Customize a Model

Here we introduce how to customize a TPP model with the support of EasyTPP.

Create a new TPP Model Class

Assume we are building a PyTorch model. We need to initialize the model by inheriting class EasyTPP.model.torch_model.TorchBaseModel.

from easy_tpp.model.torch_model.torch_basemodel import TorchBaseModel

# Custom Torch TPP implementations need to
# inherit from the TorchBaseModel interface
class NewModel(TorchBaseModel):
    def __init__(self, model_config):
        super(NewModel, self).__init__(model_config)

    # Forward along the sequence, output the states / intensities at the event times
    def forward(self, batch):
        ...
        return states

    # Compute the loglikelihood loss
    def loglike_loss(self, batch):
        ....
        return loglike

    # Compute the intensities at given sampling times
    # Used in the Thinning sampler
    def compute_intensities_at_sample_times(self, batch, sample_times, **kwargs):
        ...
        return intensities

If we are building a Tensorflow model, we start with the following code

from easy_tpp.model.torch_model.tf_basemodel import TfBaseModel

# Custom Tf TPP implementations need to
# inherit from the TorchBaseModel interface
class NewModel(TfBaseModel):
    def __init__(self, model_config):
        super(NewModel, self).__init__(model_config)

    # Forward along the sequence, output the states / intensities at the event times
    def forward(self, batch):
        ...
        return states


    # Compute the loglikelihood loss
    def loglike_loss(self, batch):
        ....
        return loglike

    # Compute the intensities at given sampling times
    # Used in the Thinning sampler
    def compute_intensities_at_sample_times(self, batch, sample_times, **kwargs):
        ...
        return intensities

Rewrite Relevant Methods

There are three important functions needed to be implemented:

• forward: the input is the batch data and the output is states at each step.

• loglike_loss: it computes the loglikihood loss given the batch data.

• compute_intensities_at_sample_times: it computes the intensities at each sampling steps.