model_api.py
# Copyright 2022 The GPflow Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Classes and other infrastructure for defining and creating models.
Concrete models factories are found in ``models.py``.
"""
from abc import ABC, abstractmethod
from typing import AbstractSet, Callable
import numpy as np
from benchmark.dataset_api import DatasetReq, XYData
from benchmark.registry import TaggedRegistry
from benchmark.tag import Tag, TagReq
from gpflow.models import GPModel
class ModelTag(Tag["ModelTag"]):
pass
ModelReq = TagReq[ModelTag]
REGRESSION = ModelTag("REGRESSION")
SPARSE = ModelTag("SPARSE")
VARIATIONAL = ModelTag("VARIATIONAL")
class ModelFactory(ABC):
"""
A way to instantiate a model.
The factory itself should be cheap to create, though it may take some time to actually create
the model.
"""
name: str
"""
Name of this model / model factory.
"""
tags: AbstractSet[ModelTag]
"""
Tags representing properties / capabilities of this model.
"""
dataset_req: DatasetReq
"""
This model is compatible with datasets that has these tags.
"""
@abstractmethod
def create_model(self, data: XYData, rng: np.random.Generator) -> GPModel:
"""
Create the model.
Any model parameters should be randomly initialised, using the `rng`.
"""
MODEL_FACTORIES: TaggedRegistry[ModelFactory, ModelTag] = TaggedRegistry()
ModelFactoryFn = Callable[[XYData, np.random.Generator], GPModel]
"""
A function that can be used as a :class:`ModelFactory`.
Any model parameters should be randomly initialised, using the ``Generator``.
"""
class FnModelFactory(ModelFactory):
"""
Adapter from a function to a :class:`ModelFactory`.
"""
def __init__(
self, name: str, tags: AbstractSet[ModelTag], dataset_req: DatasetReq, fn: ModelFactoryFn
) -> None:
self.name = name
self.tags = tags
self.dataset_req = dataset_req
self._fn = fn
def create_model(self, data: XYData, rng: np.random.Generator) -> GPModel:
assert self.dataset_req.satisfied(data.tags)
return self._fn(data, rng)
def make_model_factory(
tags: AbstractSet[ModelTag], dataset_req: DatasetReq
) -> Callable[[ModelFactoryFn], FnModelFactory]:
"""
Decorator for turning a function into a :class:`ModelFactory`.
"""
def wrap(fn: ModelFactoryFn) -> FnModelFactory:
name = fn.__name__
factory = FnModelFactory(name, tags, dataset_req, fn)
MODEL_FACTORIES.add(factory)
return factory
return wrap