Metadata-Version: 2.1
Name: MLLytics
Version: 0.1.15
Summary: A library of tools for easier evaluation of ML models.
Home-page: https://github.com/scottclay/MLLytics
Author: Scott Clay
Author-email: scottclay8@gmail.com
License: MIT
Description: # MLLytics
        
        ## Installation instructions
        ```pip install MLLytics```
        or
        ```python setup.py install```
        or
        ``` conda env create -f environment.yml```
        
        ## Update pypi instructions (for me)
        Creates the package
        ```python setup.py sdist bdist_wheel```
        Upload package
        ```twine upload --repository pypi *version_files*```
        
        ## Future
        ### Improvements and cleanup
        * Allow figure size and font sizes to be passed into plotting functions
        * Comment all functions and classes
        * Add type hinting to all functions and classes (https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html)
        * Example guides for each function in jupyter notebooks
        * ~MultiClassMetrics should inherit from ClassMetrics~
        * REGRESSION
        
        ### Cosmetic
        * Fix size of confusion matrix
        * Check works with matplotlib 3
        * Tidy up legends and annotation text on plots
        * Joy plots
        * Brier score for calibration plot
        * Tidy up cross validation and plots (also repeated cross-validation)
        * Acc-thresholds graph
        
        
        
        ### Big push
        * Scoring functions
        * MultiClassMetrics class to inherit from ClassMetrics and share common functions
        * More output stats in overviews
        * Update reliability plot https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/
        * Tests
        * Switch from my metrics to sklearn metrics where it makes sense? aka
        ```fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])```
        and more general macro/micro average metrics from: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
        * Additional metrics (sensitivity, specificity, precision, negative predictive value, FPR, FNR,
        false discovery rate, accuracy, F1 score
        
        
        ## Contributing Authors
        * Scott Clay
        * David Sullivan
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Description-Content-Type: text/markdown
