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autobmt

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0.2.0 autobmt-0.2.0-py2.py3-none-any.whl

Wheel Details

Project: autobmt
Version: 0.2.0
Filename: autobmt-0.2.0-py2.py3-none-any.whl
Download: [link]
Size: 82209
MD5: c82852e17cb29a9f6db2e497c73b2b04
SHA256: 5657bd3efec90689f5a0d71b3989227757dbb55b4d3764d64cbdb0190749c8ba
Uploaded: 2024-04-04 13:07:09 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: autobmt
Version: 0.2.0
Summary: a modeling tool that automatically builds scorecards and tree models.
Author: RyanZheng
Author-Email: zhengruiping000[at]163.com
Home-Page: https://github.com/ZhengRyan/autobmt
License: MIT license
Keywords: autobmt
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Requires-Dist: pandas
Requires-Dist: scikit-learn (>=0.21)
Requires-Dist: statsmodels (>=0.11.1)
Requires-Dist: XlsxWriter (>=1.3.7)
Requires-Dist: matplotlib (>=3.1.2)
Requires-Dist: openpyxl (>=3.0.7)
Requires-Dist: bayesian-optimization (==1.1.0)
Requires-Dist: shap (>=0.40.0)
Requires-Dist: joblib (>=0.12)
Requires-Dist: xgboost (<=1.5.0,>=1.2.0)
Requires-Dist: lightgbm (>=3.1.0)
Requires-Dist: seaborn (>=0.10.0)
[Description omitted; length: 2360 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.36.2)
Root-Is-Purelib: true
Tag: py2-none-any
Tag: py3-none-any

RECORD

Path Digest Size
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autobmt/auto_build_scorecard.py sha256=C5b5xqpApWOo2fCM1iviUqpyegls0ttoediY2KMFTl4 18328
autobmt/auto_build_tree_model.py sha256=j4k-w81URbBSNHLsXrycldxyrDSFPJ-HIXg3SG-9_tA 10581
autobmt/auto_build_tree_model_lgb.py sha256=SqlulaknFsO9eCe61z71-ZDxnFrxx8tjjSn6AQR-IJU 11025
autobmt/bayes_opt_tuner.py sha256=uc5iNpea0IAFY0iT3tA42M2dFyzoRLJvP5e9Zz0W154 13566
autobmt/category_label_encoder.py sha256=BXZm6wqIVkTCPZmPPLbiBwLfrsB3wcj6wDEC24LpsBU 3734
autobmt/category_woe_encoder.py sha256=OJAmbFvikYkowAa07NJN-We80QFctTFLxbnznSveT6g 5735
autobmt/detector.py sha256=rA414EFMg6h3FXED2JhY-VLNHCe2EiicqWP-DZmWIEk 4234
autobmt/feature_binning.py sha256=ql_ZDfWvq2wpfL8zVeQJ8BUdoVkciwb-Ea81EMaHJIQ 17099
autobmt/feature_selection.py sha256=J7gAwORlMIygH_DZpFNKv10YJmn_OEgmOqVX2EZQAe8 31163
autobmt/logger_utils.py sha256=ZXBt7cFxavJ-FplhRFsKRgJtClBGfa6zAaWnpkrhp3U 728
autobmt/metrics.py sha256=912kRQhYvpHNoCeTQVyk1iOQuGj5kGJ7ojOVFpKx-bQ 6847
autobmt/plot.py sha256=LP5eugMuvU_XLa1tW7t4uVKnf4ZV6M2Zlkn8fsNICaU 16016
autobmt/report2excel.py sha256=VZ7az7wAmnUNyurM9gdQZ4oEHq1y6JgZyrOjhAIOdgI 28367
autobmt/scorecard.py sha256=fmE4aTlj9bzIDg_xpZKF4CIZPs5Q04s8-Wnc4h9X3Jo 13959
autobmt/statistics.py sha256=1AOVm5lmn4VPbOgVM3qivehPBeFSeco5YicN6iX1EgQ 33519
autobmt/stepwise.py sha256=qHB9e9I-qbSxIeUOnm_cPSIFTDsWg3qXRuWBYMl8blY 24138
autobmt/transformer.py sha256=BNrEAJYhydHNDmUWCgecvCVFReM24urGHh4XiByhXIk 22583
autobmt/utils.py sha256=vwR6b8q67-Vn0Vkte75MH5Byn_A2QlxI0sSU018mVdk 21055
autobmt-0.2.0.dist-info/LICENSE sha256=QPj0_OuRQ6hWIqLsOLg6rS4IjJoa0OfIh6SzKAmFvtM 1068
autobmt-0.2.0.dist-info/METADATA sha256=8QdJTVxWJHHO9ptniR59mHRLlDZmnOjgSJob54PpA-A 4707
autobmt-0.2.0.dist-info/WHEEL sha256=Z-nyYpwrcSqxfdux5Mbn_DQ525iP7J2DG3JgGvOYyTQ 110
autobmt-0.2.0.dist-info/top_level.txt sha256=WirxvcMwtttviUmchu9Jjq0J7A6PoHUtaRegjbrQWBw 8
autobmt-0.2.0.dist-info/RECORD — —

top_level.txt

autobmt