transformer module¶

A transformer to apply operations on NumPy arrays.

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

See also

scaler dimension_reduction

class gemseo.mlearning.transform.transformer.Transformer(name='Transformer', **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 “Transformer”.
**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:: Transformer

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 (Union[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 (Union[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, Union[bool, int, float, numpy.ndarray, str, NoneType]]¶: The parameters of the transformer.

class gemseo.mlearning.transform.transformer.TransformerFactory(*args, **kwargs)[source]¶

Bases: Factory

A factory of Transformer.

Parameters:

base_class – The base class to be considered.
module_names – The fully qualified modules names to be searched.
args (Any) –
kwargs (Any) –

static cache_clear()¶

Clear the cache.

Return type:: None

create(class_name, **options)¶

Return an instance of a class.

Parameters:

class_name (str) – The name of the class.
**options (Any) – The arguments to be passed to the class constructor.

Returns:

The instance of the class.

Raises:

TypeError – If the class cannot be instantiated.

Return type:

Any

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[Any]

get_default_options_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_options_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=None)¶

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 (str | None) – 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.

Returns: