2.82604893 0.43465748], y = -0.012279935067206867, Best = -0.01175766925941466
Iter 108: x = [2.82574734 0.43576819], y = -0.01153450297370113, Best = -0.01153450297370113
Iter 109: x = [2.82277347 0.43389066], y = -0.012390032246291639, Best = -0.01153450297370113
Iter 110: x = [2.81730369 0.43273485], y = -0.012509538111509745, Best = -0.01153450297370113
Iter 111: x = [2.82665891 0.43414353], y = -0.012701638890714727, Best = -0.01153450297370113
Iter 112: x = [2.83767763 0.43739193], y = -0.01193281616604846, Best = -0.01153450297370113
Iter 113: x = [2.82726794 0.43477305], y = -0.012353680815337184, Best = -0.01153450297370113
Iter 114: x = [2.83706038 0.43372371], y = -0.014579681817161265, Best = -0.01153450297370113
Iter 115: x = [2.81399489 0.43293162], y = -0.0120490117352694, Best = -0.01153450297370113
Iter 116: x = [2.82358294 0.43412057], y = -0.012335840565402115, Best = -0.01153450297370113
Iter 117: x = [2.81277472 0.43239172], y = -0.012252859500843875, Best = -0.01153450297370113
Iter 118: x = [2.8103593 0.43051053], y = -0.0131653360210775, Best = -0.01153450297370113
Iter 119: x = [2.82484102 0.43558352], y = -0.0115502597719194, Best = -0.01153450297370113
Iter 120: x = [2.82031538 0.43278322], y = -0.012824727790769912, Best = -0.01153450297370113
Iter 121: x = [2.82935036 0.43547723], y = -0.012144296188742467, Best = -0.01153450297370113
Iter 122: x = [2.83047176 0.43656758], y = -0.011567294204675335, Best = -0.01153450297370113
Iter 123: x = [2.8242902 0.43445937], y = -0.012199021403649379, Best = -0.01153450297370113
Iter 124: x = [2.82546606 0.43464455], y = -0.01221798407004494, Best = -0.01153450297370113
Iter 125: x = [2.75390819 0.41967335], y = -0.015565133346141517, Best = -0.01153450297370113
Iter 126: x = [2.81743265 0.43365839], y = -0.011951998530680569, Best = -0.01153450297370113
Iter 127: x = [2.81744748 0.43379662], y = -0.011870317933430775, Best = -0.01153450297370113
Iter 128: x = [2.82643135 0.43498626], y = -0.012110370450982535, Best = -0.01153450297370113
Iter 129: x = [2.82936598 0.43524773], y = -0.012300190436620286, Best = -0.01153450297370113
Iter 130: x = [2.82667899 0.4348612 ], y = -0.01222237866723448, Best = -0.01153450297370113
Iter 131: x = [2.82715867 0.43508052], y = -0.012136390628676046, Best = -0.01153450297370113
Iter 132: x = [2.83995685 0.43746788], y = -0.012197290674562874, Best = -0.01153450297370113
Iter 133: x = [2.82502457 0.4347007 ], y = -0.012129004698662696, Best = -0.01153450297370113
Iter 134: x = [2.81926461 0.43214716], y = -0.013121382522752629, Best = -0.01153450297370113
Iter 135: x = [2.83299843 0.43668067], y = -0.011801928837520483, Best = -0.01153450297370113
Iter 136: x = [2.82710357 0.43508359], y = -0.0121276909935151, Best = -0.01153450297370113
Iter 137: x = [2.82555829 0.43531029], y = -0.01180027853779025, Best = -0.01153450297370113
Iter 138: x = [2.81915734 0.43438105], y = -0.011690621406422503, Best = -0.01153450297370113
Iter 139: x = [2.8094572 0.43333774], y = -0.011433396525451367, Best = -0.011433396525451367
Iter 140: x = [2.8197034 0.43451222], y = -0.011666699897677192, Best = -0.011433396525451367
Iter 141: x = [2.81664381 0.43324983], y = -0.012119789450024841, Best = -0.011433396525451367
Iter 142: x = [2.81825255 0.43324158], y = -0.012293921346483586, Best = -0.011433396525451367
Iter 143: x = [2.82537503 0.4355309 ], y = -0.011641156420260972, Best = -0.011433396525451367
Iter 144: x = [2.82561643 0.43353707], y = -0.012982538223115557, Best = -0.011433396525451367
Iter 145: x = [2.82083618 0.43025525], y = -0.014680518890423294, Best = -0.011433396525451367
Iter 146: x = [2.83052872 0.43243048], y = -0.014516989187375432, Best = -0.011433396525451367
Iter 147: x = [2.81795729 0.43426724], y = -0.011640543604890937, Best = -0.011433396525451367
Iter 148: x = [2.81693122 0.43223881], y = -0.012784990635717738, Best = -0.011433396525451367
Iter 149: x = [2.82203178 0.43474476], y = -0.011766000028479422, Best = -0.011433396525451367
Iter 150: x = [2.83248172 0.43667212], y = -0.01174289326111282, Best = -0.011433396525451367
from matplotlib.animation import FuncAnimation
from mpl_toolkits.mplot3d import Axes3D # 导入3D工具
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
from scipy.optimize import basinhopping, Bounds
from functools import partial
# 设置随机种子以保证可重复性
np.random.seed(0)
# ========== 定义 Beale 函数（补充） ==========
def beale(x):
x1, x2 = x[0], x[1]
term1 = (1.5 - x1 + x1*x2) ** 2
term2 = (2.25 - x1 + x1*x2**2) ** 2
term3 = (2.625 - x1 + x1*x2**3) ** 2
return term1 + term2 + term3
def noisy_neg_beale(x):
noise = np.random.random() - 0.5
return -beale(x) + 0.1 * noise
# ========== 初始化训练数据 ==========
n_init = 5
X_init = 6 * np.random.rand(n_init, 2) - 3
y_init = np.array([noisy_neg_beale(x) for x in X_init])
X_train = X_init.copy()
y_train = y_init.copy()
# ========== 高斯过程模型 ==========
kernel = C(1.0, (1e-3, 1e3)) * RBF(1.0, (1e-2, 1e2))
gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10, alpha=1e-6)
# ========== UCB 采集函数 ==========
beta = 1.0
def ucb(x, model, beta):
x = np.atleast_2d(x)
mu, std = model.predict(x, return_std=True)
return (mu + np.sqrt(beta) * std).flatten() # 返回一维数组
# ========== 动画参数 ==========
n_iter = 200 # 总迭代次数
grid_res = 50 # 网格分辨率
x1_grid = np.linspace(-3.1, 3.1, grid_res)
x2_grid = np.linspace(-3.1, 3.1, grid_res)
X1, X2 = np.meshgrid(x1_grid, x2_grid)
X_grid = np.c_[X1.ravel(), X2.ravel()]
# 创建3D图形
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
# 设置固定视角
ax.view_init(elev=30, azim=-60)
