merlin.models.tf.ColumnBasedSampleWeight

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

Bases: merlin.models.tf.core.base.Block

Allows using columns (features or targets) as sample weights for a give ModelOutput.

Examples

It can be used for example for binary class weights, using the same column as the weight column and setting binary_class_weights.

``` inputs = mm.InputBlockV2(music_streaming_data.schema) output_block = mm.BinaryOutput(“like”,

post=mm.ColumnBasedSampleWeight(

weight_column_name=”like”, binary_class_weights=((1.0, 5.0)

)

)

model = mm.Model(inputs, mm.MLPBlock([64]), output_block) ```

Another use case is computing a loss only for a subset of the examples. That is useful in multi-task learning, where one of target is conditioned on the other target (e.g. the user can only like if he viewed the video). So you can use the positive views (view==1)as the sample space for training the “like” prediction task.

``` inputs = mm.InputBlockV2(music_streaming_data.schema) output_block = mm.ParallelBlock(

“view/binary_output”: mm.BinaryOutput(“view”), “like/binary_output”: mm.BinaryOutput(“like”,

post=mm.ColumnBasedSampleWeight(

weight_column_name=”view”,

)

)

)

model = mm.Model(inputs, mm.MLPBlock([64]), output_block) ```

Parameters

• weight_column_name (Optional[str]) – The column name to be used as weight. If should be present in the schema either as an input feature (i.e., tagged as Tags.CONTINUOUS or Tags.CATEGORICAL) or target feature (i.e., tagged as Tags.TARGET). It is optional if binary_class_weights is set (assuming the target column will be used as weight column in that case).

• binary_class_weights (Optional[Tuple[float, float]], optional) – If provided, it allows setting the weights to which negative (0) and positive values (1) of weight column should be converted to result the final sample weights, by default None. It expects a two elements tuple: (negative_value, positive_value)

__init__(weight_column_name: Optional[str] = None, binary_class_weights: Optional[Tuple[float, float]] = None, **kwargs)

Methods

__init__([weight_column_name, …])
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.
as_tabular([name])
build(input_shapes)
build_from_config(config)
call(inputs[, features, targets, training, …])
call_outputs(outputs[, training])
check_schema([schema])
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.
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)	Creates a layer from its config.
from_layer(layer)
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)
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.
select_by_name(name)
select_by_tag(tags)
set_schema([schema])
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

REQUIRES_SCHEMA
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.
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.
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.
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, features=None, targets=None, training=False, testing=False, target_name=None, **kwargs) → Union[merlin.models.tf.core.prediction.Prediction, tensorflow.python.framework.ops.Tensor]

compute_output_shape(input_shape)

get_config()