IMBENS: class-imbalanced ensemble learning in Python.

Related tags

Deep Learningpythondata-sciencemachine-learningdata-miningscikit-learnsklearnpython3classificationensembleensemble-learningclass-imbalanceensemble-modelimbalanced-dataimbalanced-learninglong-tailmul
Overview

IMBENS: class-imbalanced ensemble learning in Python.

Documentation Status

Links: [Documentation] [Gallery] [PyPI] [Changelog] [Source] [Download] [็ŸฅไนŽ/Zhihu] [ไธญๆ–‡README] [arXiv]

Paper: IMBENS: Ensemble Class-imbalanced Learning in Python

imbalanced-ensemble (IMBENS, imported as imbalanced_ensemble) is a Python toolbox for quick implementation, modification, evaluation, and visualization of ensemble learning algorithms for class-imbalanced data. The problem of learning from imbalanced data is known as imbalanced learning or long-tail learning (under multi-class scenario). See related papers/libraries/resources here.

Currently (v0.1), IMBENS includes more than 15 ensemble imbalanced learning algorithms, from the classical SMOTEBoost (2003), RUSBoost (2010) to recent SPE (2020), from resampling to cost-sensitive learning. More algorithms will be included in the future. We also provide detailed documentation and examples across various algorithms. See full list of implemented methods here.

IMBENS is featured for:

  • ๐ŸŽ Unified, easy-to-use APIs, detailed documentation and examples.
  • ๐ŸŽ Capable for out-of-the-box multi-class imbalanced (long-tailed) learning.
  • ๐ŸŽ Optimized performance with parallelization when possible using joblib.
  • ๐ŸŽ Powerful, customizable, interactive training logging and visualizer.
  • ๐ŸŽ Full compatibility with other popular packages like scikit-learn and imbalanced-learn.

API Demo:

# Train an SPE classifier
from imbalanced_ensemble.ensemble import SelfPacedEnsembleClassifier
clf = SelfPacedEnsembleClassifier(random_state=42)
clf.fit(X_train, y_train)

# Predict with an SPE classifier
y_pred = clf.predict(X_test)

Table of Contents

Citing us

If you find IMBENS helpful in your work or research, please consider citing our work. We would greatly appreciate citations to the following paper [PDF]:

@article{liu2021imbens,
  title={IMBENS: Ensemble Class-imbalanced Learning in Python},
  author={Liu, Zhining and Wei, Zhepei and Yu, Erxin and Huang, Qiang and Guo, Kai and Yu, Boyang and Cai, Zhaonian and Ye, Hangting and Cao, Wei and Bian, Jiang and Wei, Pengfei and Jiang, Jing and Chang, Yi},
  journal={arXiv preprint arXiv:2111.12776},
  year={2021}
}

Installation

It is recommended to use pip for installation.
Please make sure the latest version is installed to avoid potential problems:

$ pip install imbalanced-ensemble            # normal install
$ pip install --upgrade imbalanced-ensemble  # update if needed

Or you can install imbalanced-ensemble by clone this repository:

$ git clone https://github.com/ZhiningLiu1998/imbalanced-ensemble.git
$ cd imbalanced-ensemble
$ pip install .

imbalanced-ensemble requires following dependencies:

Highlights

  • ๐ŸŽ Unified, easy-to-use API design.
    All ensemble learning methods implemented in IMBENS share a unified API design. Similar to sklearn, all methods have functions (e.g., fit(), predict(), predict_proba()) that allow users to deploy them with only a few lines of code.
  • ๐ŸŽ Extended functionalities, wider application scenarios.
    All methods in IMBENS are ready for multi-class imbalanced classification. We extend binary ensemble imbalanced learning methods to get them to work under the multi-class scenario. Additionally, for supported methods, we provide more training options like class-wise resampling control, balancing scheduler during the ensemble training process, etc.
  • ๐ŸŽ Detailed training log, quick intuitive visualization.
    We provide additional parameters (e.g., eval_datasets, eval_metrics, training_verbose) in fit() for users to control the information they want to monitor during the ensemble training. We also implement an EnsembleVisualizer to quickly visualize the ensemble estimator(s) for providing further information/conducting comparison. See an example here.
  • ๐ŸŽ Wide compatiblilty.
    IMBENS is designed to be compatible with scikit-learn (sklearn) and also other compatible projects like imbalanced-learn. Therefore, users can take advantage of various utilities from the sklearn community for data processing/cross-validation/hyper-parameter tuning, etc.

List of implemented methods

Currently (v0.1.3, 2021/06), 16 ensemble imbalanced learning methods were implemented:
(Click to jump to the document page)

Note: imbalanced-ensemble is still under development, please see API reference for the latest list.

5-min Quick Start with IMBENS

Here, we provide some quick guides to help you get started with IMBENS.
We strongly encourage users to check out the example gallery for more comprehensive usage examples, which demonstrate many advanced features of IMBENS.

A minimal working example

Taking self-paced ensemble [1] as an example, it only requires less than 10 lines of code to deploy it:

>>> from imbalanced_ensemble.ensemble import SelfPacedEnsembleClassifier
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> 
>>> X, y = make_classification(n_samples=1000, n_classes=3,
...                            n_informative=4, weights=[0.2, 0.3, 0.5],
...                            random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...                            X, y, test_size=0.2, random_state=42)
>>> clf = SelfPacedEnsembleClassifier(random_state=0)
>>> clf.fit(X_train, y_train)
SelfPacedEnsembleClassifier(...)
>>> clf.predict(X_test)  
array([...])

Visualize ensemble classifiers

The imbalanced_ensemble.visualizer sub-module provide an ImbalancedEnsembleVisualizer. It can be used to visualize the ensemble estimator(s) for further information or comparison. Please refer to visualizer documentation and examples for more details.

Fit an ImbalancedEnsembleVisualizer

from imbalanced_ensemble.ensemble import SelfPacedEnsembleClassifier
from imbalanced_ensemble.ensemble import RUSBoostClassifier
from imbalanced_ensemble.ensemble import EasyEnsembleClassifier
from sklearn.tree import DecisionTreeClassifier

# Fit ensemble classifiers
init_kwargs = {'base_estimator': DecisionTreeClassifier()}
ensembles = {
    'spe': SelfPacedEnsembleClassifier(**init_kwargs).fit(X_train, y_train),
    'rusboost': RUSBoostClassifier(**init_kwargs).fit(X_train, y_train),
    'easyens': EasyEnsembleClassifier(**init_kwargs).fit(X_train, y_train),
}

# Fit visualizer
from imbalanced_ensemble.visualizer import ImbalancedEnsembleVisualizer
visualizer = ImbalancedEnsembleVisualizer().fit(ensembles=ensembles)

Plot performance curves

fig, axes = visualizer.performance_lineplot()

Plot confusion matrices

fig, axes = visualizer.confusion_matrix_heatmap()

Customizing training log

All ensemble classifiers in IMBENS support customizable training logging. The training log is controlled by 3 parameters eval_datasets, eval_metrics, and training_verbose of the fit() method. Read more details in the fit documentation.

Enable auto training log

clf.fit(..., train_verbose=True)
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ             โ”ƒ                          โ”ƒ            Data: train             โ”ƒ
โ”ƒ #Estimators โ”ƒ    Class Distribution    โ”ƒ               Metric               โ”ƒ
โ”ƒ             โ”ƒ                          โ”ƒ  acc    balanced_acc   weighted_f1 โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ      1      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.838      0.877          0.839    โ”ƒ
โ”ƒ      5      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.924      0.949          0.924    โ”ƒ
โ”ƒ     10      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.954      0.970          0.954    โ”ƒ
โ”ƒ     15      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.979      0.986          0.979    โ”ƒ
โ”ƒ     20      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.990      0.993          0.990    โ”ƒ
โ”ƒ     25      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.994      0.996          0.994    โ”ƒ
โ”ƒ     30      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.988      0.992          0.988    โ”ƒ
โ”ƒ     35      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.999      0.999          0.999    โ”ƒ
โ”ƒ     40      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.995      0.997          0.995    โ”ƒ
โ”ƒ     45      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.995      0.997          0.995    โ”ƒ
โ”ƒ     50      โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.993      0.995          0.993    โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ    final    โ”ƒ {0: 150, 1: 150, 2: 150} โ”ƒ 0.993      0.995          0.993    โ”ƒ
โ”—โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ปโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ปโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”›

Customize granularity and content of the training log

clf.fit(..., 
        train_verbose={
            'granularity': 10,
            'print_distribution': False,
            'print_metrics': True,
        })
Click to view example output 
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ             โ”ƒ            Data: train             โ”ƒ
โ”ƒ #Estimators โ”ƒ               Metric               โ”ƒ
โ”ƒ             โ”ƒ  acc    balanced_acc   weighted_f1 โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ      1      โ”ƒ 0.964      0.970          0.964    โ”ƒ
โ”ƒ     10      โ”ƒ 1.000      1.000          1.000    โ”ƒ
โ”ƒ     20      โ”ƒ 1.000      1.000          1.000    โ”ƒ
โ”ƒ     30      โ”ƒ 1.000      1.000          1.000    โ”ƒ
โ”ƒ     40      โ”ƒ 1.000      1.000          1.000    โ”ƒ
โ”ƒ     50      โ”ƒ 1.000      1.000          1.000    โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ    final    โ”ƒ 1.000      1.000          1.000    โ”ƒ
โ”—โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ปโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”›

