shml: routines to automate machine learning experiments for a X -> SH -> bbyy search
This module aims to provide a set of functions that, when composed, can run a pipeline capable of:
- going from
.rootfiles toparquetfiles viauprootandawkward - constructing useful kinematic quantites for training
- applying a chosen or manual preselection
- configuring any additional processing, e.g. weight normalization, feature scaling
- access event data that's prepared for
pytorchusingshml.torch_dataset.EventDataset
still to do:
- infra to run ml experiments in a GPU or CPU environment via
pytorch-lightining
Usage
To see currently usable implemented functionality, check the examples folder.
Install
For preprocessing only:
python3 -m pip install shml
For ML extras (pytorch, plotting):
python3 -m pip install shml[ml]
1 maintainer
Metadata
1 maintainer
Release files for shml 0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| shml-0.1.tar.gz | 13.2 kB | Without provenance | Details for shml-0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| shml-0.1-py3-none-any.whl | Python 3 | none | any | Details for shml-0.1-py3-none-any.whl |
Total release size: 25.9 kB
Release files / shml-0.1.tar.gz
| Download URL | shml-0.1.tar.gz |
|---|---|
| Size | 13.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
f48aa7aceb8059e150045387fb91f1e33429e609ba57c0b3179e3c560bb9ea82
|
|
BLAKE2b-256 checksum How to use checksums |
9dfea40593a0f8ea7323b7582cae290250eef2a3b7a7a24d48ce73e3798a8405
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9
|
Release files / shml-0.1-py3-none-any.whl
| Download URL | shml-0.1-py3-none-any.whl |
|---|---|
| Size | 12.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
8e30b18bed174af327175df2246bdfb54684f5716764ac391fdcb7c92f57d9f0
|
|
BLAKE2b-256 checksum How to use checksums |
92acd0ebb9ecfd74d93aa42fdda3f5bf579db676498c8ff8f5d6d7868a14c263
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9
|
Security for shml 0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
