merlin.models.tf.ItemRetrievalScorer#

class merlin.models.tf.ItemRetrievalScorer(*args, **kwargs)[source]#

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). :param samplers: List of item samplers that provide negative samples when training=True :type samplers: List[ItemSampler], optional :param sampling_downscore_false_negatives: Identify false negatives (sampled item ids equal to the positive item and downscore them

to the sampling_downscore_false_negatives_value), by default True

Parameters
  • 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)[source]#

Methods

__init__([samplers, ...])

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)

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)

from_layer(layer)

get_build_config()

get_config()

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()

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)[source]#
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]][source]#
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[source]#
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()[source]#
get_config()[source]#
classmethod from_config(config)[source]#