Metadata-Version: 1.1
Name: stacking
Version: 0.1.3
Summary: A stacking library for ensemble learning
Home-page: https://github.com/ikki407/stacking
Author: Ikki Tanaka
Author-email: ikki0407@gmail.com
License: MIT
Description: Library for stacking(Stacked generalization)
        ============================================
        
        |PyPI version| |license|
        
        About this library(watch test folder for more detailed)
        -------------------------------------------------------
        
        1. Set train and test dataset under data/input.
        
        2. Created features from original dataset need to be under
           data/output/features.
        
        3. Models for stacking are defined in scripts under scripts folder.
        
        4. Need to define created features in that scripts.
        
        5. Just run ``sh run.sh`` (``python scripts/XXX.py``)
        
        --------------
        
        Getting started: 30 seconds to stacking
        ---------------------------------------
        
        --------------
        
        Installation
        ------------
        
        To install stacking, ``cd`` to the stacking folder and run the install
        command:
        
        ::
        
            sudo python setup.py install
        
        You can also install stacking from PyPI:
        
        ::
        
            pip install stacking
        
        --------------
        
        Tree of files
        -------------
        
        -  base\_fixed\_fold.py (class of stacking)
        -  data/
        -  input/
        
           -  train.csv (train dataset)
           -  test.csv (test dataset)
        
        -  output/
        
           -  features/
           -  features.csv (features user created)
           -  temp/
           -  temp.csv (files saved in stacking)
        
        -  scripts/
        -  script.csv (main script where concrete models defined)
        
        --------------
        
        Details of scripts
        ------------------
        
        -  base.py:
        -  Base models for stacking are defined here (using
           sklearn.base.BaseEstimator).
        -  Some models are defined here. e.g., XGBoost, Keras, Vowpal Wabbit.
        -  These models are wrapped as scikit-learn like (using
           sklearn.base.ClassifierMixin, sklearn.base.RegressorMixin).
        -  That is, model class has some methods, fit(), predict\_proba(), and
           predict().
        
        New user-defined models can be added here.
        
        Scikit-learn models can be used.
        
        Base model have some arguments.
        
        -  's': Stacking. Saving a oof(out-of-fold)
           prediction({model\_name}\_all\_fold.csv) and average of test
           prediction based on train-fold models({model\_name}\_test.csv). These
           files will be used for next level stacking.
        
        -  't': Training with all data and predict
           test({model\_name}\_TestInAllTrainingData.csv). In this training, no
           validation data are used.
        
        -  'st': Stacking and then training with all data and predict test ('s'
           and 't').
        
        -  'cv': Only cross validation without saving the prediction.
        
        Define several models and its parameters used for stacking. Define task
        details on the top of script. Train and test feature set are defined
        here. Need to define CV-fold index.
        
        Any level stacking can be defined.
        
        --------------
        
        TODO LIST
        ---------
        
        Need to be more general library.
        
        Please check isuues!!
        
        .. |PyPI version| image:: https://badge.fury.io/py/stacking.svg
           :target: https://badge.fury.io/py/stacking
        .. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000
           :target: https://github.com/ikki407/stacking/LICENSE
        
Keywords: stacking,ensemble,machine learning,cross validation,sckit-learn,XGBoost,Keras,Vowpal Wabbit
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: MIT License
