Parameter Utils

class deep_qa.common.params.Params(params: typing.Dict[str, typing.Any], history: str = '')[source]


Represents a parameter dictionary with a history, and contains other functionality around parameter passing and validation for DeepQA.

There are currently two benefits of a Params object over a plain dictionary for parameter passing:

  1. We handle a few kinds of parameter validation, including making sure that parameters representing discrete choices actually have acceptable values, and making sure no extra parameters are passed.
  2. We log all parameter reads, including default values. This gives a more complete specification of the actual parameters used than is given in a JSON / HOCON file, because those may not specify what default values were used, whereas this will log them.

The convention for using a Params object in DeepQA is that you will consume the parameters as you read them, so that there are none left when you’ve read everything you expect. This lets us easily validate that you didn’t pass in any extra parameters, just by making sure that the parameter dictionary is empty. You should do this when you’re done handling parameters, by calling Params.assert_empty().

DEFAULT = <object object>
_abc_cache = <_weakrefset.WeakSet object>
_abc_negative_cache = <_weakrefset.WeakSet object>
_abc_negative_cache_version = 47
_abc_registry = <_weakrefset.WeakSet object>

Sometimes we need to just represent the parameters as a dict, for instance when we pass them to a Keras layer(so that they can be serialised).


quiet: bool, optional (default = False)

Whether to log the parameters before returning them as a dict.

assert_empty(class_name: str)[source]

Raises a ConfigurationError if self.params is not empty. We take class_name as an argument so that the error message gives some idea of where an error happened, if there was one. class_name should be the name of the calling class, the one that got extra parameters (if there are any).

get(key: str, default: typing.Any = <object object>)[source]

Performs the functionality associated with dict.get(key) but also checks for returned dicts and returns a Params object in their place with an updated history.

pop(key: str, default: typing.Any = <object object>)[source]

Performs the functionality associated with dict.pop(key), along with checking for returned dictionaries, replacing them with Param objects with an updated history.

If key is not present in the dictionary, and no default was specified, we raise a ConfigurationError, instead of the typical KeyError.

pop_choice(key: str, choices: typing.List[typing.Any], default_to_first_choice: bool = False)[source]

Gets the value of key in the params dictionary, ensuring that the value is one of the given choices. Note that this pops the key from params, modifying the dictionary, consistent with how parameters are processed in this codebase.


key: str

Key to get the value from in the param dictionary

choices: List[Any]

A list of valid options for values corresponding to key. For example, if you’re specifying the type of encoder to use for some part of your model, the choices might be the list of encoder classes we know about and can instantiate. If the value we find in the param dictionary is not in choices, we raise a ConfigurationError, because the user specified an invalid value in their parameter file.

default_to_first_choice: bool, optional (default=False)

If this is True, we allow the key to not be present in the parameter dictionary. If the key is not present, we will use the return as the value the first choice in the choices list. If this is False, we raise a ConfigurationError, because specifying the key is required (e.g., you have to specify your model class when running an experiment, but you can feel free to use default settings for encoders if you want).

deep_qa.common.params.pop_choice(params: typing.Dict[str, typing.Any], key: str, choices: typing.List[typing.Any], default_to_first_choice: bool = False, history: str = '?.') → typing.Any[source]

Performs the same function as Params.pop_choice(), but is required in order to deal with places that the Params object is not welcome, such as inside Keras layers. See the docstring of that method for more detail on how this function works.

This method adds a history parameter, in the off-chance that you know it, so that we can reproduce Params.pop_choice() exactly. We default to using ”?.” if you don’t know the history, so you’ll have to fix that in the log if you want to actually recover the logged parameters.

deep_qa.common.params.replace_none(dictionary: typing.Dict[str, typing.Any]) → typing.Dict[str, typing.Any][source]