tfm.uplift.models.two_tower_uplift_model.TwoTowerUpliftModel

Training and inference model for a BaseTwoTowerUpliftNetwork layer.

tfm.uplift.models.two_tower_uplift_model.TwoTowerUpliftModel(
    treatment_indicator_feature_name: str,
    uplift_network: base_uplift_networks.BaseTwoTowerUpliftNetwork,
    inverse_link_fn: (Callable[[tf.Tensor], tf.Tensor] | None) = None,
    **kwargs
)

treatment_indicator_feature_name the name of the feature representing the treatment_indicator tensor, which should be castable to a boolean tensor (False for control and True for treatment). This tensor is required during training and evaluation to compute the true logits needed for loss computation.
uplift_network a layer for computing control and treatment logits. Its input is expected to be a dictionary of feature tensors and its output is exptected to be a TwoTowerNetworkOutputs instance.
inverse_link_fn a function for computing the control and treatment predictions from their respective logits. If left as None it is functionally equivalent to the identity function.
**kwargs base model keyword arguments.

Methods

call

View source

call(
    inputs: types.DictOfTensors,
    training: (bool | None) = None,
    mask: (tf.Tensor | None) = None
) -> (types.TwoTowerPredictionOutputs | types.TwoTowerTrainingOutputs)

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args
inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.