semvec/pypi-packages huggingface dataset:
musodium 1.0.0
Pythonic Music Player
License: MIT
Keywords: music,python,player,ytdl

# musodium
Pythonic Music Player.

# Why not just use lavalink?
Well musodium is mean't to be a direct ancestor to lavalink.
but faster and more productive. we also plan to use the full power
of yt-dlp to support as much websites as possible.
pypysodium 1.0.0
Pythonic Music Player
License: MIT
Keywords: music,python,player,ytdl

# pysodium
Pythonic Music Player.

# Why not just use lavalink?
Well pysodium is mean't to be a direct ancestor to lavalink.
but faster and more productive. we also plan to use the full power
of yt-dlp to support as much websites as possible.
adval 0.0.9
Adversarial validation for train-test datasets
License: MIT
Keywords: adversarial validation train-test-split data-sceince machine-learning

Adversarial Validator
==============================

The code is Python 3

What is Adversarial Validation?
The objective of any predictive modelling project is to create a model using the training data, 
and afterwards apply this model to the test data. 
However, for the best results it is essential that 
the training data is a representative sample of the data 
we intend to use it on (*i.e.* the test data), 
otherwise our model will, at best, under-perform, or at worst, be completely useless.   

***Adversarial Validation*** is a very clever and 
very simple way to let us know if our test data and our training data are similar; 
we combine our `train` and `test` data, 
labeling them with say a `0` for the training data and a `1` for the test data, 
mix them up, then see if we are able to correctly re-identify them using a binary classifier.

If we cannot correctly classify them, *i.e.* we obtain an area under the 
[receiver operating characteristic curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) (ROC) 
of 0.5 then they are indistinguishable and we are good to go.

However, if we can classify them (ROC > 0.5) then we have a problem, 
either with the whole dataset or more likely with some features in particular, 
which are probably from  different distributions in the test and train datasets.
If we have a problem, 
we can look at the feature that was most out of place. 
The problem may be that there were values that were only seen in, 
say, training data, but not in the test data. 
If the contribution to the ROC is very high from one feature, 
it may well be a good idea to remove that feature from the model.


Adversarial Validation to reduce overfitting
The key to avoid overfitting is to create a situation 
where the local cross-vlidation (CV) score is representative of the competition score. 
When we have a ROC of 0.5 then your local data is representative of the test data, 
thus your local CV score should now be representative of the Public LB score.

Procedure:

- drop the training data target column 
- label the `test` and `train` data with `0` and `1` (it doesn't really matter which is which)
- combine the training and test data into one big dataset
- perform the binary classification, for example using XGboost
- look at our AUC ROC score

Installation
------------------------

Fast install:

::

    pip install adval

Example on Mobile Price Classification Dataset
--------------------------------------------------------------------------------

.. code:: python

    from adval.validation import adVal 
    
    # In this dataset: 
    # target = "price_range"
    # 95 = Â½ threshold similarity ratio you want
    # Id Column = "id"
    # run module
    k = adVal(train, test, 95,  "price_range", "id")

    # get auc_score
    k.auc_score()
LTK-cicy 0.1.0
Linked Reads Tool Kit
License: MIT
Requires Python: >=3.6.0

📦 setup.py (for humans)
=======================

This repo exists to provide [an example setup.py] file, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices for `setup.py`, as well as some
commented–out nice–to–haves.

For example, this `setup.py` provides a `$ python setup.py upload`
command, which creates a *universal wheel* (and *sdist*) and uploads
your package to [PyPi] using [Twine], without the need for an annoying
`setup.cfg` file. It also creates/uploads a new git tag, automatically.

In short, `setup.py` files can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)

[Check out the example!][an example setup.py]

Installation
-----

```bash
cd your_project

# Download the setup.py file:
#  download with wget
wget https://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py -O setup.py

#  download with curl
curl -O https://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py
```

To Do
-----

-   Tests via `$ setup.py test` (if it's concise).

Pull requests are encouraged!

More Resources
--------------

-   [What is setup.py?] on Stack Overflow
-   [Official Python Packaging User Guide](https://packaging.python.org)
-   [The Hitchhiker's Guide to Packaging]
-   [Cookiecutter template for a Python package]

License
-------

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.

  [an example setup.py]: https://github.com/navdeep-G/setup.py/blob/master/setup.py
  [PyPi]: https://docs.python.org/3/distutils/packageindex.html
  [Twine]: https://pypi.python.org/pypi/twine
  [image]: https://farm1.staticflickr.com/628/33173824932_58add34581_k_d.jpg
  [What is setup.py?]: https://stackoverflow.com/questions/1471994/what-is-setup-py
  [The Hitchhiker's Guide to Packaging]: https://the-hitchhikers-guide-to-packaging.readthedocs.io/en/latest/creation.html
  [Cookiecutter template for a Python package]: https://github.com/audreyr/cookiecutter-pypackage