Package: torchRDS
RDSImplementation ofReinforced Data Sampling for Model Diversification.Requirementsnumpytorchscikit-learnpandastqdmMachine Learning TasksThis repository supports multiple machine learning tasks on multivariate, textual and visual data:Binary ClassificationMulti-Class ClassificationRegressionInstallationpip install torchRDSUsagefromtorchRDS.RDSimportRDStrainer=RDS(data_file="datasets/madelon.csv",target=[0],task="classification",measure="auc",model_classes=["models.MDL_RF","models.MDL_MLP","models.MDL_LR"],learn="deterministic",ratio=0.7695,iters=100)sample=trainer.train()print("No of observations in training set: ",sum(sample))Real-World Use CasesPlease contact us if you want to be listed here for real-world competitions or use cases.Experiment ResultsExperiments have been conducted on four datasets as the following.DatasetTaskChallengeSize of DataEvaluationYearMADELONBinary ClassificationNIPS 2013 Feature Selection Challenge2,600 x 500 (multivariate)AUC2003DRRegressionDrug Reviews (Kaggle Hackathon)215,063 x 6 (multivariate, text)R^22018MNISTMulticlass ClassificationHand Written Digit Recognition70,000 x 28 x 28 (image)Micro-F11998KLPBinary ClassificationKalapa Credit Scoring Challenge50,000 x 64 (multivariate, text)AUC2020MADELON - ResultsSampling#SampleClass RatioLRRFMLPEnsemblePublicTrainTestTrainTestPreset20006001.00001.0000.6019.8106.5590.6783.9063Random2000600.99201.0270.5742.7729.5774.6453.9002Stratified20006001.00001.0000.5673.7470.6153.6360.8828RDS^{DET}20015991.0375.9137.6192.8050.6228.6973.8915RDS^{STO}20215791.0010.9966.6192.8050.6050.6947.9106DR - ResultsSamplingTrainTestRidgeMLPCNNEnsemblePublicPreset161,29753,766.4580.5787.7282.6660.7637Random161,29753,766.4597.4179.7353.6485.7503RDS^{DET}162,07052,993.4646.5776.7355.6692.7649RDS^{STO}161,94453,119.4647.5370.7509.6562.7600MNIST - ResultsSampling#SampleClass RatioLRRFCNNEnsemblePublicTrainTestTrainTestPreset6000010000.8571.1429.9647.9524.9824.9819.9917Random5950010500.8500.1500.9603.9465.9779.9768.9914Stratified5950010500.8500.1500.9625.9510.9795.9792.9901RDS^{DET}5993810062.8562.1438.9495.9382.9757.9769.9927RDS^{STO}5949610504.8499.1501.9583.9486.9851.9830.9931KLP - ResultsSampling#SampleClass RatioLRRFMLPEnsemblePublicTrainTestTrainTestPreset3000020000.0165.0186.5799.5517.5635.5723.5953Simple3000020000.0169.0179.5886.5374.5914.5856.6042Stratified3000020000.0173.0173.5952.5608.5780.5983.6014RDS^{DET}2999920001.0180.0163.6045.5350.5802.6057.5362RDS^{STO}3003119969.0172.0174.5997.5491.6354.6072.6096Citing this workPlease consider citing us if this work is useful in your research:@misc{nguyen2020reinforced,
    title={Reinforced Data Sampling for Model Diversification},
    author={Hoang D. Nguyen and Xuan-Son Vu and Quoc-Tuan Truong and Duc-Trong Le},
    year={2020},
    eprint={2006.07100},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}ReferencesLee, S., Prakash, S.P.S., Cogswell, M., Ranjan, V., Crandall, D. and Batra, D., 2016. Stochastic multiple choice learning for training diverse deep ensembles. InAdvances in Neural Information Processing Systems(pp. 2119-2127).Peng, M., Zhang, Q., Xing, X., Gui, T., Huang, X., Jiang, Y.G., Ding, K. and Chen, Z., 2019, July. Trainable undersampling for class-imbalance learning. InProceedings of the AAAI Conference on Artificial Intelligence(Vol. 33, pp. 4707-4714).Gong, Z., Zhong, P. and Hu, W., 2019. Diversity in machine learning.IEEE Access,7, pp.64323-64350.