A unified classifiability analysis framework based on meta-learner and its application in spectroscopic profiling data [J]. Applied Intelligence, 2021, doi: 10.1007/s10489-021-02810-8
pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, Volume 18, June 2022, 101007, doi: 10.1016/j.softx.2022.101007
Since 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.
pip install cla (pyCLAMs for versions under 1.0.0)
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()'.
Download 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)))
- python -m cla.gui.run
- Open http://localhost:5005/ in your browser.
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
classification.SVM.Margin - the linear-SVC's margin width
test.student, test.student.min, test.student.min.log10, test.student.T, test.student.T.max
test.KW, test.KW.min, test.KW.min.log10, test.KW.H, test.KW.H.max
test.Median, test.Median.min, test.Median.min.log10, test.Median.CHI2, test.Median.CHI2.max
Integrate some existing packages and reorganize the package structure.
| module | sub-module | description | standalone pypi package (if any) | publication |
| cla | cla.metrics | Provides various classifiability analysis metrics. | pyCLAMs | pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, Volume 18, June 2022, 101007, doi: 10.1016/j.softx.2022.101007 |
| cla.unify | Provide a method for unifying multiple atom metrics. | N/A | A unified classifiability analysis framework based on meta-learner and its application in spectroscopic profiling data [J]. Applied Intelligence, 2021, doi: 10.1007/s10489-021-02810-8 | |
| cla.vis | Data visualization and plotting functions. | N/A | N/A | |
| cla.gui | Provide a user-friendly GUI. | wCLAMs | N/A |