在本文中，我们将给您介绍关于为什么unittest不将numpy数组视为序列？的详细内容，并且为您解答为什么numpy不能用的相关问题，此外，我们还将为您提供关于AnacondaNumpy错误“Imp

在本文中，我们将给您介绍关于为什么 unittest 不将 numpy 数组视为序列？的详细内容，并且为您解答为什么numpy不能用的相关问题，此外，我们还将为您提供关于Anaconda Numpy 错误“Importing the Numpy C Extension Failed”是否有另一种解决方案、cvxpy 和 numpy 之间的版本冲突：“针对 API 版本 0xe 编译的模块，但此版本的 numpy 是 0xd”、Jupyter 中的 Numpy 在打印时出错（Python 版本 3.8.8）：TypeError: 'numpy.ndarray' object is not callable、numpy 协方差矩阵 numpy.cov的知识。

• 为什么 unittest 不将 numpy 数组视为序列？（为什么numpy不能用）
• Anaconda Numpy 错误“Importing the Numpy C Extension Failed”是否有另一种解决方案
• cvxpy 和 numpy 之间的版本冲突：“针对 API 版本 0xe 编译的模块，但此版本的 numpy 是 0xd”
• Jupyter 中的 Numpy 在打印时出错（Python 版本 3.8.8）：TypeError: 'numpy.ndarray' object is not callable
• numpy 协方差矩阵 numpy.cov

为什么 unittest 不将 numpy 数组视为序列？（为什么numpy不能用）

Numpy 数组与 Python 序列具有不同的相等语义；在 Numpy 中，相等是按元素计算的。例如：

>>> [1,2,3] == [1,3]
True

>>> np.array([1,3]) == [1,3]
array([ True,True,True])

这打破了 assertSequenceEqual 所做的假设，导致测试失败。

为了测试 numpy 数组的相等性，您应该更喜欢 numpy.testing.assert_array_equal 之类的实用程序：

>>> np.testing.assert_array_equal(np.array([1,3]),[1,3])

我可能对这个问题给予了更多的关注，但我很好奇断言是如何工作的。

（从另一个内置包的开发工作中，argparse 我知道文档从来都不是准确的。文档中无法捕获代码的细微差别。文档必须平衡对大多数用户的有用性，与更挑剔的用户的期望背道而驰。）

这是该方法的完整代码：

Signature: M.assertSequenceEqual(seq1,seq2,msg=None,seq_type=None)
Source:   
    def assertSequenceEqual(self,seq1,seq_type=None):
        """An equality assertion for ordered sequences (like lists and tuples).

        For the purposes of this function,a valid ordered sequence type is one
        which can be indexed,has a length,and has an equality operator.

        Args:
            seq1: The first sequence to compare.
            seq2: The second sequence to compare.
            seq_type: The expected datatype of the sequences,or None if no
                    datatype should be enforced.
            msg: Optional message to use on failure instead of a list of
                    differences.

对于此函数而言，有效的有序序列类型是 它可以被索引，有长度，并且有一个相等运算符。

ndarray 有一个相等运算符，但 numpy 不允许在 if 语句中使用它。

        """
        if seq_type is not None:
            seq_type_name = seq_type.__name__
            if not isinstance(seq1,seq_type):
                raise self.failureException('First sequence is not a %s: %s'
                                        % (seq_type_name,safe_repr(seq1)))
            if not isinstance(seq2,seq_type):
                raise self.failureException('Second sequence is not a %s: %s'
                                        % (seq_type_name,safe_repr(seq2)))
        else:
            seq_type_name = "sequence"

        differing = None
        try:
            len1 = len(seq1)
        except (TypeError,NotImplementedError):
            differing = 'First %s has no length.    Non-sequence?' % (
                    seq_type_name)

        if differing is None:
            if seq1 == seq2:
                return

这是 numpy 抛出错误的步骤。对于像 list 和 string 这样的内置序列类型，这是一个非常好的表达式。我想他们可以将它包装在 try/except ValueError 中。但是是由核心 python 来预测外部库失败的所有方式，还是库本身应该注意提供自己有用的测试？

其余代码尝试确定序列的不同之处，无论是长度还是元素值。

            differing = '%ss differ: %s != %s\n' % (
                    (seq_type_name.capitalize(),) +
                    _common_shorten_repr(seq1,seq2))

            for i in range(min(len1,len2)):
                try:
                    item1 = seq1[i]
                except (TypeError,IndexError,NotImplementedError):
                    differing += ('\nUnable to index element %d of first %s\n' %
                                 (i,seq_type_name))
                    break

                try:
                    item2 = seq2[i]
                except (TypeError,NotImplementedError):
                    differing += ('\nUnable to index element %d of second %s\n' %
                                 (i,seq_type_name))
                    break

                if item1 != item2:
                    differing += ('\nFirst differing element %d:\n%s\n%s\n' %
                                 ((i,) + _common_shorten_repr(item1,item2)))
                    break
            else:
                if (len1 == len2 and seq_type is None and
                    type(seq1) != type(seq2)):
                    # The sequences are the same,but have differing types.
                    return

