This repository provides a numpy and scipy based implementation on Unweighted Influence Data Subsampling (UIDS) on the Logistic Regression model. The UIDS can achieve good result when the data set quality is not good, such as noisy labels, or there is distribution shift between training and test set, by dropping several bad cases.
Less Is Better: Unweighted Data Subsampling via Influence Function
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@inproceedings{wang2019less,
title={Less Is Better: Unweighted Data Subsampling via Influence Function},
author={Wang, Zifeng and Zhu, Hong and Dong, Zhenhua and He, Xiuqiang and Huang, Shao-Lun},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020}
}In practice, it is common that some of the collected data are not good or even harmful for our model. On the other, sometimes the data distribution is not stable. There often lies distribution shift between the training set and the test set, causing the degradation performance of the classical supervised machine learning algorithms.
Subsampling tries to build a tool to quantify each data’s quality, thereby keeping good examples and dropping bad examples to improve model’s generalization ability. Previous works concentrate on Weighted subsampling, that is, trying to maintain the model performance when dropping several data.
By contrast, our work attempts to obtain a superior model by subsampling.
The different between them can be shown as the image below:
- (a) means if the blue points (training samples) within the red circle are removed, the new optimal decision boundary is still same as the former one
- (b) if removing blue points in the red circle, the new decision boundary shifts from the left, while achieves better performance on the Te set
The main process of doing subsampling is as follows:
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(a) first train a model on the full data set
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(b) compute the influence function (IF) for each sample in training set
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(c) compute the sampling probability of each sample in training set
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(d) doing subsampling and train a subset-model and the reduced data set
To accelerate the computation of Influence Function, we modify the original scipy/optimize module to realize the Hessian-free Preconditioned Truncated Newton Method [Hsia et al., 2018] for Logistic Regression.
The details can be referred to ./optimize/optimize.py.
We have prepared simple demo on Logistic Regression and SVM, see in Demo_on_Logistic_Regression.ipynb and Demo_on_SVM.ipynb
The experiment results would be shown as following. We have the Sig-UIDS obtain ACC and AUC much better than the Full-set-model and subset-model obtained by random sampling.
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MNIST: Result Summary on Te (ACC and AUC)
[SigUIDS] acc 0.984281, auc 0.998802 # 4994
[Random] acc 0.900139, auc 0.950359 # 4995
[Full] acc 0.916782, auc 0.961824 # 8325
============================================================For other data sets, we provide a simple tool to proceed the data set from the raw text to the processed scipy.sparse matrix, which supports pretty large and high dimensional data set in practice (more than 10-million-feature data set):
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python -u process_data.py -p 2 -b 10 -n 1000 -f fm data/XXX.txt
Args:
-p: # of threads used in processing
-b: # of lines processed in a thread
-n: the maximum # of features for the raw data set
-f: should be "fm" or "ffm" indicating the format of the raw text data, the "fm" stores one sample in a line as "feature_id:value", while the "ffm" has "field_id:feature_id:value".
=================================================================You can find a toy ffm formatted data set on ./data/toy.ffm.
Then you could refer to the demo notebook to do your own experiments on other data sets ^ ^
We especially thank the insights and advice from Professor Chih-Jen Lin for the theory and writing of this work.