# SelfPacedEnsembleClassifier

class imbalanced_ensemble.ensemble.SelfPacedEnsembleClassifier(base_estimator=None, n_estimators: int = 50, k_bins: int = 5, soft_resample_flag: bool = False, replacement: bool = True, estimator_params=(), n_jobs=None, random_state=None, verbose=0)

A self-paced ensemble (SPE) Classifier for class-imbalanced learning.

Self-paced Ensemble (SPE) [1] is an ensemble learning framework for massive highly imbalanced classification. It is an easy-to-use solution to class-imbalanced problems, features outstanding computing efficiency, good performance, and wide compatibility with different learning models.

This implementation extends SPE to support multi-class classification.

Parameters
base_estimatorestimator object, default=None

The base estimator to fit on self-paced under-sampled subsets of the dataset. Support for sample weighting is NOT required, but need proper classes_ and n_classes_ attributes. If None, then the base estimator is DecisionTreeClassifier().

n_estimatorsint, default=50

The number of base estimators in the ensemble.

k_binsint, default=5

The number of hardness bins that were used to approximate hardness distribution. It is recommended to set it to 5. One can try a larger value when the smallest class in the data set has a sufficient number (say, > 1000) of samples.

soft_resample_flagbool, default=False

Whether to use weighted sampling to perform soft self-paced under-sampling, rather than explicitly cut samples into k-bins and perform hard sampling.

replacementbool, default=True

Whether samples are drawn with replacement. If False and soft_resample_flag = False, may raise an error when a bin has insufficient number of data samples for resampling.

estimator_paramslist of str, default=tuple()

The list of attributes to use as parameters when instantiating a new base estimator. If none are given, default parameters are used.

n_jobsint, default=None

The number of jobs to run in parallel for predict(). None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

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.

verboseint, default=0

Controls the verbosity when predicting.

Attributes
base_estimatorestimator

The base estimator from which the ensemble is grown.

base_sampler_SelfPacedUnderSampler

The base sampler.

estimators_list of estimator

The collection of fitted base estimators.

samplers_list of SelfPacedUnderSampler

The collection of fitted samplers.

classes_ndarray of shape (n_classes,)

The classes labels.

n_classes_int

The number of classes.

feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.

estimators_n_training_samples_list of ints

The number of training samples for each fitted base estimators.

BalanceCascadeClassifier

EasyEnsembleClassifier

Bag of balanced boosted learners.

RUSBoostClassifier

References

1

Liu, Z., Cao, W., Gao, Z., Bian, J., Chen, H., Chang, Y., & Liu, T. Y. “Self-paced ensemble for highly imbalanced massive data classification.” 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2010: 841-852.

Examples

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


Methods

 fit(X, y, *[, sample_weight]) Build a SPE classifier from the training set (X, y). get_params([deep]) Get parameters for this estimator. Predict class for 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.
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, **kwargs)

Build a SPE 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 under-sampling. All other classes that have more samples than the target class will be considered as majority classes. They will be under-sampled until the number of samples is equalized. The remaining minority classes (if any) will stay unchanged.

n_target_samplesint or dict, default=None

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

• If int, all classes that have more than the n_target_samples samples will be under-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
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 class for X.

The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a predict_proba method, then it resorts to voting.

Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns
yndarray of shape (n_samples,)

The predicted classes.

predict_proba(X)

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a predict_proba method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class.

Parameters
X{array-like, sparse matrix} of shape = [n_samples, n_features]

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns
parray of shape = [n_samples, n_classes]

The class probabilities of the input samples.

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.