I am doing multilabel classification, where I try to predict correct labels for each document and here is my code: mlb = MultiLabelBinarizer() X = dataframe['body'].values y = mlb.fit_transform(dataframe['tag'].values) classifier = Pipeline([ ('vectorizer', CountVectorizer(lowercase=True, stop_words='english', max_df =
我正在做多标签分类，我试图预测每个文档的正确标签，这里是我的代码： mlb = MultiLabelBinarizer() X = dataframe['body'].values y = mlb.fit_transform(dataframe['tag'].values) classifier = Pipeline([ ('vectorizer', CountVectorizer(lowercase=True, stop_words='english', max_df = 0.8, min_df
While I've written unit tests for most of the code I've done, I only recently got my hands on a copy of TDD by example by Kent Beck. I have always regretted certain design decisions I made since they prevented the application from being 'testable'. I read through the book and while some of it looks alien, I felt that I could manage it and decided to try it out on my current proj
虽然我为大部分代码编写了单元测试，但最近我只通过Kent Beck举例说明了TDD的副本。 我一直对我做出的某些设计决定感到后悔，因为它们阻止了应用程序的'可测试'。 我读了这本书，虽然有些看起来很陌生，但我觉得我可以管理它，并决定尝试一下我当前的项目，这个项目基本上是一个客户端/服务器系统，两个部分通过这个系统进行通信。 USB。 一个在小工具上，另一个在主机上。 该应用程序是在Python中。 我开始了，
I have a web-application. As part of this, I need users of the app to be able to write (or copy and paste) very simple scripts to run against their data. The scripts really can be very simple, and performance is only the most minor issue. And example of the sophistication of script I mean are something like: ratio = 1.2345678 minimum = 10 def convert(money) return money * ratio end if
我有一个网络应用程序。 作为其中的一部分，我需要应用程序的用户能够编写（或复制和粘贴）非常简单的脚本来针对他们的数据运行。 脚本确实可以非常简单，而且性能只是最小的问题。 我的意思是脚本的复杂性的例子如下所示： ratio = 1.2345678 minimum = 10 def convert(money) return money * ratio end if price < minimum cost = convert(minimum) else cost = convert(price) end 价格和成本是一个全
Possible Duplicate: Any AOP support library for Python? I am familiar with the AspectJ extension for the Java language. I want to know if there is such a thing for Python. Don't get me wrong, I do not mean a library but a language extension like AspectJ is to Java. Python does not need something like a "language extension" for being able to work in an Aspect Oriented way.
可能重复： 任何Python的AOP支持库？ 我熟悉Java语言的AspectJ扩展。 我想知道Python是否有这样的事情。 不要误解我的意思，我不是指一个库，而是像AspectJ这样的语言扩展对Java。 Python不需要像“语言扩展”那样能够以面向方面的方式工作。 这仅仅是由于Python本身的动态机制。 谷歌搜索将产生一些项目 - 尽管看起来只是像库，但它是Python所需要的。 我没有这样做 - 这是事实，你可以反省类和方法，并在运行时改
What can we put in a setup.py file to prevent pip from collecting and attempting to install a package when using an unsupported Python version? For example magicstack is a project listed with the trove classifier: Programming Language :: Python :: 3 :: Only So I expect the following behaviour if pip --version is tied to python 2.7: $ pip install magicstack Collecting magicstack Could not f
在使用不支持的Python版本时，我们可以在setup.py文件中放入什么来防止pip收集并尝试安装软件包？ 例如magicstack是一个与magicstack分类器一起列出的项目： Programming Language :: Python :: 3 :: Only 所以我期望下面的行为，如果pip --version绑定到python 2.7： $ pip install magicstack Collecting magicstack Could not find a version that satisfies the requirement magicstack (from versions: ) No matching
