Hi Gael,
I think using list may cause problems with what I was doing. So I decided to
change things in a way that I only need a scalar and everything works now.
But thanks for your help !
From: Gael Varoquaux [mailto:[email protected]]
Sent: Monday, February 16, 2015 5:39 PM
To: [email protected]
Cc: [email protected]
Subject: Re: [Scikit-learn-general] which methods do I need to implement for a
regressor?
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, at 20:02, "Pagliari, Roberto"
<[email protected]<mailto:[email protected]>> 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:[email protected]<mailto:[email protected]>]
Sent: Monday, February 16, 2015 1:04 PM
To:
[email protected]<mailto:[email protected]>
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
<[email protected]<mailto:[email protected]>> 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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