Fit

gpjax.fit

ModuleModel = TypeVar('ModuleModel', bound=Module) module-attribute

__all__ = ['fit', 'get_batch'] module-attribute

fit(*, model: ModuleModel, objective: Union[AbstractObjective, Callable[[ModuleModel, Dataset], ScalarFloat]], train_data: Dataset, optim: ox.GradientTransformation, key: KeyArray, num_iters: Optional[int] = 100, batch_size: Optional[int] = -1, log_rate: Optional[int] = 10, verbose: Optional[bool] = True, unroll: Optional[int] = 1, safe: Optional[bool] = True) -> Tuple[ModuleModel, Array]

Train a Module model with respect to a supplied Objective function. Optimisers used here should originate from Optax.

Example:

    >>> import jax.numpy as jnp
    >>> import jax.random as jr
    >>> import optax as ox
    >>> import gpjax as gpx
    >>>
    >>> # (1) Create a dataset:
    >>> X = jnp.linspace(0.0, 10.0, 100)[:, None]
    >>> y = 2.0 * X + 1.0 + 10 * jr.normal(jr.key(0), X.shape)
    >>> D = gpx.Dataset(X, y)
    >>>
    >>> # (2) Define your model:
    >>> class LinearModel(gpx.base.Module):
            weight: float = gpx.base.param_field()
            bias: float = gpx.base.param_field()

            def __call__(self, x):
                return self.weight * x + self.bias

    >>> model = LinearModel(weight=1.0, bias=1.0)
    >>>
    >>> # (3) Define your loss function:
    >>> class MeanSquareError(gpx.objectives.AbstractObjective):
            def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float:
                return jnp.mean((train_data.y - model(train_data.X)) ** 2)
    >>>
    >>> loss = MeanSqaureError()
    >>>
    >>> # (4) Train!
    >>> trained_model, history = gpx.fit(
            model=model, objective=loss, train_data=D, optim=ox.sgd(0.001), num_iters=1000
        )

Parameters:

Name	Type	Description	Default
model	Module	The model Module to be optimised.	required
objective	Objective	The objective function that we are optimising with respect to.	required
train_data	Dataset	The training data to be used for the optimisation.	required
optim	GradientTransformation	The Optax optimiser that is to be used for learning a parameter set.	required
num_iters	Optional[int]	The number of optimisation steps to run. Defaults to 100.	100
batch_size	Optional[int]	The size of the mini-batch to use. Defaults to -1 (i.e. full batch).	-1
key	Optional[KeyArray]	The random key to use for the optimisation batch selection. Defaults to jr.key(42).	required
log_rate	Optional[int]	How frequently the objective function's value should be printed. Defaults to 10.	10
verbose	Optional[bool]	Whether to print the training loading bar. Defaults to True.	True
unroll	int	The number of unrolled steps to use for the optimisation. Defaults to 1.	1

Returns

Tuple[Module, Array]: A Tuple comprising the optimised model and training
    history respectively.

fit_scipy(*, model: ModuleModel, objective: Union[AbstractObjective, Callable[[ModuleModel, Dataset], ScalarFloat]], train_data: Dataset, max_iters: Optional[int] = 500, verbose: Optional[bool] = True, safe: Optional[bool] = True) -> Tuple[ModuleModel, Array]

Train a Module model with respect to a supplied Objective function. Optimisers used here should originate from Optax. todo

Parameters:

Name	Type	Description	Default
model	Module	The model Module to be optimised.	required
objective	Objective	The objective function that we are optimising with respect to.	required
train_data	Dataset	The training data to be used for the optimisation.	required
max_iters	Optional[int]	The maximum number of optimisation steps to run. Defaults to 500.	500
verbose	Optional[bool]	Whether to print the information about the optimisation. Defaults to True.	True

Returns

Tuple[Module, Array]: A Tuple comprising the optimised model and training
    history respectively.

get_batch(train_data: Dataset, batch_size: int, key: KeyArray) -> Dataset

Batch the data into mini-batches. Sampling is done with replacement.

Parameters:

Name	Type	Description	Default
train_data	Dataset	The training dataset.	required
batch_size	int	The batch size.	required
key	KeyArray	The random key to use for the batch selection.	required

Returns

Dataset: The batched dataset.