Metadata-Version: 2.4
Name: momo-ml
Version: 0.1.4
Summary: A production-oriented model monitoring library for detecting performance drift, data drift, and prediction drift, with automated reporting for ML systems.
Author: YHJYH
License: MIT License
        
        Copyright (c) 2026 Werner Heisenberg
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/YHJYH/momo_ml
Project-URL: Source, https://github.com/YHJYH/momo_ml
Project-URL: Issues, https://github.com/YHJYH/momo_ml/issues
Keywords: model monitoring,drift detection,MLOps,machine learning,analytics
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: plotly
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: flake8; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Dynamic: license-file

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# momo-ml

**momo-ml** (**MO**del **MO**nitoring for **ML**) is a production‑oriented library for systematically, scalably, and automatically monitoring model quality.  
It covers key monitoring dimensions such as **performance drift**, **data drift**, and **prediction drift**, and also supports automatic generation of visual reports.

This project is suitable for data science, ML engineering, MLOps, and other scenarios – it can be integrated into various monitoring and governance workflows after model deployment.

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## 📌 Features

### **1. Performance Drift**
Monitor changes in model prediction performance over different time windows:
- Metrics: AUC / F1 / Precision / Recall / RMSE, etc.
- Compare reference window vs. current window.
- Rolling window trend analysis.

### **2. Data Drift**
Detect shifts in the stability of input data distributions:
- Population Stability Index (PSI)
- KL Divergence, KS Test
- Statistical changes in numerical features (mean / var / quantile shift)
- Distribution drift in categorical features (frequency shift)

### **3. Prediction Drift**
Monitor abnormal model output behavior:
- Changes in output distribution
- Binning stability (e.g., deciles shift)
- Prediction differences across groups/segments

### **4. Automated Reporting**
Unified report generation capabilities:
- Automatically generate drift charts (matplotlib / plotly)
- One‑click HTML or PDF report generation
- Extensible storage or push to custom dashboards

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## 🔧 Installation

```bash
pip install momo-ml
