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While I'm here, I wanted to point out that at sktime, we've started to interface pyts algorithms, upon popular demand, to have them indexed for users who are searching for TSC and time series transformations.
Users will be able to find interfaced estimators using the indexing utility all_estimators in sktime.registry, and use them as components in sktime pipelines and composites. For this, users will need to install pyts, and they recieve an informative error message to this effect when they attempt to construct pipelines. Proper credit to pyts is of course also given, by name of estimator (uses pyts brand) and in docstring, feedback appreciated.
The estimators are also regularly tested against standard API contracts, that's how we found #158.
adding new pyts estimators is formulaic, we have written a general adapter that can be inherited from
We're not sure yet about distances.
Further, the knn classifier is neat, but we're wondering what is the best way to allow it to take abstract distances, which are first order citizens in sktime (also estimators). Perhaps there is a collaboration opportunity here.
The text was updated successfully, but these errors were encountered:
fkiraly
changed the title
[ENH] making pyts searchable via sktime
[ENH] making pyts searchable via sktime, interfaces & collaboration
Mar 19, 2024
PS: we weren't sure about authorship or maintainership - kindly be welcome to add yourself/yourselves to the author or maintainer fields of any of the adapters. Maintainer field only if you would like to take on maintenance of the adapter in sktime, of course. Both fields are shown in the searchable estimator overview and are one of our primary means of giving individual credit: https://www.sktime.net/en/latest/estimator_overview.html
I just went ahead and added you to the author fields, @johannfaouzi: sktime/sktime#6270
You will be visible in the estimator overview in first position.
While I'm here, I wanted to point out that at
sktime
, we've started to interfacepyts
algorithms, upon popular demand, to have them indexed for users who are searching for TSC and time series transformations.Users will be able to find interfaced estimators using the indexing utility
all_estimators
insktime.registry
, and use them as components insktime
pipelines and composites. For this, users will need to installpyts
, and they recieve an informative error message to this effect when they attempt to construct pipelines. Proper credit topyts
is of course also given, by name of estimator (usespyts
brand) and in docstring, feedback appreciated.The estimators are also regularly tested against standard API contracts, that's how we found #158.
If you would like to help out, or observe:
sktime
is here, feel free to comment: [ENH] interfacingpyts
estimators sktime/sktime#5850pyts
estimators is formulaic, we have written a general adapter that can be inherited fromWe're not sure yet about distances.
Further, the knn classifier is neat, but we're wondering what is the best way to allow it to take abstract distances, which are first order citizens in
sktime
(also estimators). Perhaps there is a collaboration opportunity here.The text was updated successfully, but these errors were encountered: