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Self-paced Ensemble for classification on class-imbalanced data.

Project description

Self-paced Ensemble

"Self-paced Ensemble for Highly Imbalanced Massive Data Classification" (ICDE 2020). [PDF] [Slides] [arXiv] [PyPI]

Self-paced Ensemble (SPE) 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.

Cite Us

If you find this repository/work helpful, please cite our work:

@inproceedings{
    liu2020self-paced-ensemble,
    title={Self-paced Ensemble for Highly Imbalanced Massive Data Classification},
    author={Liu, Zhining and Cao, Wei and Gao, Zhifeng and Bian, Jiang and Chen, Hechang and Chang, Yi and Liu, Tie-Yan},
    booktitle={2020 IEEE 36th International Conference on Data Engineering (ICDE)},
    pages={841--852},
    year={2020},
    organization={IEEE}
}

Install

Our SPE implementation requires following dependencies:

You can install SPE from PyPI by running:

$ pip install self-paced-ensemble

Or you can install SPE by clone this repository:

$ git clone https://github.com/ZhiningLiu1998/self-paced-ensemble.git
$ cd self-paced-ensemble
$ python setup.py install

Table of Contents

Background

SPE performs strictly balanced under-sampling in each iteration and is therefore very computationally efficient. In addition, SPE does not rely on calculating the distance between samples to perform resampling. It can be easily applied to datasets that lack well-defined distance metrics (e.g. with categorical features / missing values) without any modification. Moreover, as a generic ensemble framework, our methods can be easily adapted to most of the existing learning methods (e.g., C4.5, SVM, GBDT, and Neural Network) to boost their performance on imbalanced data. Compared to existing imbalance learning methods, SPE works particularly well on datasets that are large-scale, noisy, and highly imbalanced (e.g. with imbalance ratio greater than 100:1). Such kind of data widely exists in real-world industrial applications. The figure below gives an overview of the SPE framework.

image

Usage

Documentation

Our SPE implementation can be used much in the same way as the ensemble classifiers in sklearn.ensemble.

Parameters Description
base_estimator object, optional (default=sklearn.tree.DecisionTreeClassifier())
The base estimator to fit on self-paced under-sampled subsets of the dataset. NO need to support sample weighting. Built-in fit(), predict(), predict_proba() methods are required.
hardness_func function, optional (default=lambda y_true, y_pred: np.absolute(y_true-y_pred))
User-specified classification hardness function.
Input: y_true and y_pred Output: hardness (1-d array)
n_estimator int, optional (default=10)
The number of base estimators in the ensemble.
k_bins int, optional (default=10)
The number of hardness bins that were used to approximate hardness distribution.
estimator_params list 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_jobs int, default=None
The number of jobs to run in parallel for :meth:predict. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors.
random_state int / RandomState instance / None, optional (default=None)
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 numpy.random.
verbose int, default=0
Controls the verbosity when fitting and predicting.
Methods Description
fit(self, X, y, label_maj=None, label_min=None) Build a self-paced ensemble of estimators from the training set (X, y).
label_maj/label_min specify the label of majority/minority class.
By default, we let the minority class be positive class (label_min=1).
predict(self, X) Predict class for X.
predict_proba(self, X) Predict class probabilities for X.
predict_log_proba(self, X) Predict class log-probabilities for X.
score(self, X, y) Returns the average precision score on the given test data and labels.
Attributes Description
base_estimator_ estimator
The base estimator from which the ensemble is grown.
estimators_ list of estimator
The collection of fitted base estimators.

Examples

A minimal example

>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=100, n_features=4,
...                         n_informative=3, n_redundant=0,
...                         n_classes=2, random_state=0, 
...                         shuffle=False)
>>> clf = SelfPacedEnsembleClassifier(
...         base_estimator=DecisionTreeClassifier(), 
...         n_estimators=10,
...         verbose=1).fit(X, y)
>>> clf.predict([[0, 0, 0, 0]])
array([1])

A non-minimal working example (It demonstrates some of the features of SPE)

import numpy as np
from sklearn.tree import DecisionTreeClassifier
from self_paced_ensemble import SelfPacedEnsembleClassifier
from utils import (
    make_binary_classification_target, 
    imbalance_train_test_split,
    load_covtype_dataset)

