我尝试加载已保存的SVM模型时遇到此错误。我尝试卸载sklearn,NumPy和SciPy,然后再次重新安装最新版本(使用pip)。我仍然收到此错误。为什么?

In [1]: import sklearn; print sklearn.__version__
0.18.1
In [3]: import numpy; print numpy.__version__
1.11.2
In [5]: import scipy; print scipy.__version__
0.18.1
In [7]: import pandas; print pandas.__version__
0.19.1

In [10]: clf = joblib.load('model/trained_model.pkl')
---------------------------------------------------------------------------
RuntimeWarning                            Traceback (most recent call last)
<ipython-input-10-5e5db1331757> in <module>()
----> 1 clf = joblib.load('sentiment_classification/model/trained_model.pkl')

/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/numpy_pickle.pyc in load(filename, mmap_mode)
    573                     return load_compatibility(fobj)
    574
--> 575                 obj = _unpickle(fobj, filename, mmap_mode)
    576
    577     return obj

/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/numpy_pickle.pyc in _unpickle(fobj, filename, mmap_mode)
    505     obj = None
    506     try:
--> 507         obj = unpickler.load()
    508         if unpickler.compat_mode:
    509             warnings.warn("The file '%s' has been generated with a "

/usr/lib/python2.7/pickle.pyc in load(self)
    862             while 1:
    863                 key = read(1)
--> 864                 dispatch[key](self)
    865         except _Stop, stopinst:
    866             return stopinst.value

/usr/lib/python2.7/pickle.pyc in load_global(self)
   1094         module = self.readline()[:-1]
   1095         name = self.readline()[:-1]
-> 1096         klass = self.find_class(module, name)
   1097         self.append(klass)
   1098     dispatch[GLOBAL] = load_global

/usr/lib/python2.7/pickle.pyc in find_class(self, module, name)
   1128     def find_class(self, module, name):
   1129         # Subclasses may override this
-> 1130         __import__(module)
   1131         mod = sys.modules[module]
   1132         klass = getattr(mod, name)

/usr/local/lib/python2.7/dist-packages/sklearn/svm/__init__.py in <module>()
     11 # License: BSD 3 clause (C) INRIA 2010
     12
---> 13 from .classes import SVC, NuSVC, SVR, NuSVR, OneClassSVM, LinearSVC, \
     14         LinearSVR
     15 from .bounds import l1_min_c

/usr/local/lib/python2.7/dist-packages/sklearn/svm/classes.py in <module>()
      2 import numpy as np
      3
----> 4 from .base import _fit_liblinear, BaseSVC, BaseLibSVM
      5 from ..base import BaseEstimator, RegressorMixin
      6 from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \

/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py in <module>()
      6 from abc import ABCMeta, abstractmethod
      7
----> 8 from . import libsvm, liblinear
      9 from . import libsvm_sparse
     10 from ..base import BaseEstimator, ClassifierMixin

__init__.pxd in init sklearn.svm.libsvm (sklearn/svm/libsvm.c:10207)()

RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 80

更新:好,通过遵循here
pip uninstall -y scipy scikit-learn
pip install --no-binary scipy scikit-learn

该错误现在消失了,尽管我仍然不知道为什么会首先发生该错误...

最佳答案

根据MAINT: silence Cython warnings about changes dtype/ufunc size. - numpy/numpy:



支票由Cython插入(因此,任何用它编译的模块中都存在)。

长话短说,这些警告在numpy 的特定情况下应该是良性的,并且这些消息自numpy 1.8 (此提交进入的分支)以来就被过滤掉了。而 scikit-learn 0.18.1 is compiled against numpy 1.6.1

要自己过滤这些警告,您可以执行相同的as the patch does:

import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")

当然,对于,如果您有balls工具,则可以使用numpy¹而不是将本地的pip install --no-binary :all: 重新编译为源代码中所有受影响的模块。

更长的故事:补丁的支持者claims应该没有特别针对numpy的风险,并且第3方软件包是有意针对较早版本构建的:



结果是Cython的devs agreed to trust the numpy team with maintaining binary compatibility by hand,因此我们可以期望使用具有破坏性的ABI更改的版本会产生特制的异常或某些其他显式的显示停止器。

¹以前可用的--no-use-wheel选项已被删除since pip 10.0.0

08-27 21:47
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