I'm trying to install pytorch (http://pytorch.org/) on Linux, and according to my machine configuration, the command I should run is: pip install https://s3.amazonaws.com/pytorch/whl/torch-0.1.6.post17-cp27-cp27mu-linux_x86_64.whl On one machine (Linux distribution Slackware 14.1) the installation fails with error: torch-0.1.6.post17-cp27-cp27mu-linux_x86_64.whl is not a supported wheel o
我试图在Linux上安装pytorch（http://pytorch.org/），根据我的机器配置，我应该运行的命令是： pip install https://s3.amazonaws.com/pytorch/whl/torch-0.1.6.post17-cp27-cp27mu-linux_x86_64.whl 在一台机器上（Linux发行版Slackware 14.1），安装失败，错误： torch-0.1.6.post17-cp27-cp27mu-linux_x86_64.whl is not a supported wheel on this platform. ，而另一个（Ubuntu 15.10）则成功。 根据我的理解，这个
I have trained a Tensorflow Model, and now I want to export the "function" to use it in my python program. Is that possible, and if yes, how? Any help would be nice, could not find much in the documentation. (I dont want to save a session!) I have now stored the session as you suggested. I am loading it now like this: f = open('batches/batch_9.pkl', 'rb') input = pickle.load(f) f
我已经训练了一个Tensorflow模型，现在我想导出“函数”在我的python程序中使用它。 这是可能的，如果是的话，怎么样？ 任何帮助都会很好，在文档中找不到太多东西。 （我不想保存会话！） 我已经按照你的建议保存了会话。 我正在像这样加载它： f = open('batches/batch_9.pkl', 'rb') input = pickle.load(f) f.close() sess = tf.Session() saver = tf.train.Saver() saver.restore(sess, 'trained_network.ckpt') y_pre
I am trying to find (line and column position) all the references of a specific function declaration when parsing a C++ source file via libclang in Python. For example: #include <iostream> using namespace std; int addition (int a, int b) { int r; r=a+b; return r; } int main () { int z, q; z = addition (5,3); q = addition (5,5); cout << "The first result is " <<
我试图在Python中通过libclang解析C ++源文件时， 查找（行和列位置）特定函数声明的所有引用 。 例如： #include <iostream> using namespace std; int addition (int a, int b) { int r; r=a+b; return r; } int main () { int z, q; z = addition (5,3); q = addition (5,5); cout << "The first result is " << z; cout << "The second result is " << q; } 因此，对于
How to do this thing? I have two table Table1: -id -table2_id_1 -table2_id_2 Table2: -id -table3_id Table3: -id -table4_id -table5_id -table6_id Table4, Table5 and Table6: -id -name -date Main table is Table1 db(db.Table1).select() I need to join twice Table2(colums) in witch i need to join Table3(in each table2_id_1 and table2_id_2 field table3_id is equals), than join Table4,Table
如何做这件事？ 我有两张桌子 表格1： -id -table2_id_1 -table2_id_2 表2： -id -table3_id 表3： -id -table4_id -table5_id -table6_id 表4，表5和表6： -id -name -date 主表是Table1 db(db.Table1).select() 我需要连接两次Table2（colums）中的女巫我需要连接Table3（在每个table2_id_1和table2_id_2字段中table3_id是等于），比连接Table4，Table5，Table6 我不知道，如果我真的得到了你想要做的事情，但是
Just checking to see if anybody listening has already generated a sort-of-working set of mypy/typeshed stubs for Pandas. I naively ran stubgen over the local Pandas install which generated some errors. I can go with what I have to start with, but was hoping someone else had pushed the ball further. (Nothing obvious turned up on GitHub, though there is an old ticket for stubs.) I have not yet
只要检查一下，是否有人在听已经为大熊猫生成了一组mypy / typeshed存根。 我天真地在当地Pandas安装上运行stubgen，从而产生了一些错误。 我可以和我一起开始，但希望有人推动球进一步。 （没有什么明显的GitHub，虽然有一个旧票的存根。） 我还没有找到存根的熊猫，但有人为NumPy创建了一些：https：//github.com/machinalis/mypy-data/tree/master/numpy-mypy 一个临时选项可以根据mypy如何看待pandas对象来定义自定义