zea.Config

class zea.Config(dictionary=None, __parent__=None, **kwargs)

Bases: dict

Config class.

This Config class extends a normal dictionary with dot notation access.

Features:

• from_path method to load a config from a yaml file (local or huggingface hub).

• save_to_yaml method to save the config to a yaml file.

• copy method to create a deep copy of the config.

• Normal dictionary methods such as keys, values, items, pop, update, get.

• Propose similar attribute names if a non-existing attribute is accessed.

• Freeze the config object to prevent new attributes from being added.

• Load config object from yaml file.

• Logs all accessed attributes such that you can check if all attributes have been accessed.

We took inspiration from the following sources:

• EasyDict

• keras.utils.Config

But this implementation is superior :)

Initializes a Config object.

Parameters:

• dictionary (dict, optional) – A dictionary containing key-value pairs to initialize the Config object. Defaults to None.

• **kwargs – Additional key-value pairs to initialize the Config object. Will override values in the dictionary if they have the same key.

as_dict(func_on_leaves=None)

Convert the config to a normal dictionary (recursively).

Parameters:

func_on_leaves (callable, optional) – Function to apply to each leaf node. The function should take three arguments: the config object, the key, and the value. You can change the key and value inside the function. Defaults to None.

clear()

Clear the config object.

copy()

Deep copy the config object.

This is useful when you want to modify the config object without changing the original. Does not preserve the access history or frozen state!

freeze()

Freeze config object.

This means that no new attributes can be added. Only existing attributes can be modified.

classmethod from_hf(repo_id, path, **kwargs)

Load config object from huggingface hub.

Parameters:

• repo_id (str) – huggingface hub repo id. For example: “zeahub/configs”

• path (str) – path to the config file in the repo. For example: “train_config.yaml”

• **kwargs – additional arguments to pass to the hf_hub_download function. For example, use repo_type=”dataset” to download from a dataset repo, or revision=”main” to download from a specific branch.

Returns:

config object.

Return type:

Config

classmethod from_path(path, loader=<class 'yaml.loader.FullLoader'>, **kwargs)

Load config object from a file path.

Parameters:

• path (str or Path) – The path to the config file. Can be a string or a Path object. Additionally can be a string with the prefix ‘hf://’, in which case it will be resolved to a huggingface path.

• loader (yaml.Loader, optional) – YAML loader used after the file is resolved.

• **kwargs – Additional Hugging Face Hub arguments for hf:// paths, for example repo_type or revision.

Returns:

config object.

Return type:

Config

Example

>>> from zea import Config
>>> config = Config.from_path("hf://zeahub/configs/config_camus.yaml")

classmethod from_yaml(path, **kwargs)

Load config object from yaml file.

fromkeys(keys, value=None)

Returns a config with the specified keys and value

get(key, default=None)

Returns the value of the specified key

items()

Returns a list containing a tuple for each key value pair

keys()

Returns a list containing the config’s keys

pop(key, default=None)

Removes the element with the specified key

popitem()

Removes the last inserted key-value pair

save_to_yaml(path)

Deprecated alias for to_yaml().

serialize()

Return a dict of this config object with all Path objects converted to strings.

setdefault(key, default=None)

Returns the value of the specified key. If the key does not exist: insert the key, with the specified value

to_json()

Return the config as a json string.

to_tensor(keep_as_is=None)

Convert the attributes in the object to keras tensors

to_yaml(path)

Save config contents to a YAML file.

unfreeze()

Unfreeze config object. This means that new attributes can be added.

update(dictionary=None, **kwargs)

Updates the config with the specified key-value pairs

update_recursive(dictionary=None, **kwargs)

Recursively update the config with the provided dictionary and keyword arguments.

If a key corresponds to another Config object, the update_recursive method is called recursively on that object. This makes it possible to update nested Config objects without replacing them.

If a value is a list and the corresponding config value is also a list, each element is updated recursively if it is a Config, otherwise replaced.

Example

>>> from zea import Config
>>> config = Config({"a": 1, "b": {"c": 2, "d": 3}})
>>> config.update_recursive({"a": 4, "b": {"c": 5}})
>>> # Notice how "d" is kept and only "c" is updated.
>>> print(config)
Config({'a': 4, 'b': {'c': 5, 'd': 3}})

Parameters:

• dictionary (dict | None) – Dictionary to update from.

• **kwargs – Additional key-value pairs to update.

values()

Returns a list of all the values in the config