gemseo / mlearning / core

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factory module

The factory to create the machine learning algorithms.

This module contains a factory to instantiate an MLAlgo from its class name. This factory also provides a list of available machine learning algorithms and allows testing if a machine learning algorithm is available.

class gemseo.mlearning.core.factory.MLAlgoFactory[source]

Bases: BaseFactory

This factory instantiates an MLAlgo from its class name.

The class can be either internal or external. In this second case, it can be either implemented in a module referenced in the GEMSEO_PATH or in a module The class can be either internal or external. In the second case, it can be either implemented in a module referenced in the GEMSEO_PATH environment variable or in a module starting with gemseo_ and referenced in the PYTHONPATH environment variable.

Return type:

Any

create(ml_algo, **options)[source]

Create an instance of a machine learning algorithm.

Parameters:

  • ml_algo (str) – The name of a machine learning algorithm (its class name).

  • **options (Dataset | TransformerType | MLAlgoParameterType | None) – The options of the machine learning algorithm.

Returns:

The instance of the machine learning algorithm.

Raises:

TypeError – If the class cannot be instantiated.

Return type:

MLAlgo

get_class(name)

Return a class from its name.

Parameters:

name (str) – The name of the class.

Returns:

The class.

Raises:

ImportError – If the class is not available.

Return type:

type[T]

get_default_option_values(name)

Return the constructor kwargs default values of a class.

Parameters:

name (str) – The name of the class.

Returns:

The mapping from the argument names to their default values.

Return type:

dict[str, str | int | float | bool]

get_default_sub_option_values(name, **options)

Return the default values of the sub options of a class.

Parameters:

  • name (str) – The name of the class.

  • **options (str) – The options to be passed to the class required to deduce the sub options.

Returns:

The JSON grammar.

Return type:

JSONGrammar

get_library_name(name)

Return the name of the library related to the name of a class.

Parameters:

name (str) – The name of the class.

Returns:

The name of the library.

Return type:

str

get_options_doc(name)

Return the constructor documentation of a class.

Parameters:

name (str) – The name of the class.

Returns:

The mapping from the argument names to their documentation.

Return type:

dict[str, str]

get_options_grammar(name, write_schema=False, schema_path='')

Return the options JSON grammar for a class.

Attempt to generate a JSONGrammar from the arguments of the __init__ method of the class.

Parameters:

  • name (str) – The name of the class.

  • write_schema (bool) –

    If True, write the JSON schema to a file.

    By default it is set to False.

  • schema_path (Path | str) –

    The path to the JSON schema file. If None, the file is saved in the current directory in a file named after the name of the class.

    By default it is set to “”.

Returns:

The JSON grammar.

Return type:

JSONGrammar

get_sub_options_grammar(name, **options)

Return the JSONGrammar of the sub options of a class.

Parameters:

  • name (str) – The name of the class.

  • **options (str) – The options to be passed to the class required to deduce the sub options.

Returns:

The JSON grammar.

Return type:

JSONGrammar

is_available(name)

Return whether a class can be instantiated.

Parameters:

name (str) – The name of the class.

Returns:

Whether the class can be instantiated.

Return type:

bool

load(directory)[source]

Load an instance of machine learning algorithm from the disk.

Parameters:

directory (str | Path) – The name of the directory containing an instance of a machine learning algorithm.

Returns:

The instance of the machine learning algorithm.

Return type:

MLAlgo

update()

Search for the classes that can be instantiated.

The search is done in the following order:

  1. The fully qualified module names

  2. The plugin packages

  3. The packages from the environment variables

Return type:

None

PLUGIN_ENTRY_POINT: ClassVar[str] = 'gemseo_plugins'

The name of the setuptools entry point for declaring plugins.

property class_names: list[str]

The sorted names of the available classes.

failed_imports: dict[str, str]

The class names bound to the import errors.

property models: list[str]

The available machine learning algorithms.