semvec/pypi-packages huggingface dataset:
XGBfnc 0.1.6
Flat, node classification model
License: MIT

* Written by Miguel Romero
* Last update: 07/07/21

Classification of nodes with structural properties
--------------------------------------------------

This package aims to evaluate whether the structural (topological)
properties of a network are useful for predicting node attributes of
nodes (i.e., node classification). It uses a combination of multiple
machine learning techniques, such as, XGBoost and the SMOTE sampling
technique.

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

The xgbfnc package can be install using pip, the requirements will be
automatically installed::

  python3 -m pip install xgbfnc

The source code and examples can be found in the
`GitHub repository <https://github.com/migueleci/XGBfnc>`_.

Documentation
-------------

Documentation of the package can be found `here <https://xgbfnc.readthedocs.io/en/latest/>`_.

Example
-------

The example illustrates how the algorithm can be used to check whether
the structural properties of the gene co-expression network improve the
performance of the prediction of gene functions for rice
(*Oryza sativa Japonica*). In this example, a gene co-expression network
gathered from ATTED II is used.

How to run the example?
^^^^^^^^^^^^^^^^^^^^^^^

The complete source code of the example can be found in the
`GitHub repository <https://github.com/migueleci/XGBfnc>`_. First, the *xgbfnc*
package need to be imported::

  from xgbfnc import xgbfnc
  from xgbfnc import data

After creating adjacency matrix ``adj`` for the network, the structural
properties are computed using the module `data` of the package::

  df, strc_cols = data.compute_strc_prop(adj)

This method returns a DataFrame with the structural properties of the network
and a list of the names of these properties (i.e., column names). After adding
the additional features of the network to the DataFrame, the XGBfnc module is
used to instantiate the XGBfnc class::

  test = XGBfnc()
  test.load_data(df, strc_cols, y, term, output_path='output')
  ans, pred, params = test.structural_test()

The data of the network is loaded using the ``load_data`` method. And the
structural test is execute using the ``structural_test`` method. The test
returns a boolean value which indicates whether the structural properties
help to improve the prediction performance, the prediction for the model
including the structural properties and its best parameters.

To run the example execute the following commands::

  cd test/
  python3 test_small.py
askfm.py 0.6.6
[No longer maintained] Ask.fm crawler
License: MIT

# askfm.py [![Test](https://github.com/utgwkk/askfm.py/actions/workflows/ci.yml/badge.svg)](https://github.com/utgwkk/askfm.py/actions/workflows/ci.yml) [![PyPI version](https://badge.fury.io/py/askfm.py.svg)](https://badge.fury.io/py/askfm.py)
Ask.fm crawler

## WARNING: this library is no longer maintained

askfm.py is no longer maintained. Please consider using other library if you want to crawl ask.fm.

## Install

```
$ pip install askfm.py
```

## Example

This program shows recent 25 answers of [ezoeryou](https://ask.fm/ezoeryou).

```python
import askfm
crawler = askfm.Crawler('ezoeryou')

for pair in crawler.crawl():
  print(pair.question, '->', pair.answer)
```

## Document

### `askfm.Crawler(username)`

Initializes ask.fm crawler.

* `username`: the username you want to crawl answers.

### `askfm.Crawler.crawl(page)`

Fetches at most 25 answers and returns iterator of list of answers.

* `page`: the offset page. The default value is `0`, this means crawing at the top of answers.

### `askfm.Crawler.pager(page)`

A generator that yields `askfm.Crawler.crawl(page)` continuously.
Uses like this:

```python
# offset_page is given
for page in crawler.pager(offset_page):
  for pair in page:
    print(pair.question, pair.answer)
```

* `page`: the offset page. The default value is `0`, this means crawing at the top of answers.

### `askfm.Answers`

An iterator of continuous `askfm.Pair` objects.

### `askfm.Pair`

An object representing for pair of question and answer.
You can get them with `askfm.Pair.question` and `askfm.Pair.answer`.
zkeys 0.2.0
Display Zsh key bindings in more human-readable formats
License: MIT
Requires Python: >=3.8

# zkeys

Display Zsh key bindings in more human-readable formats.

For example, print a table of key bindings, sorted by widget (i.e. function):

```text
% zkeys
...
^B        backward-char
^[[D      backward-char
^[OD      backward-char
^?        backward-delete-char
^H        backward-delete-char
^[^?      backward-kill-word
^[^H      backward-kill-word
^[B       backward-word
^[b       backward-word
^A        beginning-of-line
^[OH      beginning-of-line
^[C       capitalize-word
^[c       capitalize-word
^L        clear-screen
^[^L      clear-screen
...
```

Instead of:

```text
% bindkey
"^@" set-mark-command
"^A" beginning-of-line
"^B" backward-char
"^D" delete-char-or-list
"^E" end-of-line
"^F" forward-char
"^G" send-break
"^H" backward-delete-char
...
"^[B" backward-word
"^[C" capitalize-word
"^[D" kill-word
"^[F" forward-word
...
```

Run `zkeys -h` to see more sorting and grouping options.

By default, `zkeys` runs `bindkey -L` in a Zsh subprocess. It can also read from standard input, which is faster, and enables displaying the current shell configuration:

```sh
bindkey -L | zkeys
```

To learn about Zsh key bindings, see:

- <https://zsh.sourceforge.io/Doc/Release/Zsh-Line-Editor.html>
- <https://zsh.sourceforge.io/Doc/Release/User-Contributions.html#Widgets>

## Installing

Requires Python 3.8 or newer.

Install the latest release from [PyPI](https://pypi.org/project/zkeys/) using [pipx](https://pypa.github.io/pipx/) (recommended) or [pip](https://pip.pypa.io/en/stable/):

```sh
pipx install zkeys

python3 -m pip install -U zkeys
```

To install the latest version from GitHub, replace `zkeys` with `git+https://github.com/bhrutledge/zkeys.git`.