BaseConfig#

class caf.toolkit.config_base.BaseConfig[source]#

Base class for storing model parameters.

Contains functionality for reading / writing parameters to config files in the YAML format.

See also

pydantic.BaseModel: handles converting data to Python types.
pydantic.field_validator: custom validation for attributes.
pydantic.model_validator: custom validation for class.

Examples

Example of creating a config class and initialising it with values, values will be validated and converted to correct type on initialisation.

>>> from pathlib import Path
>>> from caf.toolkit import BaseConfig
>>> class ExampleParameters(BaseConfig):
...    import_folder: Path
...    name: str
...    some_option: bool = True
>>> parameters = ExampleParameters(
...    import_folder="Test Folder",
...    name="Test",
...    some_option=False,
... )

Example of instance of class after initialisation, the path differs depending on operating system.

>>> parameters
ExampleParameters(
    import_folder=WindowsPath('Test Folder'),
    name='Test',
    some_option=False,
)

Config class can be converted to YAML or saved with BaseConfig.save_yaml().

>>> print(parameters.to_yaml())
import_folder: Test Folder
name: Test
some_option: no

Config class data can be loaded from a YAML config file using BaseConfig.load_yaml().

>>> yaml_text = '''
... import_folder: Test Folder
... name: Test
... some_option: no
... '''
>>> loaded_parameters = ExampleParameters.from_yaml(yaml_text)
>>> loaded_parameters == parameters
True

Attributes

`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.

Methods

`__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_orm`(obj)
`from_yaml`(text)	Parse class attributes from YAML text.
`json`(*[, include, exclude, by_alias, ...])
`load_yaml`(path)	Read YAML file and load the data using from_yaml.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, include, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`save_yaml`(path[, datetime_comment, ...])	Write data from self to a YAML file.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`to_yaml`()	Convert attributes from self to YAML string.
`update_forward_refs`(**localns)
`validate`(value)
`write_example`(path_, /[, comment_])	Write examples to a config file.

Attributes Documentation

model_computed_fields = {}#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra#

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {}#

model_fields_set#

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.