One of the design principles of AllenNLP is the use of a modular, declarative language (JSON) for defining experiments and models.
This is implemented by giving each AllenNLP class a method
that contains the logic for instantiating a class instance from a JSON-like
Params object. Historically you had to implement your own
method on every class you wanted to instantiate this way, even though
most of the time you were simply popping off params and handing them to the
constructor (making sure that you popped them using the same default values
as in the constructor.)
It turns out that in those simple cases, we can generate a
method automatically. This implementation lives in the
Registrable subclass automatically gets it, and you can have your
Registrable classes subclass from it as well.
The inclusion of
extras allows for non-FromParams parameters to be passed
as well. For instance, all of our
Model subclasses require a
Vocabulary parameter. Accordingly, the
train command calls
model = Model.from_params(params=params.pop('model'), vocab=vocab)
As an AllenNLP user, you will probably never need to worry about this. However, if you do, note that the extra arguments must be called by keyword. Prior to this default implementation it was possible to call them positionally but this is no longer the case.
In some cases you might want the construction of class instances from_params to include more elaborate logic than “pop off params and hand them to the constructor”. In this case your class just needs to explicitly implement its own from_params method.
Mixin to give a from_params method to classes. We create a distinct base class for this because sometimes we want non-Registrable classes to be instantiatable from_params.
from_params(params: allennlp.common.params.Params, **extras) → T¶
This is the automatic implementation of from_params. Any class that subclasses FromParams (or Registrable, which itself subclasses FromParams) gets this implementation for free. If you want your class to be instantiated from params in the “obvious” way – pop off parameters and hand them to your constructor with the same names – this provides that functionality.
If you need more complex logic in your from from_params method, you’ll have to implement your own method that overrides this one.
create_kwargs(cls: typing.Type[T], params: allennlp.common.params.Params, **extras) → typing.Dict[str, typing.Any]¶
Given some class, a Params object, and potentially other keyword arguments, create a dict of keyword args suitable for passing to the class’s constructor.
The function does this by finding the class’s constructor, matching the constructor arguments to entries in the params object, and instantiating values for the parameters using the type annotation and possibly a from_params method.
Any values that are provided in the extras will just be used as is. For instance, you might provide an existing Vocabulary this way.
Optional[X] annotations are actually represented as Union[X, NoneType]. For our purposes, the “Optional” part is not interesting, so here we throw it away.
takes_arg(obj, arg: str) → bool¶
Checks whether the provided obj takes a certain arg. If it’s a class, we’re really checking whether its constructor does. If it’s a function or method, we’re checking the object itself. Otherwise, we raise an error.