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A stacking library for ensemble learning

Project 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``)

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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

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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.

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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

Project details


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stacking-0.1.3.tar.gz (12.5 kB) Copy SHA256 hash SHA256 Source None Jul 20, 2016

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