semvec/pypi-packages huggingface dataset:
cifrus 1.0.4
Official implementation of Class-invariante Feature Range Uniform Sampling (CiFRUS)
Requires Python: >=3.9

# Class-invariant Feature Range Uniform Sampling (CiFRUS)

## Introduction

This is the official implementation of the tabular data augmentation method described in the following KDD 2024 paper: [A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability](https://doi.org/10.1145/3637528.3671736). The core augmentation method is class-invariant and supports majority-based prediction of unlabeled instances. Each unlabeled instance can be expanded into a set of augmented instances, followed by classifier prediction and aggregation of the predicted labels or class probabilities.

## Installation

### Installing with Pip

`CiFRUS` can be installed from [PyPI](https://pypi.org/project/cifrus/) with `pip`:

```
python -m pip install cifrus
```

The PyPI package only contains the core augmentation module. To acquire the datasets and code for conducting experiments, please fork this repository.

### From source
`cifrus.py` can be directly copied to the project repository.

## Usage

`CiFRUS` is compatible with [scikit-learn](https://scikit-learn.org/stable/) and [imbalanced-learn](https://imbalanced-learn.org/stable/). Given feature matrix `X` where each row is a sample and corresponding class labels `y`, augmentation can be performed as follows:

```
from cifrus.cifrus import CiFRUS
cfrs = CiFRUS()
X_resampled, y_resampled = cfrs.fit_resample(X, y)
```

By default, `CiFRUS` augmentation increases the number of samples in the majority class five-fold, and balances the minority classes.

The majority-based prediction of `CiFRUS` can be used by passing the classifier's `predict_proba()` function as a parameter to the `resample_predict_proba` function of the `CiFRUS` object, as demonstrated below:

```
# any sklearn compatible classifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier() 
clf.fit(X_resampled, y_resampled)

# X_test is resampled, and the probabilities are aggregated internally
y_pred = cfrs.resample_predict_proba(clf.predict_proba, X_test)
```

## License

This work is licensed under the [Creative Commons Attribution 4.0 International License](https://github.com/sakhawathossain/CiFRUS/blob/main/LICENSE).

## Citation

If you use `CiFRUS` in your scientific research, please consider citing us:

```
@inproceedings{saimon2024novel,
    author = {Saimon, Sakhawat Hossain and Najnin, Tanzira and Ruan, Jianhua},
    title = {A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability},
    doi = {10.1145/3637528.3671736},
    booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
    pages = {2512–2523},
    year = {2024},
}
```
odoo14-addon-remaining-days-widget-reformat-state 14.0.1.0.1.dev1
Reformat text shown with remaining_days widget
Requires Python: >=3.6

======================================
Remaining Days Widget - Reformat State
======================================

.. 
   !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
   !! This file is generated by oca-gen-addon-readme !!
   !! changes will be overwritten.                   !!
   !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
   !! source digest: sha256:22329a784f99d01302fb755815f691731fea96dc63a4a55806b18992de20525e
   !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

.. |badge1| image:: https://img.shields.io/badge/maturity-Beta-yellow.png
    :target: https://odoo-community.org/page/development-status
    :alt: Beta
.. |badge2| image:: https://img.shields.io/badge/github-sygel--technology%2Fsy--web-lightgray.png?logo=github
    :target: https://github.com/sygel-technology/sy-web/tree/14.0/remaining_days_widget_reformat_state
    :alt: sygel-technology/sy-web

|badge1| |badge2|

This module allows to remove format of text shown in views using the remaining_days
widget. A list of states can be passed so the reformatting will only happen if the
instance is in one of those states.
This module does nothing on its own and it requires certain technical knowledge in
order to include the functionalities provided in other modules.

**Table of contents**

.. contents::
   :local:

Usage
=====

To remove format in fields that use the remaining_days widget, it is necessary to do
the following in another module:

#. Include this module in the other module's dependencies list in manifest.
#. Include options="{'reformat': True}" in the fields that use the remaining_days
   widget so the text is displayed without an specific format.
#. If the reformating only has to be performed if the instance is in certain states,
   the options declaration should be "options="{'reformat': True, 'reformat_states':
   ['state_1', 'state_2']}". It is possible to include as many states as needed in the
   reformat_states list.

