A low-code interpretable machine learning toolbox in Python.
Project description
PiML: a low-code interpretable machine learning toolbox in Python
PiML (or π·ML, /ˈpaɪ·ˈem·ˈel/) is a new Python toolbox for Interpretable Machine Learning model development and validation. Through low-code automation and high-code programming, PiML supports various machine learning models in the following two categories:
- Inherently interpretable models:
- EBM: Explainable Boosting Machine (Nori, et al. 2019; Lou, et al. 2013)
- GAMI-Net: Generalized Additive Model with Structured Interactions (Yang, Zhang and Sudjianto, 2021)
- ReLU-DNN: Deep ReLU Networks using Aletheia Unwrapper (Sudjianto, et al. 2020)
- Arbitrary black-box models,e.g.
- LightGBM or XGBoost of varying depth
- RandomForest of varying depth
- Residual Deep Neural Networks
Low-code Examples
Click the ipynb links to run examples in Google Colab:
Begin your own PiML journey with this demo notebook.
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 Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distributions
Close
Hashes for PiML-0.1.3-cp39-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9113825c10f8d1c37225d7ddeb3413508ce281354fefd832dd2f1ec85ecfb340 |
|
MD5 | 53a632d328927a423edfa876e19293da |
|
BLAKE2b-256 | ad4b23b8ccaf6cfcc54d00592d006f7ccb3af9c0631bb9fd9c3cb70ea0f46e17 |
Close
Hashes for PiML-0.1.3-cp39-none-manylinux_2_5_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 81d872f3edf486f9bcb607292aecae9b15302e8cafbd61e9e807ea83452ba821 |
|
MD5 | c4156941afb113f144680aad2192b280 |
|
BLAKE2b-256 | 28351acc13c7ef3f45dbb82a88571df7e56078f4d15c2d787449551ca8a69323 |
Close
Hashes for PiML-0.1.3-cp39-none-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3f6b0619b242196fbbccd6e61f1aa5264493b0b7b29d41d70b6a8ce0702339cf |
|
MD5 | a12d37c8f6f114c7ecd7910c68b8f555 |
|
BLAKE2b-256 | 0bd20d9138b80d8681010de3103153bd17b9f620a404ecb6a6d95139203ca206 |
Close
Hashes for PiML-0.1.3-cp38-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | af4f229b08dbc635fa97bbe5f8940ff78a9d9bf1cb55da5803afafa0139ae18d |
|
MD5 | 18fe40192e71434ad5fc5f013ca860c9 |
|
BLAKE2b-256 | 6c2cff892ead9b2f09fc4af244304bceddeb21eb17744c93bae41e5f98ef9974 |
Close
Hashes for PiML-0.1.3-cp38-none-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b1b93fce47327e4348a12e4541a6cd1ae8a139ad69bb206400a085e627454c53 |
|
MD5 | 8c9b618656a8f2deede83aa02688c875 |
|
BLAKE2b-256 | 968c7347a12bed36609e51ccdc3c15db88b80171a6ae24a1ba009d27c2543f56 |
Close
Hashes for PiML-0.1.3-cp38-none-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cb01708f5abb69c4b974a1c9b639ef2492645c4247df34dc79d2f1d47caa76e0 |
|
MD5 | db1598de2b6dfacfecdb615c039b3f46 |
|
BLAKE2b-256 | 8cc7edd185d4a795f2e5878e797431c7560db7a7a7ae72dcc5f45cdda1aa528b |
Close
Hashes for PiML-0.1.3-cp37-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a50b8ba95b3900f67ac7e3dacf3856a3e66329fd4f7a0ae26d06f938bf531d7a |
|
MD5 | f3bb1365b1b0b297e8cbe698573be221 |
|
BLAKE2b-256 | 50e7cf8666d4c1319edcbef2392dfb049d4a2bf24ad9545a54cc3248db0faad7 |
Close
Hashes for PiML-0.1.3-cp37-none-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 307539ed3618d89d37ff9867def98047fecf9436c40a5e6e0e71ab1b0c7d30af |
|
MD5 | 7557edfcfa5c94db5fae7f5eb568fe07 |
|
BLAKE2b-256 | 56d204341c095d6162d7e1ce35d6a8e279ba43b03eff989040906674b120b702 |
Close
Hashes for PiML-0.1.3-cp37-none-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1bbfc50b8c3388c9df2d91f1899f0806ed8e6e095f48a84bb1b065266091eb67 |
|
MD5 | b96bfc8157525d843142dc2ecbf1f532 |
|
BLAKE2b-256 | ee4b42c56f274006d69e8f6e04037df3e652248ed57bea33093520634c316f44 |