Lightweight preprocessing and reversible Modular Linear Tokenization (MLT) utilities for categorical and continuous data.
Project description
🧩 light-mlt
Lightweight preprocessing and reversible Modular Linear Tokenization (MLT) utilities for categorical and continuous data.
✨ Overview
light-mlt implements Modular Linear Tokenization (MLT) — a deterministic and reversible method for encoding high-cardinality categorical identifiers into compact numerical vectors.
Unlike hashing or one-hot encodings, MLT guarantees bijective mappings, provides explicit dimensionality control, and integrates seamlessly with machine learning pipelines.
It was developed as part of applied research on scalable tokenization and efficient preprocessing for tabular and recommendation systems.
🚀 Installation
pip install light-mlt
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file light_mlt-0.1.2.tar.gz.
File metadata
- Download URL: light_mlt-0.1.2.tar.gz
- Upload date:
- Size: 9.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
16607c6d402a21206c1d65d23aae70cbb9cd6950757d60b161ab17fb48a94bb2
|
|
| MD5 |
9edd50799e93cc62d36c5d3745c22650
|
|
| BLAKE2b-256 |
e6aeb094cc582336570ee91c73700ef845cdafdd0980a45bd25709b46593411b
|
File details
Details for the file light_mlt-0.1.2-py3-none-any.whl.
File metadata
- Download URL: light_mlt-0.1.2-py3-none-any.whl
- Upload date:
- Size: 8.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
21ce749975fbd37de8ca70094d79c2b2a2987e37971700c1c162a6976d61b626
|
|
| MD5 |
7cd43548f6d0224870758f6e6870fa39
|
|
| BLAKE2b-256 |
e6bc32ce2eea23f3c83969eb07e90c86275eaf3e7e85494aaba5337067d32b91
|
