semvec/pypi-packages huggingface dataset:
scholarmetrics 0.2.2
Compute scholarly metrics in Python with Pandas and NumPy Edit
License: MIT
Keywords: metrics,researchers,scientists,academics

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scholarmetrics
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Compute scholarly metrics in Python with Pandas and NumPy.


**Documentation**: https://scholarmetrics.readthedocs.io.

.. image:: https://img.shields.io/pypi/v/scholarmetrics.svg
        :target: https://pypi.python.org/pypi/scholarmetrics

.. image:: https://readthedocs.org/projects/scholarmetrics/badge/?version=latest
        :target: https://scholarmetrics.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status

.. image:: https://codeclimate.com/github/Michael-E-Rose/scholarmetrics/badges/gpa.svg
        :target: https://codeclimate.com/github/Michael-E-Rose/scholarmetrics
        :alt: Code Climate

.. image:: https://travis-ci.org/Michael-E-Rose/scholarmetrics.svg?branch=master
        :target: https://travis-ci.org/Michael-E-Rose/scholarmetrics
        :alt: Build Status


Examples
--------

* J.E. Hirsch's h-index or Hirsch-index:

.. code:: python

    >>> from scholarmetrics import hindex
    >>> citations = [6, 10, 5, 46, 0, 2]
    >>> hindex(citations)
    4

* Euclidean index:

.. code:: python

    >>> from scholarmetrics import euclidean
    >>> citations = [6, 10, 5, 46, 0, 2]
    >>> euclidean(citations)
    47.75981574503821


Contributing
------------

.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
     :target: https://github.com/Michael-E-Rose/scholarmetrics/issues
     :alt: Contributions welcome

Please see `CONTRIBUTING.rst <CONTRIBUTING.rst>`_.

For a list of contributors see `AUTHORS.rst <AUTHORS.rst>`_.

License
-------
MIT License, see `LICENSE <LICENSE>`_.
octopus-ml 3.2.0
A collection of handy ML and data validation tools
License: MIT

# Octopus-ML
 
[![PyPI Latest Release](https://img.shields.io/pypi/v/octopus-ml.svg)](https://pypi.org/project/octopus-ml/)
[![License](https://img.shields.io/pypi/l/octopus-ml.svg)](https://github.com/gershonc//octopus-ml/blob/master/LICENSE)
[![Python Version](https://img.shields.io/pypi/pyversions/pandas-profiling)](https://pypi.org/project/octopus-ml/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
[![Binder](https://mybinder.org/badge.svg)](https://hub.gke2.mybinder.org/user/gershonc-octopus-ml-k5of97xu/tree)
[![Downloads](https://pepy.tech/badge/octopus-ml)](https://pepy.tech/project/octopus-ml)
[![Colab Demo](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1T1T15AZe0w7Zcld_09jxg-Yhq2pBefVZ?usp=sharing)


<!-- 
[![Code Coverage](https://codecov.io/gh/pandas-profiling/octopus-ml/branch/master/graph/badge.svg?token=gMptB4YUnF)](https://codecov.io/gh/octopus/octopus)
-->
Set of handy ML and data tools - starting from data exploration, visualization, pre-processing, hyper parameter tuning, modeling and all the way to final ML model evaluation 
<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/octopus_know_your_data.png" width="540" height="460" /></center>

Check out the octopus-ml demo notebook on Colab <a href="https://colab.research.google.com/drive/1T1T15AZe0w7Zcld_09jxg-Yhq2pBefVZ?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
 
## Installation
The module can be easily installed with pip:

```conslole
> pip install octopus-ml
```

This module depends on `Scikit-learn`, `NumPy`, `Pandas`, `TQDM`, `lightGBM` as defualt classifier. Optionally you can get also some nice visualisations if you have `Seaborn` installed.



## Usage
The module contains ML and Data related methods:

```python
from octopus_ml import plot_imp, adjusted_classes, cv, cv_plot, roc_curve_plot, ...

```
<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/oc_plot_cv.png" width="820"/></center>
<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/oc_plot_prediction_distribution.png" width="720" /></center>

## Selected visualizations:
<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/target_distribution_plot.png" width="520"/></center>
<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/histogram_on_target.png" width="520"/></center>


<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/oc_plot_roc.png" width="520"/></center>
<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/oc_plot_confusion_matrix.png" width="580" /></center>
<br><center><img src="https://raw.githubusercontent.com/gershonc/octopus-ml/main/images/oc_plot_feature_imp.png" width="820"/></center>