# mesa
**Repository Path**: IncubatorShokuhou/mesa
## Basic Information
- **Project Name**: mesa
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-19
- **Last Updated**: 2025-02-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
MESA: Meta-sampler for imbalanced learning
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler (NeurIPS 2020)
**MESA is a ***meta-learning-based ensemble learning framework*** for solving class-imbalanced learning problems. It is a task-agnostic general-purpose solution that is able to boost most of the existing machine learning models' performance on imbalanced data.**
# Cite Us
**If you find this repository helpful in your work or research, we would greatly appreciate citations to the following paper:**
```
@inproceedings{liu2020mesa,
title={MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler},
author={Liu, Zhining and Wei, Pengfei and Jiang, Jing and Cao, Wei and Bian, Jiang and Chang, Yi},
booktitle={Conference on Neural Information Processing Systems},
year={2020},
}
```
# Table of Contents
- [Cite Us](#cite-us)
- [Table of Contents](#table-of-contents)
- [Background](#background)
- [About MESA](#about-mesa)
- [Pros and Cons of MESA](#pros-and-cons-of-mesa)
- [Requirements](#requirements)
- [Usage](#usage)
- [Running main.py](#running-mainpy)
- [Running mesa-example.ipynb](#running-mesa-exampleipynb)
- [Visualization and Results](#visualization-and-results)
- [From mesa-example.ipynb](#from-mesa-exampleipynb)
- [Class distribution of Mammography dataset](#class-distribution-of-mammography-dataset)
- [Visualize the meta-training process](#visualize-the-meta-training-process)
- [Comparison with baseline methods](#comparison-with-baseline-methods)
- [Other results](#other-results)
- [Dataset description](#dataset-description)
- [Comparisons of MESA with under-sampling-based EIL methods](#comparisons-of-mesa-with-under-sampling-based-eil-methods)
- [Comparisons of MESA with over-sampling-based EIL methods](#comparisons-of-mesa-with-over-sampling-based-eil-methods)
- [Comparisons of MESA with resampling-based EIL methods](#comparisons-of-mesa-with-resampling-based-eil-methods)
- [Miscellaneous](#miscellaneous)
- [References](#references)
- [Contributors ✨](#contributors-)
# Background
## About MESA
We introduce a novel ensemble imbalanced learning (EIL) framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns a parameterized sampling strategy (i.e., meta-sampler) from data to optimize the final metric beyond following random heuristics. It consists of three parts: ***meta sampling*** as well as ***ensemble training*** to build ensemble classifiers, and ***meta-training*** to optimize the meta-sampler.
The figure below gives an overview of the MESA framework.
## Pros and Cons of MESA
Here are some personal thoughts on the advantages and disadvantages of MESA. More discussions are welcome!
**Pros:**
- 🍎 *Wide compatiblilty.*
We decoupled the model-training and meta-training process in MESA, making it compatible with most of the existing machine learning models.
- 🍎 *High data efficiency.*
MESA performs strictly balanced under-sampling to train each base-learner in the ensemble. This makes it more data-efficient than other methods, especially on highly skewed data sets.
- 🍎 *Good performance.*
The sampling strategy is optimized for better final generalization performance, we expect this can provide us with a better ensemble model.
- 🍎 *Transferability.*
We use only task-agnostic meta-information during meta-training, which means that a meta-sampler can be directly used in unseen new tasks, thereby greatly reducing the computational cost brought about by meta-training.
**Cons:**
- 🍏 *Meta-training cost.*
Meta-training repeats the ensemble training process multiple times, which can be costly in practice (By shrinking the dataset used in meta-training, the computational cost can be reduced at the cost of minor performance loss).
- 🍏 *Need to set aside a separate validation set for training.*
The meta-state is formed by computing the error distribution on both the training and validation sets.
- 🍏 *Possible unstable performance on small datasets.*
Small datasets may cause the obtained error distribution statistics to be inaccurate/unstable, which will interfere with the meta-training process.
# Requirements
**Main dependencies:**
- [Python](https://www.python.org/) (>=3.5)
- [PyTorch](https://pytorch.org/) (=1.0.0)
- [Gym](https://gym.openai.com/) (>=0.17.3)
- [pandas](https://pandas.pydata.org/) (>=0.23.4)
- [numpy](https://numpy.org/) (>=1.11)
- [scikit-learn](https://scikit-learn.org/stable/) (>=0.20.1)
- [imbalanced-learn](https://imbalanced-learn.readthedocs.io/en/stable/index.html) (=0.5.0, optional, for baseline methods)
To install requirements, run:
```Shell
pip install -r requirements.txt
```
> **NOTE**: this implementation requires an old version of PyTorch (v1.0.0).
> You may want to start a new conda environment to run our code. The step-by-step guide is as follows (using torch-cpu for an example):
> - `conda create --name mesa python=3.7.11`
> - `conda activate mesa`
> - `conda install pytorch-cpu==1.0.0 torchvision-cpu==0.2.1 cpuonly -c pytorch`
> - `pip install -r requirements.txt`
>
> These commands should help you to get ready for running mesa. If you have any further questions, please feel free to open an issue or drop me an email.
# Usage
A typical usage example:
```python
# load dataset & prepare environment
args = parser.parse_args()
rater = Rater(args.metric)
X_train, y_train, X_valid, y_valid, X_test, y_test = load_dataset(args.dataset)
base_estimator = DecisionTreeClassifier()
# meta-training
mesa = Mesa(
args=args,
base_estimator=base_estimator,
n_estimators=10)
mesa.meta_fit(X_train, y_train, X_valid, y_valid, X_test, y_test)
# ensemble training
mesa.fit(X_train, y_train, X_valid, y_valid)
# evaluate
y_pred_test = mesa.predict_proba(X_test)[:, 1]
score = rater.score(y_test, y_pred_test)
```
## Running [main.py](https://github.com/ZhiningLiu1998/mesa/blob/master/main.py)
Here is an example:
```powershell
python main.py --dataset Mammo --meta_verbose 10 --update_steps 1000
```
You can get help with arguments by running:
```powershell
python main.py --help
```
```
optional arguments:
# Soft Actor-critic Arguments
-h, --help show this help message and exit
--env-name ENV_NAME
--policy POLICY Policy Type: Gaussian | Deterministic (default:
Gaussian)
--eval EVAL Evaluates a policy every 10 episode (default:
True)
--gamma G discount factor for reward (default: 0.99)
--tau G target smoothing coefficient(τ) (default: 0.01)
--lr G learning rate (default: 0.001)
--lr_decay_steps N step_size of StepLR learning rate decay scheduler
(default: 10)
--lr_decay_gamma N gamma of StepLR learning rate decay scheduler
(default: 0.99)
--alpha G Temperature parameter α determines the relative
importance of the entropy term against the reward
(default: 0.1)
--automatic_entropy_tuning G
Automaically adjust α (default: False)
--seed N random seed (default: None)
--batch_size N batch size (default: 64)
--hidden_size N hidden size (default: 50)
--updates_per_step N model updates per simulator step (default: 1)
--update_steps N maximum number of steps (default: 1000)
--start_steps N Steps sampling random actions (default: 500)
--target_update_interval N
Value target update per no. of updates per step
(default: 1)
--replay_size N size of replay buffer (default: 1000)
# Mesa Arguments
--cuda run on CUDA (default: False)
--dataset N the dataset used for meta-training (default: Mammo)
--metric N the metric used for evaluate (default: aucprc)
--reward_coefficient N
--num_bins N number of bins (default: 5). state-size = 2 *
num_bins.
--sigma N sigma of the Gaussian function used in meta-sampling
(default: 0.2)
--max_estimators N maximum number of base estimators in each meta-
training episode (default: 10)
--meta_verbose N number of episodes between verbose outputs. If 'full'
print log for each base estimator (default: 10)
--meta_verbose_mean_episodes N
number of episodes used for compute latest mean score
in verbose outputs.
--verbose N enable verbose when ensemble fit (default: False)
--random_state N random_state (default: None)
--train_ir N imbalance ratio of the training set after meta-
sampling (default: 1)
--train_ratio N the ratio of the data used in meta-training. set
train_ratio<1 to use a random subset for meta-training
(default: 1)
```
## Running [mesa-example.ipynb](https://github.com/ZhiningLiu1998/mesa/blob/master/mesa-example.ipynb)
We include a highly imbalanced dataset [Mammography](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.datasets.fetch_datasets.html#imblearn.datasets.fetch_datasets) (#majority class instances = 10,923, #minority class instances = 260, imbalance ratio = 42.012) and its variants with flip label noise for quick testing and visualization of MESA and other baselines.
You can use [mesa-example.ipynb](https://github.com/ZhiningLiu1998/mesa/blob/master/mesa-example.ipynb) to quickly:
- conduct a comparative experiment
- visualize the meta-training process of MESA
- visualize the experimental results of MESA and other baselines
**Please check [mesa-example.ipynb](https://github.com/ZhiningLiu1998/mesa/blob/master/mesa-example.ipynb) for more details.**
# Visualization and Results
## From [mesa-example.ipynb](https://github.com/ZhiningLiu1998/mesa/blob/master/mesa-example.ipynb)
### Class distribution of Mammography dataset
### Visualize the meta-training process
### Comparison with baseline methods
## Other results
### Dataset description
### Comparisons of MESA with under-sampling-based EIL methods
### Comparisons of MESA with over-sampling-based EIL methods
### Comparisons of MESA with resampling-based EIL methods
# Miscellaneous
**Check out our previous work [Self-paced Ensemble](https://github.com/ZhiningLiu1998/self-paced-ensemble) (ICDE 2020).
It is a simple heuristic-based method, but being very fast and works reasonably well.**
**This repository contains:**
- Implementation of MESA
- Implementation of 7 ensemble imbalanced learning baselines
- `SMOTEBoost` [1]
- `SMOTEBagging` [2]
- `RAMOBoost` [3]
- `RUSBoost` [4]
- `UnderBagging` [5]
- `BalanceCascade` [6]
- `SelfPacedEnsemble` [7]
- Implementation of 11 resampling imbalanced learning baselines [8]
> **NOTE:** The implementations of the above baseline methods are based on [imbalanced-algorithms](https://github.com/dialnd/imbalanced-algorithms) and [imbalanced-learn](https://github.com/scikit-learn-contrib/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] | Sheng Chen, Haibo He, and Edwardo A Garcia. 2010. RAMOBoost: ranked minority oversampling in boosting. IEEE Transactions on Neural Networks 21, 10 (2010), 1624–1642.|
| [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. 185–197, 2010.|
| [5] | 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.|
| [6] | 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. |
| [7] | 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.
| [8] | 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. |
## Contributors ✨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!