OverBoostClassifier

class imbalanced_ensemble.ensemble.OverBoostClassifier(base_estimator=None, n_estimators: int = 50, *, learning_rate: float = 1.0, algorithm: str = 'SAMME.R', early_termination: bool = False, random_state=None)

Random over-sampling integrated in the learning of AdaBoost.

OverBoost is similar to SMOTEBoost [1], but use RandomOverSampler instead of SMOTE. It alleviates the problem of class balancing by Randomly over-samples the sample at each iteration of the boosting algorithm.

This OverBoost implementation supports multi-class classification.

Parameters
base_estimatorestimator object, default=None

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. If None, then the base estimator is DecisionTreeClassifier(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 trade-off between learning_rate and n_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.

early_terminationbool, default=False

Whether to enable early termination for AdaBoost training. If True, AdaBoost training can be terminated early when the error is zero or the sum of the sample weights is non-positive.

random_stateint, RandomState instance or None, default=None

Control the randomization of the algorithm. Within each iteration, a different seed is generated for each sampler. If the base estimator accepts a random_state attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls.

  • 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.

Attributes
base_estimator_estimator

The base estimator from which the ensemble is grown.

base_sampler_SMOTE

The base sampler.

estimators_list of classifiers

The collection of fitted sub-estimators.

samplers_list of SMOTE

The collection of used samplers.

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.

estimators_n_training_samples_list of ints

The number of training samples for each fitted base estimators.

feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.

See also

SMOTEBoostClassifier

SMOTE over-sampling integrated in AdaBoost.

KmeansSMOTEBoostClassifier

Kmeans-SMOTE over-sampling integrated in AdaBoost.

OverBaggingClassifier

Bagging with intergrated random over-sampling.

References

1

Chawla, N. V., Lazarevic, A., Hall, L. O., & Bowyer, K. W. “SMOTEBoost: Improving prediction of the minority class in boosting.” European conference on principles of data mining and knowledge discovery. Springer, Berlin, Heidelberg, (2003): 107-119.

Examples

>>> from imbalanced_ensemble.ensemble import OverBoostClassifier
>>> 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 = OverBoostClassifier(random_state=0)
>>> clf.fit(X, y)  
OverBoostClassifier(...)
>>> clf.predict(X)  
array([...])

Methods

decision_function(X)

Compute the decision function of X.

fit(X, y, *[, sample_weight, target_label, ...])

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

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict classes for X.

predict_log_proba(X)

Predict class log-probabilities for X.

predict_proba(X)

Predict class probabilities for X.

score(X, y[, sample_weight])

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

set_params(**params)

Set the parameters of this estimator.

staged_decision_function(X)

Compute decision function of X for each boosting iteration.

staged_predict(X)

Return staged predictions for X.

staged_predict_proba(X)

Predict class probabilities for X.

staged_score(X, y[, sample_weight])

Return staged scores for X, y.

decision_function(X)

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

property feature_importances_

The impurity-based 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: impurity-based 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(X, y, *, sample_weight=None, target_label: int = None, n_target_samples: int = None, balancing_schedule: str = 'uniform', eval_datasets: dict = None, eval_metrics: dict = None, train_verbose: bool = False)

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

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.

yarray-like of shape (n_samples,)

The target values (class labels).

sample_weightarray-like of shape (n_samples,), default=None

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

target_labelint, default=None

Specify the class targeted by the over-sampling. All other classes that have less samples than the target class will be considered as minority classes. They will be over-sampled until the number of samples is equalized. The remaining majority classes (if any) will stay unchanged.

n_target_samplesint or dict, default=None

Specify the desired number of samples (of each class) after the over-sampling.

  • If int, all classes that have less than the n_target_samples samples will be over-sampled until the number of samples is equalized.

  • If dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.

balancing_schedulestr, or callable, default=’uniform’

Scheduler that controls how to sample the data set during the ensemble training process.

