Nope! Mostly because of lack of experience with mixins.
I've done some reading and I think I can come up with a few mixins doing
the trick by dynamically adding their methods to an already instantiated
object. I'll play with that and I hope to show you something soon! Or at
least I will have better
How could I make mixins work in this case?
If I define the class `FancyEstimatorMixin`, in order to get a drop-in
replacement for a sklearn
object wouldn't I need to monkey-patch the scikit-learn `BaseEstimator`
class to inherit from my mixin?
Or am I misunderstanding something?
(BTW monkey-patch
Hi Manolo!
Your code looks nice, but my use case is a bit different. I have a mixed
set of parameters, some come from my wrapper,
and some from the wrapped estimator. The logic I am going for is something
like
"If you know about this parameter, then deal with it, if not, then pass it
along to the
Hi everyone,
I was using scikit-learn KMeans algorithm to cluster pretrained
word-vectors. There are a few things which I found to be surprising and
wanted to get some feedback on.
- Based upon the 'labels_' assigned to each word-vector (i.e. cluster
memberships), I compute every cluster centroid
On 04/16/2018 04:07 PM, Sidak Pal Singh wrote:
Hi everyone,
I was using scikit-learn KMeans algorithm to cluster pretrained
word-vectors. There are a few things which I found to be surprising
and wanted to get some feedback on.
- Based upon the 'labels_' assigned to each word-vector (i.e.