            if len1 > len2:
                differing += ('\nFirst %s contains %d additional '
                             'elements.\n' % (seq_type_name,len1 - len2))
                try:
                    differing += ('First extra element %d:\n%s\n' %
                                  (len2,safe_repr(seq1[len2])))
                except (TypeError,NotImplementedError):
                    differing += ('Unable to index element %d '
                                  'of first %s\n' % (len2,seq_type_name))
            elif len1 < len2:
                differing += ('\nSecond %s contains %d additional '
                             'elements.\n' % (seq_type_name,len2 - len1))
                try:
                    differing += ('First extra element %d:\n%s\n' %
                                  (len1,safe_repr(seq2[len1])))
                except (TypeError,NotImplementedError):
                    differing += ('Unable to index element %d '
                                  'of second %s\n' % (len1,seq_type_name))
        standardMsg = differing
        diffMsg = '\n' + '\n'.join(
            difflib.ndiff(pprint.pformat(seq1).splitlines(),pprint.pformat(seq2).splitlines()))

        standardMsg = self._truncateMessage(standardMsg,diffMsg)
        msg = self._formatMessage(msg,standardMsg)
        self.fail(msg)
File:      /usr/lib/python3.8/unittest/case.py

差异诊断旨在处理列表等序列，具有 len 的对象和元素明智的迭代是有意义的。

带有列表的示例：

In [309]: M.assertSequenceEqual([1,3],[1.,2.,3.])
In [310]: M.assertSequenceEqual([1,4])
Traceback (most recent call last):
  File "<ipython-input-310-3cc2193995b9>",line 1,in <module>
    M.assertSequenceEqual([1,4])
  File "/usr/lib/python3.8/unittest/case.py",line 1100,in assertSequenceEqual
    self.fail(msg)
  File "/usr/lib/python3.8/unittest/case.py",line 753,in fail
    raise self.failureException(msg)
AssertionError: Sequences differ: [1,3] != [1.0,2.0,4]

First differing element 2:
3
4

- [1,3]
+ [1.0,4]

In [311]: M.assertSequenceEqual([1,3,0],4])
Traceback (most recent call last):
  File "<ipython-input-311-1a7c548c65ee>",0] != [1.0,4]

First differing element 2:
3
4

First sequence contains 1 additional elements.
First extra element 3:
0

- [1,0]
+ [1.0,4]

对于列表中的列表：

In [314]: M.assertSequenceEqual([[1,3]],[[1,4]])
Traceback (most recent call last):
  File "<ipython-input-314-e71a55865cae>",in <module>
    M.assertSequenceEqual([[1,4]])
  File "/usr/lib/python3.8/unittest/case.py",in fail
    raise self.failureException(msg)
AssertionError: Sequences differ: [[1,3]] != [[1,4]]

First differing element 0:
[1,3]
[1,4]

- [[1,3]]
?         ^

+ [[1,4]]
?         ^

在不深入细节的情况下，目前尚不清楚诊断步骤是否会更好地处理 numpy 数组。数组的 len 只是第一维的大小。

将其与 numpy 自己的测试人员报告的错误进行对比。当 unittest 提供更适合多维数组的测试器时，为什么要强制 numpy 使用数组？

In [319]: numpy.testing.assert_array_almost_equal([[1,4]])
Traceback (most recent call last):
  File "<ipython-input-319-9392a2140ddd>",in <module>
    numpy.testing.assert_array_almost_equal([[1,4]])
  File "/usr/local/lib/python3.8/dist-packages/numpy/testing/_private/utils.py",line 1042,in assert_array_almost_equal
    assert_array_compare(compare,x,y,err_msg=err_msg,verbose=verbose,File "/usr/local/lib/python3.8/dist-packages/numpy/testing/_private/utils.py",line 840,in assert_array_compare
    raise AssertionError(msg)
AssertionError: 
Arrays are not almost equal to 6 decimals

Mismatched elements: 1 / 3 (33.3%)
Max absolute difference: 1
Max relative difference: 0.25
 x: array([[1,3]])
 y: array([[1,4]])

虽然测试阵列可能很小，但生产阵列通常非常大。这种统计报告风格比 unittest 方法更详细的差异更有用。

注意 numpy 测试做了什么。我给了它列表，它转换为 numpy 数组。这是 numpy 中的常见做法。在开始时编写将输入转换为“标准”类型的代码要简单得多，而不是试图在整个代码中考虑不同的行为。就其价值而言，numpy 有一个非常好的 tolist 方法

In [322]: M.assertSequenceEqual([1,np.array([1.,3.]).tolist())

你忘记展示失败了！

In [1]: import unittest
   ...: unittest.TestCase().assertSequenceEqual([1,3.]))
Traceback (most recent call last):
  File "<ipython-input-1-fb3ef7cd88ef>",line 2,in <module>
    unittest.TestCase().assertSequenceEqual([1,3.]))
  File "/usr/lib/python3.8/unittest/case.py",line 1043,in assertSequenceEqual
    if seq1 == seq2:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