  • If str, using the predefined balancing schedule. Possible choices are:

    • 'uniform': resample to target distribution for all base estimators;

    • 'progressive': The resample class distributions are progressive interpolation between the original and the target class distribution. Example: For a class \(c\), say the number of samples is \(N_{c}\) and the target number of samples is \(N'_{c}\). Suppose that we are training the \(t\)-th base estimator of a \(T\)-estimator ensemble, then we expect to get \((1-\frac{t}{T}) \cdot N_{c} + \frac{t}{T} \cdot N'_{c}\) samples after resampling;

  • If callable, function takes 4 positional arguments with order ('origin_distr': dict, 'target_distr': dict, 'i_estimator': int, 'total_estimator': int), and returns a 'result_distr': dict. For all parameters of type dict, the keys of type int correspond to the targeted classes, and the values of type str correspond to the (desired) number of samples for each class.

eval_datasetsdict, default=None

Dataset(s) used for evaluation during the ensemble training process. The keys should be strings corresponding to evaluation datasets’ names. The values should be tuples corresponding to the input samples and target values.

Example: eval_datasets = {'valid' : (X_valid, y_valid)}

eval_metricsdict, default=None

Metric(s) used for evaluation during the ensemble training process.

  • If None, use 3 default metrics:

    • 'acc': sklearn.metrics.accuracy_score()

    • 'balanced_acc': sklearn.metrics.balanced_accuracy_score()

    • 'weighted_f1': sklearn.metrics.f1_score(average='weighted')

  • If dict, the keys should be strings corresponding to evaluation metrics’ names. The values should be tuples corresponding to the metric function (callable) and additional kwargs (dict).

    • The metric function should at least take 2 named/keyword arguments, y_true and one of [y_pred, y_score], and returns a float as the evaluation score. Keyword arguments:

      • y_true, 1d-array of shape (n_samples,), true labels or binary label indicators corresponds to ground truth (correct) labels.

      • When using y_pred, input will be 1d-array of shape (n_samples,) corresponds to predicted labels, as returned by a classifier.

      • When using y_score, input will be 2d-array of shape (n_samples, n_classes,) corresponds to probability estimates provided by the predict_proba method. In addition, the order of the class scores must correspond to the order of labels, if provided in the metric function, or else to the numerical or lexicographical order of the labels in y_true.

    • The metric additional kwargs should be a dictionary that specifies the additional arguments that need to be passed into the metric function.

Example: {'weighted_f1': (sklearn.metrics.f1_score, {'average': 'weighted'})}

train_verbosebool, int or dict, default=False

Controls the verbosity during ensemble training/fitting.

  • If bool: False means disable training verbose. True means print training information to sys.stdout use default setting:

    • 'granularity' : int(n_estimators/10)

    • 'print_distribution' : True

    • 'print_metrics' : True

  • If int, print information per train_verbose rounds.

  • If dict, control the detailed training verbose settings. They are:

    • 'granularity': corresponding value should be int, the training information will be printed per granularity rounds.

    • 'print_distribution': corresponding value should be bool, whether to print the data class distribution after resampling. Will be ignored if the ensemble training does not perform resampling.

    • 'print_metrics': corresponding value should be bool, whether to print the latest performance score. The performance will be evaluated on the training data and all given evaluation datasets with the specified metrics.

Warning

Setting a small 'granularity' value with 'print_metrics' enabled can be costly when the training/evaluation data is large or the metric scores are hard to compute. Normally, one can set 'granularity' to n_estimators/10 (this is used by default).

Returns
selfobject

Returns self.

get_params(deep=True)

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
paramsdict

Parameter names mapped to their values.

predict(X)

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. COO, DOK, and LIL are converted to CSR.

Returns
yndarray of shape (n_samples,)

The predicted classes.

predict_log_proba(X)

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. 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(X)

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. 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(X, y, sample_weight=None)

Return 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
Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns
scorefloat

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

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). 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
selfestimator instance

Estimator instance.

staged_decision_function(X)

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. 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, 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)

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
Xarray-like 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(X)

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. 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(X, y, sample_weight=None)

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. COO, DOK, and LIL are converted to CSR.

yarray-like of shape (n_samples,)

Labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Yields
zfloat

Examples using imbalanced_ensemble.ensemble.OverBoostClassifier