lenskit.data.Vocabulary#
- class lenskit.data.Vocabulary(keys=None, name=None)#
Bases:
object
Vocabularies of terms, tags, entity IDs, etc. for the LensKit data model.
This class supports bidirectional mappings between key-like data and congiguous nonnegative integer indices. Its key use is to facilitate the user and item ID vocabularies in
Dataset
, but it can also be used for things like item tags.It is currently a wrapper around
pandas.Index
, but supports the ability to add additional vocabulary terms after the vocabulary has been created. New terms do not change the index positions of previously-known identifiers.- Parameters:
keys (pd.Index | ArrayLike | Iterable[Hashable] | None)
name (str | None)
- __init__(keys=None, name=None)#
- Parameters:
keys (pd.Index | ArrayLike | Iterable[Hashable] | None)
name (str | None)
Methods
__init__
([keys, name])add_terms
(terms)copy
()Return a (cheap) copy of this vocabulary.
id
(num)Alias for
term()
for greater readability for entity ID vocabularies.ids
([nums])Alias for
terms()
for greater readability for entity ID vocabularies.number
()Look up the number for a vocabulary term.
numbers
(terms[, missing])Look up the numbers for an array of terms or IDs.
term
(num)Look up the term with a particular number.
terms
([nums])Get a list of terms, optionally for an array of term numbers.
Attributes
The property as a Pandas index.
Current vocabulary size.
The name of the vocabulary (e.g. “user”, “item”).
- number(term: object, missing: Literal['error'] = 'error') int #
- number(term: object, missing: Literal['none'] | None) int | None
Look up the number for a vocabulary term.
- numbers(terms, missing='error')#
Look up the numbers for an array of terms or IDs.
- term(num)#
Look up the term with a particular number. Negative indexing is not supported.
- terms(nums=None)#
Get a list of terms, optionally for an array of term numbers.
- copy()#
Return a (cheap) copy of this vocabulary. It retains the same mapping, but will not be updated if the original vocabulary has new terms added. However, since new terms are always added to the end, it will be compatible with the original vocabulary for all terms recorded at the time of the copy.
This method is useful for saving known vocabularies in model training.
- Return type: