[ENH] Implementation of TS2Vec

[gasperpetelin]

The first approach for implementing TS2Vec is still a work in progress.

Usage:

from aeon.transformations.collection.contrastive_based import TS2Vec
X, _ = load_unit_test(split="train")

# Select the device
transformer = TS2Vec(device="cpu") # or
transformer = TS2Vec(device="cuda") # or
transformer = TS2Vec(device="cuda:0")

transformer.fit_transform(X)

Reference Issues/PRs

Implements TS2Vec as discussed in #2753

What does this implement/fix? Explain your changes.

Implements TS2Vec

Does your contribution introduce a new dependency? If yes, which one?

No new dependencies. The code is mostly based on Pytorch from original author

Any other comments?

The original code has MIT licence

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[MatthewMiddlehurst]

Testing with soft dependencies also needs to be isolated i.e. https://github.com/aeon-toolkit/aeon/blob/main/aeon/classification/feature_based/tests/test_tsfresh.py

[gasperpetelin]

Thanks for pointing me in the correct direction. skipif was added for the torch dependency.

[TonyBagnall]

hi, thanks very much for this. I think we need to have a read of the paper, I'm not familiar myself, apologies if we are a little slow but if we introduce a new category of collection transformer we need to make sure we understand it. Are there other algorithms in this class that could follow on?

[TonyBagnall]

from my initial look, it seems that this transformer outputs a multivariate time series of embeddings, so input a collection of shape (n_cases, n_channels, n_timepoints) to outputs (n_cases, n_embeddings, n_timepoints), is that correct? If so, the output type is not tabular, it is a collection of transformed time series. Have I understood correctly?

Also, our estimators generally dont work with tensors. Could you format the output to by a 3D numpy array (n_cases, n_embeddings, n_timepoints),?

I'd like to try this for classification, could be interesting.

ah, having talked to hadi the collection output is an option for ts2vec, usual output is tabular.

[hadifawaz1999]

Waiting on the self supervised PR to get in then will review this, @TonyBagnall check the self supervised PR if you want to know more about how the structure of the module is gonna be for now (#2385 )

[gasperpetelin]

Hi. Just to clarify a few things about dimensions. I specifically fixed the TS2Vec implementation parameter to encoding_window='full_series', meaning that the output is always tabular with a shape of (n_cases, n_embeddings). Original TS2Vec implementation supports different encoding_window types, and one of them returns a shape of (n_cases, n_embeddings, n_timepoints), but I think for classification, (n_cases, n_embeddings) is the most relevant. In this regard, it is similar to TSFresh or Rocket.

Example:

from aeon.transformations.collection.contrastive_based import TS2Vec
from aeon.datasets import load_classification

for dataset in ['BasicMotions', 'Car']:
    print(dataset)
    X, y = load_classification(name=dataset) 
    transformer = TS2Vec(device="cuda", output_dim=320)
    print('\tinput:', X.shape, type(X))
    rtrn = transformer.fit_transform(X)
    print('\toutput:', rtrn.shape, type(rtrn))

Output:

BasicMotions
        input: (80, 6, 100) <class 'numpy.ndarray'>
        output: (80, 320) <class 'numpy.ndarray'>
Car
        input: (120, 1, 577) <class 'numpy.ndarray'>
        output: (120, 320) <class 'numpy.ndarray'>

When #2385 is merged, I can move the model into self_supervised module. As far as I know, contrastive models are just a subcategory of self-supervised ones.