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MECHANISTIC INTERPRETABILITY ON TRANSFORMER-BASED TIME SERIES CLASSIFICATION MODEL

Author: Matiss Kalnare

Supervisor Niki van Stein

This repository contains a PyTorch-based toolkit created as part of a BSc thesis, focused on interpretability techique adaptions to Transformer-based Time Series (TST) classification model. It provides two pipelines to probe and interpret the inner workings of a model.

1. Activation Patching / Causal Tracing: causal intervention on specific parts (Heads, MLP layers, etc.) to form / confirm hypothesis about influential components wihtin the Transformer

2. Sparse Autoencoders: a neural network trained on the activation values of a specific layer within the Transformer that highlights possible interpretable features / concepts that the Transformer has learned

Included is a modular training script for a TST, some pretrained models on benchmark datasets, and Interactive Jupyter notebooks to follow work through the two mechanistic interpretability methods.

Components

  • TSTtrainer.py Core training and evaluation script for Transformer-based TSC models. Defines convolutional patch embeddings, learnable postional encodings, and a Transformer encoder with a classification head

  • TSTpatching.ipynb Pipeline for causal tracing. Loads a pre-trained model, performs selective patching across various components, and measures downstream influence on predicitons. Forms causal / attribution graphs

  • SAE.ipynb Pipeline for training and analyzing sparse autoencoders. Visualiuzes learned features / concepts.

  • Pre-trained models Ready to use weights for Yoga (univariate), JapaneseVowels (multivariate) datasets to experiment on

Installation

# Clone
git clone https://github.com/mathiisk/TSTpatching.git
cd TSTpatching

# Install packages
pip install -r requirements.txt

CUDA-enabled GPU recommended but not necessary

Usage

  1. Training a new model
python TSTtrainer.py --dataset DATASETNAME --epochs NUMEPOCHS --batch_size BATCHSIZE

Specify Dataset name from https://www.timeseriesclassification.com/dataset.php, e.g, JapaneseVowels

Specify number of epochs for trianing, e.g., 100

Specify batch size, e.g., 4

The trained weights will be saved as TST_.pth in the project root

  1. Run Activation Patching Experiments
jupyter notebook TSTpatching.ipynb

Follow the cells to load a pre-trained model, apply patching, and analyze the results

  1. Run Sparse Autoencoder Experiments Follow the cells to train a sparse autoencoder, visualize sparse codes

BSc Thesis Context

This repository aims to support a BSc thesis investigating whether mechanistic interpretability methods from NLP (activation patching, sparse autoencoders) can be adapted to Transformer-based time series classifiers. The primary goal is to determine the feasibility and insight potential of these methods in revealing internal causal components within TST models