semvec/pypi-packages huggingface dataset:
braceexpand 0.1.7
Bash-style brace expansion for Python
License: MIT

Bash-style brace expansion for Python
=====================================

|build-status-img| |PyPI|

Implements Brace Expansion as described in
`bash(1) <http://man7.org/linux/man-pages/man1/bash.1.html#EXPANSION>`__,
with the following limitations:

-  A pattern containing unbalanced braces will raise an
   ``UnbalancedBracesError`` exception. In bash, unbalanced braces will
   either be partly expanded or ignored.

-  A mixed-case character range like ``'{Z..a}'`` or ``'{a..Z}'`` will
   not include the characters :literal:`[]^_\`` between ``Z`` and ``a``.

``braceexpand`` is tested with Python 2.7, and 3.6+

Installation
------------

Install the ``braceexpand`` package from pypi:

::

   $ pip install braceexpand

Examples
--------

The ``braceexpand`` function returns an iterator over the expansions
generated from a pattern.

.. code:: python

   >>> from braceexpand import braceexpand

   # Integer range
   >>> list(braceexpand('item{1..3}'))
   ['item1', 'item2', 'item3']

   # Character range
   >>> list(braceexpand('{a..c}'))
   ['a', 'b', 'c']

   # Sequence
   >>> list(braceexpand('index.html{,.backup}'))
   ['index.html', 'index.html.backup']

   # Nested patterns
   >>> list(braceexpand('python{2.{5..7},3.{2,3}}'))
   ['python2.5', 'python2.6', 'python2.7', 'python3.2', 'python3.3']

   # Prefixing an integer with zero causes all numbers to be padded to
   # the same width.
   >>> list(braceexpand('{07..10}'))
   ['07', '08', '09', '10']

   # An optional increment can be specified for ranges.
   >>> list(braceexpand('{a..g..2}'))
   ['a', 'c', 'e', 'g']

   # Ranges can go in both directions.
   >>> list(braceexpand('{4..1}'))
   ['4', '3', '2', '1']

   # Numbers can be negative
   >>> list(braceexpand('{2..-1}'))
   ['2', '1', '0', '-1']

   # Unbalanced braces raise an exception.
   >>> list(braceexpand('{1{2,3}'))
   Traceback (most recent call last):
       ...
   UnbalancedBracesError: Unbalanced braces: '{1{2,3}'

   # By default, the backslash is the escape character.
   >>> list(braceexpand(r'{1\{2,3}'))
   ['1{2', '3']

   # Setting 'escape' to False disables backslash escaping.
   >>> list(braceexpand(r'\{1,2}', escape=False))
   ['\\1', '\\2']

License
-------

braceexpand is licensed under the MIT License. See the included file
``LICENSE`` for details.

.. |build-status-img| image:: https://travis-ci.org/trendels/braceexpand.svg
   :target: https://travis-ci.org/trendels/braceexpand
.. |PyPI| image:: https://img.shields.io/pypi/v/braceexpand
   :target: https://pypi.python.org/pypi/braceexpand
RecurrsiveFeatureSelector 1.1.6
Recurrsively selecting features for machine learning task.
License: MIT
Requires Python: >=3.6
Keywords: Recurrsive features selection machine learning data science computer science

# <img src="https://raw.githubusercontent.com/HindyDS/RecurrsiveFeatureSelector/main/logo/RFS2.png" height="200">

[![Open Source Love](https://badges.frapsoft.com/os/v2/open-source.svg?v=103)](https://github.com/ellerbrock/open-source-badges/)
[![PyPI version](https://badge.fury.io/gh/HindyDS%2FRecurrsiveFeatureSelector.svg)](https://pypi.org/project/RecurrsiveFeatureSelector/)
[![MIT Licence](https://badges.frapsoft.com/os/mit/mit.svg?v=103)](https://opensource.org/licenses/mit-license.php)

RecurrsiveFeatureSelector aims to select the best features in machine learning tasks according to corresponding score with other incredible packages like numpy, pandas and sklearn.

This package is inspired by: 
PyData DC 2016 | A Practical Guide to Dimensionality Reduction 
Vishal Patel
October 8, 2016

- **Examples:** https://github.com/HindyDS/RecurrsiveFeatureSelector/tree/main/examples
- **Email:** hindy888@hotmail.com
- **Source code:** https://github.com/HindyDS/RecurrsiveFeatureSelector/tree/main/RecurrsiveFeatureSelector
- **Bug reports:** https://github.com/HindyDS/RecurrsiveFeatureSelector/issues

It requires at least six arguments to run:

- model: machine learning model
- X (array): features space
- y (array): target
- cv (int): number of folds in a (Stratified)KFold
- scoring (str): see https://scikit-learn.org/stable/modules/model_evaluation.html 

- max_round (int): number of rounds that you wanted RFS to stop searching
- chances_to_fail (int): how many times RFS can fail to find better subset of features 
- jump_start (list): starting point for RFS to search, must be corresponds to the columns of X
- n_digit (int): Decimal places for scoring

If you have any ideas for this packge please don't hesitate to bring forward!