Metadata-Version: 2.1
Name: AIGrammar
Version: 0.0.6
Summary: AIGrammar Python package
Home-page: https://github.com/ZaruhiNavasardyan/AIGrammar
Author: metric
License: UNKNOWN
Download-URL: https://github.com/ZaruhiNavasardyan/AIGrammar/archive/refs/tags/V_006.tar.gz
Description: # AIGrammar
        
        ## About
        AIGrammar is all in one and easy to use package for model diagnostic and vulnerability checks. It enable with a simple line of code to check model metrics and prediction generalizability, feature contribution, and model vulnerability against adversarial attacks. 
        
        **Data**
        - Multicollinearity
        - Data drift
        
        **Model**
        - Metric metric comparison
          - roc_auc vs average precision
        - Optimal threshold vs 50% threshold
        
        **Feature importance**
        - Too high importance
        - 0 impact
        - Negative influence (FLOFO)
        - Causes of overfitting
        
        **Adversarial Attack**
        - Model vulnerability identification based on one feature minimal change for getting opposite outcome.
        
        
        **Usage**
        Python 3.7+ required.
        Installation: ``pip install AIGrammar``
        
        Example:
        ``` from AIGrammar import AIGrammar
        aig = AIGrammar(train, test, model, target_name)
        aig.measure_all(X0_shap_values, X1_shap_values)
        
        print(aig.diagnosis)
        print(aig.warnings)
        ```
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
