教你如何在Windows10环境下顺利安装minepy并成功运行！_no module named 'minepy

在学习使用sklearn做单机特征工程这篇文章时，发现在计算互信息时from minepy import MINE代码运行出错ModuleNotFoundError: No module named 'minepy'

尝试解决错误第一步

根据ModuleNotFoundError: No module named 'minepy'提示，缺少minepy模块，于是到Anaconda环境下执行命令pip install minepy：

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting minepy
  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/84/92/84b624219daa189a44a06641a165e1099ec9a2c8e2dff24f748025d798e2/minepy-1.2.6.tar.gz (496 kB)
  Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy>=1.3.0 in f:\anaconda\envs\sklearn-env\lib\site-packages (from minepy) (1.23.0)
Building wheels for collected packages: minepy
  Building wheel for minepy (setup.py) ... error
  error: subprocess-exited-with-error

  × python setup.py bdist_wheel did not run successfully.
  │ exit code: 1
  ╰─> [10 lines of output]
      running bdist_wheel
      running build
      running build_py
      creating build
      creating build\lib.win-amd64-cpython-39
      creating build\lib.win-amd64-cpython-39\minepy
      copying minepy\__init__.py -> build\lib.win-amd64-cpython-39\minepy
      running build_ext
      building 'minepy.mine' extension
      error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building wheel for minepy
  Running setup.py clean for minepy
Failed to build minepy
Installing collected packages: minepy
  Running setup.py install for minepy ... error
  error: subprocess-exited-with-error

  × Running setup.py install for minepy did not run successfully.
  │ exit code: 1
  ╰─> [12 lines of output]
      running install
      F:\anaconda\envs\sklearn-env\lib\site-packages\setuptools\command\install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
        warnings.warn(
      running build
      running build_py
      creating build
      creating build\lib.win-amd64-cpython-39
      creating build\lib.win-amd64-cpython-39\minepy
      copying minepy\__init__.py -> build\lib.win-amd64-cpython-39\minepy
      running build_ext
      building 'minepy.mine' extension
      error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: legacy-install-failure

× Encountered error while trying to install package.
╰─> minepy

note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.
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安装失败！

尝试解决错误第二步

进入minepy的官网：

https://www.lfd.uci.edu/~gohlke/pythonlibs/

这个网站包含了很多Python扩展包的非官方Windows二进制文件，还真有minepy：

结合自己的Python版本，我下载的是：

minepy-1.2.6-cp39-cp39-win_amd64.whl
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把它下载下来后，我把它拷贝到了：

F:\anaconda\envs\sklearn-env\Scripts
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文件夹里面。

在Anaconda的Prompt中直接用命令：

pip install F:\anaconda\envs\sklearn-env\Scripts\minepy-1.2.6-cp39-cp39-win_amd64.whl
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最后的结果为：

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing f:\anaconda\envs\sklearn-env\scripts\minepy-1.2.6-cp39-cp39-win_amd64.whl
Requirement already satisfied: numpy>=1.3.0 in f:\anaconda\envs\sklearn-env\lib\site-packages (from minepy==1.2.6) (1.23.0)
Installing collected packages: minepy
Successfully installed minepy-1.2.6
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安装成功！

测试

我们常用的代码如下：

from sklearn.feature_selection import SelectKBest
from minepy import MINE
from sklearn.datasets import load_iris
iris=load_iris()
# 由于MINE的设计不是函数式的，定义mic方法将其为函数式的，返回一个二元组，二元组的第2项设置成固定的P值0.5
def mic(x, y):
    m = MINE()
    m.compute_score(x, y)
    return (m.mic(), 0.5)
#选择K个最好的特征，返回特征选择后的数据
SelectKBest(lambda X, Y: np.array(map(lambda x:mic(x, Y), X.T)).T, k=2).fit_transform(iris.data,iris.target)
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我们跑出来的结果错误：

float() argument must be a string or a number, not 'map'
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解决办法：

将获取到的map值放入list列表中，然后再使用。

即：

SelectKBest(lambda X, Y: np.array(list(map(lambda x:mic(x, Y), X.T))).T[0], k=2).fit_transform(iris.data,iris.target)
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我们的运行结果为：

运行成功，全部搞定！