brew.selection.dynamic package¶

Submodules¶

brew.selection.dynamic.base module¶

class brew.selection.dynamic.base.DCS(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: object

get_neighbors(x, return_distance=False)[source]¶

select(ensemble, x)[source]¶

brew.selection.dynamic.knora module¶

class brew.selection.dynamic.knora.KNORA(Xval, yval, K=5, weighted=False, knn=None)[source]¶: Bases: brew.selection.dynamic.base.DCS

class brew.selection.dynamic.knora.KNORA_ELIMINATE(Xval, yval, K=5, weighted=False, knn=None, v2007=False)[source]¶

Bases: brew.selection.dynamic.knora.KNORA

K-nearest-oracles Eliminate.

The KNORA Eliminate reduces the neighborhood until finds an ensemble of classifiers that correctly classify all neighbors.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

`weighted`: bool, (makes no difference in knora_eliminate) – Bool that defines if the classifiers uses weights or not

Examples

>>> from brew.selection.dynamic.knora import KNORA_ELIMINATE
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0],
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>>
>>> dt = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=dt, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> ke = KNORA_ELIMINATE(X, y, K=5)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=ke)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.knora.KNORA_UNION: KNORA Union.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.
brew.selection.dynamic.ola.OLA: Overall Local Accuracy.

References

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. “Dynamic selection of classifiers—A comprehensive review.” Pattern Recognition 47.11 (2014): 3665-3680.

Hung-Ren Ko, A., Robert Sabourin, and A. de Souza Britto. “K-nearest oracle for dynamic ensemble selection.” Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on. Vol. 1. IEEE, 2007

select(ensemble, x)[source]¶

class brew.selection.dynamic.knora.KNORA_UNION(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.knora.KNORA

K-nearest-oracles Union.

The KNORA union reduces the neighborhood until finds an ensemble of classifiers that correctly classify all neighbors.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

`weighted`: bool, (makes no difference in knora_eliminate) – Bool that defines if the classifiers uses weights or not

Examples

>>> from brew.selection.dynamic.knora import KNORA_UNION
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0],
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>>
>>> dt = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=dt, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> ku = KNORA_UNION(X, y, K=5)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=ku)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.knora.KNORA_ELIMINATE: Knora Eliminate.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.
brew.selection.dynamic.ola.OLA: Overall Local Accuracy.

References

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. “Dynamic selection of classifiers—A comprehensive review.” Pattern Recognition 47.11 (2014): 3665-3680.

Hung-Ren Ko, A., Robert Sabourin, and A. de Souza Britto. “K-nearest oracle for dynamic ensemble selection.” Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on. Vol. 1. IEEE, 2007.

select(ensemble, x)[source]¶

brew.selection.dynamic.lca module¶

class brew.selection.dynamic.lca.LCA(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.base.DCS

select(ensemble, x)[source]¶

class brew.selection.dynamic.lca.LCA2(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.base.DCS

Local Class Accuracy.

The Local Class Accuracy selects the best classifier for a sample using it’s K nearest neighbors.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

Examples

>>> from brew.selection.dynamic.lca import LCA
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0],
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>> tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=tree, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> lca = LCA(X, y, K=3)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=lca)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.ola.OLA: Overall Local Accuracy.

References

Woods, Kevin, Kevin Bowyer, and W. Philip Kegelmeyer Jr. “Combination of multiple classifiers using local accuracy estimates.” Computer Vision and Pattern Recognition, 1996. Proceedings CVPR‘96, 1996 IEEE Computer Society Conference on. IEEE, 1996.

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

select(ensemble, x)[source]¶

brew.selection.dynamic.ola module¶

class brew.selection.dynamic.ola.OLA(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.base.DCS

select(ensemble, x)[source]¶

class brew.selection.dynamic.ola.OLA2(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.base.DCS

Overall Local Accuracy.

