Is there a reason why you are (still) not respecting the API constraints for
custom estimators given in the documentation?
__init__ should only set parameters on self that have (exactly) the same name
as the arguments passed to it.
Your __init__ should be:
self.k = k
self.nestimators = nestimators
and self.clf_ should be constructed in fit, not in init.
Vlad
> On 29 Apr 2015, at 14:44, Pagliari, Roberto <rpagli...@appcomsci.com> wrote:
>
> I'm not sure why but when I do something like the below, nestimators becomes
> zero, despite the default value of 2000.
>
>
> class myCustomTransformer(BaseEstimator, TransformerMixin):
> def __init__(self, k=5, nestimators=2000):
> self.k_ = k
> self.clf_ = RandomForestClassifier(n_estimators=nestimators)
>
> def fit(self, X, y):
> # some stuff
> return self
>
> def transform(self, X):
> # do some stuff
> return some_stuff
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