# 定义更新函数
def update(frame):
global X_train, y_train, gpr
# 如果是第0帧，直接使用初始数据；否则执行一次贝叶斯优化迭代并更新数据
if frame > 0:
# 拟合当前 GP
gpr.fit(X_train, y_train)
# 最大化 UCB 获取下一个采样点
def objective(x):
return -ucb(x, gpr, beta)
minimizer_kwargs = {
"method": "L-BFGS-B",
"bounds": [(-3.1, 3.1), (-3.1, 3.1)],
"tol": 1e-7
}
x0 = np.random.uniform(-3.1, 3.1, size=2)
result = basinhopping(objective, x0,
minimizer_kwargs=minimizer_kwargs,
niter=50, stepsize=0.5, T=1.0)
x_next = result.x
# 评估真实函数
y_next = noisy_neg_beale(x_next)
# 更新数据集
X_train = np.vstack([X_train, x_next])
y_train = np.append(y_train, y_next)
# 重新拟合 GP（使用最新数据）
gpr.fit(X_train, y_train)
# 计算当前 UCB 在网格上的值（用于曲面）
ucb_vals = ucb(X_grid, gpr, beta).reshape(X1.shape)
# 清除之前的绘图
ax.clear()
# 绘制 UCB 曲面
surf = ax.plot_surface(X1, X2, ucb_vals, cmap='viridis', alpha=0.8, linewidth=0, antialiased=True)
# 计算每个训练点的 UCB 值（用于点的高度）
ucb_train = ucb(X_train, gpr, beta) # 形状 (N,)
# 绘制所有采样点（红色，落在曲面上）
ax.scatter(X_train[:, 0], X_train[:, 1], ucb_train,
color='red', s=40, label='Sampled points', depthshade=False)
# 标记当前最佳点（使得 -beale 最大的点，即 y_train 最大值）
best_idx = np.argmax(y_train)
best_x = X_train[best_idx]
best_y = y_train[best_idx]
best_ucb = ucb_train[best_idx] # 该点的 UCB 值作为 z 坐标
ax.scatter(best_x[0], best_x[1], best_ucb,
color='yellow', edgecolors='black', s=150, marker='*',
label=f'Best: {best_y:.3f}', depthshade=False)
# 设置坐标轴标签和标题
ax.set_title(f'Iteration {frame}/{n_iter} (Total points: {len(X_train)})')
ax.set_xlabel('x1')
ax.set_ylabel('x2')
ax.set_zlabel('UCB')
ax.set_xlim(-3.1, 3.1)
ax.set_ylim(-3.1, 3.1)
# 根据 UCB 值的范围自动设置 z 轴范围（可选）
ax.set_zlim(ucb_vals.min(), ucb_vals.max())
ax.legend(loc='upper left', bbox_to_anchor=(0.02, 0.98))
# 重新设置视角（因为 clear 会重置）
ax.view_init(elev=30, azim=-60)
return []
# 创建动画
ani = FuncAnimation(fig, update, frames=n_iter+1, interval=200, repeat=False)
# 若需保存为 MP4（需 ffmpeg），取消下面注释
ani.save('bayesian_optimization_beale_3d.mp4', writer='ffmpeg', fps=8)
plt.show()e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:440: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__length_scale is close to the specified lower bound 0.01. Decreasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 3 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 11 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 19 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 18 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 11 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 7 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 19 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 19 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 8 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 15 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 13 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 16 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 16 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 19 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 24 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 11 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 6 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 8 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 5 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 5 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 4 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 25 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 4 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 15 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 20 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 19 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 3 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 18 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 15 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 9 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 24 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 19 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 17 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 6 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 18 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 14 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 6 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 13 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 17 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 13 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 17 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 5 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 23 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 12 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 5 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 21 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 14 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 2 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 17 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 4 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 28 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 21 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 18 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 0 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 5 iteration(s) (status=2):
ABNORMAL:
You might also want to scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
_check_optimize_result("lbfgs", opt_res)
e:\Projects\papers-reproduce\Add-GP-UCB\.venv\Lib\site-packages\sklearn\gaussian_process\kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.
warnings.warn(