该方法使用 if seq1 == seq2:。对于 seq1 == seq2 结果是布尔数组，它没有特殊情况。这是一个简单的 if 语句。当在 Python 上下文中使用需要标量布尔值（例如 numpy）的数组（具有多个元素）时，if 会引发 ValueError。

numpy 有自己的测试工具，例如 https://numpy.org/doc/stable/reference/generated/numpy.testing.assert_array_equal.html

您的第一个数组是整数 dtype，第二个是浮点数。它们应该相等还是不相等？为什么？我们建议对浮动进行 allclose 测试。

单元素数组

In [46]: unittest.TestCase().assertSequenceEqual([1],np.array([1.]))
In [47]: unittest.TestCase().assertSequenceEqual([1],np.array([3.]))
Traceback (most recent call last):
  File "<ipython-input-47-049a26b28f34>",in <module>
    unittest.TestCase().assertSequenceEqual([1],np.array([3.]))
  File "/usr/lib/python3.8/unittest/case.py",in fail
    raise self.failureException(msg)
AssertionError: Sequences differ: [1] != array([3.])

First differing element 0:
1
3.0

- [1]
+ array([3.])

numpy 提供 numpy.testing.assert_array_almost_equal 和其他测试方法，用于 unittest（和其他测试框架）。 pandas 提供 pandas.testing.assert_series_equal 等。

因此，实现标准库支持的是第三方库，而不是相反。

assertSequenceEqual(first,second,seq_type=None)¶ 两个序列相等。如果提供了 seq_type，则首先 第二个必须是 seq_type 的实例，否则将引发故障。 如果序列不同，则构建错误消息 显示了两者之间的差异。这个方法不叫 直接通过 assertEqual() 实现，但用于实现 assertListEqual() 和 assertTupleEqual()。

还有 numpy 函数。

Anaconda Numpy 错误“Importing the Numpy C Extension Failed”是否有另一种解决方案

  \Traceback (most recent call last):
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\__init__.py",line 22,in <module>
from . import multiarray
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\multiarray.py",line 12,in <module>
from . import overrides
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\overrides.py",line 7,in <module>
from numpy.core._multiarray_umath import (
ImportError: DLL load Failed while importing _multiarray_umath: The specified module Could not be found.

During handling of the above exception,another exception occurred:

Traceback (most recent call last):
File "c:\API\citi-bike.py",line 4,in <module>
import numpy as np
File "C:\Conda\envs\gcp\lib\site-packages\numpy\__init__.py",line 150,in <module>
from . import core
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\__init__.py",line 48,in <module>
raise ImportError(msg)
ImportError:

IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!

Importing the numpy C-extensions Failed. This error can happen for
many reasons,often due to issues with your setup or how NumPy was
installed.

We have compiled some common reasons and troubleshooting tips at:

https://numpy.org/devdocs/user/troubleshooting-importerror.html

Please note and check the following:

* The Python version is: python3.9 from "C:\Conda\envs\gcp\python.exe"
* The NumPy version is: "1.21.1"

and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.

Original error was: DLL load Failed while importing _multiarray_umath: The specified module Could not be found.

RuntimeError: module compiled against API version 0xe but this version of numpy is 0xd
Traceback (most recent call last):
  File "test.py",line 1,in <module>
    import cvxpy
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\__init__.py",line 18,in <module>
    from cvxpy.atoms import *
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\__init__.py",line 20,in <module>
    from cvxpy.atoms.geo_mean import geo_mean
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\geo_mean.py",in <module>
    from cvxpy.utilities.power_tools import (fracify,decompose,approx_error,lower_bound,File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\utilities\power_tools.py",in <module>
    from cvxpy.atoms.affine.reshape import reshape
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\affine\reshape.py",in <module>
    from cvxpy.atoms.affine.hstack import hstack
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\affine\hstack.py",in <module>
    from cvxpy.atoms.affine.affine_atom import AffAtom
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\affine\affine_atom.py",line 22,in <module>
    from cvxpy.cvxcore.python import canonInterface
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\cvxcore\python\__init__.py",line 3,in <module>
    import _cvxcore
ImportError: numpy.core.multiarray Failed to import

cvxpy 
numpy~=1.19.2 # tensorflow 2.4.1 requires this version

import cvxpy

if __name__ == ''__main__'':
    pass

import numpy as np
    for i in range (5):
        n = np.random.rand ()
    print (n)

Results
0.6604903457995978
0.8236300859753154
0.16067650689842816
0.6967868357083673
0.4231597934445466

import numpy as np
    for i in range (5):
        n = np.random.rand ()
    print (n)

-------------------------------------------------- ------
TypeError Traceback (most recent call last)
<ipython-input-78-0c6a801b3ea9> in <module>
       2 for i in range (5):
       3 n = np.random.rand ()
---->  4 print (n)

       TypeError: ''numpy.ndarray'' object is not callable