Hi,
I posted a while back about this, and am reposting now since I have made
progress on this topic. As you are probably aware, the sklearn Pipeline
only supports transformers for X, and the number of samples must stay
the same.
I work with time series where the learning pipeline relies on
transformations like resampling, segmentation, etc that change the
target and number of samples in the data set. In order to address this,
I created an sklearn compatible pipeline that handles transformers that
alter X, y, and sample_weight together. It can undergo model selection
using the sklearn tools, and integrates with all the sklearn
transformers and estimators. It also has some new options for setting
hyper-parameters with callables and in reference to other parameters.
The implementation is in my time series package seglearn:
https://github.com/dmbee/seglearn
- Best
David Burns
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