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https://github.com/fukatani/stacked_generalization

Library for machine learning stacking generalization.
https://github.com/fukatani/stacked_generalization

machine-learning

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Library for machine learning stacking generalization.

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stacked\_generalization

=======================



Implemented machine learning ***stacking technic[1]*** as handy library

in Python. Feature weighted linear stacking is also available. (See

https://github.com/fukatani/stacked\_generalization/tree/master/stacked\_generalization/example)



Including simple model cache system Joblibed claasifier and Joblibed

Regressor.



Feature

-------



1) Any scikit-learn model is availavle for Stage 0 and Stage 1 model.

'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''



And stacked model itself has the same interface as scikit-learn library.

''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''



You can replace model such as *RandomForestClassifier* to *stacked

model* easily in your scripts. And multi stage stacking is also easy.



ex.



.. code:: python



    from stacked_generalization.lib.stacking import StackedClassifier

    from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier

    from sklearn.linear_model import LogisticRegression, RidgeClassifier

    from sklearn import datasets, metrics

    iris = datasets.load_iris()



    # Stage 1 model

    bclf = LogisticRegression(random_state=1)



    # Stage 0 models

    clfs = [RandomForestClassifier(n_estimators=40, criterion = 'gini', random_state=1),

            GradientBoostingClassifier(n_estimators=25, random_state=1),

            RidgeClassifier(random_state=1)]



    # same interface as scikit-learn

    sl = StackedClassifier(bclf, clfs)

    sl.fit(iris.target, iris.data)

    score = metrics.accuracy_score(iris.target, sl.predict(iris.data))

    print("Accuracy: %f" % score)



More detail example is here.

https://github.com/fukatani/stacked\_generalization/blob/master/stacked\_generalization/example/cross\_validation\_for\_iris.py



https://github.com/fukatani/stacked\_generalization/blob/master/stacked\_generalization/example/simple\_regression.py



2) Evaluation model by out-of-bugs score.

'''''''''''''''''''''''''''''''''''''''''



Stacking technic itself uses CV to stage0. So if you use CV for entire

stacked model, ***each stage 0 model are fitted n\_folds squared

times.*** Sometimes its computational cost can be significent, therefore

we implemented CV only for stage1[2].



For example, when we get 3 blends (stage0 prediction), 2 blends are used

for stage 1 fitting. The remaining one blend is used for model test.

Repitation this cycle for all 3 blends, and averaging scores, we can get

oob (out-of-bugs) score ***with only n\_fold times stage0 fitting.***



ex.



.. code:: python



    sl = StackedClassifier(bclf, clfs, oob_score_flag=True)

    sl.fit(iris.data, iris.target)

    print("Accuracy: %f" % sl.oob_score_)



3) Caching stage1 blend\_data and trained model. (optional)

'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''



If cache is exists, recalculation for stage 0 will be skipped. This

function is useful for stage 1 tuning.



.. code:: python



    sl = StackedClassifier(bclf, clfs, save_stage0=True, save_dir='stack_temp')



Feature of Joblibed Classifier / Regressor

------------------------------------------



Joblibed Classifier / Regressor is simple cache system for scikit-learn

machine learning model. You can use it easily by minimum code

modification.



At first fitting and prediction, model calculation is performed

normally. At the same time, model fitting result and prediction result

are saved as *.pkl* and *.csv* respectively.



**At second fitting and prediction, if cache is existence, model and

prediction results will be loaded from cache and never recalculation.**



e.g.



.. code:: python



    from sklearn import datasets

    from sklearn.cross_validation import StratifiedKFold

    from sklearn.ensemble import RandomForestClassifier

    from stacked_generalization.lib.joblibed import JoblibedClassifier



    # Load iris

    iris = datasets.load_iris()



    # Declaration of Joblibed model

    rf = RandomForestClassifier(n_estimators=40)

    clf = JoblibedClassifier(rf, "rf")



    train_idx, test_idx = list(StratifiedKFold(iris.target, 3))[0]



    xs_train = iris.data[train_idx]

    y_train = iris.target[train_idx]

    xs_test = iris.data[test_idx]

    y_test = iris.target[test_idx]



    # Need to indicate sample for discriminating cache existence.

    clf.fit(xs_train, y_train, train_idx)

    score = clf.score(xs_test, y_test, test_idx)



See also

https://github.com/fukatani/stacked\_generalization/blob/master/stacked\_generalization/lib/joblibed.py



Software Requirement

--------------------



-  Python (2.7 or 3.5 or later)

-  numpy

-  scikit-learn

-  pandas



Installation

------------



::



    pip install stacked_generalization



License

-------



MIT License. (http://opensource.org/licenses/mit-license.php)



Copyright

---------



Copyright (C) 2016, Ryosuke Fukatani



Many part of the implementation of stacking is based on the following.

Thanks!

https://github.com/log0/vertebral/blob/master/stacked\_generalization.py



Other

-----



Any contributions (implement, documentation, test or idea...) are

welcome.



References

----------



[1] L. Breiman, "Stacked Regressions", Machine Learning, 24, 49-64

(1996). [2] J. Sill1 et al, "Feature Weighted Linear Stacking",

https://arxiv.org/abs/0911.0460, 2009.



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