Bug Tracker
===========

Bugs are tracked on `GitHub Issues <https://github.com/sygel-technology/sy-web/issues>`_.
In case of trouble, please check there if your issue has already been reported.
If you spotted it first, help us to smash it by providing a detailed and welcomed
`feedback <https://github.com/sygel-technology/sy-web/issues/new?body=module:%20remaining_days_widget_reformat_state%0Aversion:%2014.0%0A%0A**Steps%20to%20reproduce**%0A-%20...%0A%0A**Current%20behavior**%0A%0A**Expected%20behavior**>`_.

Do not contact contributors directly about support or help with technical issues.

Credits
=======

Authors
~~~~~~~

* Sygel

Contributors
~~~~~~~~~~~~

* Manuel Regidor <manuel.regidor@sygel.es>
* Valentín Vinagre <valentin.vinagre@sygel.es>

Maintainers
~~~~~~~~~~~

This module is part of the `sygel-technology/sy-web <https://github.com/sygel-technology/sy-web/tree/14.0/remaining_days_widget_reformat_state>`_ project on GitHub.

You are welcome to contribute.
odoo14-addon-stock-manual-shipping-weight 14.0.1.0.1
The shipping weight field in pickings can be manually edited.
Requires Python: >=3.6

.. image:: https://img.shields.io/badge/licence-AGPL--3-blue.svg
	:target: http://www.gnu.org/licenses/agpl
	:alt: License: AGPL-3


============================
Stock manual shipping weight
============================
This module allows you to edit the weight for shipping field through a wizard.


Installation
============
To install this module, you need to:

#. Just install.


Configuration
=============

To configure this module, you need to:

#. Just install.


Usage
=====

To use this module, you need to:

#. Choose any inventory transfer.
#. Once in the transfer form view, go to the "Additional Info" tab
#. In this tab, locate the Weight for shipping field.
#. This field will now have a button that enables us to make manual weight changes to the shipment.
#. If we click on the button, a wizard will open that allows us to specify the new value for shipping weight


ROADMAP
=======

* ...


Bug Tracker
===========

Bugs and errors are managed in `issues of GitHub <https://github.com/sygel-technology/sy-stock-logistics-workflow/issues>`_.
In case of problems, please check if your problem has already been
reported. If you are the first to discover it, help us solving it by indicating
a detailed description `here <https://github.com/sygel-technology/sy-stock-logistics-workflow/issues/new>`_.

Do not contact contributors directly about support or help with technical issues.


Credits
=======

Authors
~~~~~~~

* Sygel, Odoo Community Association (OCA)


Contributors
~~~~~~~~~~~~

* Valentin Vinagre <valentin.vinagre@sygel.es>
* Roger Sans <roger.sans@sygel.es>


Maintainer
~~~~~~~~~~

This module is maintained by Sygel.

.. image:: https://www.sygel.es/logo.png
   :alt: Sygel
   :target: https://www.sygel.es


This module is part of the `Sygel/sy-stock-logistics-workflow <https://github.com/sygel-technology/sy-stock-logistics-workflow>`_.

To contribute to this module, please visit https://github.com/sygel-technology.
testing-step-generator 0.1.2
A CLI tool for initializing and generating output.
Requires Python: >=3.11

# Kenny's Testing Step Generate

This is a CLI that should generate testing steps.

## Installation

Simply run `pip install testing-step-generator`. This was designed using `Python 3.11`,
so you should probably have that installed.

If you don't have python installed, I highly recommend using `pyenv`, and for this
you would want to set your global python to 3.11

## Commands

### init

You need to run this `init` command, using `tsg init` before you do anything.
This will prompt you to enter your OpenAi API key, which you can get once you
make an OpenAi develop account. Follow their [guide](https://platform.openai.com/docs/quickstart)

This will save the API key in the `.tsg` directory in your root.

### generate

Run using `tsg generate` from the branch of your project you want to get testing steps for.
By default this will comapre it against the `master` branch. However, you can specify a branch
with `-b` or `--branch`, e.g., `tsg generate --branch other_branch`

# Building and deploying (for me)

So that I don't forget how I did all of this.

To test the cli locally, use `pip install -e .` which will build it for you.
Then you can get rid of it with `pip uninstall testing-step-generator`

To build it, use `python -m build`

To upload it to TestPyPi for testing:

```
twine upload --repository testpypi dist/*
```

Then to upload it to the real deal:

```
twine upload dist/*
```