Add evaluation dataset(s)

  clf.fit(..., 
          eval_datasets={
              'valid': (X_valid, y_valid)
          })
Click to view example output 
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ             โ”ƒ            Data: train             โ”ƒ            Data: valid             โ”ƒ
โ”ƒ #Estimators โ”ƒ               Metric               โ”ƒ               Metric               โ”ƒ
โ”ƒ             โ”ƒ  acc    balanced_acc   weighted_f1 โ”ƒ  acc    balanced_acc   weighted_f1 โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ      1      โ”ƒ 0.939      0.961          0.940    โ”ƒ 0.935      0.933          0.936    โ”ƒ
โ”ƒ     10      โ”ƒ 1.000      1.000          1.000    โ”ƒ 0.971      0.974          0.971    โ”ƒ
โ”ƒ     20      โ”ƒ 1.000      1.000          1.000    โ”ƒ 0.982      0.981          0.982    โ”ƒ
โ”ƒ     30      โ”ƒ 1.000      1.000          1.000    โ”ƒ 0.983      0.983          0.983    โ”ƒ
โ”ƒ     40      โ”ƒ 1.000      1.000          1.000    โ”ƒ 0.983      0.982          0.983    โ”ƒ
โ”ƒ     50      โ”ƒ 1.000      1.000          1.000    โ”ƒ 0.983      0.982          0.983    โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ    final    โ”ƒ 1.000      1.000          1.000    โ”ƒ 0.983      0.982          0.983    โ”ƒ
โ”—โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ปโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ปโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”›

Customize evaluation metric(s)

from sklearn.metrics import accuracy_score, f1_score
clf.fit(..., 
        eval_metrics={
            'acc': (accuracy_score, {}),
            'weighted_f1': (f1_score, {'average':'weighted'}),
        })
Click to view example output 
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ             โ”ƒ     Data: train      โ”ƒ     Data: valid      โ”ƒ
โ”ƒ #Estimators โ”ƒ        Metric        โ”ƒ        Metric        โ”ƒ
โ”ƒ             โ”ƒ  acc    weighted_f1  โ”ƒ  acc    weighted_f1  โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ      1      โ”ƒ 0.942      0.961     โ”ƒ 0.919      0.936     โ”ƒ
โ”ƒ     10      โ”ƒ 1.000      1.000     โ”ƒ 0.976      0.976     โ”ƒ
โ”ƒ     20      โ”ƒ 1.000      1.000     โ”ƒ 0.977      0.977     โ”ƒ
โ”ƒ     30      โ”ƒ 1.000      1.000     โ”ƒ 0.981      0.980     โ”ƒ
โ”ƒ     40      โ”ƒ 1.000      1.000     โ”ƒ 0.980      0.979     โ”ƒ
โ”ƒ     50      โ”ƒ 1.000      1.000     โ”ƒ 0.981      0.980     โ”ƒ
โ”ฃโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‹โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ซ
โ”ƒ    final    โ”ƒ 1.000      1.000     โ”ƒ 0.981      0.980     โ”ƒ
โ”—โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ปโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ปโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”›

About imbalanced learning

Class-imbalance (also known as the long-tail problem) is the fact that the classes are not represented equally in a classification problem, which is quite common in practice. For instance, fraud detection, prediction of rare adverse drug reactions and prediction gene families. Failure to account for the class imbalance often causes inaccurate and decreased predictive performance of many classification algorithms. Imbalanced learning aims to tackle the class imbalance problem to learn an unbiased model from imbalanced data.

For more resources on imbalanced learning, please refer to awesome-imbalanced-learning.

Acknowledgements

Many samplers and utilities are adapted from imbalanced-learn, which is an amazing project!

