Transforming y is a big deal :) You can refer to https://github.com/scikit-learn/enhancement_proposals/pull/2 and the associated issues/PR to see what is going on. This is probably an additional use case to think about when designing estimator which will be modifying y.
Regarding the pipeline, I assume that your strategy would be to resample at fit and do nothing at predict, isn't it? NB: you could actually implement this sampling in a FunctionSampler of imblearn: http://contrib.scikit-learn.org/imbalanced-learn/dev/generated/imblearn.FunctionSampler.html#imblearn.FunctionSampler and then use the imblearn pipeline which would apply the transform at fit time but not at predict. On 27 February 2018 at 18:02, David Burns <david.mo.bu...@gmail.com> wrote: > First post on this mailing list. > > I have been working with time series data for a project, and thought I > could contribute a new transformer to segment time series data using a > sliding window, with variable overlap. I have attached demonstration of how > this would fit in the existing framework. The only challenge for me here is > that the transformer needs to transform both the X and y variable in order > to perform the segmentation. I am not sure from the documentation how to > implement this in the framework. > > Overlapping segments is a great way to boost performance for time series > classifiers, so this may be a worthwhile contribution for some in this area > of ML. Ultimately, model_selection.TimeSeries.Split would need to be > modified to support overlapping segments, or a new class created to enable > validation for this. > > Please let me know if this would be a worthwhile contribution, and if so > how to go about transforming the target vector y in the framework / > pipeline? > > Thanks! > > David Burns > > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/
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