Variants of the synthetic minority oversampling technique (SMOTE) for imbalanced learning
Project description
SMOTE-variants
Introduction
The package implements 85 variants of the Synthetic Minority Oversampling Technique (SMOTE). Besides the implementations, an easy to use model selection framework is supplied to enable the rapid evaluation of oversampling techniques on unseen datasets.
The implemented techniques: [SMOTE] , [SMOTE_TomekLinks] , [SMOTE_ENN] , [Borderline_SMOTE1] , [Borderline_SMOTE2] , [ADASYN] , [AHC] , [LLE_SMOTE] , [distance_SMOTE] , [SMMO] , [polynom_fit_SMOTE] , [Stefanowski] , [ADOMS] , [Safe_Level_SMOTE] , [MSMOTE] , [DE_oversampling] , [SMOBD] , [SUNDO] , [MSYN] , [SVM_balance] , [TRIM_SMOTE] , [SMOTE_RSB] , [ProWSyn] , [SL_graph_SMOTE] , [NRSBoundary_SMOTE] , [LVQ_SMOTE] , [SOI_CJ] , [ROSE] , [SMOTE_OUT] , [SMOTE_Cosine] , [Selected_SMOTE] , [LN_SMOTE] , [MWMOTE] , [PDFOS] , [IPADE_ID] , [RWO_sampling] , [NEATER] , [DEAGO] , [Gazzah] , [MCT] , [ADG] , [SMOTE_IPF] , [KernelADASYN] , [MOT2LD] , [V_SYNTH] , [OUPS] , [SMOTE_D] , [SMOTE_PSO] , [CURE_SMOTE] , [SOMO] , [ISOMAP_Hybrid] , [CE_SMOTE] , [Edge_Det_SMOTE] , [CBSO] , [E_SMOTE] , [DBSMOTE] , [ASMOBD] , [Assembled_SMOTE] , [SDSMOTE] , [DSMOTE] , [G_SMOTE] , [NT_SMOTE] , [Lee] , [SPY] , [SMOTE_PSOBAT] , [MDO] , [Random_SMOTE] , [ISMOTE] , [VIS_RST] , [GASMOTE] , [A_SUWO] , [SMOTE_FRST_2T] , [AND_SMOTE] , [NRAS] , [AMSCO] , [SSO] , [NDO_sampling] , [DSRBF] , [Gaussian_SMOTE] , [kmeans_SMOTE] , [Supervised_SMOTE] , [SN_SMOTE] , [CCR] , [ANS] , [cluster_SMOTE]
Comparison and evaluation
For a detailed comparison and evaluation of all the implemented techniques see https://www.researchgate.net/publication/334732374_An_empirical_comparison_and_evaluation_of_minority_oversampling_techniques_on_a_large_number_of_imbalanced_datasets
Citation
If you use this package in your research, please consider citing the below papers.
Preprint describing the package: https://www.researchgate.net/publication/333968087_smote-variants_a_Python_Implementation_of_85_Minority_Oversampling_Techniques
BibTex for the package:
@article{smote-variants,
author={Gy\"orgy Kov\'acs},
title={smote-variants: a Python Implementation of 85 Minority Oversampling Techniques},
journal={Neurocomputing},
note={(IF-2019=4.07)},
volume={366},
pages={352--354},
year={2019},
group={journal},
code= {https://github.com/analyticalmindsltd/smote_variants},
doi= {10.1016/j.neucom.2019.06.100}
}Preprint of the comparative study: https://www.researchgate.net/publication/334732374_An_empirical_comparison_and_evaluation_of_minority_oversampling_techniques_on_a_large_number_of_imbalanced_datasets
BibTex for the comparison and evaluation:
@article{smote-comparison,
author={Gy\"orgy Kov\'acs},
title={An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets},
journal={Applied Soft Computing},
note={(IF-2019=4.873)},
volume={83},
pages={105662},
year={2019},
link={https://www.sciencedirect.com/science/article/pii/S1568494619304429},
group={journal},
code={https://github.com/analyticalmindsltd/smote_variants},
doi={10.1016/j.asoc.2019.105662}
}Documentation
For a detailed documentation see http://smote-variants.readthedocs.io.
For a YouTube tutorial check https://www.youtube.com/watch?v=GSK7akQPM60
Best practices
Normalization/standardization/scaling/feature selection
Most of the oversampling techniques operate in the Euclidean space implied by the attributes. Therefore it is extremely important to normalize/scale the attributes appropriatly. With no knowledge on the importance of attributes, the normalization/standardization is a good first try. Having some domain knowledge or attribute importances from bootstrap classification, the scaling of attribute ranges according to their importances is also reasonable. Alternatively, feature subset selection might also improve the results by making oversampling work in the most suitable subspace.
Model selection for the number of samples to be generated
Classification after oversampling is highly sensitive to the number of minority samples being generated. Balancing the dataset is rarely the right choice, as most of the classifiers operate the most efficiently if the density of positive and negative samples near the decision boundary is approximately the same. If the manifolds of the positive and negative classes do not have the same size approximately, balancing the dataset cannot achieve this. Moreover, in certain regions it can even revert the situation: if the manifold of the minority class is much smaller than that of the majority class, balancing will turn the minority class into the majority in the local environments along the decision boundary.
