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fix train_only_size #652

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fix train_only_size #652

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5f1042b
fix train_only_size
bos1988 Apr 16, 2023
7d35852
fix zero-candidate case
bos1988 Apr 21, 2023
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11 changes: 6 additions & 5 deletions implicit/evaluation.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -187,16 +187,17 @@ cpdef leave_k_out_split(

# get only users with n + 1 interactions
candidate_mask = counts > K + 1
if sum(candidate_mask) == 0:
return ratings.tocsr(), csr_matrix(ratings.shape)
unique_candidate_users = unique_users[candidate_mask]

# keep a given subset of users _only_ in the training set.
if train_only_size > 0.0:
train_only_mask = ~np.isin(
unique_users, _choose(random_state, len(unique_users), train_only_size)
)
candidate_mask = train_only_mask & candidate_mask
adjusted_ratio = min(1, (1 - train_only_size) / (unique_candidate_users.shape[0] / (unique_users.shape[0] + 1)))
train_only_mask = _choose(random_state, len(unique_candidate_users), adjusted_ratio)
unique_candidate_users = unique_candidate_users[train_only_mask]

# get unique users who appear in the test set
unique_candidate_users = unique_users[candidate_mask]
full_candidate_mask = np.isin(users, unique_candidate_users)

# get all users, items and ratings that match specified requirements to be
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