pyCLAMs 0.4.0

An integrated Python toolkit for classifiability analysis

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:

  # 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)))

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