The solution is to apply model selection for the number of samples being generated. Almost all techniques implemented in the `smote-variants` package have a parameter called `proportion`. This parameter controls how many samples to generate, namely, the number of minority samples generated is `proportion*(N_maj - N_min)`, that is, setting the proportion parameter to 1 will balance the dataset. It is highly recommended to carry out cross-validated model selection for a range like `proportion` = 0.1, 0.2, 0.5, 1.0, 2.0, 5.0.
Sample Usage
Binary oversampling:
import smote_variants as sv
import imbalanced_databases as imbd
dataset= imbd.load_iris0()
X, y= dataset['data'], dataset['target']
oversampler= sv.distance_SMOTE()
# X_samp and y_samp contain the oversampled dataset
X_samp, y_samp= oversampler.sample(X, y)Multiclass oversampling:
import smote_variants as sv
import sklearn.datasets as datasets
dataset= datasets.load_wine()
X, y= dataset['data'], dataset['target']
oversampler= sv.MulticlassOversampling(sv.distance_SMOTE())
# X_samp and y_samp contain the oversampled dataset
X_samp, y_samp= oversampler.sample(X, y)Selection of the best oversampler:
import os.path
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import smote_variants as sv
import sklearn.datasets as datasets
cache_path= os.path.join(os.path.expanduser('~'), 'smote_test')
if not os.path.exists(cache_path):
os.makedirs(cache_path)
dataset= datasets.load_breast_cancer()
dataset= {'data': dataset['data'], 'target': dataset['target'], 'name': 'breast_cancer'}
knn_classifier= KNeighborsClassifier()
dt_classifier= DecisionTreeClassifier()
# samp_obj and cl_obj contain the oversampling and classifier objects which give the
# best performance together
samp_obj, cl_obj= sv.model_selection(dataset= dataset,
samplers= sv.get_n_quickest_oversamplers(5),
classifiers= [knn_classifier, dt_classifier],
cache_path= cache_path,
n_jobs= 5,
max_samp_par_comb= 35)
# training the best techniques using the entire dataset
X_samp, y_samp= samp_obj.sample(dataset['data'], dataset['target'])
cl_obj.fit(X_samp, y_samp)Integration with sklearn pipelines:
import smote_variants as sv
import imblearn.datasets as imb_datasets
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
libras= imb_datasets.fetch_datasets()['libras_move']
X, y= libras['data'], libras['target']
oversampler= sv.MulticlassOversampling(sv.distance_SMOTE())
classifier= KNeighborsClassifier(n_neighbors= 5)
# Constructing a pipeline which contains oversampling and classification as the last step.
model= Pipeline([('scale', StandardScaler()), ('clf', sv.OversamplingClassifier(oversampler, classifier))])
model.fit(X, y)Integration with sklearn grid search:
import smote_variants as sv
import imblearn.datasets as imb_datasets
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
libras= imb_datasets.fetch_datasets()['libras_move']
X, y= libras['data'], libras['target']
oversampler= sv.MulticlassOversampling(sv.distance_SMOTE())
classifier= KNeighborsClassifier(n_neighbors= 5)
# Constructing a pipeline with oversampling and classification as the last step
model= Pipeline([('scale', StandardScaler()), ('clf', sv.OversamplingClassifier(oversampler, classifier))])
param_grid= {'clf__oversampler':[sv.distance_SMOTE(proportion=0.5),
sv.distance_SMOTE(proportion=1.0),
sv.distance_SMOTE(proportion=1.5)]}
# Specifying the gridsearch for model selection
grid= GridSearchCV(model, param_grid= param_grid, cv= 3, n_jobs= 1, verbose= 2, scoring= 'accuracy')
# Fitting the pipeline
grid.fit(X, y)The competition
We have kicked off a competition to find the best general purpose oversampling technique. The competition is ongoing, the preliminary results are available at the page https://smote-variants.readthedocs.io/en/latest/competition.html
All the numerical results are reproducible by the 005_evaluation example script, downloading the database foldings from the link below and following the instructions in the script. Anyone is open to join the competition by implementing an oversampling technique as part of the smote_variants package. The below database foldings can be used to evaluate the technique, and compare the results to the already implemented ones. Once the code is added to a feature branch, the evaluation will be repeated by the organizers and the results added to the rankings page.
Database foldings: https://drive.google.com/open?id=1PKw1vETVUzaToomio1-RGzJ9_-buYjOW
Other downloads
If someone is interested in the results of the evaluation of 85 oversamplers on 104 imbalanced datasets, the raw and aggregated results as structured pickle files are avaialble at the below links:
Raw results: https://drive.google.com/open?id=12CfB3184nchLIwStaHhrjcQK7Ari18Mo
Aggregated results: https://drive.google.com/open?id=19JGikRYXQ6-eOxaFVrqkF64zOCiSdT-j
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