merlin.models.tf.ContrastiveOutput

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

Bases: merlin.models.tf.outputs.base.ModelOutput

Categorical output

Parameters

• to_call (Union[Schema, ColumnSchema,) –

  EmbeddingTable, ‘CategoricalTarget’,

  ’EmbeddingTablePrediction’, ‘DotProduct’]

  The target feature to predict. To perform weight-tying [1] technique, you should provide the EmbeddingTable or EmbeddingTablePrediction related to the target feature.

• negative_samplers (ItemSamplersType) – List of samplers for negative sampling, by default None

• pre (Optional[Block], optional) – Optional block to transform predictions before computing the binary logits, by default None

• post (Optional[Block], optional) – Optional block to transform the binary logits, by default None

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

• name (str, optional) – The name of the task, by default None

• default_loss (Union[str, tf.keras.losses.Loss], optional) – Default loss to use for categorical-classification by default ‘categorical_crossentropy’

• get_default_metrics (Callable, optional) – A function returning the list of default metrics to use for categorical-classification

• store_negative_ids (bool, optional) – Whether to store negative ids for post-processing by default False

• logq_sampling_correction (bool, optional) – The LogQ correction is a standard technique for sampled softmax and popularity-biased sampling. It subtracts from the logits the log expected count/prob of the positive and negative samples in order to not overpenalize the popular items for being sampled more often as negatives. It can be enabled if a single negative sampler is provided and if it provides the sampler provides the sampling probabilities (i.e. implements with_sampling_probs()). Another alternative for performing logQ correction is using ContrastiveOutput(…, post=PopularityLogitsCorrection(item_frequencies)), where you need to provide the items frequency probability distribution (prior). Default is False.

• References –

• ---------- –

• Inan ([1] Hakan) –

• Khosravi (Khashayar) –

• vectors (and Richard Socher. 2016. Tying word) –

• classifiers (and word) –

• arXiv (1611.01462 (2016).) –

• Notes –

• ---------- –

• DotProduct() (In case to_call is set as) –

• inferred (schema of target couldn't be) –

• therefore –

• arg (the user should feed a schema only with ITEM_ID feature as schema) –

:param : :param which is treated as a kwargs arg below.: :param Example usage::

outputs=mm.ContrastiveOutput(
    to_call=DotProduct(),
    negative_samplers="in-batch",
    schema=schema.select_by_tag(Tags.ITEM_ID),
    logits_temperature = 0.2,
)

Parameters

• arg. (The schema arg is not needed when we pass the schema to to_call) –

• usage:: (Example) –

  outputs=mm.ContrastiveOutput(

  to_call=schema.select_by_tag(Tags.ITEM_ID), negative_samplers=”in-batch”, logits_temperature = 0.2,

  )

__init__(to_call: typing.Union[merlin.schema.schema.Schema, merlin.schema.schema.ColumnSchema, merlin.models.tf.inputs.embedding.EmbeddingTable, merlin.models.tf.outputs.classification.CategoricalTarget, merlin.models.tf.outputs.classification.EmbeddingTablePrediction, merlin.models.tf.outputs.base.DotProduct], negative_samplers: typing.Union[merlin.models.tf.outputs.sampling.base.CandidateSampler, typing.Sequence[typing.Union[merlin.models.tf.outputs.sampling.base.CandidateSampler, str]], str], target_name: typing.Optional[str] = None, pre: typing.Optional[keras.engine.base_layer.Layer] = None, post: typing.Optional[keras.engine.base_layer.Layer] = None, logits_temperature: float = 1.0, name: typing.Optional[str] = None, default_loss: typing.Union[str, keras.losses.Loss] = 'categorical_crossentropy', default_metrics_fn: typing.Callable[[], typing.Sequence[keras.metrics.base_metric.Metric]] = <function default_categorical_prediction_metrics>, downscore_false_negatives=True, false_negative_score: float = -655.04, query_name: str = 'query', candidate_name: str = 'candidate', store_negative_ids: bool = False, logq_sampling_correction: typing.Optional[bool] = False, **kwargs)

Methods

__init__(to_call, negative_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.
build(input_shape)
build_from_config(config)
call(inputs[, features, targets, training, ...])
call_contrastive(inputs, features, targets)
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)
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.
create_default_metrics()
embedding_lookup(ids)
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)
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_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_task_name(target_name)	Returns the name of the task :param target_name: Name of the target :type target_name: str
get_weights()	Returns the current weights of the layer, as NumPy arrays.
outputs(query_embedding, positive, negative)	Method to compute the dot product between the query embeddings and positive/negative candidates
sample_negatives(positive, features[, ...])	Method to sample negatives from self.negative_samplers
set_negative_samplers(negative_samplers)
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
to_dataset([gpu])
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.
has_candidate_weights
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.
keys
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.
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.

build(input_shape)

call(inputs, features=None, targets=None, training=False, testing=False)

call_contrastive(inputs, features, targets, training=False, testing=False)

outputs(query_embedding: tensorflow.python.framework.ops.Tensor, positive: merlin.models.tf.outputs.sampling.base.Candidate, negative: merlin.models.tf.outputs.sampling.base.Candidate) → merlin.models.tf.core.prediction.Prediction

Method to compute the dot product between the query embeddings and positive/negative candidates

Parameters

• query_embedding (tf.Tensor) – tensor of query embeddings.

• positive (Candidate) – Store the ids and metadata (such as embeddings) of the positive candidates.

• negative (Candidate) – Store the ids and metadata (such as embeddings) of the sampled negative candidates.

Returns

a Prediction object with the prediction scores, the targets and the negative candidates ids if specified.

Return type

Prediction

sample_negatives(positive: merlin.models.tf.outputs.sampling.base.Candidate, features: Dict[str, tensorflow.python.framework.ops.Tensor], training=False, testing=False) → Tuple[merlin.models.tf.outputs.sampling.base.Candidate, merlin.models.tf.outputs.sampling.base.Candidate]

Method to sample negatives from self.negative_samplers

Parameters

• positive_items (Items) – Positive items (ids and metadata)

• features (TabularData) – Dictionary of input raw tensors

• training (bool, optional) – Flag for train mode, by default False

• testing (bool, optional) – Flag for test mode, by default False

Returns

Tuple of candidates with sampled negative ids and the provided positive ids added with the sampling probability

Return type

Tuple[Candidate, Candidate]

embedding_lookup(ids: tensorflow.python.framework.ops.Tensor)

to_dataset(gpu=None) → merlin.io.dataset.Dataset

property has_candidate_weights: bool

property keys: List[str]

set_negative_samplers(negative_samplers: Union[merlin.models.tf.outputs.sampling.base.CandidateSampler, Sequence[Union[merlin.models.tf.outputs.sampling.base.CandidateSampler, str]], str])

get_config()

classmethod from_config(config)