我正在尝试使用 GaussianProcessRegressor
适合GP,我注意到我的超参数仍处于初始值 . 我在gpr.py中做了一些踩踏,但是无法找到确切的原因 . 使用初始值进行预测会产生零线 .
我的数据包含5400个样本,每个样本有12个特征,映射到单个输出变量 . 即使设计可能不是那么好,我仍然期待一些学习 .
所需文件:
import pandas as pd
import numpy as np
import time
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel,WhiteKernel
designmatrix = pd.read_csv('features.txt', index_col = 0)
y = pd.read_csv('output.txt', header=None, index_col = 0)
# The RBF kernel is a stationary kernel. It is also known as the “squared exponential” kernel.
# It is parameterized by a length-scale parameter length_scale>0, which can either be a scalar (isotropic variant of the kernel)
# or a vector with the same number of dimensions as the inputs X (anisotropic variant of the kernel).
#
# The ConstantKernel can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as
# part of a sum-kernel, where it modifies the mean of the Gaussian process.
#
# The main use-case of the White kernel is as part of a sum-kernel where it explains the noise-component of the signal.
# Tuning its parameter corresponds to estimating the noise-level: k(x_1, x_2) = noise_level if x_1 == x_2 else 0
kernel = ConstantKernel(0.1, (1e-23, 1e5)) *
RBF(0.1*np.ones(designmatrix.shape[1]), (1e-23, 1e10) ) + WhiteKernel(0.1, (1e-23, 1e5))
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=0)
print('Training')
t = time.time()
gp = gp.fit(designmatrix, y)
elapsed = time.time() - t
print(elapsed)
score = gp.score(designmatrix, y)
print(score)
print("initial params")
params = gp.get_params()
print(params)
print("learned kernel params")
print(gp.kernel_.get_params())
结果如下:
initial params
{'alpha': 1e-10, 'copy_X_train': True, 'kernel__k1': 1**2, 'kernel__k2': RBF(len
gth_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), 'kernel__k1__constant_value': 1
.0, 'kernel__k1__constant_value_bounds': (1e-05, 100000.0), 'kernel__k2__length_
scale': array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]), 'ke
rnel__k2__length_scale_bounds': (1e-05, 100000.0), 'kernel': 1**2 * RBF(length_s
cale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), 'n_restarts_optimizer': 0, 'normaliz
e_y': False, 'optimizer': 'fmin_l_bfgs_b', 'random_state': None}
learned kernel params
{'k1': 1**2, 'k2': RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), 'k1__
constant_value': 1.0, 'k1__constant_value_bounds': (1e-05, 100000.0), 'k2__lengt
h_scale': array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]), '
k2__length_scale_bounds': (1e-05, 100000.0)}
所以,内核参数不变......
-
有没有办法检查警告?
-
我做错了什么,或者有什么我可以检查的?
任何帮助将非常感激...
本
1 回答
NOT AN ANSWER (YET)
Begin Note
对于SO问题,数据太大,我们测试您的问题需要很长时间 . 我已将您的代码更改为仅包含每个文件的前600行 . 你粘贴在这里的方式代码也没有运行,我已经解决了这个问题 .
End Note
使用
python 3.6.4
,scikit-learn==0.19.1
和numpy==1.14.2
.正如您在
n_restarts_optimizer
的文档中看到的那样,如果要优化内核超参数,则需要将其大于0 .因此,在代码中将值从
0
更改为2
会产生以下输出:并输出:
你可以编辑你的问题,以便你的观察可以复制吗?