[MNT, ENH, DOC] Rework similarity search #2473

baraline · 2024-12-26T19:00:13Z

Reference Issues/PRs

Fixes #2341, #2236, #2028, #2020, #1806, #2475, #2538

What does this implement/fix? Explain your changes.

The previous structure for similarity search was not in line with the structure we would expect considering other aeon modules, the lack of distinct base classes for some tasks, as well as the initial design choice (due to the lack of practical experience with using and expanding the module) lead to some really complex code when working on #2341 to make everything work together. Further expanding the module would have made thing worse.

To make the module more flexible and comprehensible, the following rework is proposed in this PR (AEP to be updated acordingly):

The module structure is now :

|-similarity_search
|------series
|------------neighbors #NN on subsequences 
|------------motifs #Motif extraction on subsequences methods
|------collection
|------------neighbors # series methods for subsequence NN adapted to collection case or Approximate NN on whole series
|------------motifs #Series methods adapted to collection case

Base classes are BaseSimilaritySearch, BaseSeriesSimilaritySearch, BaseCollectionSimilaritySearch
Implemented estimators are :

(series/neighbors) MassSNN : subsequence nearest neighbors and distance profile computation
(series/neighbors) DummySNN : brute force subsequence nearest neighbors
(series/motifs) StompMotifs : top k motifs extraction (supports motifs pairs, k-motif or r-motifs)
(collection/neighbors) RandomProjectionIndexANN: Approximate nearest neighbors on whole series using a random projection LSH method.

The sufix of the estimators (SNN/ANN/Motifs) remains an open discussion, not sure it's the right way to go.

I removed the support for collections for Stomp and Mass for now to focus on the "expected and well known" use cases, I'll make them in another PR.

All similarity search estimators now use fit/predict interface, with predict returning two arrays (NN/Motifs indexes, and NN/Motifs distances).

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

No.

Any other comments?

As this is still a WIP, I would love some inputs on the structure (notably from @patrickzib !) to make the module more future-proof to future additions and easier to use.

TODO list :

Finish to include testing suite for base estimators in the testing module for the SubsequenceSearch part and fix them
Implement LSH index as a simple first case for BaseCollectionSimilaritySearch
Implement tests for base classes and estimators
Update API docs / doc pages
Update notebooks
Check docstrings
Cleanup TODOs in the code
updated aeon's CODEOWNERS to receive notifications about future changes to these files.

…class-with-attimo-algorithm

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…class-with-attimo-algorithm

patrickzib · 2025-01-02T13:53:45Z

Thank you very much for working on this.

Some thoughts:

Focus the module on two distinct tasks : find_neighbors and find_motifs for all type for similarity search estimators. Similarly to the fit/ predict interface we already know well, here, we first fit and then either find_motifs or find_neighbors ("predict" keyword don't make much sense here). We give a collection to use as database in fit, and a single series in find_neighbors or find_motifs to use as query for the search.

That is an interesting problem. Here is my view:

For whole series similarity search fit requires a dataset of time series of equal length, and find_neighbors would get one or many query series of this length.

For subsequence similarity search fit requires a single time series, and find_neighbors commonly gets a single query sequence which length is shorter than the single series length. It would be fine however, to extend it to multiple short sequences.

There is only one whole series consensus motif search paper, which would be the use case of whole matching and motif discovery. The input to fit would be the whole dataset, and find_motif has no input series X. not sure, what an input series X should trigger.

Most papers solve the problem of motif discovery in a single long time series, defined as subsequences of the time series. Here, fit gets a single series, and find_motif has no input series X.

Distinguish between two kind of similarity search tasks with the two submodules, SeriesSearch and SubsequencesSearch. The SubsequencesSearch focuses on tasks for which the goal is to find motifs or neighbors in subsequences of time series (e.g. Matrix Profiles, Motiflets, etc.). the SeriesSearch focuses on task using whole series (e.g. Indexes such as LSH, iSAX, etc.)

