保存（泡菜）Scipy KDE答案

【问题标题】：Save (pickle) Scipy KDE保存（泡菜）Scipy KDE
【发布时间】：2018-12-05 18:10:34
【问题描述】：

如何腌制或保存 scipy kde 以供以后使用？

import scipy.stats as scs
from sklearn.externals import joblib

kde = scs.gaussian_kde(data, bw_method=.15)
joblib.dump(kde, 'test.pkl')

我在上面尝试并收到此错误：

PicklingError: Can't pickle <function gaussian_kde.set_bandwidth.<locals>.<lambda> at 0x1a5b6fb7b8>: it's not found as scipy.stats.kde.gaussian_kde.set_bandwidth.<locals>.<lambda>

【问题讨论】：

标签： python scikit-learn scipy

【解决方案1】：

看起来 joblib 在使用 set_bandwith 方法时遇到了问题，我猜是因为方法中的 lambda 函数 -- pickling lambdas has been discussed here。

with open('test.pkl', 'wb') as fo:  
    joblib.dump(lambda x,y: x+y, fo)

PicklingError: Can't pickle <function <lambda> at 0x7ff89495d598>: it's not found as __main__.<lambda>

cloudpickle 和 dill 据我所知都可以工作：

import cloudpickle
import dill

with open('test.cp.pkl', 'wb') as f:
    cloudpickle.dump(kde, f)  

with open('test.dill.pkl', 'wb') as f:
    dill.dump(kde, f)

with open('test.cp.pkl', 'rb') as f:
    kde_cp = cloudpickle.load(f)

with open('test.dill.pkl', 'rb') as f:
    kde_dill = dill.load(f)

检查一些数据：

import numpy as np

print(np.array_equal(kde.dataset, kde_cp.dataset))
True
print(np.array_equal(kde.dataset, kde_dill.dataset))
True
print(np.array_equal(kde_cp.dataset, kde_dill.dataset))
True

kde.pdf(10) == kde_cp.pdf(10) == kde_dill.pdf(10)
array([ True])

【讨论】：