semvec/pypi-packages huggingface dataset:
WSGIProxy2 0.5.1
A WSGI Proxy with various http client backends
License: MIT
Keywords: wsgi proxy

Installation
============

With pip::

  $ pip install WSGIProxy2

Install optionnal backends::

  $ pip install requests urllib3


Usage
=====

Create a proxy::

  >>> from wsgiproxy import HostProxy
  >>> proxy = HostProxy(application_url)

Then use it. Here is an example with WebOb but you can use it like a classic
WSGI application::

  >>> from webob import Request
  >>> req = Request.blank('/form.html')
  >>> resp = req.get_response(proxy)
  >>> print(resp.text)
  <html>...
  ...</html>

The Proxy application accept some keyword arguments. Those arguments are passed
to the client during the process.

If no client as specified then python httplib is used. It's recommended to use
a more robust client able to manage a connection pool and stuff.

Use `urllib3 <http://pypi.python.org/pypi/urllib3>`_::

  >>> proxy = HostProxy(application_url, client='urllib3')

Use `requests <http://pypi.python.org/pypi/requests>`_. This client support response streaming::

  >>> proxy = HostProxy(application_url, client='requests')


Changes
=======

0.5.1 (2021-08-26)
------------------

- remove stale dep on six

- use github actions as CI


0.5.0 (2021-08-18)
------------------

- Drop support for python3.6 and bellow

0.4.6 (2019-02-22)
------------------

- PATCH added into (default) list of allowed methods


0.4.5 (2018-09-19)
------------------

- Allow to use URIs with no path


0.4.4 (2017-06-02)
------------------

- Clean up connection before returning result. This removes some
  ResourceWarnings when testing


0.4.3 (2017-02-17)
------------------

- Add OPTIONS to defaults allowed methods

- Drop restkit support

- Drop py26 support


0.4.2 (2014-12-20)
------------------

- Undo webob's unquoting to handle paths with percent quoted utf8 characters
  [Laurence Rowe]


0.4.1 (2013-12-21)
------------------

- Include README_fixt.py in release


0.4 (2013-12-21)
----------------

- fix tests.

- change the way requests iter response


0.3 (2013-09-12)
----------------

Make allowed_methods check optional

0.2
---

Return the data not gzip decoded when using request

0.1
---

Initial release
IRonTeste 0.0.6
A sample test package
License: MIT
Requires Python: >=2


<!-- README.md is generated from README.Rmd. Please edit that file -->

IRon
====

[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)

A Python Package for solving **I**mbalanced **R**egressi**on** tasks

Introduction
------------

Imbalanced domain learning has almost exclusively focused on solving classification tasks, where the objective is to predict cases labelled with a rare class accurately. Such a well-defined approach for regression tasks lacked due to two main factors. First, standard regression tasks assume that each value is equally important to the user. Second, standard evaluation metrics focus on assessing the performance of the model on the most common cases. This package contains methods to tackle imbalanced domain learning problems in regression tasks, where the objective is to predict extreme (rare) values.

The methods contained in this package are: - an automatic and non-parametric method to obtain such relevance functions, building on the concept of relevance proposed by Torgo and Ribeiro (2007); - visualisation tools; - suite of evaluation measures for optimisation/validation processes; - the squared-error relevance area measure, an evaluation metric tailored for imbalanced regression tasks.

Requirements
------------
For windows python32 is needed.

Installation
------------

<!-- You can install the last released version from [CRAN](https://CRAN.R-project.org) with: -->
<!--``` {r, eval=FALSE} -->
<!-- install.packages("IRon") -->
<!-- ``` -->
<!-- You can also install the latest development version from GitHub: -->
<!-- ```{r, eval=FALSE} -->
<!-- install.packages("devtools") -->
<!-- devtools::install_github("nunompmoniz/IRon") -->
<!-- ``` -->
To install from github use the following command lines:

``` r
pip install IRon  # You need to install this package!

```



References
----------

-   Rita Ribeiro and Nuno Moniz (2020). "Imbalanced Regression and Extreme Value Prediction". Machine Learning. 109, pp 1803â€“1835, Springer. https://doi.org/10.1007/s10994-020-05900-9
-   LuÃ­s Torgo and Rita Ribeiro (2007). "Utility-based regression". Proceedings of ECML/PKDD 2007, J. N. Kok, J. Koronacki, R. Lopez deMantaras, S. Matwin, D. Mladenic, and A. Skowron, Eds., pp. 597â€“604