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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