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|PyPI version| |license|
Set train and test dataset under data/input.
Created features from original dataset need to be under data/output/features.
Models for stacking are defined in scripts under scripts folder.
Need to define created features in that scripts.
Just run sh run.sh
(python scripts/XXX.py
)
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
base_fixed_fold.py (class of stacking)
data/
input/
output/
scripts/
script.csv (main script where concrete models defined)
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.
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
FAQs
A stacking library for ensemble learning
We found that stacking demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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