A computation graph library for feature engineering with Numpy data
Project description
Megatron is a framework for building computation graphs for
feature engineering in machine learning, with Numpy arrays as the data type.
Use Megatron if you want to:
- Do feature engineering in a modular and functional way, building up features one step at a time
- Use disk space to save time by caching feature sets for easy reloading
- Train feature engineering modules on training data and apply them to testing data
- Write custom functions for complex transformations, but access built-in functions for quick and common transformations
- Build feature engineering like you build Keras models (the API is heavily inspired by Keras)
Or any combination of these.
Megatron is distributed under the MIT license.
feature engineering in machine learning, with Numpy arrays as the data type.
Use Megatron if you want to:
- Do feature engineering in a modular and functional way, building up features one step at a time
- Use disk space to save time by caching feature sets for easy reloading
- Train feature engineering modules on training data and apply them to testing data
- Write custom functions for complex transformations, but access built-in functions for quick and common transformations
- Build feature engineering like you build Keras models (the API is heavily inspired by Keras)
Or any combination of these.
Megatron is distributed under the MIT license.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Megatron-0.3.6.tar.gz
(20.7 kB
view details)
File details
Details for the file Megatron-0.3.6.tar.gz
.
File metadata
- Download URL: Megatron-0.3.6.tar.gz
- Upload date:
- Size: 20.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.25.0 CPython/3.7.0
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9d8ec29e4c898d4c24e8d5f8caa4612f955a263c234b1b40f46b7b6a08c5af50 |
|
MD5 | 405152a0d53356d57a78cee9d85a76ee |
|
BLAKE2b-256 | 88b0e340e4d8d7fcff062fd462592a0cb52fdb91813496b7facac15e398830b2 |