Have base classes for families of method to limit code duplication (e.g. BaseMatrixProfile, and STOMP, where most existing code was ported), so the we can focus on implementing the computational logic when adding new estimators.

What is the difference between BaseMatrixProfile and STOMP?

At least for Motiflets, we cannot use STOMP/MP, as it only gives a 1-NN profile, but we need k-NN profiles. Same problem would be the case, if you want to solve k-nearest neighbors similarity search.

Questions to answers for motif search :

Do we want to make providing X optional in find_motifs ? Providing X means that we search for subsequences in X that are motifs in the collection given in fit. Not providing X would mean that we search for motifs in the collection given in fit only. I think it would make sense to make it optional, but would love some comment from people actually doing motif discovery.

I think that X is not meaningful for motif discovery.

baraline · 2025-01-02T14:20:37Z

Thanks for the inputs @patrickzib😄

For whole series similarity search fit requires a dataset of time series of equal length, and find_neighbors would get one or many query series of this length.

Completely in line with this, but what about the case of unequal length series, with, for example, elastic distance measures? Wouldn't that be a plausible use case? (all whole series estimators don't have to support it)

For subsequence similarity search fit requires a single time series, and find_neighbors commonly gets a single query sequence which length is shorter than the single series length. It would be fine however, to extend it to multiple short sequences.

For this case, I'm defining a length parameter during __init__, and accept a 2D subsequence of shape (n_channels, length) for find_neighbors, ensuring that length is not bigger than the length of any series given in fit. However, any reason why you think we should restrain ourselves to a single series for fit ?

There is only one whole series consensus motif search paper, which would be the use case of whole matching and motif discovery. The input to fit would be the whole dataset, and find_motif has no input series X. not sure, what an input series X should trigger.

This is the tricky one for me too. I'm not sure how giving X during find_motifs on whole series would fit any use case.

Most papers solve the problem of motif discovery in a single long time series, defined as subsequences of the time series. Here, fit gets a single series, and find_motif has no input series X.

I've been kinda frustrated by this limitation for practical use cases, wouldn't it be fine to loop on series of a collection with the motif discovery methods and then merge the results ? That's how I implemented STOMP for now for example. For each subsequence in X, it computes the distance profile to all series in a collection, and keep the top k among all of them (also storing the sample ID and timepoint ID).

What is the difference between BaseMatrixProfile and STOMP?
At least for Motiflets, we cannot use STOMP/MP, as it only gives a 1-NN profile, but we need k-NN profiles. Same problem would be the case, if you want to solve k-nearest neighbors similarity search.

BaseMatrixProfile (which inherit BaseSubsequenceSearch) is simply a base class that defines abstract compute_matrix_profile and compute_distance_profile methods to be implemented by child classes such as STOMP and the likes (STUMP, etc...). The logic for finding neighbors / motifs is then handled in the BaseMatrixProfile. I wanted to leave the door open to alternative methods and not just focus on matrix profiles, hence the split.

As stated above, I already extended STOMP to support k-NN profiles for collections (multivariate and unequal length compatible).

I suppose that in this context, motiflets would either inherit from BaseMatrixProfile if you need to implement methods like compute_matrix_profile and compute_distance_profile. Otherwise, It would inherit from BaseSubsequenceSearch and make its own methods to answer the find_neighbors/find_motifs tasks. (I would need to read the paper again!)

Note that it's possible to simply raise a "NotImplementedError" or something similar if an estimator would only support neighbors or motifs search.

My goal here is to find a base class structure that enables us to move most common code to there and focus on the computational optimisations of each method in the child classes.

I think that X is not meaningful for motif discovery.

In the context of motif search in a single series I agree, but wouldn't there be some interest when dealing with a collection ? For example find motifs in the collection at the condition that they are similar to a subsequence in X ? (This is pure speculation)

patrickzib · 2025-01-02T16:13:42Z

Completely in line with this, but what about the case of unequal length series, with, for example, elastic distance measures? Wouldn't that be a plausible use case? (all whole series estimators don't have to support it)

Sure. I did not think of this.

