Anaconda 安装 ml_metrics package

http://www.cnblogs.com/klchang/p/5588930.html

ml_metrics is the Python implementation of Metrics implementations a library of various supervised machine learning evaluation metrics.

首先，打开 Anaconda Prompt,

按如下步骤操作

1、搜索 ml_metrics 包

[Anaconda2] C:\Users\klchang> anaconda search -t conda ml_metricsUsing anaconda-server api site https://api.anaconda.orgRun 'anaconda show <USER/PACKAGE>' to get more details:Packages:Name | Version | Package Types | Platforms------------------------- | ------ | --------------- | ---------------chdoig/ml_metrics | 0.1.3 | conda | osx-64: Machine Learning Evaluation Metricsdan_blanchard/ml_metrics | 0.1.3 | conda | linux-64: https://github.com/benhamner/Metrics/tree/master/Pythonm0nhawk/ml_metrics | 0.1.4 | conda | linux-64, win-32,win-64, linux-32, osx-64Found 3 packages

2、显示 ml_metrics 包的信息

[Anaconda2] C:\Users\klchang> anaconda show m0nhawk/ml_metricsUsing anaconda-server api site https://api.anaconda.orgName: ml_metricsSummary:Access: publicPackage Types: condaVersions:+ 0.1.3+ 0.1.4To install this package with conda run:conda install --channel https://conda.anaconda.org/m0nhawk ml_metrics

3、安装最新版本的ml_metrics 包

[Anaconda2] C:\Users\klchang>conda install --channel https://conda.anaconda.org/m0nhawk ml_metrics==0.1.4Fetching package metadata: ......Solving package specifications: ................Package plan for installation in environment E:\Users\klchang\Anaconda2:The following packages will be downloaded:package | build---------------------------|-----------------mkl-11.3.3 | 1 110.0 MB defaultsvs2008_runtime-9.00.30729.1| 1 1.2 MB defaultspython-2.7.11 | 4 23.1 MB defaultsconda-env-2.4.5 | py27_0 65 KB defaultsmenuinst-1.4.1 | py27_0 105 KB defaultsnumpy-1.11.0 | py27_1 3.0 MB defaultspycosat-0.6.1 | py27_1 83 KB defaultspytz-2016.4 | py27_0 171 KB defaultspyyaml-3.11 | py27_4 169 KB defaultsrequests-2.10.0 | py27_0 615 KB defaultssetuptools-21.2.1 | py27_0 763 KB defaultswheel-0.29.0 | py27_0 121 KB defaultsconda-4.0.7 | py27_0 228 KB defaultspip-8.1.1 | py27_1 1.5 MB defaultspython-dateutil-2.5.3 | py27_0 236 KB defaultspandas-0.18.1 | np111py27_0 7.0 MB defaultsml_metrics-0.1.4 | 0 31 KB m0nhawk------------------------------------------------------------Total: 148.4 MBThe following NEW packages will be INSTALLED:mkl: 11.3.3-1 defaultsml_metrics: 0.1.4-0 m0nhawkvs2008_runtime: 9.00.30729.1-1 defaultsThe following packages will be UPDATED:conda: 3.18.6-py27_0 defaults --> 4.0.7-py27_0 defaultsconda-env: 2.4.4-py27_2 defaults --> 2.4.5-py27_0 defaultsmenuinst: 1.2.1-py27_0 defaults --> 1.4.1-py27_0 defaultsnumpy: 1.10.1-py27_0 defaults --> 1.11.0-py27_1 defaultspandas: 0.17.0-np110py27_0 defaults --> 0.18.1-np111py27_0 defaultspip: 7.1.2-py27_0 defaults --> 8.1.1-py27_1 defaultspycosat: 0.6.1-py27_0 defaults --> 0.6.1-py27_1 defaultspython: 2.7.10-4 defaults --> 2.7.11-4 defaultspython-dateutil: 2.4.2-py27_0 defaults --> 2.5.3-py27_0 defaultspytz: 2015.6-py27_0 defaults --> 2016.4-py27_0 defaultspyyaml: 3.11-py27_2 defaults --> 3.11-py27_4 defaultsrequests: 2.8.1-py27_0 defaults --> 2.10.0-py27_0 defaultssetuptools: 18.5-py27_0 defaults --> 21.2.1-py27_0 defaultswheel: 0.26.0-py27_1 defaults --> 0.29.0-py27_0 defaultsProceed ([y]/n)? ymenuinst-1.4.1 100% |###############################| Time: 0:00:00 161.14 kB/sFetching packages ...mkl-11.3.3-1.t 100% |###############################| Time: 0:02:39 725.30 kB/svs2008_runtime 100% |###############################| Time: 0:00:02 424.65 kB/spython-2.7.11- 100% |###############################| Time: 0:00:24 984.44 kB/sconda-env-2.4. 100% |###############################| Time: 0:00:00 101.80 kB/snumpy-1.11.0-p 100% |###############################| Time: 0:00:05 580.68 kB/spycosat-0.6.1- 100% |###############################| Time: 0:00:00 97.22 kB/spytz-2016.4-py 100% |###############################| Time: 0:00:01 161.02 kB/spyyaml-3.11-py 100% |###############################| Time: 0:00:01 104.81 kB/srequests-2.10. 100% |###############################| Time: 0:00:03 180.66 kB/ssetuptools-21. 100% |###############################| Time: 0:00:02 293.96 kB/swheel-0.29.0-p 100% |###############################| Time: 0:00:01 109.30 kB/sconda-4.0.7-py 100% |###############################| Time: 0:00:01 142.15 kB/spip-8.1.1-py27 100% |###############################| Time: 0:00:05 307.28 kB/spython-dateuti 100% |###############################| Time: 0:00:01 160.14 kB/spandas-0.18.1- 100% |###############################| Time: 0:00:38 189.41 kB/sml_metrics-0.1 100% |###############################| Time: 0:00:00 45.44 kB/sExtracting packages ...[ COMPLETE ]|##################################################| 100%Unlinking packages ...[ COMPLETE ]|##################################################| 100%Linking packages ...[ COMPLETE ]|##################################################| 100%

 

4、测试 ml_metrics 包，以 apk，mapk度量函数为例，（apk为average precision@k的缩写, mapk为mean average precision@k的缩写）

[Anaconda2] C:\Users\klchang> pythonPython 2.7.11 |Anaconda 2.4.0 (64-bit)| (default, Feb 16 2016, 09:58:36) [MSC v.1500 64 bit (AMD64)] on win32Type "help", "copyright", "credits" or "license" for more information.Anaconda is brought to you by Continuum Analytics.Please check out: http://continuum.io/thanks and https://anaconda.org>>> import ml_metrics as metrics>>> actual = [1]>>> predicted = [1,2,3,4,5]>>> print 'Answer=%s predicted=%s' % (actual,predicted)Answer=[1] predicted=[1, 2, 3, 4, 5]>>> print 'AP@5 =', metrics.apk(actual,predicted,5)AP@5 = 1.0>>> predicted = [2,1,3,4,5]>>> print 'Answer=%s predicted=%s' % (actual, predicted)Answer=[1] predicted=[2, 1, 3, 4, 5]>>> print 'AP@5 =', metrics.apk(actual, predicted, 5)AP@5 = 0.5>>> predicted = [3,2,1,4,5]>>> print 'Answer=%s predicted=%s' % (actual,predicted)Answer=[1] predicted=[3, 2, 1, 4, 5]>>> print 'AP@5 =', metrics.apk(actual,predicted,5)AP@5 = 0.333333333333>>>>>> predicted = [4,2,3,1,5]>>> print 'Answer=%s predicted=%s' % (actual,predicted)Answer=[1] predicted=[4, 2, 3, 1, 5]>>> print 'AP@5 =', metrics.apk(actual,predicted,5)AP@5 = 0.25>>>>>> predicted = [2,3,4,5,1]>>> print 'Answer=%s predicted=%s' % (actual,predicted)Answer=[1] predicted=[2, 3, 4, 5, 1]>>> print 'AP@5 =', metrics.apk(actual,predicted,5)AP@5 = 0.2>>>>>> print 'MAP@5 = ', metrics.mapk([[1],[1],[1],[1],[1]],[[1,2,3,4,5],[2,1,3,4,5],[3,2,1,4,5],[4,2,3,1,5],[4,2,3,5,1]],5)MAP@5 = 0.456666666667