Package: cla
cla (classifiability analysis)A unified classifiability analysis framework based on meta-learner and its application in spectroscopic profiling data [J]. Applied Intelligence, 2021, doi: 10.1007/s10489-021-02810-8pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, Volume 18, June 2022, 101007, doi: 10.1016/j.softx.2022.101007WarningSince 0.3.x, we have reorganized the package structure. Any upper app should be revised accordingly.Since 1.0.0, we stopped pyCLAMs and switch to cla.Installationpip install cla (pyCLAMs for versions under 1.0.0)pip install rpy2Install 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 useDownload the sample dataset from the /data folder
Use the following sample code to use the package:# import clams # (for versions < 1.0.0)  
  from cla import metrics # (for versions > 1.0.0)  

  # 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
  metrics.get_metrics(X,y) # Return a dictionary of all metrics

  # get metrics as JSON
  metrics.get_json(X,y)

  # get an html report and display in Jupyter notebook
  from IPython.display import display, HTML
  display(HTML(metrics.get_html(X,y)))Start the web GUIpython -m cla.gui.runOpenhttp://localhost:5005/in your browser.3. A ready-to-use online demo is http://spacs.brahma.pub/research/CLAMetrics and functions added since the original publication1. metricsclassification.Mean_KLD - mean KLD (Kullback-Leibler divergence) between ground truth and predicted one-hot encodingscorrelation.r2 - R2, the R-squared effect sizetest.CHISQ, test.CHISQ.log10, test.CHISQ.CHI2 - Chi-squared testclassification.McNemar, classification.McNemar.CHI2 - McNemar test on the groud-truth and classifier's predictionclassification.SVM.Margin - the linear-SVC's margin widthtest.student, test.student.min, test.student.min.log10, test.student.T, test.student.T.maxtest.KW, test.KW.min, test.KW.min.log10, test.KW.H, test.KW.H.maxtest.Median, test.Median.min, test.Median.min.log10, test.Median.CHI2, test.Median.CHI2.max2. refactorIntegrate some existing packages and reorganize the package structure.modulesub-moduledescriptionstandalone pypi package (if any)publicationclacla.metricsProvides various classifiability analysis metrics.pyCLAMspyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, Volume 18, June 2022, 101007, doi: 10.1016/j.softx.2022.101007cla.unifyProvide a method for unifying multiple atom metrics.N/AA unified classifiability analysis framework based on meta-learner and its application in spectroscopic profiling data [J]. Applied Intelligence, 2021, doi: 10.1007/s10489-021-02810-8cla.visData visualization and plotting functions.N/AN/Acla.guiProvide a user-friendly GUI.wCLAMsN/A