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Optimization for big values of num_list_per_user #526

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Optimization for big values of num_list_per_user #526

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13 changes: 6 additions & 7 deletions tensorflow_recommenders/examples/movielens.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,13 +112,10 @@ def _sample_list(
size=num_examples_per_list,
replace=False,
)
sampled_movie_titles = [
feature_lists["movie_title"][idx] for idx in sampled_indices
]
sampled_ratings = [
feature_lists["user_rating"][idx]
for idx in sampled_indices
]

sampled_movie_titles = feature_lists["movie_title"][sampled_indices].tolist()

sampled_ratings = feature_lists["user_rating"][sampled_indices].tolist()

return (
tf.concat(sampled_movie_titles, 0),
Expand Down Expand Up @@ -174,6 +171,8 @@ def sample_listwise(
tensor_slices = {"user_id": [], "movie_title": [], "user_rating": []}

for user_id, feature_lists in example_lists_by_user.items():
feature_lists["movie_title"] = np.array(feature_lists["movie_title"])
feature_lists["user_rating"] = np.array(feature_lists["user_rating"])
for _ in range(num_list_per_user):

# Drop the user if they don't have enough ratings.
Expand Down