For subsequence similarity search fit requires a single time series, and find_neighbors commonly gets a single query sequence which length is shorter than the single series length. It would be fine however, to extend it to multiple short sequences.

For this case, I'm defining a length parameter during __init__, and accept a 2D subsequence of shape (n_channels, length) for find_neighbors, ensuring that length is not bigger than the length of any series given in fit. However, any reason why you think we should restrain ourselves to a single series for fit ?

Simplicity :) But I agree that you could have multiple series in fit, too - this would mimic the Shapelet use case, I suppose?

Most papers solve the problem of motif discovery in a single long time series, defined as subsequences of the time series. Here, fit gets a single series, and find_motif has no input series X.

I've been kinda frustrated by this limitation for practical use cases, wouldn't it be fine to loop on series of a collection with the motif discovery methods and then merge the results ? That's how I implemented STOMP for now for example. For each subsequence in X, it computes the distance profile to all series in a collection, and keep the top k among all of them (also storing the sample ID and timepoint ID).

Sorry, yes, that is what the authors refer to as consensus motif:
https://www.cs.ucr.edu/~eamonn/consensus_Motif_ICDM_Long_version.pdf

BaseMatrixProfile (which inherit BaseSubsequenceSearch) is simply a base class that defines abstract compute_matrix_profile and compute_distance_profile methods to be implemented by child classes such as STOMP and the likes (STUMP, etc...).

I see. I personally do not like to use the terms matrix-profile for simple k-NN distances or k-NN indices though. It was a brilliant re-framing of EK, such that all 1-NN algorithms are now suddenly an instance of matrix profile. Yet, the concept is much older.

As stated above, I already extended STOMP to support k-NN profiles for collections (multivariate and unequal length compatible).

Great.

I think that X is not meaningful for motif discovery.

In the context of motif search in a single series I agree, but wouldn't there be some interest when dealing with a collection ? For example find motifs in the collection at the condition that they are similar to a subsequence in X ? (This is pure speculation)

I would not say that this is impossible, but I have not seen it. :)

baraline · 2025-01-02T16:23:36Z

Simplicity :) But I agree that you could have multiple series in fit, too - this would mimic the Shapelet use case, I suppose?

I'm not 100% sure what you mean, but in a sense yes ? For example with a brute force neighbour search, just compute the distance of the subsequence given in find_neighbors to all candidates subsequences in all series of the collection given in fit, and take the k best overall, (considering neighbouring matches/self matches if specified by parameters).

I see. I personally do not like to use the terms matrix-profile for simple k-NN distances or k-NN indices though. It was a brilliant re-framing of EK, such that all 1-NN algorithms are now suddenly an instance of matrix profile. Yet, the concept is much older.

I'm not against the idea of a different naming, especially if methods labelled differently from MPs would fit in the base class without much change of parameter/interface. Would you have any proposal? Something like BaseNeighborhoodSearch ?

patrickzib · 2025-01-06T11:51:45Z

I'm not against the idea of a different naming, especially if methods labelled differently from MPs would fit in the base class without much change of parameter/interface. Would you have any proposal? Something like BaseNeighborhoodSearch ?

In sklearn it is simply NearestNeighbors ? :) And it returns indices and distances.

https://scikit-learn.org/1.5/modules/neighbors.html

…ithm

MatthewMiddlehurst

Base class comments

aeon/similarity_search/collection/_base.py

aeon/similarity_search/collection/tests/test_base.py

aeon/similarity_search/series/_base.py

baraline · 2025-04-15T19:39:48Z

Based on your comment @MatthewMiddlehurst, I think another set of base class for neighbours / motifs estimator is necessary to not have weird thing in the series & collection base classes.
I wanted to avoid creating too many bases classes, but it seems necessary to have "cleaner" ones.
I'll put it in place and then you can check back if it feels better.

