Skip to content
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

Jump to bottom

Allow for Nans in input of Permutation Importance #381

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Allow for Nans in input of Permutation Importance #381

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f372520
Allow for Nans in input of Permutation Importance
May 22, 2020
fc22f65
Update sklearn requirement to 0.20
Nov 11, 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
2 changes: 1 addition & 1 deletion eli5/sklearn/permutation_importance.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -196,7 +196,7 @@ def fit(self, X, y, groups=None, **fit_params):
self.estimator_ = clone(self.estimator)
self.estimator_.fit(X, y, **fit_params)

X = check_array(X)
X = check_array(X, force_all_finite=False)

if self.cv not in (None, "prefit"):
si = self._cv_scores_importances(X, y, groups=groups, **fit_params)
Expand Down
12 changes: 11 additions & 1 deletion tests/test_sklearn_permutation_importance.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -189,4 +189,14 @@ def test_cv_sample_weight(iris_train):
perm = PermutationImportance(model, cv=5, random_state=42).fit(X, y)

# passing a vector of weights filled with one should be the same as passing no weights
assert (perm.feature_importances_ == perm_weights.feature_importances_).all()
assert (perm.feature_importances_ == perm_weights.feature_importances_).all()

def test_allow_nans(iris_train):
xgboost = pytest.importorskip('xgboost')

X, y, feature_names, target_names = iris_train
X[0, 0] = np.nan

perm = PermutationImportance(xgboost.XGBClassifier(), cv=5)
# There should be not error thrown during fitting of the model
perm.fit(X, y)