An integrated Python toolkit for classifiability analysis
Project description
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
Installation
pip install pyCLAMs pip install rpy2 You should also have the R runtime with the ECol library (https://github.com/lpfgarcia/ECoL) installed.
How to use
Download the sample dataset from the /data folder Use the following sample code to use the package:
# import the library from pyCLAMs 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
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