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Merged
feldlime merged 10 commits into from
Jul 24, 2025
Merged

Updated lock dependancies #293

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cb510f0
Updated lock dependancies
feldlime Jul 20, 2025
a049d51
fixed cupy dependancy
feldlime Jul 21, 2025
0ba71de
fixed bpr test
feldlime Jul 21, 2025
f60c789
fixed extras
feldlime Jul 21, 2025
7677150
fixed torch ranker attribute error
feldlime Jul 21, 2025
b61f899
Merge branch 'main' into feature/update_lock_dependancies
feldlime Jul 22, 2025
af4041d
fixed flacky tests
feldlime Jul 22, 2025
0bbdaaf
formatted
feldlime Jul 22, 2025
44a0fb8
fixed macOS
feldlime Jul 23, 2025
ce45033
updated changelog
feldlime Jul 23, 2025
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3,054 changes: 1,592 additions & 1,462 deletions poetry.lock
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11 changes: 8 additions & 3 deletions pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -96,15 +96,20 @@ pytorch-lightning = [
ipywidgets = {version = ">=7.7,<8.2", optional = true}
plotly = {version="^5.22.0", optional = true}
nbformat = {version = ">=4.2.0", optional = true}

# cupy-cuda12x is incompatible with MacOS.
# It's possible to install pure `cupy` and it will be fully compatible
# but it will work only with CPU and will be slower than `numpy`.
# That's why we don't install cupy at all on MacOS and handle it during the import.
cupy-cuda12x = [
{version = "^13.3.0", python = "<3.13", optional = true},
{version = "^13.4.0", python = ">=3.13", optional = true},
{version = "^13.3.0", python = "<3.13", markers = "sys_platform != 'darwin'", optional = true},
{version = "^13.4.0", python = ">=3.13", markers = "sys_platform != 'darwin'", optional = true},
]
# This is a dependency of cupy-cuda12x
# poetry can't resolve appropriate version of fastrlock for Python 3.13
# and cupy-cuda12x, so we add the version restriction here manually to avoid
# installing older version of fastrlock which is incompatible with Python 3.13
fastrlock = {version = "^0.8.3", optional = true}
fastrlock = {version = "^0.8.3", markers = "sys_platform != 'darwin'", optional = true}

[tool.poetry.extras]
lightfm = ["rectools-lightfm"]
Expand Down
11 changes: 5 additions & 6 deletions rectools/models/rank/rank_torch.py
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Expand Up @@ -136,12 +136,11 @@ def rank(
)

if filter_pairs_csr is not None:
mask = (
torch.from_numpy(filter_pairs_csr[cur_user_emb_inds].toarray()[:, sorted_object_whitelist]).to(
scores.device
)
!= 0
)
# Convert cur_user_emb_inds to numpy to avoid
# AttributeError: 'torch.dtype' object has no attribute 'kind'
cur_user_filter_pairs_csr = filter_pairs_csr[cur_user_emb_inds.cpu().numpy()]
whitelisted_filter_matrix = cur_user_filter_pairs_csr.toarray()[:, sorted_object_whitelist]
mask = torch.from_numpy(whitelisted_filter_matrix).to(scores.device) != 0
scores = torch.masked_fill(scores, mask, mask_values)

top_scores, top_inds = torch.topk(
Expand Down
19 changes: 10 additions & 9 deletions tests/models/nn/transformers/test_bert4rec.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,9 +217,9 @@ def get_val_mask_func(interactions: pd.DataFrame, val_users: tp.List[int]) -> np
False,
pd.DataFrame(
{
Columns.User: [10, 10, 10, 30, 30, 30, 40, 40, 40],
Columns.Item: [12, 13, 11, 12, 13, 11, 12, 13, 11],
Columns.Rank: [1, 2, 3, 1, 2, 3, 1, 2, 3],
Columns.User: [30, 30, 30, 40, 40, 40],
Columns.Item: [12, 13, 11, 12, 13, 11],
Columns.Rank: [1, 2, 3, 1, 2, 3],
}
),
pd.DataFrame(
Expand Down Expand Up @@ -289,8 +289,6 @@ def get_trainer() -> Trainer:
similarity_module_kwargs={"distance": u2i_dist},
)
model.fit(dataset=dataset_devices)
users = np.array([10, 30, 40])
actual = model.recommend(users=users, dataset=dataset_devices, k=3, filter_viewed=filter_viewed)
if accelerator == "cpu" and n_devices == 1:
expected = expected_cpu_1
elif accelerator == "cpu" and n_devices == 2:
Expand All @@ -299,6 +297,9 @@ def get_trainer() -> Trainer:
expected = expected_gpu_1
else:
expected = expected_gpu_2
users = np.unique(expected[Columns.User])
actual = model.recommend(users=users, dataset=dataset_devices, k=3, filter_viewed=filter_viewed)

