merlin.models.tf.TopKEncoder

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

Bases: merlin.models.tf.core.encoder.Encoder, merlin.models.tf.models.base.BaseModel

Block that can be used for top-k prediction & evaluation, initialized from a trained retrieval model

Parameters

• query_encoder (Union[Encoder, tf.keras.layers.Layer],) – The layer to use for encoding the query features

• topk_layer (Union[str, tf.keras.layers.Layer, TopKOutput]) – The layer to use for computing the top-k predictions. You can also pass the name of registered top-k layer. The current supported strategies are [brute-force-topk] By default “brute-force-topk”

• candidates (Union[tf.Tensor, Dataset]) – The candidate embeddings to use for the Top-k index. You can pass a tensor of pre-trained embeddings or a merlin.io.Dataset of pre-trained embeddings, indexed by the candidates ids. This is required when topk_layer is a string By default None

• candidate_encoder (Union[Encoder, tf.keras.layers.Layer],) – The layer to use for encoding the item features

• k (int, Optional) – Number of candidates to return, by default 10

• pre (Optional[tf.keras.layers.Layer]) – A block to use before encoding the input query By default None

• post (Optional[tf.keras.layers.Layer]) – A block to use after getting the top-k prediction scores By default None

• target (str, optional) – The name of the target. This is required when multiple targets are provided. By default None

__init__(query_encoder: Union[merlin.models.tf.core.encoder.Encoder, keras.engine.base_layer.Layer], topk_layer: Union[str, keras.engine.base_layer.Layer, merlin.models.tf.outputs.topk.TopKOutput] = 'brute-force-topk', candidates: Optional[Union[tensorflow.python.framework.ops.Tensor, merlin.io.dataset.Dataset]] = None, candidate_encoder: Optional[Union[merlin.models.tf.core.encoder.Encoder, keras.engine.base_layer.Layer]] = None, k: int = 10, pre: Optional[keras.engine.base_layer.Layer] = None, post: Optional[keras.engine.base_layer.Layer] = None, target: Optional[str] = None, **kwargs)

Methods

__init__(query_encoder[, topk_layer, ...])
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.
adjust_predictions_and_targets(predictions, ...)	Adjusts the predictions and targets to ensure compatibility with most Keras losses and metrics.
batch_predict(dataset, batch_size[, ...])	Batched top-k prediction using Dask.
build(input_shape)	Creates the variables of the layer.
build_from_config(config)
call(inputs, *[, targets, training, testing])	Calls the model on new inputs and returns the outputs as tensors.
call_train_test(x[, y, sample_weight, ...])	Apply the model's call method during Train or Test modes and prepare Prediction (v2) or PredictionOutput (v1 - depreciated) objects
compile([optimizer, loss, metrics, ...])	Extend the compile method of BaseModel to set the threshold k of the top-k encoder.
compile_from_config(config)
compute_loss([x, y, y_pred, sample_weight])	Compute the total loss, validate it, and return it.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_metrics(prediction_outputs[, ...])	Overrides Model.compute_metrics() for some custom behaviour
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.
encode(dataset, index, batch_size, **kwargs)	Encodes the given dataset and index.
encode_candidates(dataset[, index_column, ...])	Method to generate candidates embeddings
evaluate([x, y, batch_size, verbose, ...])
evaluate_generator(generator[, steps, ...])	Evaluates the model on a data generator.
export(filepath)	Create a SavedModel artifact for inference (e.g.
finalize_state()	Finalizes the layers state after updating layer weights.
fit(*args, **kwargs)	Fit model
fit_generator(generator[, steps_per_epoch, ...])	Fits the model on data yielded batch-by-batch by a Python generator.
from_candidate_dataset(query_encoder, ...[, ...])	Class method to initialize a TopKEncoder from a dataset of raw candidates features.
from_config(config[, custom_objects])	Creates a new instance of the class by deserializing.
get_build_config()
get_compile_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_layer([name, index])	Retrieves a layer based on either its name (unique) or index.
get_metrics_result()	Returns the model's metrics values as a dict.
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_weight_paths()	Retrieve all the variables and their paths for the model.
get_weights()	Retrieves the weights of the model.
index_candidates(candidates[, identifiers])
load_weights(filepath[, skip_mismatch, ...])	Loads all layer weights from a saved files.
make_predict_function([force])	Creates a function that executes one step of inference.
make_test_function([force])	Creates a function that executes one step of evaluation.
make_train_function([force])	Creates a function that executes one step of training.
metrics_results()	Logic to consolidate metrics results extracted from standard Keras Model.compute_metrics()
outputs_by_name()	Returns the task names from the model outputs
outputs_by_target()	Method to index the model's prediction blocks by target names.
predict(x[, batch_size, verbose, steps, ...])
predict_generator(generator[, steps, ...])	Generates predictions for the input samples from a data generator.
predict_on_batch(x)	Returns predictions for a single batch of samples.
predict_step(data)	Custom predict step to obtain the outputs
prediction_tasks_by_name()
prediction_tasks_by_target()	Method to index the model's prediction tasks by target names.
reset_metrics()	Resets the state of all the metrics in the model.
reset_states()
save(export_path[, include_optimizer, ...])	Saves the model to export_path as a Tensorflow Saved Model.
save_spec([dynamic_batch])	Returns the tf.TensorSpec of call args as a tuple (args, kwargs).
save_weights(filepath[, overwrite, ...])	Saves all layer weights.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
summary([line_length, positions, print_fn, ...])	Prints a string summary of the network.
test_on_batch(x[, y, sample_weight, ...])	Test the model on a single batch of samples.
test_step(data)	Custom test step using the compute_loss method.
to_json(**kwargs)	Returns a JSON string containing the network configuration.
to_yaml(**kwargs)	Returns a yaml string containing the network configuration.
train_compute_metrics(outputs, compiled_metrics)	Returns metrics for the outputs of this step.
train_on_batch(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
train_step(data)	Performs a training step.
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.
distribute_reduction_method	The method employed to reduce per-replica values during training.
distribute_strategy	The tf.distribute.Strategy this model was created under.
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.
first	Returns the first block of the model.
has_schema	Returns True as this class does contain a 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_schema	Get the input schema if it's defined.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	InputSpec instance(s) describing the input format for this layer.
jit_compile	Specify whether to compile the model with XLA.
last	Returns the last block of the model.
layers
losses	List of losses added using the add_loss() API.
metrics	Return metrics added using compile() or add_metric().
metrics_names	Returns the model's display labels for all outputs.
model_outputs	Returns a list with the ModelOutput in the model
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
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.
prediction_tasks	Returns the Prediction tasks in the model.
run_eagerly	Settable attribute indicating whether the model should run eagerly.
schema	Returns the schema of the model.
state_updates	Deprecated, do NOT use!
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
to_call	Provides the list of blocks to be called during the execution of the model.
topk_layer
trainable
trainable_variables
trainable_weights
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.

classmethod from_candidate_dataset(query_encoder: Union[merlin.models.tf.core.encoder.Encoder, keras.engine.base_layer.Layer], candidate_encoder: Union[merlin.models.tf.core.encoder.Encoder, keras.engine.base_layer.Layer], dataset: merlin.io.dataset.Dataset, top_k: int = 10, index_column: Optional[Union[str, merlin.schema.schema.ColumnSchema, merlin.schema.schema.Schema, merlin.schema.tags.Tags]] = None, **kwargs)

Class method to initialize a TopKEncoder from a dataset of raw candidates features.

Parameters

• query_encoder (Union[Encoder, tf.keras.layers.Layer]) – The encoder layer to use for computing the query embeddings.

• candidate_encoder (Union[Encoder, tf.keras.layers.Layer]) – The encoder layer to use for computing the candidates embeddings.

• dataset (merlin.io.Dataset) – Raw candidate features dataset

• index_column (Union[str, ColumnSchema, Schema, Tags], optional) – The column to use as candidates identifiers, this will be used for returning the topk ids of candidates with the highest scores. If not specified, the candidates indices will be used instead. by default None

• top_k (int, optional) – Number of candidates to return, by default 10

Returns

a TopKEncoder indexed by the pre-trained embeddings of the candidates in the specified dataset

Return type

TopKEncoder

compile(optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, steps_per_execution=None, jit_compile=None, k: Optional[int] = None, **kwargs)

Extend the compile method of BaseModel to set the threshold k of the top-k encoder.

property topk_layer

index_candidates(candidates, identifiers=None)

encode_candidates(dataset: merlin.io.dataset.Dataset, index_column: Optional[Union[str, merlin.schema.schema.ColumnSchema, merlin.schema.schema.Schema, merlin.schema.tags.Tags]] = None, candidate_encoder: Optional[Union[merlin.models.tf.core.encoder.Encoder, keras.engine.base_layer.Layer]] = None, **kwargs) → merlin.io.dataset.Dataset

Method to generate candidates embeddings

Parameters

• dataset (merlin.io.Dataset) – Raw candidate features dataset

• index_column (Union[str, ColumnSchema, Schema, Tags], optional) – The column to use as candidates identifiers, this will be used for returning the topk ids of candidates with the highest scores. If not specified, the candidates indices will be used instead. by default None

• candidate_encoder (Union[Encoder, tf.keras.layers.Layer], optional) – The encoder layer to use for computing the candidates embeddings. If not specified, the candidate_encoder set in the constructor will be used instead. by default None

Returns

A merlin dataset of candidates embeddings, indexed by index_column.

Return type

merlin.io.Dataset

batch_predict(dataset: Union[merlin.io.dataset.Dataset, merlin.models.tf.loader.Loader], batch_size: int, output_schema: Optional[merlin.schema.schema.Schema] = None, **kwargs) → merlin.io.dataset.Dataset

Batched top-k prediction using Dask.

Parameters

• dataset (Union[merlin.io.Dataset, merlin.models.tf.loader.Loader]) – Raw queries features dataset or Loader

• batch_size (int) – The number of queries to process at each prediction step

• output_schema (Schema, optional) – The columns to output from the input dataset

Returns

A merlin dataset with the top-k predictions, the candidates identifiers and related scores.

Return type

merlin.io.Dataset

fit(*args, **kwargs)

Fit model