# load dataset
X_train, X_test, y_train, y_test = load_covtype_dataset(subset=0.1, random_state=42)

def absolute_error(y_true, y_pred):
    """Self-defined classification hardness function"""
    return np.absolute(y_true - y_pred)

# ensemble training
spe = SelfPacedEnsembleClassifier(
    base_estimator=DecisionTreeClassifier(),
    hardness_func=absolute_error,
    n_estimators=10,
    verbose=1).fit(X_train, y_train, label_maj=0, label_min=1)

# predict & evaluate
y_pred_proba_test = clf.predict_proba(X_test)[:, 1]
print ('\nTest AUPRC score: ', average_precision_score(y_test, y_pred_proba_test))

Outputs should be like:

Dataset used: 		Forest covertypes from UCI (10.0% random subset)
Positive target:	7
Imbalance ratio:	27.328
----------------------------------------------------
# Samples       : 46480
# Features      : 54
# Classes       : 2
Classes         : 0/1
Class Dist      : 44840/1640
Imbalance Ratio : 27.34/1.00
----------------------------------------------------
SPE Training: 100%|██████████| 10/10 [00:00<00:00, 23.65it/s]
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished

Test AUPRC score:  0.9106885803103659

Conducting comparative experiments

We also provide a simple framework (run_example.py) for conveniently comparing the performance of our method and other baselines. It is also a more complex example of how to use our implementation of ensemble methods to perform classification. To use it, simply run:

python run_example.py --method=SPEnsemble --n_estimators=10 --runs=10

Outputs should be like:

Dataset used:           Forest covertypes from UCI (10.0% random subset)
Positive target:        7
Imbalance ratio:        27.328

Running method:         SPEnsemble - 10 estimators in 10 independent run(s) ...
SPEnsemble running: 100%|███████████████████████| 10/10 [00:11<00:00,  1.16s/it]
ave_run_time:           0.412s
------------------------------
Metrics:
AUCPRC  mean:0.910  std:0.009
F1      mean:0.872  std:0.006
G-mean  mean:0.873  std:0.007
MCC     mean:0.868  std:0.007
Arguments Description
--method string, optional (default='SPEnsemble')
support: SPEnsemble, SMOTEBoost, SMOTEBagging, RUSBoost, UnderBagging, Cascade, all
When all, the script will run all supported methods.
--n_estimators int, optional (default=10)
The number of base estimators in the ensemble.
--runs int, optional (default=10)
The number of independent runs for evaluating method performance.

Results

Dataset links: Credit Fraud, KDDCUP, Record Linkage, Payment Simulation.

image

Comparisons of SPE with traditional resampling/ensemble methods in terms of performance & computational efficiency.

image

image

image

Miscellaneous

This repository contains:

  • Implementation of Self-paced Ensemble
  • Implementation of 5 ensemble-based imbalance learning baselines
    • SMOTEBoost [1]
    • SMOTEBagging [2]
    • RUSBoost [3]
    • UnderBagging [4]
    • BalanceCascade [5]
  • Implementation of resampling based imbalance learning baselines [6]
  • Additional experimental results

NOTE: The implementations of [1],[3] and resampling methods are based on imbalanced-algorithms and imbalanced-learn.

References

# Reference
[1] N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, Smoteboost: Improving prediction of the minority class in boosting. in European conference on principles of data mining and knowledge discovery. Springer, 2003, pp. 107–119
[2] S. Wang and X. Yao, Diversity analysis on imbalanced data sets by using ensemble models. in 2009 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2009, pp. 324–331.
[3] C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 40, no. 1, pp. 185–197, 2010.
[4] R. Barandela, R. M. Valdovinos, and J. S. Sanchez, “New applications´ of ensembles of classifiers,” Pattern Analysis & Applications, vol. 6, no. 3, pp. 245–256, 2003.
[5] X.-Y. Liu, J. Wu, and Z.-H. Zhou, “Exploratory undersampling for class-imbalance learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539–550, 2009.
[6] Guillaume Lemaître, Fernando Nogueira, and Christos K. Aridas. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research, 18(17):1–5, 2017.

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