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Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification

🚨 NEW Version 0.2.0: Mantis pre-training is now available! 🚨

Overview

MANTIS is an open-source python package with a pre-trained time series classification foundation model implemented by Huawei Noah's Ark Lab.

This is a repository the model checkpoint. Please refer to the GitHub repository of the package and the technical report on arXiv for more details.

Installation

Pip installation

It can be installed via pip by running:

The requirements can be verified at pyproject.toml

Editable mode using Poetry

First, install Poetry and add the path to the binary file to your shell configuration file. For example, on Linux systems, you can do this by running:

curl -sSL https://install.python-poetry.org | python3 -
export PATH="/home/username/.local/bin:$PATH"

Now you can create a virtual environment that is based on one of your already installed Python interpreters. For example, if your default Python is 3.9, then create the environment by running:

poetry env use 3.9

Alternatively, you can specify a path to the interpreter. For example, to use an Anaconda Python interpreter:

poetry env use /path/to/anaconda3/envs/my_env/bin/python

If you want to run any command within the environment, instead of activating the environment manually, you can use poetry run:

poetry run <command>

For example, to install the dependencies and run tests:

poetry install
poetry run pytest

If dependencies are not resolving correctly, try re-generating the lock file:

poetry lock
poetry install

Getting started

Please refer to [`getting

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