stacking 0.1.2

A stacking library for ensemble learning

Library for stacking
====================

|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() and predict\_proba().

New user-defined models can be added here.

Scikit-learn models can be used.

Base model have some arguments.

- 's': Stacking. Svaing a oof prediction({model\_name}\_all\_fold.csv)
and average of test prediction based on fold-train
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). This is useful to get
the single model performance.

- 'st': Stacking and then training with all data and predict test ('s'
and 't').

- 'cv': Only cross validation without saving the prediction.

Define task details top of script.

- features.py: Create features based on original dataset.

- scripts/XXX.py: Define several models and its parameters used for
stacking. 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