merlin.models.tf.CategoryEncoding

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

Bases: merlin.models.tf.core.tabular.TabularBlock

A preprocessing layer which encodes integer features.

This layer provides options for condensing data into a categorical encoding. It accepts integer values as inputs, and it outputs a dense or sparse representation of those inputs. Only categorical features with “CATEGORICAL” as Tag can be transformed, and other features without this Tag would be discarded. It outputs a TabularData (Dict of features), where each feature is a 2D tensor computed based on the outputmode.

schemaOptional[Schema]

The Schema with the input features

output_mode: Optional[str]

Specification for the output of the layer. Defaults to “multi_hot”. Values can be “one_hot”, “multi_hot” or “count”, configuring the transformation layer as follows: - “one_hot”: Encodes each individual element in the input into a tensor with shape

(batch_size, feature_cardinality), containing a 1 at the element index. It accepts both 1D tensor or 2D tensor if squeezable (i.e., if the last dimension is 1).

• “multi_hot”: Encodes each categorical value from the 2D input features into a

  multi-hot representation with shape (batch_size, feature_cardinality), with 1 at the indices present in the sequence and 0 for the other position. If 1D feature is provided, it behaves the same as “one_hot”.

• “count”: also expects 2D tensor like “multi_hot” and outputs the features

  with shape (batch_size, feature_cardinality). But instead of returning “multi-hot” values, it outputs the frequency (count) of the number of items each item occurs in each sample.

sparse: Optional[Boolean]

If true, returns a SparseTensor instead of a dense Tensor. Defaults to False. Setting sparse=True is recommended for high-cardinality features, in order to avoid out-of-memory errors.

count_weights: Optional[Union(tf.Tensor, tf.RaggedTensor, tf.SparseTensor)]

count_weights is used to calculate weighted sum of times a token at that index appeared when output_mode is “count”

__init__(schema: Optional[merlin.schema.schema.Schema] = None, output_mode='one_hot', sparse=False, count_weights=None, **kwargs)

Methods

__init__([schema, output_mode, sparse, ...])
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.
apply_to_all(inputs[, columns_to_filter])
as_tabular([name])
build(input_shapes)
build_from_config(config)
calculate_batch_size_from_input_shapes(...)
call(inputs, **kwargs)
call_outputs(outputs[, training])
check_schema([schema])
compute_call_output_shape(input_shapes)
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shapes)
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_features(features[, pre, post, ...])	Initializes a TabularLayer instance where the contents of features will be filtered out
from_layer(layer)
from_schema(schema[, tags, allow_none])	Instantiate a TabularLayer instance from a DatasetSchema.
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)
post_call(inputs[, transformations, ...])	Method that's typically called after the forward method for post-processing.
pre_call(inputs[, transformations])	Method that's typically called before the forward method for pre-processing.
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.
repr_add()
repr_extra()
repr_ignore()
select_by_name(name)
select_by_tag(tags)
set_aggregation(value)	param value
set_post(value)
set_pre(value)
set_schema([schema])
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
super()
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.
aggregation	rtype: TabularAggregation, optional
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.
is_input
is_tabular
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.
post	rtype: SequentialTabularTransformations, optional
pre	rtype: SequentialTabularTransformations, optional
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.

call(inputs: Dict[str, tensorflow.python.framework.ops.Tensor], **kwargs) → Dict[str, tensorflow.python.framework.ops.Tensor]

compute_output_shape(input_shapes)

get_config()

REQUIRES_SCHEMA = True