tslearn-team / tslearn-team/tslearn
partial_fit in NonMyopicEarlyClassifier
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- Dominant language
- Python
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Description
Problem:
I have some long time series I want to classify using NonMyopicEarlyClassifier, but then I exceed the RAM limit when I use the fit() function.
Proposed solution:
I want to use partial_fit() to train my NonMyopicEarlyClassifier, so I don't have to load all my data into the RAM at the same time, but rather train using a generator.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the NonMyopicEarlyClassifier implementation and trace how fit() consumes training data. Compare its estimator interface with the requested partial_fit() behavior, then verify that training can consume a generator without requiring the complete dataset in RAM. Done means the classifier supports incremental training for long time series.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100