allennlp.common.params#

infer_and_cast#

infer_and_cast(value:Any)

In some cases we'll be feeding params dicts to functions we don't own; for example, PyTorch optimizers. In that case we can't use pop_int or similar to force casts (which means you can't specify int parameters using environment variables). This function takes something that looks JSON-like and recursively casts things that look like (bool, int, float) to (bool, int, float).

Params#

Params(self, params:Dict[str, Any], history:str='') -> None

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

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 file, because those may not specify what default values were used, whereas this will log them.

Consumption

The convention for using a Params object in AllenNLP 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.

as_dict#

Params.as_dict(self, quiet:bool=False, infer_type_and_cast:bool=False)

Sometimes we need to just represent the parameters as a dict, for instance when we pass them to PyTorch code.

Parameters

  • quiet: bool, optional (default = False)

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

  • infer_type_and_cast: bool, optional (default = False)

    If True, we infer types and cast (e.g. things that look like floats to floats).

as_flat_dict#

Params.as_flat_dict(self)

Returns the parameters of a flat dictionary from keys to values. Nested structure is collapsed with periods.

as_ordered_dict#

Params.as_ordered_dict(
    self,
    preference_orders: List[List[str]] = None,
) -> collections.OrderedDict

Returns Ordered Dict of Params from list of partial order preferences.

Parameters

  • preference_orders: List[List[str]], optional

    preference_orders is list of partial preference orders. ["A", "B", "C"] means "A" > "B" > "C". For multiple preference_orders first will be considered first. - Keys not found, will have last but alphabetical preference. Default Preferences: [["dataset_reader", "iterator", "model", "train_data_path", "validation_data_path", "test_data_path", "trainer", "vocabulary"], ["type"]]

assert_empty#

Params.assert_empty(self, class_name:str)

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).

DEFAULT#

The most base type

duplicate#

Params.duplicate(self) -> 'Params'

Uses copy.deepcopy() to create a duplicate (but fully distinct) copy of these Params.

from_file#

Params.from_file(
    params_file: str,
    params_overrides: str = '',
    ext_vars: dict = None,
) -> 'Params'

Load a Params object from a configuration file.

Parameters

  • params_file: str

    The path to the configuration file to load.

  • params_overrides: str, optional

    A dict of overrides that can be applied to final object. - e.g. {"model.embedding_dim": 10}

  • ext_vars: dict, optional

    Our config files are Jsonnet, which allows specifying external variables for later substitution. Typically we substitute these using environment variables; however, you can also specify them here, in which case they take priority over environment variables. - e.g. {"HOME_DIR": "/Users/allennlp/home"}

get#

Params.get(self, key:str, default:Any=<object object at 0x7f292aaa0cb0>)

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.

get_hash#

Params.get_hash(self) -> str

Returns a hash code representing the current state of this Params object. We don't want to implement __hash__ because that has deeper python implications (and this is a mutable object), but this will give you a representation of the current state. We use zlib.adler32 instead of Python's builtin hash because the random seed for the latter is reset on each new program invocation, as discussed here: https://stackoverflow.com/questions/27954892/deterministic-hashing-in-python-3.

pop#

Params.pop(
    self,
    key: str,
    default: Any = <object object at 0x7f292aaa0cb0>,
    keep_as_dict: bool = False,
) -> Any

Performs the functionality associated with dict.pop(key), along with checking for returned dictionaries, replacing them with Param objects with an updated history (unless keep_as_dict is True, in which case we leave them as dictionaries).

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

pop_bool#

Params.pop_bool(
    self,
    key: str,
    default: Any = <object object at 0x7f292aaa0cb0>,
) -> bool

Performs a pop and coerces to a bool.

pop_choice#

Params.pop_choice(
    self,
    key: str,
    choices: List[Any],
    default_to_first_choice: bool = False,
    allow_class_names: bool = True,
) -> Any

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.

Parameters

  • 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).

  • allow_class_names: bool, optional (default = True)

    If this is True, then we allow unknown choices that look like fully-qualified class names. This is to allow e.g. specifying a model type as my_library.my_model.MyModel and importing it on the fly. Our check for "looks like" is extremely lenient and consists of checking that the value contains a '.'.

pop_float#

Params.pop_float(
    self,
    key: str,
    default: Any = <object object at 0x7f292aaa0cb0>,
) -> float

Performs a pop and coerces to a float.

pop_int#

Params.pop_int(
    self,
    key: str,
    default: Any = <object object at 0x7f292aaa0cb0>,
) -> int

Performs a pop and coerces to an int.

pop_choice#

pop_choice(
    params: Dict[str, Any],
    key: str,
    choices: List[Any],
    default_to_first_choice: bool = False,
    history: str = '?.',
    allow_class_names: bool = True,
) -> Any

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.

unflatten#

unflatten(flat_dict:Dict[str, Any]) -> Dict[str, Any]

Given a "flattened" dict with compound keys, e.g. {"a.b": 0} unflatten it: {"a": {"b": 0}}

with_fallback#

with_fallback(
    preferred: Dict[str, Any],
    fallback: Dict[str, Any],
) -> Dict[str, Any]

Deep merge two dicts, preferring values from preferred.