Hi Sam.
GridSearchCV will do cross-validation, which requires to "transform" the
test data.
The shape of the test-data will be different from the shape of the
training data.
You need to have the ability to compute the kernel between the training
data and new test data.
A more hacky solution would be to compute the full kernel matrix in
advance and pass that to GridSearchCV.
You probably don't need it here, but you should also checkout what the
_pairwise attribute does in cross-validation,
because that it likely to come up when playing with kernels.
Hth,
Andy
On 08/02/2017 08:38 AM, Sam Barnett wrote:
Dear all,
I have created a 2-step pipeline with a custom transformer followed by
a simple SVC classifier, and I wish to run a grid-search over it. I am
able to successfully create the transformer and the pipeline, and each
of these elements work fine. However, when I try to use the fit()
method on my GridSearchCV object, I get the following error:
57 # during fit.
58 if X.shape != self.input_shape_:
---> 59 raise ValueError('Shape of input is different from
what was seen '
60 'in `fit`')
61
ValueError: Shape of input is different from what was seen in `fit`
For a full breakdown of the problem, I have written a Jupyter notebook
showing exactly how the error occurs (this also contains all .py files
necessary to run the notebook). Can anybody see how to work through this?
Many thanks,
Sam Barnett
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