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

Fix partial_fit_items bug with only growing by a single item #563

Merged
merged 1 commit into from
Apr 12, 2022
Merged

Fix partial_fit_items bug with only growing by a single item #563

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
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 implicit/cpu/als.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -315,7 +315,7 @@ def partial_fit_items(self, itemids, item_users):
# ensure that we have enough storage for any new items
items, factors = self.item_factors.shape
max_itemid = max(itemids)
if max_itemid > items:
if max_itemid >= items:
self.item_factors = np.concatenate(
[self.item_factors, np.zeros((max_itemid - items + 1, factors), dtype=self.dtype)]
)
Expand Down
2 changes: 1 addition & 1 deletion implicit/gpu/als.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -242,7 +242,7 @@ def partial_fit_items(self, itemids, item_users):
# ensure that we have enough storage for any new items
items, factors = self.item_factors.shape
max_itemid = max(itemids)
if max_itemid > items:
if max_itemid >= items:
# TODO: grow exponentially ?
self.item_factors.resize(max_itemid + 1, factors)

Expand Down
6 changes: 6 additions & 0 deletions tests/als_test.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -255,3 +255,9 @@ def test_incremental_retrain(use_gpu):
model.partial_fit_items([100], likes[1])
ids, _ = model.recommend(1, likes[1], N=2)
assert set(ids) == {1, 100}

# check to make sure we can index only a single extra item/user
model.partial_fit_users([101], likes[1])
model.partial_fit_items([101], likes[1])
ids, _ = model.recommend(101, likes[1], N=3)
assert set(ids) == {1, 100, 101}