The naming seems a bit unfortunate with seqlearn ;)
On 08/02/2018 07:25 AM, David Burns wrote:
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
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
