merlin.models.tf.ItemRetrievalTask

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

Bases: merlin.models.tf.prediction_tasks.classification.MultiClassClassificationTask

Prediction-task for item-retrieval.

Parameters

  • schema (Schema) – The schema object including features to use and their properties.

  • loss (Optional[LossType]) – Loss function. Defaults to categorical_crossentropy.

  • metrics (MetricOrMetrics) – List of top-k ranking metrics. Defaults to a number of ranking metrics.

  • samplers (List[ItemSampler]) – List of samplers for negative sampling, by default [InBatchSampler()]

  • post_logits (Optional[PredictionBlock]) – Optional extra pre-call block for post-processing the logits, by default None. You can for example use post_logits = mm.PopularitySamplingBlock(item_fequency) for populariy sampling correction.

  • target_name (Optional[str]) – If specified, name of the target tensor to retrieve from dataloader. Defaults to None.

  • task_name (Optional[str]) – name of the task. Defaults to None.

  • task_block (Block) – The Block that applies additional layers op to inputs. Defaults to None.

  • logits_temperature (float) – Parameter used to reduce the model overconfidence, so that logits / T. Defaults to 1.

Returns

The item retrieval prediction task

Return type

PredictionTask

__init__(schema: merlin.schema.schema.Schema, loss: Optional[Union[str, keras.losses.Loss]] = 'categorical_crossentropy', metrics: Union[Sequence[Union[keras.metrics.base_metric.Metric, Type[keras.metrics.base_metric.Metric]]], keras.metrics.base_metric.Metric, Type[keras.metrics.base_metric.Metric]] = [RecallAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), MRRAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), NDCGAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), AvgPrecisionAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), PrecisionAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() )], samplers: Sequence[merlin.models.tf.blocks.sampling.base.ItemSampler] = (), target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[tensorflow.python.keras.legacy_tf_layers.base.Layer] = None, post_logits: Optional[merlin.models.tf.blocks.core.base.Block] = None, logits_temperature: float = 1.0, cache_query: bool = False, store_negative_ids: bool = False, **kwargs)[source]

Methods

__init__(schema[, loss, metrics, 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.

attach_metrics_calculation_to_loss(outputs, …)

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

build_task(input_shape, schema, body, **kwargs)

calculate_metrics(outputs[, sample_weight, …])

Method to compute the metrics.

call(inputs[, training, eval_sampling])

child_name(name)

compute_loss(predictions, targets[, …])

Method to compute the loss and metrics during training/evaluation.

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.

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_schema(schema[, feature_name, loss, …])

Create from Schema.

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_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_weights()

Returns the current weights of the layer, as NumPy arrays.

metric_result_dict([mode])

metric_results([mode])

pre_call(inputs, **kwargs)

Apply PredictionTask to inputs to get predictions scores

pre_loss(outputs, **kwargs)

Apply call_outputs method of pre block to transform predictions and targets before computing loss and metrics.

repr_add()

reset_metrics()

set_retrieval_cache_query(value)

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

DEFAULT_LOSS

DEFAULT_METRICS

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.

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.

pre_eval_topk

retrieval_scorer

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

task_name

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.
DEFAULT_LOSS = 'categorical_crossentropy'
DEFAULT_METRICS: Union[Sequence[Union[keras.metrics.base_metric.Metric, Type[keras.metrics.base_metric.Metric]]], keras.metrics.base_metric.Metric, Type[keras.metrics.base_metric.Metric]] = [RecallAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), MRRAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), NDCGAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), AvgPrecisionAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() ), PrecisionAt(   (total): <tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>   (count): <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>   (_fn_kwargs): Dict() )]
call(inputs, training=False, eval_sampling=False, **kwargs)[source]
property retrieval_scorer
set_retrieval_cache_query(value: bool)[source]