Library for machine learning stacking generalization.

Related tags

Deep Learning machine-learning
Overview

Build Status

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.

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.

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.

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.

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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Comments
  • something goes wrong when I use command line

    something goes wrong when I use command line "pip install stacked_generalization"

    here is the detail. pip install stacked_generalization Collecting stacked_generalization Using cached https://files.pythonhosted.org/packages/d0/99/e42d99355f00068aa2f8eef5f88c1610558f10217e2704067b5134629397/stacked_generalization-0.0.4.zip Complete output from command python setup.py egg_info: Traceback (most recent call last): File "", line 1, in File "/tmp/pip-build-mm6gy5sl/stacked-generalization/setup.py", line 28, in long_description=read_md('Readme.md'), File "/tmp/pip-build-mm6gy5sl/stacked-generalization/setup.py", line 13, in read_md = lambda f: pypandoc.convert(f, 'rst') File "/home/wxk/anaconda3/lib/python3.6/site-packages/pypandoc/init.py", line 66, in convert raise RuntimeError("Format missing, but need one (identified source as text as no " RuntimeError: Format missing, but need one (identified source as text as no file with that name was found).

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

    Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-mm6gy5sl/stacked-generalization/

    Please tell me how to fix it if you know it.

    opened by wEEang763162 2
  • fix import util

    fix import util

    you can try it with pip3 install -e [email protected]:geoHeil/stacked_generalization.git@fixImportUtil#egg=stacked-generalization

    I fixed the failing import of utils

    opened by geoHeil 1
  • ModuleNotFoundError and ImportError due to deprecated sklearn sub-modules

    ModuleNotFoundError and ImportError due to deprecated sklearn sub-modules

    Hi,

    I was trying to rerun the example, after doing:

    from stacked_generalization.lib.stacking import FWLSRegressor

    I got the following errors, for stacking.py and util.py:

    ModuleNotFoundError: No module named 'sklearn.cross_validation'
ImportError: cannot import name 'joblib' from 'sklearn.externals'

    According to https://stackoverflow.com/questions/30667525/importerror-no-module-named-sklearn-cross-validation and https://stackoverflow.com/questions/61893719/importerror-cannot-import-name-joblib-from-sklearn-externals, it seems to be related to the deprecation of sub-modules. It worked for me to replace all the:

    from sklearn.cross_validation import StratifiedKFold, KFold
from sklearn.externals import joblib

    by

    from sklearn.model_selection import StratifiedKFold, KFold
import joblib

    Thanks.

    opened by ShaunFChen 0
  • Improve memory management.

    Improve memory management.

    Current version of stacked_genelarization continue to holt all stage0 model after fitting. But in case of huge stacking model (~100), stacked_genelarization needs huge memory.

    By adding lazy evaluation, I want to improve memory management. i.e. hold stage0 model only when predicting, and release memory. (Save model to joblib cache and load only when needed.)

    opened by fukatani 0
Owner
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