Retrieves K highest scoring items and their ids from a large dataset.
Inherits From: TopK
tfrs.layers.factorized_top_k.Streaming(
query_model: Optional[tf.keras.Model] = None,
k: int = 10,
handle_incomplete_batches: bool = True,
num_parallel_calls: int = tf.data.AUTOTUNE,
sorted_order: bool = True
) -> None
Used to efficiently retrieve top K query-candidate scores from a dataset,
along with the top scoring candidates' identifiers.
Args |
query_model
|
Optional Keras model for representing queries. If provided,
will be used to transform raw features into query embeddings when
querying the layer. If not provided, the layer will expect to be given
query embeddings as inputs.
|
k
|
Number of top scores to retrieve.
|
handle_incomplete_batches
|
When True, candidate batches smaller than k
will be correctly handled at the price of some performance. As an
alternative, consider using the drop_remainer option when batching the
candidate dataset.
|
num_parallel_calls
|
Degree of parallelism when computing scores. Defaults
to autotuning.
|
sorted_order
|
If the resulting scores should be returned in sorted order.
setting this to False may result in a small increase in performance.
|
Raises |
ValueError if candidate elements are not tuples.
|
Methods
call
View source
call(
queries: Union[tf.Tensor, Dict[Text, tf.Tensor]], k: Optional[int] = None
) -> Tuple[tf.Tensor, tf.Tensor]
Query the index.
Args |
queries
|
Query features. If query_model was provided in the constructor,
these can be raw query features that will be processed by the query
model before performing retrieval. If query_model was not provided,
these should be pre-computed query embeddings.
|
k
|
The number of candidates to retrieve. If not supplied, defaults to the
k value supplied in the constructor.
|
Returns |
Tuple of (top candidate scores, top candidate identifiers).
|
Raises |
ValueError if index has not been called.
|
index
View source
index(
candidates: tf.data.Dataset, identifiers: Optional[tf.data.Dataset] = None
) -> 'Streaming'
Not implemented. Please call index_from_dataset
instead.
index_from_dataset
View source
index_from_dataset(
candidates: tf.data.Dataset
) -> 'TopK'
Builds the retrieval index.
When called multiple times the existing index will be dropped and a new one
created.
Args |
candidates
|
Dataset of candidate embeddings or (candidate identifier,
candidate embedding) pairs. If the dataset returns tuples,
the identifiers will be used as identifiers of top candidates
returned when performing searches. If not given, indices into the
candidates dataset will be given instead.
|
Raises |
ValueError if the dataset does not have the correct structure.
|
is_exact
View source
is_exact() -> bool
Indicates whether the results returned by the layer are exact.
Some layers may return approximate scores: for example, the ScaNN layer
may return approximate results.
Returns |
True if the layer returns exact results, and False otherwise.
|
query_with_exclusions
View source
@tf.function
query_with_exclusions(
queries: Union[tf.Tensor, Dict[Text, tf.Tensor]],
exclusions: tf.Tensor,
k: Optional[int] = None
) -> Tuple[tf.Tensor, tf.Tensor]
Query the index.
Args |
queries
|
Query features. If query_model was provided in the constructor,
these can be raw query features that will be processed by the query
model before performing retrieval. If query_model was not provided,
these should be pre-computed query embeddings.
|
exclusions
|
[query_batch_size, num_to_exclude] tensor of identifiers to
be excluded from the top-k calculation. This is most commonly used to
exclude previously seen candidates from retrieval. For example, if a
user has already seen items with ids "42" and "43", you could set
exclude to [["42", "43"]] .
|
k
|
The number of candidates to retrieve. Defaults to constructor k
parameter if not supplied.
|
Returns |
Tuple of (top candidate scores, top candidate identifiers).
|
Raises |
ValueError if index has not been called.
ValueError if queries is not a tensor (after being passed through
the query model).
|