plp-antispam-rust 1.0.2

Fast Rust-based anti-spam inference engine for email classification

plp-antispam-rust

Fast Rust-based anti-spam inference engine for email classification, achieving 10x speedup over pure Python with 99.9% accuracy.

Features

• High Performance: 2,300+ emails/second inference speed (vs 220 emails/s in Python)
• Production Ready: 99.9% accuracy on real-world spam datasets
• Cross-Platform: Works on Linux, macOS (Intel/ARM), and Windows
• Python 3.9+: Compatible with Python 3.9, 3.10, 3.11, 3.12, 3.13+ (abi3 stable ABI)
• Zero Dependencies: Self-contained binary wheels, no external dependencies

Installation

pip install plp-antispam-rust

Usage

import plpas
from plpas import RustEnsemble

# Load pre-trained models
ensemble = RustEnsemble.load("path/to/models")

# Predict single email
prediction = ensemble.predict(["email text here"])
print(f"Spam probability: {prediction[0]}")

# Batch prediction (faster)
emails = ["email 1", "email 2", "email 3"]
predictions = ensemble.predict(emails)

Architecture

4-model stacked ensemble:

• Model 1: Naive Bayes + Word Unigrams
• Model 2: Logistic Regression + Word Bigrams
• Model 3: SVM + Character Trigrams
• Model 4: XGBoost + Word 1-2 Grams
• Meta-Classifier: Logistic Regression with StandardScaler

All models are pre-trained and exported to MessagePack format for fast Rust deserialization.

Performance

Implementation	Speed (emails/s)	Accuracy
Python (sklearn)	220	99.93%
Rust (this package)	2,311	99.90%
Speedup	10.5x	-0.03%

License

MIT

Author

Paul Lucian Pinzariu