pd.testing.assert_frame_equal(actual.drop(columns=Columns.Score), expected)
pd.testing.assert_frame_equal(
actual.sort_values([Columns.User, Columns.Score], ascending=[True, False]).reset_index(drop=True),
Expand Down Expand Up @@ -392,9 +393,9 @@ def test_u2i_losses(
False,
pd.DataFrame(
{
Columns.User: [10, 10, 30, 30, 40, 40],
Columns.Item: [13, 11, 13, 11, 13, 11],
Columns.Rank: [1, 2, 1, 2, 1, 2],
Columns.User: [30, 30, 40, 40],
Columns.Item: [13, 11, 13, 11],
Columns.Rank: [1, 2, 1, 2],
}
),
),
Expand All @@ -421,7 +422,7 @@ def test_with_whitelist(
similarity_module_type=DistanceSimilarityModule,
)
model.fit(dataset=dataset_devices)
users = np.array([10, 30, 40])
users = np.unique(expected[Columns.User])
items_to_recommend = np.array([11, 13, 17])
actual = model.recommend(
users=users,
Expand Down
14 changes: 7 additions & 7 deletions tests/models/nn/transformers/test_sasrec.py
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Original file line number Diff line number Diff line change
Expand Up @@ -556,9 +556,9 @@ def test_with_whitelist(
None,
pd.DataFrame(
{
Columns.TargetItem: [12, 12, 12, 14, 14, 14, 17, 17, 17],
Columns.Item: [12, 13, 14, 14, 12, 15, 17, 13, 14],
Columns.Rank: [1, 2, 3, 1, 2, 3, 1, 2, 3],
Columns.TargetItem: [12, 12, 12, 17, 17, 17],
Columns.Item: [12, 13, 14, 17, 13, 14],
Columns.Rank: [1, 2, 3, 1, 2, 3],
}
),
),
Expand All @@ -567,9 +567,9 @@ def test_with_whitelist(
None,
pd.DataFrame(
{
Columns.TargetItem: [12, 12, 12, 14, 14, 14, 17, 17, 17],
Columns.Item: [13, 14, 11, 12, 15, 17, 13, 14, 11],
Columns.Rank: [1, 2, 3, 1, 2, 3, 1, 2, 3],
Columns.TargetItem: [12, 12, 12, 17, 17, 17],
Columns.Item: [13, 14, 11, 13, 14, 11],
Columns.Rank: [1, 2, 3, 1, 2, 3],
}
),
),
Expand Down Expand Up @@ -607,7 +607,7 @@ def test_i2i(
similarity_module_type=DistanceSimilarityModule,
)
model.fit(dataset=dataset)
target_items = np.array([12, 14, 17])
target_items = np.unique(expected[Columns.TargetItem])
actual = model.recommend_to_items(
target_items=target_items,
dataset=dataset,
Expand Down
8 changes: 4 additions & 4 deletions tests/models/test_implicit_bpr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,9 +91,9 @@ def dataset(self) -> Dataset:
False,
pd.DataFrame(
{
Columns.User: [10, 10, 20, 20],
Columns.Item: [11, 17, 11, 17],
Columns.Rank: [1, 2, 1, 2],
Columns.User: [20, 20],
Columns.Item: [11, 17],
Columns.Rank: [1, 2],
}
),
pd.DataFrame(
Expand All @@ -120,7 +120,7 @@ def test_basic(
self._init_model_factors_inplace(base_model, dataset)
model = ImplicitBPRWrapperModel(model=base_model).fit(dataset)
actual = model.recommend(
users=np.array([10, 20]),
users=np.unique(expected_cpu[Columns.User]),
dataset=dataset,
k=2,
filter_viewed=filter_viewed,
Expand Down