…tps://github.com/aeon-toolkit/aeon into 2341-enh-indexsearch-class-with-attimo-algorithm

baraline · 2025-04-19T23:35:10Z

Tried to make additional base classes, but this made the whole module structure feel too overcomplicated. Reverted and instead factored some functions away from the base class and made a fit_predict for stomp to avoid the weird X=None possibility in predict. Open to suggestions if you have some

aeon/similarity_search/series/_base.py

MatthewMiddlehurst · 2025-04-30T12:58:13Z

Its all experimental. Better to just get it in when clear issues are resolved then iterate.

MatthewMiddlehurst · 2025-04-30T13:08:53Z

It is also possible I am misunderstanding the goal a bit. If you want to do something that fits a collection and predicts on a series, we probably need a new base class for that. The collection and series bases are not really built for it. Not saying has to be done in this PR, but probably where we want to end up.

baraline · 2025-05-01T14:41:46Z

I think we need to explore this a bit further down the road when more estimators are added to see if it would really fit a use case.
This could also be solved with a simple wrapper like predict_series calling self.predict([X]), but let's give it some thought first agree.

…ithm

Stale review, comments addressed and fixed.

MatthewMiddlehurst

This is too big to reasonably review just looking at the code and text. Generally I'm fine with the changes, and the module is experimental so stuff can be modified and removed as required. No one has raised any major objections in the duration of this PR.

In the future I would like to see some generic testing added to make sure these adhere so some structure.

baraline · 2025-05-09T18:37:16Z

Agreed, will resume work on this module during summer when things cool down, and testing will definitly be an important part of the work

