gemseo / mlearning / transformers

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base_transformer module

A transformer to apply operations on NumPy arrays.

The abstract BaseTransformer class implements the concept of a data transformer. Inheriting classes shall implement the BaseTransformer.fit(), BaseTransformer.transform() and possibly BaseTransformer.inverse_transform() methods.

See also

scaler dimension_reduction

class gemseo.mlearning.transformers.base_transformer.BaseTransformer(name='', **parameters)[source]

Bases: object

A data transformer fitted from some samples.

Parameters:
  • name (str) –

    A name for this transformer.

    By default it is set to “”.

  • **parameters (ParameterType) – The parameters of the transformer.

compute_jacobian(data)[source]

Compute the Jacobian of transform().

Parameters:

data (ndarray) – The data where the Jacobian is to be computed, shaped as (n_observations, n_features) or (n_features, ).

Returns:

The Jacobian matrix, shaped according to data.

Return type:

NoReturn

compute_jacobian_inverse(data)[source]

Compute the Jacobian of the inverse_transform().

Parameters:

data (ndarray) – The data where the Jacobian is to be computed, shaped as (n_observations, n_features) or (n_features, ).

Returns:

The Jacobian matrix, shaped according to data..

Return type:

NoReturn

duplicate()[source]

Duplicate the current object.

Returns:

A deepcopy of the current instance.

Return type:

BaseTransformer

fit(data, *args)[source]

Fit the transformer to the data.

Parameters:
  • data (ndarray) – The data to be fitted, shaped as (n_observations, n_features) or (n_observations, ).

  • args (float | int | str)

Return type:

None

fit_transform(data, *args)[source]

Fit the transformer to the data and transform the data.

Parameters:
  • data (ndarray) – The data to be transformed, shaped as (n_observations, n_features) or (n_observations, ).

  • args (float | int | str)

Returns:

The transformed data, shaped as data.

Return type:

ndarray

inverse_transform(data)[source]

Perform an inverse transform on the data.

Parameters:

data (ndarray) – The data to be inverse transformed, shaped as (n_observations, n_features) or (n_features, ).

Returns:

The inverse transformed data, shaped as data.

Return type:

NoReturn

abstract transform(data)[source]

Transform the data.

Parameters:

data (ndarray) – The data to be transformed, shaped as (n_observations, n_features) or (n_features, ).

Returns:

The transformed data, shaped as data.

Return type:

ndarray

CROSSED: ClassVar[bool] = False

Whether the fit() method requires two data arrays.

property is_fitted: bool

Whether the transformer has been fitted from some data.

name: str

The name of the transformer.

property parameters: dict[str, bool | int | float | ndarray | str | None]

The parameters of the transformer.

class gemseo.mlearning.transformers.base_transformer.TransformerFactory[source]

Bases: BaseFactory

A factory of transformers.

Return type:

Any

create(class_name, *args, **kwargs)

Return an instance of a class.

Parameters:
  • class_name (str) – The name of the class.

  • **args (Any) – The positional arguments to be passed to the class constructor.

  • **kwargs (Any) – The keyword arguments to be passed to the class constructor.

Returns:

The instance of the class.

Raises:

TypeError – If the class cannot be instantiated.

Return type:

T

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:

StrKeyMapping

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

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

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.

Examples using BaseTransformer

KL-SVD on Burgers equation

KL-SVD on Burgers equation

PCA on Burgers equation

PCA on Burgers equation

Pipeline

Pipeline

Scalers

Scalers