It is unclear why evaluation metrics (auc_score, precision_at_k) fail when train and test data have high overlap

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Hi,

When I run this code to get my test performance:
```
test_auc = auc_score(model, test_interactions = test_data_matrix, train_interactions = train_data_matrix).mean()
```
I get an error:

> ValueError: Test interactions matrix and train interactions matrix share 745082 interactions. This will cause incorrect evaluation, check your data split.

This is presumably because test_data_matrix is all my data (2.5 years of retail transactions) and training data is all the data except the last 6 months. It is basically failing because train and test have a high degree of overlap.

But why? Surely the whole point of the `train_interactions` argument is so you can exclude the overlap? Shouldn't this be a warning rather than an error that fails the whole function?

Start at the auc_score evaluation call and inspect how train_interactions and test_interactions are compared when the matrices contain overlapping retail transactions. Reproduce the reported 745082-interaction overlap, then determine whether the current error behavior matches the documented evaluation contract and what outcome should count as done.

Written by the indexing model from the issue text.