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Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Fixed a few spelling/grammar mistakes on TSC docs examples (#2738) * Fix docstring inconsistencies in benchmarking module (resolves #809) (#2735) * issue#809 Fix docstrings for benchmarking functions * Fixed docstrings in results_loaders.py * Fix docstring inconsistencies in benchmarking module - resolves #809 * Fix docstring inconsistencies in benchmarking module - resolves #809 * [ENH] `best_on_top` addition in `plot_pairwise_scatter` (#2655) * Empty-Commit * best_on_top parameter added * changes * [ENH] Add dummy clusterer tags (#2551) * dummy clusterer tags * len * [ENH] Collection conversion cleanup and `df-list` fix (#2654) * collection conversion cleanup * notebook * fixes --------- Co-authored-by: Tony Bagnall <a.j.bagnall@soton.ac.uk> * [MNT] Updated the release workflows (#2638) * edit release workflows to use trusted publishing * docs * [MNT,ENH] Update to allow Python 3.13 (#2608) * python 3.13 * tensorflow * esig * tensorflow * tensorflow * esig and matrix profile * signature notebook * remove prts * fix * remove annoying deps from all_extras * Update pyproject.toml * [ENH] Hard-Coded Tests for `test_metrics.py` (#2672) * Empty-Commit * hard-coded tests * changes * Changed single ticks to double (#2640) Co-authored-by: Matthew Middlehurst <pfm15hbu@gmail.com> * 📝 Add HaroonAzamFiza as a contributor for doc (#2740) * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * [ENH,MNT] Assign Bot (assigned issues>2) (#2702) * Empty-Commit * point 2 working * changes * changes in comment message * [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) (#2704) * added extra features * added comments * optimized code * optimized code * made changes requested by moderators * fixed conflicts * fixed conflicts * fixed conflicts --------- Co-authored-by: Ramana-Raja <ramanarajakesavaraja@gamil.com> * 📝 Add Ramana-Raja as a contributor for code (#2741) * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * Release v1.1.0 (#2696) * v1.1.0 draft * finish * Automated `pre-commit` hook update (#2743) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump the github-actions group with 2 updates (#2744) Bumps the github-actions group with 2 updates: [crs-k/stale-branches](https://github.com/crs-k/stale-branches) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). 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Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [DOC] Add implementation references (#2748) * implementation references * better attribution * use gpu installs for periodic tests (#2747) * Use shape calculation in _fit to optimize QUANTTransformer (#2727) * [REF] Refactor Anomaly Detection Module into Submodules by Algorithm Family (#2694) * Refactor Anomaly Detection Module into Submodules by Algorithm Family * updated documentation and references * implemented suggested changes * minor changes * added headers for remaining algorithm family * removing tree-based header * Automated `pre-commit` hook update (#2756) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH]Type hints/forecasting (#2737) * Type hints for primitive data types in base module * Type hints for primitive data types and strings in forecating module * type hints for primitives in foreacasting module * Revert "type hints for primitives in foreacasting module" This reverts commit 575122d. * type hints for primitives in forecasting module * Automated `pre-commit` hook update (#2766) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Implement `load_model` function for ensemble classifiers (#2631) * feat: implement `load_model` function for LITETimeClassifier Implement separate `load_model` function for LITETimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * feat: implement `load_model` function for InceptionTimeClassifier Implement separate `load_model` function for InceptionTimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * fix: typo in load model function * feat: convert load_model functions to classmethods * test: implement test for save load for LITETIME and Inception classification models * Automatic `pre-commit` fixes * refactor: move loading tests to separate files * Update _ae_abgru.py (#2771) * Automated `pre-commit` hook update (#2779) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [DOC] Fix Broken [Source] Link and Improve Documentation for suppress_output() (#2677) * Fix Broken [Source] Link and Improve Documentation for suppress_output() Function * modified docstring and added tests * modified docstring example * modifying docstring examples * modifying docstring examples * updating conf file * updated docstring * base transform tidy (#2773) * DOC: Add Raises section for invalid weights in KNeighborsTimeSeriesClassifier (#1766) (#2764) Document the ValueError raised during initialization when an unsupported value is passed to the 'weights' parameter. 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Co-authored-by: Matthew Middlehurst <pfm15hbu@gmail.com> * [ENH] Fixes Issue Improve `_check_params` method in `kmeans.py` and `kmedoids.py` (#2682) * Improves _check_params * removes function and adds a var * minor changes * minor changes * minor changes * line endings to LF * use variable instead of duplicating strings * weird file change * weird file change --------- Co-authored-by: Matthew Middlehurst <pfm15hbu@gmail.com> * [ENH] Add type hints for deep learning regression classes (#2644) * type hints for cnn for regrssion * editing import modules Model & Optim * type hints for disjoint_cnn for regrssion * FIX type hints _get_test_params * ENH Change linie of importing typing * type hints for _encoder for regrssion * type hints for _fcn for regrssion * type hints for _inception_time for regrssion * type hints for _lite_time for regrssion * type hints for _mlp for regrssion * type hints for _resnet for regrssion * type hints for _base for regrssion * FIX: mypy errors in _disjoint_cnn.py file * FIX: mypy typing errors * Fix: Delete variable types, back old-verbose * FIX: add model._save in save_last_model_to_file function * FIX: Put TYPE_CHECKING downside * Fix: Put Any at the top * [DOC] Add RotationForest Classifier Notebook for Time Series Classification (#2592) * Add RotationForest Classifier Notebook for Time Series Classification * Added references and modified doc * minor modifications to notebook description * Update rotation_forest.ipynb --------- Co-authored-by: Matthew Middlehurst <pfm15hbu@gmail.com> * fix: Codeowners for benchmarking metrics AD (#2784) * [GOV] Supporting Developer role (#2775) * supporting dev role * pr req * Update governance.md * typo * Automatic `pre-commit` fixes * aeon --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT, ENH, DOC] Rework similarity search (#2473) * WIP remake module structure * Update _brute_force.py * Update test__commons.py * WIP mock and test * Add test for base subsequence * Fix subsequence_search tests * debug brute force mp * more debug of subsequence tests * more debug of subsequence tests * Add functional LSH neighbors * add notebook for sim search tasks * Updated series similarity search * Fix mistake addition in transformers and fix base classes * Fix registry and api reference * Update documentation and fix some leftover bugs * Update documentation and add default test params * Fix identifiers and test data shape for all_estimators tests * Fix missing params * Fix n_jobs params and tags, add some docs * Fix numba test bug and update testing data for sim search * Fix imports, testing data tests, and impose predict/_predict interface to all sim search estimators * Fix args * Fix extract test * update docs api and notebooks * remove notes * Patrick comments * Adress comments and clean index code * Fix Patrick comments * Fix variable suppression mistake * Divide base class into task specific * Fix typo in imports * Empty 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baraline added 4 commits December 4, 2024 22:47

