Pipeline

A collection of full training and evaluation pipelines.

class Result(model, predictions, losses, train_time, evaluation_time, metrics)[source]

A result package.

save(directory)[source]

Save the results to a directory.

Return type

None

summarize()[source]

Print results to the console.

Return type

None

pipeline(*, dataset, model, model_kwargs=None, optimizer_cls=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None, loss_cls=<class 'torch.nn.modules.loss.BCELoss'>, loss_kwargs=None, batch_size=512, epochs, context_features, drug_features, drug_molecules, train_size=None, random_state=None, metrics=None, device=None)[source]

Run the training and evaluation pipeline.

Parameters

  • dataset (Union[str, DatasetLoader, Type[DatasetLoader], None]) –

    The dataset can be specified in one of three ways:

    1. The name of the dataset

    2. A subclass of chemicalx.DatasetLoader

    3. An instance of a chemicalx.DatasetLoader

  • model (Union[str, Model, Type[Model], None]) –

    The model can be specified in one of three ways:

    1. The name of the model

    2. A subclass of chemicalx.Model

    3. An instance of a chemicalx.Model

  • model_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass through to the model constructor. Relevant if passing model by string or class.

  • optimizer_cls (Type[Optimizer]) – The class for the optimizer to use. Currently defaults to torch.optim.Adam.

  • optimizer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass through to the optimizer construction.

  • loss_cls (Type[_Loss]) – The loss to use. If none given, uses torch.nn.BCELoss.

  • loss_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass through to the loss construction.

  • batch_size (int) – The batch size

  • epochs (int) – The number of epochs to train

  • context_features (bool) – Indicator whether the batch should include biological context features.

  • drug_features (bool) – Indicator whether the batch should include drug features.

  • drug_molecules (bool) – Indicator whether the batch should include drug molecules

  • train_size (Optional[float]) – The ratio of training triples. Default is 0.8 if None is passed.

  • random_state (Optional[int]) – The random seed for splitting the triples. Default is 42. Set to none for no fixed seed.

  • metrics (Optional[Sequence[str]]) – The list of metrics to use.

  • device (Union[device, str, None]) – The device to use

Return type

Result

Returns

A result object with the trained model and evaluation results