[ENH] Implement Proximity Forest 2.0 classifier using aeon distances

[itsdivya1309]

Reference Issues/PRs

Closes: #428
Incorporates changes suggested in #1874 (maybe we can close PR #1876)

What does this implement/fix? Explain your changes.

1. Created a private distance file to parameterise DTW and ADTW distances.
2. Created a function to calculate the first_order_derivative of a time series.
3. Implemented the ProximityTree2 and ProximityForest2 class, as per the paper.
4. Added the classes to API.
5. Wrote unit tests.

To do:

• Improve the computational efficiency by implementing Early Abandoning and Pruning algorithm for elastic distance measures.

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[MatthewMiddlehurst]

I would put the distances in the same file as pf2 for now

[TonyBagnall]

have we compared results vs published?

[MatthewMiddlehurst]

Not yet, need to put it on the cluster. I have not got results for the original yet also.

[itsdivya1309]

have we compared results vs published?

Actually, the algorithm isn't complete yet. We still need to work on computational power, particularly integrating the EAP technique. I'd resume the work in a couple of days.

[MatthewMiddlehurst]

Want to run this soon. This is not blocking that, but some component seems to be non-deterministic.

[baraline]

What were the remaining things to do here ? In terms of testing, it seems that there is some kind of non-deterministic thing happens, which given the algorithm shouldn't be happening If I remember correctly.

[MatthewMiddlehurst]

Need to compare to past results and yeah something is failing the non-deterministic test

[itsdivya1309]

What were the remaining things to do here ? In terms of testing, it seems that there is some kind of non-deterministic thing happens, which given the algorithm shouldn't be happening If I remember correctly.

Hi, we need to integrate the EAP for distance measures to complete this algorithm as such. In the current implementation, I've used private distance functions, which use pruning to stop distance calculations greater than a threshold.

[itsdivya1309]

Hi, @MatthewMiddlehurst, I've corrected the code. I think we are good to compare the results now.

[MatthewMiddlehurst]

that was not the issue, its will be internal to the fit tree function. Would revert the last change as the previous was how we do that elsewhere.

Does not stop me evaluating, just have to find the time to get it working with our setup and run it 🙂