python - Scikit 在使用 fit() 函数时学习 GaussianProcessClassifier 内存错误

我有 X_train 和 y_train 作为 2 numpy.ndarrays 大小分别为 (32561, 108) 和 (32561,)。

每次我调用适合我的 GaussianProcessClassifier 时，我都会收到一个内存错误。

>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.gaussian_process import GaussianProcessClassifier
>>> from sklearn.gaussian_process.kernels import RBF
>>> X_train.shape
(32561, 108)
>>> y_train.shape
(32561,)
 >>> gp_opt = GaussianProcessClassifier(kernel=1.0 * RBF(length_scale=1.0))
>>> gp_opt.fit(X_train,y_train)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/retsim/.local/lib/python2.7/site-packages/sklearn/gaussian_process/gpc.py", line 613, in fit
    self.base_estimator_.fit(X, y)
  File "/home/retsim/.local/lib/python2.7/site-packages/sklearn/gaussian_process/gpc.py", line 209, in fit
    self.kernel_.bounds)]
  File "/home/retsim/.local/lib/python2.7/site-packages/sklearn/gaussian_process/gpc.py", line 427, in _constrained_optimization
    fmin_l_bfgs_b(obj_func, initial_theta, bounds=bounds)
  File "/home/retsim/anaconda2/lib/python2.7/site-packages/scipy/optimize/lbfgsb.py", line 199, in fmin_l_bfgs_b
    **opts)
  File "/home/retsim/anaconda2/lib/python2.7/site-packages/scipy/optimize/lbfgsb.py", line 335, in _minimize_lbfgsb
    f, g = func_and_grad(x)
  File "/home/retsim/anaconda2/lib/python2.7/site-packages/scipy/optimize/lbfgsb.py", line 285, in func_and_grad
    f = fun(x, *args)
  File "/home/retsim/anaconda2/lib/python2.7/site-packages/scipy/optimize/optimize.py", line 292, in function_wrapper
    return function(*(wrapper_args + args))
  File "/home/retsim/anaconda2/lib/python2.7/site-packages/scipy/optimize/optimize.py", line 63, in __call__
    fg = self.fun(x, *args)
  File "/home/retsim/.local/lib/python2.7/site-packages/sklearn/gaussian_process/gpc.py", line 201, in obj_func
    theta, eval_gradient=True)
  File "/home/retsim/.local/lib/python2.7/site-packages/sklearn/gaussian_process/gpc.py", line 338, in log_marginal_likelihood
    K, K_gradient = kernel(self.X_train_, eval_gradient=True)
  File "/home/retsim/.local/lib/python2.7/site-packages/sklearn/gaussian_process/kernels.py", line 753, in __call__
    K1, K1_gradient = self.k1(X, Y, eval_gradient=True)
  File "/home/retsim/.local/lib/python2.7/site-packages/sklearn/gaussian_process/kernels.py", line 1002, in __call__
    K = self.constant_value * np.ones((X.shape[0], Y.shape[0]))
  File "/home/retsim/.local/lib/python2.7/site-packages/numpy/core/numeric.py", line 188, in ones
    a = empty(shape, dtype, order)
MemoryError
>>>

为什么我会收到此错误，我该如何解决？

最佳答案

在 400 of gpc.py 线上，您正在使用的分类器的实现，创建了一个大小为 (N, N) 的矩阵，其中 N 是观察的数量。所以代码试图创建一个形状为 (32561, 32561) 的矩阵。这显然会导致一些问题，因为该矩阵有超过 10 亿个元素。

至于为什么这样做，我真的不知道 scikit-learn 的实现，但总的来说，高斯过程需要估计整个输入空间的协方差矩阵，这就是为什么如果你有高维数据，它们不是那么好. (文档说“高维”指的不仅仅是几十个。)

我对如何修复它的唯一建议是分批工作。 Scikit-learn 可能有一些实用程序可以为您生成批处理，或者您可以手动生成。

关于python - Scikit 在使用 fit() 函数时学习 GaussianProcessClassifier 内存错误，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/49524761/