cluster based memory stacking data for Attention based model Continual Learning

Data Preparation

Datasets are used in the following file structure:

│continual_learning for MIL/
├──data/
│  ├── AMI
│  │   ├── month_continual
│  │   │   ├──  summer
│  │   │   ├──  winter
│  │   │   ├──  all
│  ├── ETRI
│  │   ├── date_continual
│  │   │   ├──  pickle_0
│  │   │   ├──  pickle_10000.pkl
│  │   │   ├──  pickle_20000.pkl
│  │   │   ├──  pickle_30000.pkl
│  │   │   ├──  pickle_40000.pkl
│  │   │   ├──  pickle_50000.pkl
│  │   │   ├──  pickle_60000.pkl
│  │   │   ├──  pickle_70000.pkl
│  │   │   ├──  pickle_80000.pkl
│  │   │   ├──  pickle_90000.pkl

AMI Dataset

Time Series Regression (Elec, water, gas hotwater, hot) ( classified )

ETRI Human Life log Dataset

you can download it from here

Setups

All code was developed and tested on Nvidia RTX A4000 (48SMs, 16GB) the following environment.

• Ubuntu 18.04
• python 3.10.11
• torch 2.0.1
• numpy 1.24.3
• pandas 2.1.0
• scikit-learn 1.3.0
• scipy 1.11.2

Implement

python AMI_continual_cluster.py (summer/winter/all)
ex) python AMI_continual_cluster.py summer 

python ETRI_continual_cluster.py