WIP remake module structure

1646895

Update _brute_force.py

52b0692

Update test__commons.py

7973a30

Merge remote-tracking branch 'origin/main' into 2341-enh-indexsearch-…

7abe221

…class-with-attimo-algorithm

baraline linked an issue Dec 26, 2024 that may be closed by this pull request

[ENH] IndexSearch class with attimo or LL algorithm #2341

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aeon-actions-bot bot added documentation Improvements or additions to documentation enhancement New feature, improvement request or other non-bug code enhancement maintenance Continuous integration, unit testing & package distribution similarity search Similarity search package labels Dec 26, 2024

baraline mentioned this pull request Dec 26, 2024

adds examples for querysearch #2472

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WIP mock and test

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This was linked to issues Dec 27, 2024

[ENH] Rethink the mask parameter in similarity search #2020

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[DOC] Update similarity search notebook #2028

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[BUG] Test fail with numpy2 for stomp_squared_matrix_profile #2236

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This was referenced Dec 30, 2024

[ENH] Improved the test coverage of similarity search module #2476

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[BUG] extract_top_k_and_threshold_from_distance_profiles_one_series Function Errors Related to Numba and n_candidates Parameter #2475

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baraline added 3 commits January 1, 2025 09:36

Add test for base subsequence

bb2aa33

Merge remote-tracking branch 'origin/main' into 2341-enh-indexsearch-…

c5c9c28

…class-with-attimo-algorithm

Fix subsequence_search tests

f23c720

baraline linked an issue Jan 2, 2025 that may be closed by this pull request

[BUG] extract_top_k_and_threshold_from_distance_profiles_one_series Function Errors Related to Numba and n_candidates Parameter #2475

Closed

baraline added 2 commits January 2, 2025 13:56

debug brute force mp

c372969

more debug of subsequence tests

d7da68b

more debug of subsequence tests

da2758c

Add functional LSH neighbors

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baraline added 2 commits March 31, 2025 15:33

Fix variable suppression mistake

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Merge branch 'main' into 2341-enh-indexsearch-class-with-attimo-algor…

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…ithm

baraline requested a review from patrickzib March 31, 2025 14:06

baraline requested a review from MatthewMiddlehurst April 11, 2025 20:08

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baraline and others added 9 commits April 16, 2025 22:27

Fix Base class comments

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Divide base class into task specific

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Fix typo in imports

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Empty commit for CI

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Fix typo again

58dd7b4

Merge branch '2341-enh-indexsearch-class-with-attimo-algorithm' of ht…

38e7b55

…tps://github.com/aeon-toolkit/aeon into 2341-enh-indexsearch-class-with-attimo-algorithm

Add check_inheritance exception for similarity search

6911bd9

Revert back to non per type base classes

7fd9899

Factor check index and typo in test

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baraline requested a review from MatthewMiddlehurst April 19, 2025 23:36

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Merge branch 'main' into 2341-enh-indexsearch-class-with-attimo-algor…

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baraline merged commit 6a6aac3 into main May 10, 2025
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baraline deleted the 2341-enh-indexsearch-class-with-attimo-algorithm branch May 10, 2025 07:05

[MNT, ENH, DOC] Rework similarity search #2473

[MNT, ENH, DOC] Rework similarity search #2473

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