imblearn.ensemble
.RUSBoostClassifier¶

class
imblearn.ensemble.
RUSBoostClassifier
(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', sampling_strategy='auto', replacement=False, random_state=None)[source]¶ Random undersampling integrated in the learning of AdaBoost.
During learning, the problem of class balancing is alleviated by random undersampling the sample at each iteration of the boosting algorithm.
Read more in the User Guide.
 Parameters
 base_estimatorobject, default=None
The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper
classes_
andn_classes_
attributes. IfNone
, then the base estimator isDecisionTreeClassifier(max_depth=1)
. n_estimatorsint, default=50
The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.
 learning_ratefloat, default=1.0
Learning rate shrinks the contribution of each classifier by
learning_rate
. There is a tradeoff betweenlearning_rate
andn_estimators
. algorithm{‘SAMME’, ‘SAMME.R’}, default=’SAMME.R’
If ‘SAMME.R’ then use the SAMME.R real boosting algorithm.
base_estimator
must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations. sampling_strategyfloat, str, dict, callable, default=’auto’
Sampling information to sample the data set.
When
float
, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as where is the number of samples in the minority class and is the number of samples in the majority class after resampling.Warning
float
is only available for binary classification. An error is raised for multiclass classification.When
str
, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:'majority'
: resample only the majority class;'not minority'
: resample all classes but the minority class;'not majority'
: resample all classes but the majority class;'all'
: resample all classes;'auto'
: equivalent to'not minority'
.When
dict
, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.When callable, function taking
y
and returns adict
. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.
 replacementbool, default=False
Whether or not to sample randomly with replacement or not.
 random_stateint, RandomState instance, default=None
Control the randomization of the algorithm.
If int,
random_state
is the seed used by the random number generator;If
RandomState
instance, random_state is the random number generator;If
None
, the random number generator is theRandomState
instance used bynp.random
.
See also
BalancedBaggingClassifier
Bagging classifier for which each base estimator is trained on a balanced bootstrap.
BalancedRandomForestClassifier
Random forest applying randomunder sampling to balance the different bootstraps.
EasyEnsembleClassifier
Ensemble of AdaBoost classifier trained on balanced bootstraps.
References
 R61575984065a1
Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. “RUSBoost: A hybrid approach to alleviating class imbalance.” IEEE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans 40.1 (2010): 185197.
Examples
>>> from imblearn.ensemble import RUSBoostClassifier >>> from sklearn.datasets import make_classification >>> >>> X, y = make_classification(n_samples=1000, n_classes=3, ... n_informative=4, weights=[0.2, 0.3, 0.5], ... random_state=0) >>> clf = RUSBoostClassifier(random_state=0) >>> clf.fit(X, y) RUSBoostClassifier(...) >>> clf.predict(X) array([...])
 Attributes
 base_estimator_estimator
The base estimator from which the ensemble is grown.
 estimators_list of classifiers
The collection of fitted subestimators.
 samplers_list of RandomUnderSampler
The collection of fitted samplers.
 pipelines_list of Pipeline
The collection of fitted pipelines (samplers + trees).
 classes_ndarray of shape (n_classes,)
The classes labels.
 n_classes_int
The number of classes.
 estimator_weights_ndarray of shape (n_estimator,)
Weights for each estimator in the boosted ensemble.
 estimator_errors_ndarray of shape (n_estimator,)
Classification error for each estimator in the boosted ensemble.
feature_importances_
ndarray of shape (n_features,)The impuritybased feature importances.

__init__
(self, base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', sampling_strategy='auto', replacement=False, random_state=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

decision_function
(self, X)[source]¶ Compute the decision function of
X
. Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 Returns
 scorendarray of shape of (n_samples, k)
The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with
k == 1
, otherwisek==n_classes
. For binary classification, values closer to 1 or 1 mean more like the first or second class inclasses_
, respectively.

property
feature_importances_
¶ The impuritybased feature importances.
The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.
Warning: impuritybased feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance
as an alternative. Returns
 feature_importances_ndarray of shape (n_features,)
The feature importances.

fit
(self, X, y, sample_weight=None)[source]¶ Build a boosted classifier from the training set (X, y).
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. DOK and LIL are converted to CSR.
 yarraylike of shape (n_samples,)
The target values (class labels).
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights. If None, the sample weights are initialized to
1 / n_samples
.
 Returns
 selfobject
Returns self.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, X)[source]¶ Predict classes for X.
The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 Returns
 yndarray of shape (n_samples,)
The predicted classes.

predict_log_proba
(self, X)[source]¶ Predict class logprobabilities for X.
The predicted class logprobabilities of an input sample is computed as the weighted mean predicted class logprobabilities of the classifiers in the ensemble.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 Returns
 pndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

predict_proba
(self, X)[source]¶ Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 Returns
 pndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

score
(self, X, y, sample_weight=None)[source]¶ Return the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of self.predict(X) wrt. y.

set_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.

staged_decision_function
(self, X)[source]¶ Compute decision function of
X
for each boosting iteration.This method allows monitoring (i.e. determine error on testing set) after each boosting iteration.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 Yields
 scoregenerator of ndarray of shape (n_samples, k)
The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with
k == 1
, otherwisek==n_classes
. For binary classification, values closer to 1 or 1 mean more like the first or second class inclasses_
, respectively.

staged_predict
(self, X)[source]¶ Return staged predictions for X.
The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.
This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine the prediction on a test set after each boost.
 Parameters
 Xarraylike of shape (n_samples, n_features)
The input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 Yields
 ygenerator of ndarray of shape (n_samples,)
The predicted classes.

staged_predict_proba
(self, X)[source]¶ Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.
This generator method yields the ensemble predicted class probabilities after each iteration of boosting and therefore allows monitoring, such as to determine the predicted class probabilities on a test set after each boost.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 Yields
 pgenerator of ndarray of shape (n_samples,)
The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

staged_score
(self, X, y, sample_weight=None)[source]¶ Return staged scores for X, y.
This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 yarraylike of shape (n_samples,)
Labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Yields
 zfloat