Transform data to improve the ML algorithm quality¶
Introduction¶
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
- class gemseo.mlearning.transform.transformer.Transformer(name='Transformer', **parameters)[source]
Transformer baseclass.
- Parameters:
- compute_jacobian(data)[source]
Compute Jacobian of transformer.transform().
- compute_jacobian_inverse(data)[source]
Compute Jacobian of the transformer.inverse_transform().
- duplicate()[source]
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)[source]
Fit the transformer to the data.
- fit_transform(data, *args)[source]
Fit the transformer to the data and transform the data.
- inverse_transform(data)[source]
Perform an inverse transform on the data.
- transform(data)[source]
Transform the data.
- name: str
The name of the transformer.
- parameters: str
The parameters of the transformer.
- class gemseo.mlearning.transform.transformer.TransformerFactory(*args, **kwargs)[source]
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) –
- create(class_name, **options)
Return an instance of a class.
- 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:
- get_default_options_values(name)
Return the constructor kwargs default values of a class.
- get_default_sub_options_values(name, **options)
Return the default values of the sub options of a class.
- Parameters:
- Returns:
The JSON grammar.
- Return type:
- get_library_name(name)
Return the name of the library related to the name of a class.
- get_options_doc(name)
Return the constructor documentation of a class.
- 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:
- Returns:
The JSON grammar.
- Return type:
- get_sub_options_grammar(name, **options)
Return the JSONGrammar of the sub options of a class.
- Parameters:
- Returns:
The JSON grammar.
- Return type:
- is_available(name)
Return whether a class can be instantiated.
- update()
Search for the classes that can be instantiated.
- The search is done in the following order:
The fully qualified module names
The plugin packages
The packages from the environment variables
- Return type:
None
A pipeline to chain transformers.
The Pipeline class chains a sequence of tranformers, and provides global
fit(), transform(), fit_transform() and inverse_transform() methods.
- class gemseo.mlearning.transform.pipeline.Pipeline(name='Pipeline', transformers=None)[source]
Transformer pipeline.
- Parameters:
name (str) –
A name for this pipeline.
By default it is set to “Pipeline”.
transformers (Sequence[Transformer]) – A sequence of transformers to be chained. The transformers are chained in the order of appearance in the list, i.e. the first transformer is applied first. If transformers is an empty list or None, then the pipeline transformer behaves like an identity transformer.
- compute_jacobian(data)[source]
Compute the Jacobian of the
pipeline.transform().
- compute_jacobian_inverse(data)[source]
Compute the Jacobian of the
pipeline.inverse_transform().
- duplicate()[source]
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)
Fit the transformer to the data.
- fit_transform(data, *args)
Fit the transformer to the data and transform the data.
- inverse_transform(data)[source]
Perform an inverse transform on the data.
The data is inverse transformed sequentially, starting with the last transformer in the list.
- transform(data)[source]
Transform the data.
The data is transformed sequentially, where the output of one transformer is the input of the next.
- name: str
The name of the transformer.
- parameters: str
The parameters of the transformer.
- transformers: Sequence[Transformer]
The sequence of transformers.
Scaling¶
Scaling a variable with a linear transformation.
The Scaler class implements the default scaling method
applying to some parameter \(z\):
where \(\bar{z}\) is the scaled version of \(z\). This scaling method is a linear transformation parameterized by an offset and a coefficient.
In this default scaling method, the offset is equal to 0 and the coefficient is equal to 1. Consequently, the scaling operation is the identity: \(\bar{z}=z\). This method has to be overloaded.
See also
- class gemseo.mlearning.transform.scaler.scaler.Scaler(name='Scaler', offset=0.0, coefficient=1.0)[source]
Data scaler.
- Parameters:
- compute_jacobian(data)[source]
Compute Jacobian of transformer.transform().
- compute_jacobian_inverse(data)[source]
Compute Jacobian of the transformer.inverse_transform().
- duplicate()
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)
Fit the transformer to the data.
- fit_transform(data, *args)
Fit the transformer to the data and transform the data.
- inverse_transform(data)[source]
Perform an inverse transform on the data.
- transform(data)[source]
Transform the data.
- property coefficient: float
The scaling coefficient.
- name: str
The name of the transformer.
- property offset: float
The scaling offset.
- parameters: str
The parameters of the transformer.
Scaling a variable with a geometrical linear transformation.
The MinMaxScaler class implements the MinMax scaling method
applying to some parameter \(z\):
where \(\text{offset}=-\text{min}(z)/(\text{max}(z)-\text{min}(z))\) and \(\text{coefficient}=1/(\text{max}(z)-\text{min}(z))\).
