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        "author": "Ikki Tanaka",
        "author_email": "ikki0407@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Utilities"
        ],
        "description": "Library for stacking(Stacked generalization)\n============================================\n\n|PyPI version| |license|\n\nAbout this library(watch test folder for more detailed)\n-------------------------------------------------------\n\n1. Set train and test dataset under data/input.\n\n2. Created features from original dataset need to be under\n   data/output/features.\n\n3. Models for stacking are defined in scripts under scripts folder.\n\n4. Need to define created features in that scripts.\n\n5. Just run ``sh run.sh`` (``python scripts/XXX.py``)\n\n--------------\n\nGetting started: 30 seconds to stacking\n---------------------------------------\n\n--------------\n\nInstallation\n------------\n\nTo install stacking, ``cd`` to the stacking folder and run the install\ncommand:\n\n::\n\n    sudo python setup.py install\n\nYou can also install stacking from PyPI:\n\n::\n\n    pip install stacking\n\n--------------\n\nTree of files\n-------------\n\n-  base\\_fixed\\_fold.py (class of stacking)\n-  data/\n-  input/\n\n   -  train.csv (train dataset)\n   -  test.csv (test dataset)\n\n-  output/\n\n   -  features/\n   -  features.csv (features user created)\n   -  temp/\n   -  temp.csv (files saved in stacking)\n\n-  scripts/\n-  script.csv (main script where concrete models defined)\n\n--------------\n\nDetails of scripts\n------------------\n\n-  base.py:\n-  Base models for stacking are defined here (using\n   sklearn.base.BaseEstimator).\n-  Some models are defined here. e.g., XGBoost, Keras, Vowpal Wabbit.\n-  These models are wrapped as scikit-learn like (using\n   sklearn.base.ClassifierMixin, sklearn.base.RegressorMixin).\n-  That is, model class has some methods, fit(), predict\\_proba(), and\n   predict().\n\nNew user-defined models can be added here.\n\nScikit-learn models can be used.\n\nBase model have some arguments.\n\n-  's': Stacking. Saving a oof(out-of-fold)\n   prediction({model\\_name}\\_all\\_fold.csv) and average of test\n   prediction based on train-fold models({model\\_name}\\_test.csv). These\n   files will be used for next level stacking.\n\n-  't': Training with all data and predict\n   test({model\\_name}\\_TestInAllTrainingData.csv). In this training, no\n   validation data are used.\n\n-  'st': Stacking and then training with all data and predict test ('s'\n   and 't').\n\n-  'cv': Only cross validation without saving the prediction.\n\nDefine several models and its parameters used for stacking. Define task\ndetails on the top of script. Train and test feature set are defined\nhere. Need to define CV-fold index.\n\nAny level stacking can be defined.\n\n--------------\n\nTODO LIST\n---------\n\nNeed to be more general library.\n\nPlease check isuues!!\n\n.. |PyPI version| image:: https://badge.fury.io/py/stacking.svg\n   :target: https://badge.fury.io/py/stacking\n.. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000\n   :target: https://github.com/ikki407/stacking/LICENSE",
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        "keywords": "stacking,ensemble,machine learning,cross validation,sckit-learn,XGBoost,Keras,Vowpal Wabbit",
        "license": "MIT",
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