This is something I have wanted to fix for a while, and which I'll do
after the release and after the model_selection refactoring is merged.
On 09/20/2015 05:02 PM, Artem wrote:
You can't get validation part of current CV split in estimator either way.
On Sun, Sep 20, 2015 at 10:11 PM, okek padokek
<defdefdef1...@gmail.com <mailto:defdefdef1...@gmail.com>> wrote:
So you are suggesting to pass the validation set as a parameter to
the __init__() of the estimator? But how do I get the current
validation set from GridSearchCV? Using my above code, do you mean
something like this:
my_model = MY_MODEL()
pipe = Pipeline(steps=[("imputer", imputer), ("scaler", scaler),
('my_model', my_model)])
my_params = dict(my_model__n_epochs = [10, 20],
*my_model__validation_set = [???]*)
estimator =
GridSearchCV(pipe, my_params, verbose=5, cv=5)
estimator.fit(x_train, y_train)
?
On Sun, Sep 20, 2015 at 10:10 AM, Artem <barmaley....@gmail.com
<mailto:barmaley....@gmail.com>> wrote:
Hi
Don't pass any parameters to fit method. Current API assumes
that you set all the parameters in estimator's constructor
(__init__ method). It's a bit nasty to set validation set
during construction stage, but there's no better approach.
On Sun, Sep 20, 2015 at 3:47 PM, okek padokek
<defdefdef1...@gmail.com <mailto:defdefdef1...@gmail.com>> wrote:
Hello,
I am trying to implement my own estimator. It currently
seems to be working. My fit() function is of the form
def fit(self, X, y=None):
....
# iteratively tune the params
....
return self
I would like to modify my fit() so that it can print out
validation costs as it iterates:
def fit(self, X, y=None, X_valid=None, y_valid=None):
....
# iteratively tune the params
#occasionally print out the cost on the validation
set (X_test, y_test)
....
return self
How would I go about passing the validation set when using
a pipeline?
I currently have something like this:
my_model = MY_MODEL()
pipe = Pipeline(steps=[("imputer", imputer), ("scaler",
scaler), ('my_model', my_model)])
my_params = dict(my_model__n_epochs = [10, 20])
estimator = GridSearchCV(pipe, my_params, verbose=5, cv=5)
estimator.fit(x_train, y_train)
If I instead try
estimator.fit(x_train, y_train, x_valid, y_valid)
then I get an error telling me that fit() does not accept
the last two parameters.
How can this be done?
Thanks
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