Hi Matt,
We do have CCA and other PLS-related transformers / regressors in
scikit-learn. They are able to do dimensionality reduction on both X and
Y (which I believe correspond to spp and env), so you might want to have
a look at these. However, they're not fully compatible with the whole
ecosystem unfortunately: for example our Pipeline objects assume that
only X can be transformed, not Y.
Nicolas
On 7/31/20 12:02 PM, Gregory, Matthew wrote:
Hi all,
I'm fairly new to scikit-learn, but have been using a predictive model for a
while now that would benefit from scikit-learn's estimator API. However, I
could use some advice on how best to implement this.
Briefly, the model is a combination of dimension reduction and nearest neighbors, but the dimension
reduction step (canonical correspondence analysis - CCA) relies on two matrices to create the
synthetic feature scores for the candidates in the nearest neighbor step. The two matrices are a
"species" matrix (spp) and an "environmental" matrix (env) which are used to
create orthogonal CCA axes that are linear combinations of the environmental features.
In reading through the documentation on creating new estimators, it seems that
every estimator should provide a fit(X, y) method. Somehow I need my X
parameter to be both the spp and env matrices together. I got a lot of good
inspiration from this post on Stack Overflow:
https://stackoverflow.com/questions/45966500/use-sklearn-gridsearchcv-on-custom-class-whose-fit-method-takes-3-arguments
and can mostly understand how the OP implemented this, basically by creating a
DataHandler class that packs together the two matrices, such that the call to
fit would look like:
estimator.fit(DataHandler(spp, env), y)
I'm wondering if this is the best way to handle the design or if I'm not fully
understanding how I could use a Pipeline to accomplish the same goal. Thanks
for any guidance - boilerplate sample code would be most appreciated!
matt
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