In the MinMax scaling method, the scaling operation linearly transforms the original variable \(z\) such that the minimum of the original data corresponds to 0 and the maximum to 1.
- class gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler(name='MinMaxScaler', offset=0.0, coefficient=1.0)[source]
Min-max scaler.
- Parameters:
- compute_jacobian(data)
Compute Jacobian of transformer.transform().
- compute_jacobian_inverse(data)
Compute Jacobian of the transformer.inverse_transform().
- duplicate()
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)
Fit the transformer to the data.
- fit_transform(data, *args)
Fit the transformer to the data and transform the data.
- inverse_transform(data)
Perform an inverse transform on the data.
- transform(data)
Transform the data.
- property coefficient: float
The scaling coefficient.
- name: str
The name of the transformer.
- property offset: float
The scaling offset.
- parameters: str
The parameters of the transformer.
Scaling a variable with a statistical linear transformation.
The StandardScaler class implements the Standard scaling method
applying to some parameter \(z\):
where \(\text{offset}=-\text{mean}(z)/\text{std}(z)\) and \(\text{coefficient}=1/\text{std}(z)\).
In this standard scaling method, the scaling operation linearly transforms the original variable math:z such that in the scaled space, the original data have zero mean and unit standard deviation.
- class gemseo.mlearning.transform.scaler.standard_scaler.StandardScaler(name='StandardScaler', offset=0.0, coefficient=1.0)[source]
Standard scaler.
- Parameters:
- compute_jacobian(data)
Compute Jacobian of transformer.transform().
- compute_jacobian_inverse(data)
Compute Jacobian of the transformer.inverse_transform().
- duplicate()
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)
Fit the transformer to the data.
- fit_transform(data, *args)
Fit the transformer to the data and transform the data.
- inverse_transform(data)
Perform an inverse transform on the data.
- transform(data)
Transform the data.
- property coefficient: float
The scaling coefficient.
- name: str
The name of the transformer.
- property offset: float
The scaling offset.
- parameters: str
The parameters of the transformer.
Dimension reduction¶
Dimension reduction as a generic transformer.
The DimensionReduction class implements the concept of dimension reduction.
See also
- class gemseo.mlearning.transform.dimension_reduction.dimension_reduction.DimensionReduction(name='DimensionReduction', n_components=None, **parameters)[source]
Dimension reduction.
- Parameters:
name (str) –
A name for this transformer.
By default it is set to “DimensionReduction”.
n_components (int | None) – The number of components of the latent space. If
None, use the maximum number allowed by the technique, typicallymin(n_samples, n_features).**parameters (str) – The parameters of the transformer.
- compute_jacobian(data)
Compute Jacobian of transformer.transform().
- compute_jacobian_inverse(data)
Compute Jacobian of the transformer.inverse_transform().
- duplicate()
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)
Fit the transformer to the data.
- fit_transform(data, *args)
Fit the transformer to the data and transform the data.
- inverse_transform(data)
Perform an inverse transform on the data.
- transform(data)
Transform the data.
- property n_components: int
The number of components.
- name: str
The name of the transformer.
- parameters: str
The parameters of the transformer.
The Principal Component Analysis (PCA) to reduce the dimension of a variable.
The PCA class wraps the PCA from Scikit-learn.
Dependence¶
This dimension reduction algorithm relies on the PCA class of the scikit-learn library.
- class gemseo.mlearning.transform.dimension_reduction.pca.PCA(name='PCA', n_components=None, **parameters)[source]
Principal component dimension reduction algorithm.
- Parameters:
name (str) –
A name for this transformer.
By default it is set to “PCA”.
n_components (int | None) – The number of components of the latent space. If
None, use the maximum number allowed by the technique, typicallymin(n_samples, n_features).**parameters (str) – The optional parameters for sklearn PCA constructor.
- compute_jacobian(data)[source]
Compute Jacobian of transformer.transform().
- compute_jacobian_inverse(data)[source]
Compute Jacobian of the transformer.inverse_transform().
- duplicate()
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)
Fit the transformer to the data.
- fit_transform(data, *args)
Fit the transformer to the data and transform the data.
- inverse_transform(data)[source]
Perform an inverse transform on the data.
- transform(data)[source]
Transform the data.
- property components: ndarray
The principal components.
- property n_components: int
The number of components.
- name: str
The name of the transformer.
- parameters: str
The parameters of the transformer.
Examples¶
See the examples about: