Tabular Classification
Scikit-learn
Joblib
genomics
structural-variants
short-tandem-repeats
variant-calling
confidence-calibration
random-forest
Instructions to use khyeom/SVSTR-Score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use khyeom/SVSTR-Score with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("khyeom/SVSTR-Score", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 919 Bytes
90d0b4b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | from setuptools import setup, find_packages
setup(
name='svspr',
version='0.1.0',
description='SV-SPR: Caller-agnostic short-read SV confidence scoring (reference-only).',
author='Woohun Kim',
author_email='alex990713@gmail.com',
license='CC-BY-4.0',
package_dir={'svspr': 'src'},
packages=['svspr'],
package_data={'svspr': ['../model/*.pkl']},
include_package_data=True,
install_requires=[
'numpy>=1.21',
'pandas>=1.3',
'scikit-learn>=1.0',
'pysam>=0.20',
],
python_requires='>=3.8',
entry_points={
'console_scripts': ['svspr=svspr.cli:main'],
},
classifiers=[
'Intended Audience :: Science/Research',
'License :: OSI Approved :: Creative Commons Attribution 4.0 International',
'Programming Language :: Python :: 3',
'Topic :: Scientific/Engineering :: Bio-Informatics',
],
)
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