Thanks to visit codestin.com
Credit goes to github.com

Skip to content

[MRG] Standardize sample weights validation in DummyClassifier #15510

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Dec 26, 2019
Merged

[MRG] Standardize sample weights validation in DummyClassifier #15510

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
b39630e
Standardize sample weights validation in DummyClassifier
fbchow Nov 2, 2019
caa7871
Remove tests for each classifier
fbchow Nov 5, 2019
ee6ce0b
Remove duplicate test
salliewalecka Nov 13, 2019
8509e85
FIX Remove redundant checks
rth Dec 26, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions sklearn/dummy.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -156,7 +156,7 @@ def fit(self, X, y, sample_weight=None):

self.n_outputs_ = y.shape[1]

check_consistent_length(X, y, sample_weight)
check_consistent_length(X, y)
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think we might still want it here, not strong opinion, though...

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

even though it's checked as part of the added validation?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

+1 to avoid redundant checks (even if it doesn't cost much). _check_sample_weight should yield better error messages


if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X)
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Actually it looks like several PRs were done for this estimator as this was added on the line below in #15505. Please remove the above 3 lines.

Otherwise LGTM.

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Is it worth merging at this point? Looks like the only changes left would be one linting issue and removing sample weight from check_consistent_length. Does it make more sense to close the PR instead?

Expand Down Expand Up @@ -245,7 +245,7 @@ def predict(self, X):
classes_ = [np.array([c]) for c in constant]

y = _random_choice_csc(n_samples, classes_, class_prob,
self.random_state)
self.random_state)
else:
if self._strategy in ("most_frequent", "prior"):
y = np.tile([classes_[k][class_prior_[k].argmax()] for
Expand Down