References

# Reference
[1] Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, and Tie-Yan Liu. 2019. Self-paced Ensemble for Highly Imbalanced Massive Data Classification. 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020, pp. 841-852.
[2] 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. 539โ€“550, 2009.
[3] Chen, Chao, Andy Liaw, and Leo Breiman. โ€œUsing random forest to learn imbalanced data.โ€ University of California, Berkeley 110 (2004): 1-12.
[4] 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. 185โ€“197, 2010.
[5] Maclin, R., & Opitz, D. (1997). An empirical evaluation of bagging and boosting. AAAI/IAAI, 1997, 546-551.
[6] 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. 107โ€“119
[7] 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. 324โ€“331.
[8] Fan, W., Stolfo, S. J., Zhang, J., & Chan, P. K. (1999, June). AdaCost: misclassification cost-sensitive boosting. In Icml (Vol. 99, pp. 97-105).
[9] Shawe-Taylor, G. K. J., & Karakoulas, G. (1999). Optimizing classifiers for imbalanced training sets. Advances in neural information processing systems, 11(11), 253.
[10] Viola, P., & Jones, M. (2001). Fast and robust classification using asymmetric adaboost and a detector cascade. Advances in Neural Information Processing System, 14.
[11] Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
[12] Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
[13] Guillaume Lemaรฎtre, 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):1โ€“5, 2017.
You might also like...
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv

Hanxun Huang 11 Nov 30, 2022
BESS: Balanced Evolutionary Semi-Stacking for Disease Detection via Partially Labeled Imbalanced Tongue Data

Balanced-Evolutionary-Semi-Stacking Code for the paper ''BESS: Balanced Evolutionary Semi-Stacking for Disease Detection via Partially Labeled Imbalan

null 0 Jan 16, 2022
The Python ensemble sampling toolkit for affine-invariant MCMC

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

Dan Foreman-Mackey 1.3k Dec 31, 2022
An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)
An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)

An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)

null 0 May 6, 2022
zeus is a Python implementation of the Ensemble Slice Sampling method.
zeus is a Python implementation of the Ensemble Slice Sampling method.

zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl

Minas Karamanis 197 Dec 4, 2022
Neural Ensemble Search for Performant and Calibrated Predictions
Neural Ensemble Search for Performant and Calibrated Predictions

Neural Ensemble Search Introduction This repo contains the code accompanying the paper: Neural Ensemble Search for Performant and Calibrated Predictio

AutoML-Freiburg-Hannover 26 Dec 12, 2022
Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation
Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation Efficient Self-Ensemble Framework for Semantic Segmentation by Walid Bousselham

null 61 Dec 26, 2022
Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning
Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning

Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning This repository is official Tensorflow implementation of paper: Ensemb

Seunghyun Lee 12 Oct 18, 2022
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

SeaLion is designed to teach today's aspiring ml-engineers the popular machine learning concepts of today in a way that gives both intuition and ways of application. We do this through concise algorithms that do the job in the least jargon possible and examples to guide you through every step of the way.

Anish 324 Dec 27, 2022
Comments
  • Bug :AttributeError: can't set attribute

    Bug :AttributeError: can't set attribute

    hello ,when i use the code as follow,the will be some errors, EasyEnsembleClassifier was used

    from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import balanced_accuracy_score from sklearn.ensemble import BaggingClassifier from sklearn.tree import DecisionTreeClassifier from imbalanced_ensemble.ensemble import EasyEnsembleClassifier from collections import Counter

    X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) print('Original dataset shape %s' % Counter(y))

    Original dataset shape Counter({{1: 900, 0: 100}})

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) bbc = EasyEnsembleClassifier(random_state=42) bbc.fit(X_train, y_train) EasyEnsembleClassifier(...) y_pred = bbc.predict(X_test) print(y_pred)

    Traceback (most recent call last): File "C:/Users/Administrator/PycharmProjects/pythonProject5/test-easy.py", line 16, in bbc.fit(X_train, y_train) File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\utils_validation.py", line 602, in inner_f return f(**kwargs) File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\ensemble\under_sampling\easy_ensemble.py", line 275, in fit return self._fit(X, y, File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\utils_validation.py", line 602, in inner_f return f(**kwargs) File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\ensemble_bagging.py", line 359, in fit n_samples, self.n_features = X.shape AttributeError: can't set attribute

    bug 
    opened by leaphan 8
  • EasyEnsembleClassifier็”จไธไบ†ไบ†

    EasyEnsembleClassifier็”จไธไบ†ไบ†

    ๆ นๆฎไฝ ็š„ๅœจ่ฟ™ๅ„ฟhttps://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/classification/plot_digits.html ็š„ไปฃ็ ๏ผŒๅฐ†ๅˆ†็ฑปๅ™จๆ”นๆˆEasyEnsembleClassifierๅฏไปฅๅค็Žฐ่ฟ™ไธช้—ฎ้ข˜๏ผŒไผšๅ‡บ็Žฐ๏ผš image AttributeError: can't set attribute่ฟ™ไธช้—ฎ้ข˜ใ€‚

    bug 
    opened by hannanhtang 7
  • ENH add early_termination control for boosting-based methods

    ENH add early_termination control for boosting-based methods

    The early termination in sklearn.ensemble.AdaBoostClassifier may be too strict under certain scenarios (only 1 base classifier is trained), which greatly hinders the performance of boosting-based ensemble imbalanced learning methods.

