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和
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
numpy
¹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
。