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auto_ml

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2.9.10 auto_ml-2.9.10-py2.py3-none-any.whl

Wheel Details

Project: auto_ml
Version: 2.9.10
Filename: auto_ml-2.9.10-py2.py3-none-any.whl
Download: [link]
Size: 71607
MD5: 735759a7cb17126dcce8b96807e55dbf
SHA256: 27e1b3039376575afa64ad21fdc2f1c7cc59209f5bc5d5c071ecd8aa56a45545
Uploaded: 2018-02-22 01:13:03 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.0
Name: auto-ml
Version: 2.9.10
Summary: Automated machine learning for production and analytics
Author: Preston Parry
Author-Email: ClimbsBytes[at]gmail.com
Home-Page: https://github.com/ClimbsRocks/auto_ml
License: MIT
Keywords: machine learning,data science,automated machine learning,regressor,regressors,regression,classification,classifiers,classifier,estimators,predictors,XGBoost,Random Forest,sklearn,scikit-learn,analytics,analysts,coefficients,feature importancesanalytics,artificial intelligence,subpredictors,ensembling,stacking,blending,feature engineering,feature extraction,feature selection,production,pandas,dataframes,machinejs,deep learning,tensorflow,deeplearning,lightgbm,gradient boosting,gbm,keras,production ready,test coverage
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.6
Requires-Dist: dill (<0.3,>=0.2.5)
Requires-Dist: h5py (<3.0,>=2.7.0)
Requires-Dist: lightgbm (<2.1,>=2.0.11)
Requires-Dist: numpy (<2.0,>=1.11.0)
Requires-Dist: pandas (<1.0,>=0.18.0)
Requires-Dist: pathos (<0.3.0,>=0.2.1)
Requires-Dist: python-dateutil (<3.0,>=2.6.1)
Requires-Dist: scikit-learn (<1.0,>=0.18.1)
Requires-Dist: scipy (<2.0,>=0.14.0)
Requires-Dist: sklearn-deap2 (<0.3,>=0.2.1)
Requires-Dist: tabulate (<1.0,>=0.7.5)
[Description omitted; length: 9773 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.30.0)
Root-Is-Purelib: true
Tag: py2-none-any
Tag: py3-none-any

RECORD

Path Digest Size
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auto_ml/__init__.py sha256=1Bi24KjYooYe-9LAIkZObYVO0VcxDJuJafDaZI0EIjc 128
auto_ml/_version.py sha256=ydFkhVbbjZGATQam5049a2jGGmHmC7KUm5H6syGebZg 23
auto_ml/predictor.py sha256=ifyFpFlMxBoPmQWkWWjW8pNANITdO68Hy_iVV7vSQ2s 109347
auto_ml/utils.py sha256=iyRuFVrML_IoFsoh1UkX4xu4tuSnpmYbzK6wsHoc-pM 10179
auto_ml/utils_categorical_ensembling.py sha256=fOk9rDaT0fI3m1j0zKuTHZpBjX5E3ciRXhvckh9sBY0 3234
auto_ml/utils_data_cleaning.py sha256=tFC2uwmOid-AYCWHFqtHWHZgc9jPV8MdNQnwHEsc_8M 20144
auto_ml/utils_ensembling.py sha256=Z8VKvNS-Ow5HuBoQbeMPOnbx5YvJMBqdgBiFd5qeu2Q 6702
auto_ml/utils_feature_selection.py sha256=EnCQ0UBjHN_MYkQJbfersYzOU9tcn_4yZG8GlyYjhag 4418
auto_ml/utils_model_training.py sha256=SS7kfvZNfMCv1sc1yLaZYbKtj5fnxPOpUbeCeOP4FuQ 32339
auto_ml/utils_models.py sha256=dYfBleoU16o430DOfbPhXhhyTzCk9QySBieh2cOxOIc 30588
auto_ml/utils_scaling.py sha256=SKKCSLBOvhmoLXonr1S5meVcHvwG0w3mez4QtPF0MHA 5280
auto_ml/utils_scoring.py sha256=UDhNVvMKkBRFxpcSoQ3sYHG9FLHd19wj0yxZdker5no 14538
auto_ml-2.9.10.dist-info/DESCRIPTION.rst sha256=K6GmpraIUYe5E4Zvhc2T88Dot7-mCFtUoT3Klc8G_sE 9773
auto_ml-2.9.10.dist-info/METADATA sha256=fSYx47Jp-j860rcPq6NokM6EJrABEx25_zs1gm_dGSc 11795
auto_ml-2.9.10.dist-info/RECORD — —
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auto_ml-2.9.10.dist-info/metadata.json sha256=v-7SHFsS7wIHuyv9fSQQPELaffv6mk0e1lknUSBL0Ig 2242
auto_ml-2.9.10.dist-info/top_level.txt sha256=t54NTOl5e4AyPrr7gIgHwQ6v0qkgFT2JEB3djJu-tVs 8

top_level.txt

auto_ml