The Overall Local Accuracy selects the best classifier for a sample using it’s K nearest neighbors.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

Examples

>>> from brew.selection.dynamic.ola import OLA
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0], [0.5, 0],
                  [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>> tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=tree, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> ola = OLA(X, y, K=3)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=ola)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.lca.LCA: Local Class Accuracy.

References

Woods, Kevin, Kevin Bowyer, and W. Philip Kegelmeyer Jr. “Combination of multiple classifiers using local accuracy estimates.” Computer Vision and Pattern Recognition, 1996. Proceedings CVPR‘96, 1996 IEEE Computer Society Conference on. IEEE, 1996.

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

select(ensemble, x)[source]¶

Module contents¶

class brew.selection.dynamic.LCA(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.base.DCS

select(ensemble, x)[source]¶

class brew.selection.dynamic.OLA(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.base.DCS

select(ensemble, x)[source]¶

class brew.selection.dynamic.APriori(Xval, yval, K=5, weighted=False, knn=None, threshold=0.1)[source]¶

Bases: brew.selection.dynamic.probabilistic.Probabilistic

A Priori Classifier Selection.

The A Priori method is a dynamic classifier selection that uses a probabilistic-based measures for selecting the best classifier.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

`threshold`: float, default = 0.1 – Threshold used to verify if there is a single best.

Examples

>>> from brew.selection.dynamic.probabilistic import APriori
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier as Tree
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0] ,
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>> tree = Tree(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=tree, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> apriori = APriori(X, y, K=3)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=apriori)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.probabilistic.APosteriori: A Posteriori DCS.
brew.selection.dynamic.ola.OLA: Overall Local Accuracy.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.

References

Giacinto, Giorgio, and Fabio Roli. “Methods for dynamic classifier selection.” Image Analysis and Processing, 1999. Proceedings. International Conference on. IEEE, 1999.

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

probabilities(clf, nn_X, nn_y, distances, x)[source]¶

class brew.selection.dynamic.APosteriori(Xval, yval, K=5, weighted=False, knn=None, threshold=0.1)[source]¶

Bases: brew.selection.dynamic.probabilistic.Probabilistic

A Priori Classifier Selection.

The A Priori method is a dynamic classifier selection that uses a probabilistic-based measures for selecting the best classifier.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

`threshold`: float, default = 0.1 – Threshold used to verify if there is a single best.

Examples

>>> from brew.selection.dynamic.probabilistic import APosteriori
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier as Tree
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1],
                  [-0.5, 0] , [0.5, 0], [1, 0],
                  [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>> tree = Tree(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=tree, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> aposteriori = APosteriori(X, y, K=3)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=aposteriori)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.probabilistic.APriori: A Priori DCS.
brew.selection.dynamic.ola.OLA: Overall Local Accuracy.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.

References

Giacinto, Giorgio, and Fabio Roli. “Methods for dynamic classifier selection.” Image Analysis and Processing, 1999. Proceedings. International Conference on. IEEE, 1999.

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

probabilities(clf, nn_X, nn_y, distances, x)[source]¶

class brew.selection.dynamic.KNORA_UNION(Xval, yval, K=5, weighted=False, knn=None)[source]¶

Bases: brew.selection.dynamic.knora.KNORA

K-nearest-oracles Union.

The KNORA union reduces the neighborhood until finds an ensemble of classifiers that correctly classify all neighbors.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

`weighted`: bool, (makes no difference in knora_eliminate) – Bool that defines if the classifiers uses weights or not

Examples

>>> from brew.selection.dynamic.knora import KNORA_UNION
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0],
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>>
>>> dt = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=dt, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> ku = KNORA_UNION(X, y, K=5)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=ku)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.knora.KNORA_ELIMINATE: Knora Eliminate.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.
brew.selection.dynamic.ola.OLA: Overall Local Accuracy.

References

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. “Dynamic selection of classifiers—A comprehensive review.” Pattern Recognition 47.11 (2014): 3665-3680.

