merlin.models.tf.ItemRetrievalScorer

class merlin.models.tf.ItemRetrievalScorer(*args, **kwargs)

Bases: merlin.models.tf.core.base.Block

Block for ItemRetrieval, which expects query/user and item embeddings as input and uses dot product to score the positive item (inputs[“item”]) and also sampled negative items (during training).

Parameters

• samplers (List[ItemSampler], optional) – List of item samplers that provide negative samples when training=True

• sampling_downscore_false_negatives (bool, optional) – Identify false negatives (sampled item ids equal to the positive item and downscore them to the sampling_downscore_false_negatives_value), by default True

• sampling_downscore_false_negatives_value (int, optional) – Value to be used to downscore false negatives when sampling_downscore_false_negatives=True, by default np.finfo(np.float32).min / 100.0

• item_id_feature_name (str) – Name of the column containing the item ids Defaults to item_id

• query_name (str) – Identify query tower for query/user embeddings, by default ‘query’

• item_name (str) – Identify item tower for item embeddings, by default’item’

• cache_query (bool) – Add query embeddings to the context block, by default False

• sampled_softmax_mode (bool) – Use sampled softmax for scoring, by default False

• store_negative_ids (bool) – Returns negative items ids as part of the output, by default False

__init__(samplers: Sequence[merlin.models.tf.blocks.sampling.base.ItemSampler] = (), sampling_downscore_false_negatives=True, sampling_downscore_false_negatives_value: float = - 655.04, item_id_feature_name: str = 'item_id', item_domain: str = 'item_id', query_name: str = 'query', item_name: str = 'item', cache_query: bool = False, sampled_softmax_mode: bool = False, store_negative_ids: bool = False, **kwargs)

Initializes the ItemRetrievalScorer class.

Methods

__init__([samplers, ...])	Initializes the ItemRetrievalScorer class.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
as_tabular([name])
build(input_shapes)	Builds the block.
build_from_config(config)
call(inputs[, training, testing])	Based on the user/query embedding (inputs[self.query_name]), uses dot product to score
call_outputs(outputs[, features, training, ...])	Based on the user/query embedding (inputs[self.query_name]), uses dot product to score
check_schema([schema])
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
connect(*block[, block_name, context])	Connect the block to other blocks sequentially.
connect_branch(*branches[, add_rest, post, ...])	Connect the block to one or multiple branches.
connect_debug_block([append])	Connect the block to a debug block.
connect_with_residual(block[, activation])	Connect the block to other blocks sequentially with a residual connection.
connect_with_shortcut(block[, ...])	Connect the block to other blocks sequentially with a shortcut connection.
copy()
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a new instance of the class from its config.
from_layer(layer)
get_build_config()
get_config()	Returns the configuration of the model as a dictionary.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_item_ids_from_inputs(inputs)
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_padding_mask_from_item_id(inputs[, ...])
get_weights()	Returns the current weights of the layer, as NumPy arrays.
parse(*block)
parse_block(input)
prepare([block, post, aggregation])	Transform the inputs of this block.
register_features(feature_shapes)
repeat([num])	Repeat the block num times.
repeat_in_parallel([num, prefix, names, ...])	Repeat the block num times in parallel.
select_by_name(name)
select_by_tag(tags)
set_required_features()	Sets the required features for the samplers.
set_schema([schema])
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

REQUIRES_SCHEMA
activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer's computations.
context
dtype	The dtype of the layer weights.
dtype_policy	The dtype policy associated with this layer.
dynamic	Whether the layer is dynamic (eager-only); set in the constructor.
has_schema
inbound_nodes	Return Functional API nodes upstream of this layer.
input	Retrieves the input tensor(s) of a layer.
input_mask	Retrieves the input mask tensor(s) of a layer.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	InputSpec instance(s) describing the input format for this layer.
losses	List of losses added using the add_loss() API.
metrics	List of metrics added using the add_metric() API.
name	Name of the layer (string), set in the constructor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables
non_trainable_weights	List of all non-trainable weights tracked by this layer.
outbound_nodes	Return Functional API nodes downstream of this layer.
output	Retrieves the output tensor(s) of a layer.
output_mask	Retrieves the output mask tensor(s) of a layer.
output_shape	Retrieves the output shape(s) of a layer.
registry
schema
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable
trainable_variables
trainable_weights	List of all trainable weights tracked by this layer.
updates
variable_dtype	Alias of Layer.dtype, the dtype of the weights.
variables	Returns the list of all layer variables/weights.
weights	Returns the list of all layer variables/weights.

build(input_shapes)

Builds the block.

Parameters

input_shapes (tuple or dict) – Shape of the input tensor.

call(inputs: Union[tensorflow.python.framework.ops.Tensor, Dict[str, tensorflow.python.framework.ops.Tensor]], training: bool = True, testing: bool = False, **kwargs) → Union[tensorflow.python.framework.ops.Tensor, Dict[str, tensorflow.python.framework.ops.Tensor]]

Based on the user/query embedding (inputs[self.query_name]), uses dot product to score

the positive item (inputs[“item”]). For the sampled-softmax mode, logits are computed by multiplying the query vector and the item embeddings matrix (self.context.get_embedding(self.item_domain))

Parameters

• inputs (Union[tf.Tensor, TabularData]) – Dict with the query and item embeddings (e.g. {“query”: <emb>}, “item”: <emb>}), where embeddings are 2D tensors (batch size, embedding size)

• training (bool, optional) – Flag that indicates whether in training mode, by default True

Returns

2D Tensor with the scores for the positive items, If training=True, return the original inputs

Return type

tf.Tensor

call_outputs(outputs: merlin.models.tf.core.base.PredictionOutput, features: Dict[str, tensorflow.python.framework.ops.Tensor] = None, training=True, testing=False, **kwargs) → PredictionOutput

Based on the user/query embedding (inputs[self.query_name]), uses dot product to score

the positive item and also sampled negative items (during training).

Parameters

• inputs (TabularData) – Dict with the query and item embeddings (e.g. {“query”: <emb>}, “item”: <emb>}), where embeddings are 2D tensors (batch size, embedding size)

• training (bool, optional) – Flag that indicates whether in training mode, by default True

Returns

all_scores: 2D Tensor with the scores for the positive items and, if training=True, for the negative sampled items too. Return tensor is 2D (batch size, 1 + #negatives)

Return type

[tf.Tensor,tf.Tensor]

set_required_features()

Sets the required features for the samplers.

get_config()

Returns the configuration of the model as a dictionary.

Returns

The configuration of the model.

Return type

dict

classmethod from_config(config)

Creates a new instance of the class from its config.

Parameters

config (dict) – A dictionary, typically the output of get_config.

Return type

A new instance of the ItemRetrievalScorer class.