Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led the algorithm to some decision is a real need in many applications. Convolutional neural networks perform well for the data series classification task; though,the explanations provided by this type of algorithms are poor for the specific case of multivariate data series. Solving this important limitation is a significant challenge. We propose a novel method that addresses the above challenge by highlighting both the temporal and dimensional discriminant information. Our contribution is two-fold: we first describe a new convolutional architecture that enables the comparison of dimensions; then, we propose a novel method that returns dCAM, a Dimension-wise ClassActivation Map specifically designed for multivariate time series.
This repository is dedicated to our paper titled "dCAM : Dimension-wise Class Activation Map for Explaining Multivariate Time Series Classification" published in the Proceedings of the 2022 International Conference on Management of Data also available on here.
Here are interesting papers that use dCAM.
The data used in this project comes from two sources:
The code is divided as follows:
The src/ folder that contains:
CNN architecture (class ConvNet)
dCNN/cCNN architecture (class ConvNet2D)
ResNet architecture (class ResNet)
dResNet/cResNet architecture (class dResNet)
Inception Time architecture (class inceptiontime)
dInception Time/cInception Time architecture (class dinceptiontime)
CNN-MTEX architecture (class ConvNetMTEX)
LSTM architecture (class LSTMClassifier)
RNN architecture (class RNNClassifier)
GRU architecture (class GRUClassifier)
the explanation/ folder:
CAM code (class CAM)
cCAM code (class cCAM)
dCAM code (class DCAM)
grad-CAM code used for CNN-MTEX (class GradCAM)
The examples/ folder that contains:
The experiments/ folder that contains:
Results_overview.ipynb: Notebook containing all the informations and the procedures to run the experiments/ scripts and generate the figures in our paper.
First, to download the datasets (unzip and move the content in the corresponding folder), please run the following commands:
cd data/UCR_UEA
https://helios2.mi.parisdescartes.fr/~themisp/dCAM/data/UCR_UEA_datasets.zip
cd data/synthetic
https://helios2.mi.parisdescartes.fr/~themisp/dCAM/data/Synthetic_datasets.zip
All python packages needed are listed in requirements.txt file and can be installed simply using the pip command:
pip install -r requirements_version.txt
If the version are not compatible in your environement, please run the following command:
pip install -r requirements.txt
Jupyter notebook should be installed. To do so, please run the following command:
pip install notebook
Overall, the required python packages are listed as follows:
If you re-use this work, please cite:
@inproceedings{DBLP:conf/sigmod/BoniolMRP22,
author = {Paul Boniol and
Mohammed Meftah and
Emmanuel Remy and
Themis Palpanas},
editor = {Zachary Ives and
Angela Bonifati and
Amr El Abbadi},
title = {dCAM: Dimension-wise Class Activation Map for Explaining Multivariate
Data Series Classification},
booktitle = {{SIGMOD} '22: International Conference on Management of Data, Philadelphia,
PA, USA, June 12 - 17, 2022},
pages = {1175--1189},
publisher = {{ACM}},
year = {2022},
url = {https://doi.org/10.1145/3514221.3526183},
doi = {10.1145/3514221.3526183},
timestamp = {Tue, 14 Jun 2022 18:31:24 +0200},
biburl = {https://dblp.org/rec/conf/sigmod/BoniolMRP22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}