Skip to content

Warning if fitted sklearn model being used #989

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
PGijsbers merged 9 commits into from
Nov 3, 2020
Merged

Warning if fitted sklearn model being used #989

Changes from 1 commit
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
d224845
sklearn model fit check
Neeratyoy Oct 28, 2020
409c6c0
Merge branch 'develop' into fix_968
Neeratyoy Oct 30, 2020
01a7c36
Reordering warning
Neeratyoy Oct 30, 2020
d08ad83
Update openml/extensions/sklearn/extension.py
mfeurer Nov 2, 2020
c72baee
Adding function to ext for checking model fit
Neeratyoy Nov 2, 2020
274506b
resolving merge conflicts
Neeratyoy Nov 2, 2020
acb8d66
Removing junk file
Neeratyoy Nov 2, 2020
3c3b3d8
Fixing sklearn version compatibility issue
Neeratyoy Nov 2, 2020
1e90133
Merge branch 'develop' into fix_968
Neeratyoy Nov 3, 2020
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
Prev Previous commit
Next Next commit
Fixing sklearn version compatibility issue
  • Loading branch information
@Neeratyoy
Neeratyoy committed Nov 2, 2020
commit 3c3b3d851a81e91a51794fe06c19368fcadc443d
17 changes: 13 additions & 4 deletions openml/extensions/sklearn/extension.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1551,13 +1551,22 @@ def check_if_model_fitted(self, model: Any) -> bool:
try:
# check if model is fitted
from sklearn.exceptions import NotFittedError
from sklearn.utils.validation import check_is_fitted

check_is_fitted(model) # raises a NotFittedError if the model has not been trained
# Creating random dummy data of arbitrary size
dummy_data = np.random.uniform(size=(10, 3))
# Using 'predict' instead of 'sklearn.utils.validation.check_is_fitted' for a more
# robust check that works across sklearn versions and models. Internally, 'predict'
# should call 'check_is_fitted' for every concerned attribute, thus offering a more
# assured check than explicit calls to 'check_is_fitted'
model.predict(dummy_data)
# Will reach here if the model was fit on a dataset with 3 features
return True
except NotFittedError:
# model is not fitted, as is required
except NotFittedError: # needs to be the first exception to be caught
# Model is not fitted, as is required
return False
except ValueError:
# Will reach here if the model was fit on a dataset with more or less than 3 features
return True

def _run_model_on_fold(
self,
Expand Down