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PerMetrics is a python library for performance metrics of machine learning models. We aim to implement allperformance metrics for problems such as regression, classification, clustering, ... problems. Helping users in allfield access metrics as fast as possible. The number of available metrics include111 (47 regression metrics, 20 classification metrics, 44 clusteringmetrics)

Please include these citations if you plan to use this library:

• LaTeX:

  @article{Thieu_PerMetrics_A_Framework_2024,author ={Thieu, Nguyen Van},doi ={10.21105/joss.06143},journal ={Journal of Open Source Software},month = mar,number ={95},pages ={6143},title ={{PerMetrics: A Framework of Performance Metrics for Machine Learning Models}},url ={https://joss.theoj.org/papers/10.21105/joss.06143},volume ={9},year ={2024}}

• APA:

  Thieu, N. V. (2024). PerMetrics: A Framework of Performance Metrics for Machine Learning Models. Journal of Open Source Software, 9(95), 6143.https://doi.org/10.21105/joss.06143

Install thecurrent PyPI release:

$ pip install permetrics

After installation, you can import Permetrics as any other Python module:

$ python>>> import permetrics>>> permetrics.__version__

Below is the most efficient and effective way to use this library compared to other libraries.The example below returns the values of metrics such as root mean squared error, mean absolute error...

frompermetricsimportRegressionMetricy_true= [3,-0.5,2,7]y_pred= [2.5,0.0,2,8]evaluator=RegressionMetric(y_true,y_pred)results=evaluator.get_metrics_by_list_names(["RMSE","MAE","MAPE","R2","NSE","KGE"])print(results["RMSE"])print(results["KGE"])

In case your y_true and y_pred data have multiple columns, and you want to return multiple outputs, something that other libraries cannot do, you can do it in Permetrics as follows:

importnumpyasnpfrompermetricsimportRegressionMetricy_true=np.array([[0.5,1], [-1,1], [7,-6]])y_pred=np.array([[0,2], [-1,2], [8,-5]])evaluator=RegressionMetric(y_true,y_pred)## The 1st wayresults=evaluator.get_metrics_by_dict({"RMSE": {"multi_output":"raw_values"},"MAE": {"multi_output":"raw_values"},"MAPE": {"multi_output":"raw_values"},})## The 2nd wayresults=evaluator.get_metrics_by_list_names(list_metric_names=["RMSE","MAE","MAPE","R2","NSE","KGE"],list_paras=[{"multi_output":"raw_values"},]*6)## The 3rd wayresult01=evaluator.RMSE(multi_output="raw_values")result02=evaluator.MAE(multi_output="raw_values")

The more complicated cases in the folder:examples. You can also read thedocumentationfor more detailed installation instructions, explanations, and examples.

There are lots of ways how you can contribute to Permetrics's development, and you are welcome to join in! For example,you can report problems or make feature requests on theissues pages. To facilitate contributions,please check for the guidelines in theCONTRIBUTING.md file.

• Official source code repository
• Official document
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• Issue tracker
• Notable changes log
• Official discussion group

• Currently, there is a huge misunderstanding among frameworks around the world about the notation of R, R2, and R^2.

• Please read the fileR-R2-Rsquared.docx to understand the differences between them and why there is such confusion.

• My recommendation is to denote the Coefficient of Determination as COD or R2, while the squared Pearson'sCorrelation Coefficient should be denoted as R^2 or RSQ (as in Excel software).

Developed by:Thieu @ 2023