gemseo / mlearning / transformers / scaler

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standard_scaler module

Scaling a variable with a statistical linear transformation.

The StandardScaler class implements the Standard scaling method applying to some parameter \(z\):

\[\bar{z} := \text{offset} + \text{coefficient}\times z = \frac{z-\text{mean}(z)}{\text{std}(z)}\]

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.

Warning

When \(\text{std}(z)=0\) and \(\text{mean}(z)\neq 0\), we use \(\bar{z}=\frac{z}{\text{mean}(z)}-1\). When \(\text{std}(z)=0\) and \(\text{mean}(z)=0\), we use \(\bar{z}=z\).

class gemseo.mlearning.transformers.scaler.standard_scaler.StandardScaler(name='', offset=0.0, coefficient=1.0)[source]

Bases: Scaler

Standard scaler.

Parameters:

  • name (str) –

    A name for this transformer.

    By default it is set to “”.

  • offset (float) –

    The offset of the linear transformation.

    By default it is set to 0.0.

  • coefficient (float) –

    The coefficient of the linear transformation.

    By default it is set to 1.0.

compute_jacobian(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

compute_jacobian_inverse(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

duplicate()

Duplicate the current object.

Returns:

A deepcopy of the current instance.

Return type:

Transformer

fit(data, *args)

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)

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, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

transform(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

CROSSED: ClassVar[bool] = False

Whether the fit() method requires two data arrays.

property coefficient: ndarray

The scaling coefficient.

property is_fitted: bool

Whether the transformer has been fitted from some data.

name: str

The name of the transformer.

property offset: ndarray

The scaling offset.

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

The parameters of the transformer.

Examples using StandardScaler

Scalers

Scalers