bm-eval-metrics 1.0.0

A package to evaluate bm metrics

📄 bm-eval-metrics

bm-eval-metrics is a Python package providing easy-to-use evaluation metrics and utilities for Data Mining and Information Retrieval modules. It helps you access and view source code for various DM and IR algorithms efficiently.

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✨ Features

• Data Mining algorithms (Hunt's, ID3, Bagging, AdaBoost, Apriori, etc.)
• Information Retrieval metrics (Jaccard, Precision/Recall/F-score, MAP, etc.)
• Near Duplicate Document detection (MinHash & LSH)
• Relevance Feedback (Rocchio & LCA)
• Source code viewer for all modules
• Built on NLTK, pandas, and scikit-learn

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📦 Installation

Install from PyPI:

pip install bm-eval-metrics

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🚀 Quick Start

Basic Usage

from eval_metrics.DM import adaboost, apriori, metrics
from eval_metrics.IR import eval_metrics, ndd, rel

# Print the source code directly
print("=== DM AdaBoost Module ===")
print(adaboost)

print("\n=== IR Evaluation Metrics ===")
print(eval_metrics)

print("\n=== IR Near Duplicate Documents ===")
print(ndd)

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🛠️ Components Overview

Component	Description
Information Retrieval (IR)
eval_metrics.IR.all	Cohesive IR File: MinHash, LSH, Rocchio, Jaccard, VS
eval_metrics.IR.all_vis	Cohesive IR File + Matplotlib visualizations & Heatmaps
eval_metrics.IR.ndd	Near Duplicate Documents (MinHash & LSH)
eval_metrics.IR.rel	Relevance feedback & query expansion (Rocchio & LCA)
eval_metrics.IR.eval_metrics	Jaccard, PRF, Compression Ratios, MAP metrics & plots
Data Mining (DM)
eval_metrics.DM.all	Cohesive DM File: Hunt's, ID3, Bagging, AdaBoost, Metrics
eval_metrics.DM.all_vis	Cohesive DM File + Graphviz & Matplotlib visualizations
eval_metrics.DM.apriori	Apriori algorithm
eval_metrics.DM.adaboost	Bagging & AdaBoost ensemble classifiers
eval_metrics.DM.bagging	Bagging ensemble classifier
eval_metrics.DM.hash	Hash-based mining
eval_metrics.DM.hunts	Hunt's decision tree algorithm
eval_metrics.DM.hunts_test	Hunt's decision tree with dataset visualization
eval_metrics.DM.id3	ID3 decision tree algorithm
eval_metrics.DM.id3_test	ID3 decision tree with dataset visualization
eval_metrics.DM.metrics	Classification metrics & curves
eval_metrics.DM.preprocessing	Data preprocessing utilities
eval_metrics.DM.lib_doc	Pandas, NumPy, Sklearn cheat sheet (DM & IR logic)
eval_metrics.DM.python_doc	Python Basics cheat sheet (Sets, Dicts, Comprehensions, etc.)

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📚 Requirements

• Python 3.8+
• nltk
• pandas
• scikit-learn (for vectorization)
• matplotlib
• graphviz

Install dependencies automatically with:

pip install bm-eval-metrics

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📂 Project Structure

eval_metrics/
│
├── src/
│   └── eval_metrics/
│       ├── __init__.py
│       ├── DM/
│       │   ├── __init__.py
│       │   ├── adaboost.py
│       │   ├── all.py
│       │   ├── ...
│       │   └── sources/
│       └── IR/
│           ├── __init__.py
│           ├── all.py
│           ├── ...
│           └── sources/
├── pyproject.toml
├── README.md
├── USAGE.md
└── INSTALLATION.md

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Open a pull request

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📄 License

This project is licensed under the MIT License.

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📬 Support

If you encounter any issues or have feature requests, please open an issue on GitHub.

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