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tabular-ml-toolkit

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0.0.35 tabular_ml_toolkit-0.0.35-py3-none-any.whl

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Project: tabular-ml-toolkit
Version: 0.0.35
Filename: tabular_ml_toolkit-0.0.35-py3-none-any.whl
Download: [link]
Size: 25711
MD5: ba8c41ef538238368f812989ae488221
SHA256: d9dcb518eae61fd6eedff907630d9f00e9de1c25739076017a3ed17100736a5d
Uploaded: 2021-12-14 23:55:38 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: tabular-ml-toolkit
Version: 0.0.35
Summary: A helper library to jumpstart your machine learning project based on tabular or structured data.
Author: Pankaj Mathur
Author-Email: psmathur.public[at]gmail.com
Home-Page: https://github.com/psmathur/tabular_ml_toolkit/tree/master/
License: Apache Software License 2.0
Keywords: machine learning,tabular data,scikit-learn,XGBoost
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.6
Requires-Dist: pip
Requires-Dist: packaging
Requires-Dist: pandas (==1.1.5)
Requires-Dist: scikit-learn (==1.0.0)
Requires-Dist: xgboost (==1.5.0)
Requires-Dist: optuna (==2.10.0)
Requires-Dist: pytorch-tabnet (==3.1.1)
Provides-Extra: dev
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 22553 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.37.0)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
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tabular_ml_toolkit/_nbdev.py sha256=9rDnkZJJICq_Ykk2dIT6Phi8nNFUmaXfezt3CuOEMT4 1060
tabular_ml_toolkit/dataframeloader.py sha256=znDVMIqFE8-kCgU59TP9S7UVTH_LmLWlFYa7vis80q8 11238
tabular_ml_toolkit/logger.py sha256=Kq11UNMoMLfWWJopysBAaOV6EK1XHxTkKu-tnswrRRM 420
tabular_ml_toolkit/preprocessor.py sha256=SgorBOOk5eNbQ5GEhWstTxnGfb0SYQ5HpPnwRxH_GEE 5416
tabular_ml_toolkit/tmlt.py sha256=IIDTJkxy_7CCNuizfnLuZ3YaHZD_Bvkoa7OKiotIetA 19970
tabular_ml_toolkit/utility.py sha256=_R-TnL81NWZWE1SBCT-5ssF_6A8_DRahbQhUUK0MZhM 6378
tabular_ml_toolkit/xgb_optuna_objective.py sha256=qCAnbbYc1GcJtEuG7S8wQcrL70maVlmT5ejQOQ63Akg 5596
tabular_ml_toolkit-0.0.35.dist-info/LICENSE sha256=xx0jnfkXJvxRnG63LTGOxlggYnIysveWIZ6H3PNdCrQ 11357
tabular_ml_toolkit-0.0.35.dist-info/METADATA sha256=DmnScXjNegjV7m7rJEDHmvMALMKIbbLLj8J3PByOo9c 23639
tabular_ml_toolkit-0.0.35.dist-info/WHEEL sha256=ewwEueio1C2XeHTvT17n8dZUJgOvyCWCt0WVNLClP9o 92
tabular_ml_toolkit-0.0.35.dist-info/entry_points.txt sha256=eqGnTHtEVMYwfXVk1Z9MhC8O2N8wuqAbr0lisLmrkxs 20
tabular_ml_toolkit-0.0.35.dist-info/top_level.txt sha256=ydE9-fn_a7Xp3u4N2iAQ88GzWBkcacci_YiVMhZ96xU 19
tabular_ml_toolkit-0.0.35.dist-info/RECORD — —

top_level.txt

tabular_ml_toolkit

entry_points.txt

[empty]