I added
import sklearn.base.TransformerMixin
but it says no module named TransofrmerMixin
From: Joel Nothman [mailto:[email protected]]
Sent: Thursday, September 11, 2014 9:37 PM
To: scikit-learn-general
Subject: Re: [Scikit-learn-general] binarizer with more levels
Good point. It should be straightforward in any case, something like:
class Quantizer(sklearn.base.TransformerMixin):
def __init__(self, thresholds):
self.thresholds = thresholds # must be sorted
def transform(X, y=None):
return np.searchsorted(self.thresholds, X)
On 12 September 2014 11:20, Pagliari, Roberto
<[email protected]<mailto:[email protected]>> wrote:
In my case I would like to do it right after scaling, while doing grid search.
This would be different to quantize the entire training set at the beginning.
Thank you,
From: Joel Nothman
[mailto:[email protected]<mailto:[email protected]>]
Sent: Thursday, September 11, 2014 9:00 PM
To: scikit-learn-general
Subject: Re: [Scikit-learn-general] binarizer with more levels
If thresholds can be provided to the constructor then they are not estimated
automatically from the training data. This is the sort of preprocessing you can
and should do with pandas.
On 12 September 2014 10:53, Pagliari, Roberto
<[email protected]<mailto:[email protected]>> wrote:
I’m getting errors about get_params_ missing etc…
I guess I need to derive my own binarizer from some other classes. Is there a
way to simplify the process?
Essentially, what I need is the binarizer, with more levels (and thresholds
provided to the constructors).
Thank you
From: Joel Nothman
[mailto:[email protected]<mailto:[email protected]>]
Sent: Thursday, September 11, 2014 5:05 PM
To: scikit-learn-general
Subject: Re: [Scikit-learn-general] binarizer with more levels
For quantizing or binning? Not currently.
On 12 September 2014 06:31, Pagliari, Roberto
<[email protected]<mailto:[email protected]>> wrote:
Is there something like the binarizer with more levels (thresholds provided
with input)
Thanks
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