    It should make more sense to add a parameter that allows the user to decide whether to enable strict early termination.

    enhancement 
    opened by ZhiningLiu1998 2
  • [BUG] Bagging-based methods do not work with base clf that do not support sample_weight

    [BUG] Bagging-based methods do not work with base clf that do not support sample_weight

    Resampling + Bagging clf (e.g., OverBagging) raises error when used with base estimators that do not support sample_weight (e.g., sklearn.KNeighborsClassifier).

    opened by ZhiningLiu1998 2
Owner
Zhining Liu
M.Sc. student at Jilin University.
Zhining Liu
GitHub Repository https://imbalanced-ensemble.readthedocs.io
Keras-retinanet - Keras implementation of RetinaNet object detection.

Keras RetinaNet Keras implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal,

Fizyr 4.3k Jan 01, 2023
Trajectory Variational Autoencder baseline for Multi-Agent Behavior challenge 2022

MABe_2022_TVAE: a Trajectory Variational Autoencoder baseline for the 2022 Multi-Agent Behavior challenge This repository contains jupyter notebooks t

Andrew Ulmer 15 Nov 08, 2022
Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"

Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"

Intelligent Robotics and Machine Vision Lab 4 Jul 19, 2022
A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset. This repo contains scripts to train RL agents to navigate the closed world and collect vi

MUGEN 11 Oct 22, 2022
XViT - Space-time Mixing Attention for Video Transformer

XViT - Space-time Mixing Attention for Video Transformer This is the official implementation of the XViT paper: @inproceedings{bulat2021space, title

Adrian Bulat 33 Dec 23, 2022
Notepy is a full-featured Notepad Python app

Notepy A full featured python text-editor Notable features Autocompletion for parenthesis and quote Auto identation Syntax highlighting Compile and ru

Mirko Rovere 11 Sep 28, 2022
PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

This is the official implementation of the following paper: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. PICARD - Parsing Incrementally for Con

ElementAI 217 Jan 01, 2023
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

This is the Vowpal Wabbit fast online learning code. Why Vowpal Wabbit? Vowpal Wabbit is a machine learning system which pushes the frontier of machin

Vowpal Wabbit 8.1k Jan 06, 2023
A task Provided by A respective Artenal Ai and Ml based Company to complete it

A task Provided by A respective Alternal Ai and Ml based Company to complete it .

Parth Madan 1 Jan 25, 2022
Deep Learning (with PyTorch)

Deep Learning (with PyTorch) This notebook repository now has a companion website, where all the course material can be found in video and textual for

Alfredo Canziani 6.2k Jan 07, 2023
Voice Conversion Using Speech-to-Speech Neuro-Style Transfer

This repo contains the official implementation of the VAE-GAN from the INTERSPEECH 2020 paper Voice Conversion Using Speech-to-Speech Neuro-Style Transfer.

Ehab AlBadawy 93 Jan 05, 2023
Pyramid addon for OpenAPI3 validation of requests and responses.

Validate Pyramid views against an OpenAPI 3.0 document Peace of Mind The reason this package exists is to give you peace of mind when providing a REST

Pylons Project 79 Dec 30, 2022
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

SEOVER-Master This code is the implementation of paper๏ผš SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

4 Feb 24, 2022
U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

U-Net Implementation By Christopher Ley This is my interpretation and implementation of the famous paper "U-Net: Convolutional Networks for Biomedical

Christopher Ley 1 Jan 06, 2022
[ICLR 2022] Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

CPDeform Code and data for paper Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics at ICLR 2022 (Spotlight). @InProceed

(Lester) Sizhe Li 29 Nov 29, 2022
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
TDN: Temporal Difference Networks for Efficient Action Recognition

TDN: Temporal Difference Networks for Efficient Action Recognition Overview We release the PyTorch code of the TDN(Temporal Difference Networks).

Multimedia Computing Group, Nanjing University 326 Dec 13, 2022
Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation) Download Synthia dataset The model uses

32 Sep 21, 2022
SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity

SSD: Single Shot MultiBox Detector Introduction Here is my pytorch implementation of 2 models: SSD-Resnet50 and SSDLite-MobilenetV2.

Viet Nguyen 149 Jan 07, 2023
Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

GANInversion_with_ConsecutiveImgs Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images" https://a

QingyangXu 38 Dec 07, 2022