Metadata-Version: 2.1
Name: py-mcc-f1
Version: 0.0.1
Summary: MCC-F1 Curve
Home-page: https://github.com/arthurcgusmao/py-mcc-f1
Author: Arthur Colombini Gusmão
License: UNKNOWN
Description: # MCC-F1 Python package
        
        Recently, the MCC-F1 curve has been proposed as an alternative, better way of assessing the performance of score-based binary classifiers [1].
        
        This Python package implements a function to compute the MCC-F1 curve, namely `mcc_f1_curve`, similarly to the `precision_recall_curve` and `roc_curve` functions of [scikit-learn](https://github.com/scikit-learn).
        
        
        ## Installation
        ```console
        pip install mcc_f1
        ```
        
        ## Usage
        ```python
        # from mcc_f1 import mcc_f1_curve
        from src import mcc_f1_curve # temp just for debugging
        
        import numpy as np
        import matplotlib.pyplot as plt
        
        from sklearn.datasets import load_breast_cancer
        from sklearn.linear_model import LogisticRegression
        from sklearn.model_selection import train_test_split
        
        # Load data and train model
        X, y = load_breast_cancer(return_X_y=True)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
        clf = LogisticRegression().fit(X_train, y_train)
        
        # Get predictions and MCC-F1 curve
        y_score = clf.predict_proba(X_test)[:,1]
        mcc, f1, thresholds = mcc_f1_curve(y_test, y_score)
        
        # Plot MCC-F1 curve
        plt.figure(figsize=(6,6))
        plt.plot(f1, mcc)
        plt.xlim(0,1)
        plt.ylim(0,1)
        ```
        
        Please refer to the function's docstring for further comments and details.
        
        
        ## Future enhancements
        
        - [ ] Function to plot the MCC-F1 curve, (e.g., `plot_precision_recall_curve`), similar to `sklearn/metrics/_plot/precision_recall_curve.py` and `sklearn/metrics/_plot/roc_curve.py`;
        - [ ] Function to compute the MCC-F1 metric, as defined in section 2.2 of the original paper.
        
        
        ## Contributing
        If you would like to contribute to this package, please follow the [common community guidelines](https://github.com/MarcDiethelm/contributing).
        
        Please, also keep in mind that the main goal of this project is to be of similar implementation and quality as scikit-learn. Pull requests should pass the existing unit-tests, and add new ones when necessary.
        
        To run the tests:
        ```console
        make test
        ```
        
        ## License
        This package is distributed under the [MIT license](./LICENSE.txt).
        
        ## References
        1. [[2006.11278] The MCC-F1 curve: a performance evaluation technique for binary classification](https://arxiv.org/abs/2006.11278)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
