You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Although I was fine making this test file depend on the definitions of various estimators, #13470 has made it clear that this can be a nuisance to developers when they add a parameter to the estimators that happen to be used in those tests (or even change a default value). I think if we replace:
from sklearn.linear_model import LogisticRegression
with something like
# Some example constructors excerpted to test pprintingclassLogisticRegression(BaseEstimator):
"""Logistic Regression (aka logit, MaxEnt) classifier. """def__init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0,
fit_intercept=True, intercept_scaling=1, class_weight=None,
random_state=None, solver='warn', max_iter=100,
multi_class='warn', verbose=0, warm_start=False, n_jobs=None,
l1_ratio=None):
pass
and do similar for other classes, then we will get most of the effect of the tests (checking that standard use cases look acceptable) without being brittle to changes in parameters of those estimators.
The text was updated successfully, but these errors were encountered:
Although I was fine making this test file depend on the definitions of various estimators, #13470 has made it clear that this can be a nuisance to developers when they add a parameter to the estimators that happen to be used in those tests (or even change a default value). I think if we replace:
from sklearn.linear_model import LogisticRegression
with something like
and do similar for other classes, then we will get most of the effect of the tests (checking that standard use cases look acceptable) without being brittle to changes in parameters of those estimators.
The text was updated successfully, but these errors were encountered: