TransformedSet#
- class libreco.data.TransformedSet(user_indices=None, item_indices=None, labels=None, sparse_indices=None, dense_values=None, train=True)[source]#
Dataset after transforming.
Often generated by calling functions in
DatasetPure
orDatasetFeat
, thenTransformedSet
is used in formal training.- Parameters:
user_indices (numpy.ndarray or None, default: None) – All user rows in data, represented in inner id.
item_indices (numpy.ndarray or None, default: None) – All item rows in data, represented in inner id.
labels (numpy.ndarray or None, default: None) – All labels in data.
sparse_indices (numpy.ndarray or None, default: None) – All sparse rows in data, represented in inner id.
dense_values (numpy.ndarray or None, default: None) – All dense rows in data.
train (bool, default: True) – Whether it is train data.
See also
- build_negative_samples(data_info, num_neg=1, item_gen_mode='random', seed=42)[source]#
Perform negative sampling on all the data contained.
- property user_indices#
All user rows in data
- property item_indices#
All item rows in data
- property sparse_indices#
All sparse rows in data
- property dense_values#
All dense rows in data
- property labels#
All labels in data
- property sparse_interaction#
User-item interaction data, in
scipy.sparse.csr_matrix
format.