merlin.models.tf.PopularityBasedSampler

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

Bases: merlin.models.tf.blocks.sampling.base.ItemSampler

Provides a popularity-based negative sampling for the softmax layer to ensure training efficiency when the catalog of items is very large. The capacity of the queue is fixed and is equal to the catalog size. For each batch, we sample max_num_samples unique negatives.

We use the default log-uniform sampler given by tensorflow:

[log_uniform_candidate_sampler](https://www.tensorflow.org/api_docs/python/tf/random/log_uniform_candidate_sampler)

We note that this default sampler requires that item-ids are encoded based on a decreasing order of their count frequency and that the classes’ expected counts are approximated based on their index order. The Categorify op provided by nvtabular supports the frequency-based encoding as default.

P.s. Ignoring the false negatives (negative items equal to the positive ones) is managed by ItemRetrievalScorer(…, sampling_downscore_false_negatives=True)

Parameters

• max_num_samples (int) – The number of unique negatives to sample at each batch.

• max_id (int) – The maximum id value to be sampled. It should be equal to the categorical feature cardinality

• min_id (int) – The minimum id value to be sampled. Useful to ignore the first categorical encoded ids, which are usually reserved for <nulls>, out-of-vocabulary or padding. Defaults to 0.

• seed (int) – Fix the random values returned by the sampler to ensure reproducibility Defaults to None

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

__init__(max_id: int, min_id: int = 0, max_num_samples: int = 100, seed: Optional[int] = None, item_id_feature_name: str = 'item_id', **kwargs)

Methods

__init__(max_id[, min_id, max_num_samples, …])
add(embeddings, items_metadata[, training])
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.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(inputs, item_weights[, training])
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)	Creates a layer from its config.
get_config()	Returns the config of the layer.
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.
sample(item_weights)
set_max_num_samples(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

activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer’s computations.
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.
max_num_samples
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.
required_features
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.

add(embeddings: tensorflow.python.framework.ops.Tensor, items_metadata: Dict[str, tensorflow.python.framework.ops.Tensor], training=True)

call(inputs: Dict[str, tensorflow.python.framework.ops.Tensor], item_weights: tensorflow.python.framework.ops.Tensor, training=True) → merlin.models.tf.blocks.core.base.EmbeddingWithMetadata

sample(item_weights) → merlin.models.tf.blocks.core.base.EmbeddingWithMetadata