Your get_params looks wrong to me: it is not returning a dictionary.
Sent from my phone. Please forgive brevity and mis spelling
On Feb 16, 2015, 20:02, at 20:02, "Pagliari, Roberto" <rpagli...@appcomsci.com>
wrote:
>Hi Vlad/All,
>Thanks for the pointers. The reason I return a copy of X is because I
>don't want to modify the dataset during grid search with cross
>validation (I'm not sure if the argument of transform is a deep copy or
>shallow copy).
>
>I implemented the class like the below. Basically a transformer that
>does nothing, with no parameters.
>
>class myTransformer(BaseEstimator, TransformerMixin):
> def __init__(self):
> pass
> def fit(self, *args, **kwargs):
> return self
> def transform(self, X, **transform_params):
> return X.copy()
> def set_params(self, **params):
> return self
> def get_params(self, deep=True):
> return None
>
>I'm getting this error when using it in a pipeline (during grid search
>cv, where the pipeline is standard scaler + myTransformer + svm ):
>'NoneType' object has no attribute 'iteritems'
>
>Do you know what the issue might be?
>
>
>Thank you,
>
>
>-----Original Message-----
>From: Vlad Niculae [mailto:zephy...@gmail.com]
>Sent: Monday, February 16, 2015 1:04 PM
>To: scikit-learn-general@lists.sourceforge.net
>Subject: Re: [Scikit-learn-general] which methods do I need to
>implement for a regressor?
>
>Hi Roberto,
>
>This is all documented in more detail here: [1]
>
>The transform looks good (just that you might want to add a flag to
>avoid memory copies when you can afford to destroy the original data).
>
>It’s not clear what the intention of `my_param` is here. It’s not user
>specified, right? Conventionally, fitted attributes are suffixed with
>an underscore (`self.my_param_`) and you shouldn’t initialize them in
>`__init__` (see [2])
>
>Also, if you do intend to have user-specified attributes, this would
>break grid search, because your `set_params` function does nothing.
>There are implementations of `set_params` and `get_params` in
>`sklearn.base.BaseEstimator`, as Gael said. Just inherit from the
>`BaseEstimator` and those should work, as long as you respect the
>scikit-learn convention that the `__init__` function doesn’t change the
>parameters (see [3])
>
>Hope this helps!
>
>Yours,
>Vlad
>
>[1]
>http://scikit-learn.org/stable/developers/index.html#rolling-your-own-estimator
>[2]
>http://scikit-learn.org/stable/developers/index.html#estimated-attributes
>[3]
>http://scikit-learn.org/stable/developers/index.html#parameters-and-init
>
>> On 16 Feb 2015, at 12:52, Pagliari, Roberto <rpagli...@appcomsci.com>
>wrote:
>>
>> I looked into some examples I found online but I’m a bit confused.
>>
>> Supposed I want to implement my own transformer, something similar to
>the standard scaler. Would this be sufficient to be used in a pipeline,
>or should it be done differently?
>>
>>
>> class ModelTransformer(TransformerMixin):
>>
>> def __init__(self, model):
>> self.my_param = None
>>
>> def fit(self, *args, **kwargs):
>> # do some stuff
>> self.my_param = something
>> return self
>>
>> def transform(self, X, **transform_params):
>> # do something with self.myparam and X.copy()
>> return X.copy()
>>
>> def set_params(**params):
>> return self
>>
>> def get_params(deep=True):
>> return None
>>
>> Thank you,
>>
>>
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