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][source]

Random under-sampling integrating in the learning of an AdaBoost classifier.

During learning, the problem of class balancing is alleviated by random under-sampling the sample at each iteration of the boosting algorithm.

Read more in the User Guide.

Parameters:
base_estimator : object, optional (default=DecisionTreeClassifier)

The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes_ and n_classes_ attributes.

n_estimators : integer, optional (default=50)

The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.

learning_rate : float, optional (default=1.)

Learning rate shrinks the contribution of each classifier by learning_rate. There is a trade-off between learning_rate and n_estimators.

algorithm : {‘SAMME’, ‘SAMME.R’}, optional (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_strategy : float, 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 majority class over the number of samples in the minority class after resampling. Therefore, the ratio is expressed as \alpha_{us} = N_{rM} / N_{m} where N_{rM} and N_{m} are the number of samples in the majority class after resampling and the number of samples in the minority class, respectively.

    Warning

    float is only available for binary classification. An error is raised for multi-class 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 a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.

replacement : bool, optional (default=False)

Whether or not to sample randomly with replacement or not.

random_state : int, RandomState instance or None, optional (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 the RandomState instance used by np.random.

References

[1]Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. “RUSBoost: A hybrid approach to alleviating class imbalance.” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 40.1 (2010): 185-197.

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)  # doctest: +ELLIPSIS
RUSBoostClassifier(...)
>>> clf.predict(X)  # doctest: +ELLIPSIS
array([...])
Attributes:
estimators_ : list of classifiers

The collection of fitted sub-estimators.

samplers_ : list of RandomUnderSampler

The collection of fitted samplers.

pipelines_ : list of Pipeline.

The collection of fitted pipelines (samplers + trees).

classes_ : ndarray, shape (n_classes,)

The classes labels.

n_classes_ : int

The number of classes.

estimator_weights_ : ndarray, shape (n_estimator,)

Weights for each estimator in the boosted ensemble.

estimator_errors_ : ndarray, shape (n_estimator,)

Classification error for each estimator in the boosted ensemble.

feature_importances_ : ndarray, shape (n_features,)

Return the feature importances (the higher, the more important the feature).

__init__(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', sampling_strategy='auto', replacement=False, random_state=None)[source][source]

Initialize self. See help(type(self)) for accurate signature.

decision_function(X)[source]

Compute the decision function of X.

Parameters:
X : {array-like, 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.

Returns:
score : array, 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, otherwise k==n_classes. For binary classification, values closer to -1 or 1 mean more like the first or second class in classes_, respectively.

feature_importances_
Return the feature importances (the higher, the more important the
feature).
Returns:
feature_importances_ : array, shape = [n_features]
fit(X, y, sample_weight=None)[source][source]

Build a boosted classifier from the training set (X, y).

Parameters:
X : {array-like, sparse matrix}, 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.

y : array-like, shape (n_samples,)

The target values (class labels).

sample_weight : array-like, shape (n_samples,), optional

Sample weights. If None, the sample weights are initialized to 1 / n_samples.

Returns:
self : object

Returns self.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
params : mapping of string to any

Parameter names mapped to their values.

predict(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 : {array-like, 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.

Returns:
y : array of shape = [n_samples]

The predicted classes.

predict_log_proba(X)[source]

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers in the ensemble.

Parameters:
X : {array-like, 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.

Returns:
p : array 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(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 : {array-like, 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.

Returns:
p : array 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(X, y, sample_weight=None)[source]

Returns the mean accuracy on the given test data and labels.

In multi-label 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:
X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:
score : float

Mean accuracy of self.predict(X) wrt. y.

set_params(**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.

Returns:
self
staged_decision_function(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 : {array-like, 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.

Returns:
score : generator of array, 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, otherwise k==n_classes. For binary classification, values closer to -1 or 1 mean more like the first or second class in classes_, respectively.

staged_predict(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:
X : array-like of shape = [n_samples, n_features]

The input samples.

Returns:
y : generator of array, shape = [n_samples]

The predicted classes.

staged_predict_proba(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 : {array-like, 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.

Returns:
p : generator of array, 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(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 : {array-like, 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.

y : array-like, shape = [n_samples]

Labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:
z : float

Examples using imblearn.ensemble.RUSBoostClassifier