brew.generation package¶

Submodules¶

brew.generation.bagging module¶

class brew.generation.bagging.Bagging(base_classifier=None, n_classifiers=100, combination_rule='majority_vote')[source]¶

Bases: brew.generation.base.PoolGenerator

fit(X, y)[source]¶

predict(X)[source]¶

class brew.generation.bagging.BaggingSK(base_classifier=None, n_classifiers=100, combination_rule='majority_vote')[source]¶

Bases: brew.generation.base.PoolGenerator

” This class should not be used, use brew.generation.bagging.Bagging instead.

fit(X, y)[source]¶

predict(X)[source]¶

brew.generation.base module¶

class brew.generation.base.PoolGenerator[source]¶

Bases: object

fit(X, y)[source]¶

predict(X)[source]¶

brew.generation.random_subspace module¶

class brew.generation.random_subspace.RandomSubspace(base_classifier=None, n_classifiers=100, combination_rule='majority_vote', max_features=0.5)[source]¶

Bases: brew.generation.base.PoolGenerator

fit(X, y)[source]¶

predict(X)[source]¶

brew.generation.smote_bagging module¶

class brew.generation.smote_bagging.SmoteBagging(base_classifier=None, n_classifiers=100, combination_rule='majority_vote', k=5)[source]¶

Bases: brew.generation.base.PoolGenerator

fit(X, y)[source]¶

predict(X)[source]¶

smote_bootstrap_sample(X, y, b, k)[source]¶

class brew.generation.smote_bagging.SmoteBaggingNew(base_classifier=None, n_classifiers=100, combination_rule='majority_vote', k=5)[source]¶

Bases: brew.generation.smote_bagging.SmoteBagging

fit(X, y)[source]¶

smote_bootstrap_sample(X, y, b, k)[source]¶

Module contents¶

class brew.generation.Bagging(base_classifier=None, n_classifiers=100, combination_rule='majority_vote')[source]¶

Bases: brew.generation.base.PoolGenerator

fit(X, y)[source]¶

predict(X)[source]¶

class brew.generation.SmoteBagging(base_classifier=None, n_classifiers=100, combination_rule='majority_vote', k=5)[source]¶

Bases: brew.generation.base.PoolGenerator

fit(X, y)[source]¶

predict(X)[source]¶

smote_bootstrap_sample(X, y, b, k)[source]¶

class brew.generation.RandomSubspace(base_classifier=None, n_classifiers=100, combination_rule='majority_vote', max_features=0.5)[source]¶

Bases: brew.generation.base.PoolGenerator

fit(X, y)[source]¶

predict(X)[source]¶