semvec/pypi-packages huggingface dataset:
kable-python-django 4.0.1
Kable Python Django SDK
Requires Python: >=3.6

# Kable Python Django SDK

Welcome to **[Kable](https://kable.io)**! Kable is the best solution for API products to manage pricing, billing, and API authentication.

This SDK is the best way to integrate Kable into your Python (Django) application. For documentation and detailed instructions about how to use this SDK, please visit https://docs.kable.io/docs/python-django.

Note: If you use Flask instead of Django, please download our [Flask SDK](https://pypi.org/project/kable-python-flask) instead.

**[Kable](https://kable.io)** | https://kable.io
kable-python-flask 4.0.1
Kable Python Flask SDK
Requires Python: >=3.6

# Kable Python Flask SDK

Welcome to **[Kable](https://kable.io)**! Kable is the best solution for API products to manage pricing, billing, and API authentication.

This SDK is the best way to integrate Kable into your Python (Flask) application. For documentation and detailed instructions about how to use this SDK, please visit https://docs.kable.io/docs/python-flask.

Note: If you use Django instead of Flask, please download our [Django SDK](https://pypi.org/project/kable-python-django) instead.

**[Kable](https://kable.io)** | https://kable.io/
pyCLAMs 0.4.1
An integrated Python toolkit for classifiability analysis
Requires Python: >=3.6

# pyCLAMs

pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, Volume 18, June 2022, 101007, doi: 10.1016/j.softx.2022.101007  
https://doi.org/10.1016/j.softx.2022.101007

# Warning

Since 0.3.x, we have reorganized the package structure. Any upper app should be revised accordingly.

# Installation 

pip install pyCLAMs
pip install rpy2
Install the R runtime and the ECol library (https://github.com/lpfgarcia/ECoL).  

  Run 'install.packages("ECoL")' in R. It will take very long time. You must wait for the installation to complete.     
  Sometimes, you may want to change the CRAN mirror. Under the "Packages" menu, click "Set CRAN Mirror".    
  After installation, you can check by R command 'installed.packages()'. 

# How to use 

Download the sample dataset from the /data folder
Use the following sample code to use the package:

<pre>
  # import the library
  import clams

  # load the dataset or generate a toy dataset by X,y = mvg(md = 2)
  df = pd.read_csv('sample.csv')
  X = np.array(df.iloc[:,:-1]) # skip first and last cols
  y = np.array(df.iloc[:,-1])

  # get all metrics
  clams.get_metrics(X,y) # Return a dictionary of all metrics

  # get metrics as JSON
  clams.get_json(X,y)

  # get an html report and display in Jupyter notebook
  from IPython.display import display, HTML
  display(HTML(clams.get_html(X,y)))
</pre>

# Extra Material
A more friendly GUI tool based on pyCLAMs can be accessed at http://spacs.brahma.pub/research/CLA

# Metrics added since the original publication

  classification.Mean_KLD - mean KLD (Kullback-Leibler divergence) between ground truth and predicted one-hot encodings  
  correlation.r2 - R2, the R-squared effect size  
  test.CHISQ, test.CHISQ.log10, test.CHISQ.CHI2 - Chi-squared test  
  classification.McNemar, classification.McNemar.CHI2 - McNemar test on the groud-truth and classifier's prediction