Hung-Ren Ko, A., Robert Sabourin, and A. de Souza Britto. “K-nearest oracle for dynamic ensemble selection.” Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on. Vol. 1. IEEE, 2007.

select(ensemble, x)[source]¶

class brew.selection.dynamic.KNORA_ELIMINATE(Xval, yval, K=5, weighted=False, knn=None, v2007=False)[source]¶

Bases: brew.selection.dynamic.knora.KNORA

K-nearest-oracles Eliminate.

The KNORA Eliminate reduces the neighborhood until finds an ensemble of classifiers that correctly classify all neighbors.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

`weighted`: bool, (makes no difference in knora_eliminate) – Bool that defines if the classifiers uses weights or not

Examples

>>> from brew.selection.dynamic.knora import KNORA_ELIMINATE
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0],
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>>
>>> dt = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=dt, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> ke = KNORA_ELIMINATE(X, y, K=5)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=ke)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.knora.KNORA_UNION: KNORA Union.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.
brew.selection.dynamic.ola.OLA: Overall Local Accuracy.

References

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. “Dynamic selection of classifiers—A comprehensive review.” Pattern Recognition 47.11 (2014): 3665-3680.

Hung-Ren Ko, A., Robert Sabourin, and A. de Souza Britto. “K-nearest oracle for dynamic ensemble selection.” Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on. Vol. 1. IEEE, 2007

select(ensemble, x)[source]¶

class brew.selection.dynamic.MCB(Xval, yval, K=5, weighted=False, knn=None, similarity_threshold=0.7, significance_threshold=0.3)[source]¶

Bases: brew.selection.dynamic.base.DCS

Multiple Classifier Behavior.

The Multiple Classifier Behavior (MCB) selects the best classifier using the similarity of the classifications on the K neighbors of the test sample in the validation set.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

Examples

>>> from brew.selection.dynamic.mcb import MCB
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0],
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>> tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=tree, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> mcb = MCB(X, y, K=3)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=mcb)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.lca.OLA: Overall Local Accuracy.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.

References

Giacinto, Giorgio, and Fabio Roli. “Dynamic classifier selection based on multiple classifier behaviour.” Pattern Recognition 34.9 (2001): 1879-1881.

select(ensemble, x)[source]¶

class brew.selection.dynamic.DSKNN(Xval, yval, K=5, weighted=False, knn=None, n_1=0.7, n_2=0.3)[source]¶

Bases: brew.selection.dynamic.base.DCS

DS-KNN

The DS-KNN selects an ensemble of classifiers based on their accuracy and diversity in the neighborhood of the test sample.

`Xval`: array-like, shape = [indeterminated, n_features] – Validation set.

`yval`: array-like, shape = [indeterminated] – Labels of the validation set.

`knn`: sklearn KNeighborsClassifier, – Classifier used to find neighborhood.

Examples

>>> from brew.selection.dynamic import DSKNN
>>> from brew.generation.bagging import Bagging
>>> from brew.base import EnsembleClassifier
>>>
>>> from sklearn.tree import DecisionTreeClassifier
>>> import numpy as np
>>>
>>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0],
                  [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
>>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
>>> tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
>>> bag = Bagging(base_classifier=tree, n_classifiers=10)
>>> bag.fit(X, y)
>>>
>>> sel = DSKNN(X, y, K=3)
>>>
>>> clf = EnsembleClassifier(bag.ensemble, selector=sel)
>>> clf.predict([-1.1,-0.5])
[1]

See also

brew.selection.dynamic.lca.OLA: Overall Local Accuracy.
brew.selection.dynamic.lca.LCA: Local Class Accuracy.

References

Santana, Alixandre, et al. “A dynamic classifier selection method to build ensembles using accuracy and diversity.” 2006 Ninth Brazilian Symposium on Neural Networks (SBRN‘06). IEEE, 2006.

select(ensemble, x)[source]¶