scaler module¶

Scaling a variable with a linear transformation.

The Scaler class implements the default scaling method applying to some parameter \(z\):

\[\bar{z} := \text{offset} + \text{coefficient}\times z\]

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

min_max_scaler standard_scaler

class gemseo.mlearning.transform.scaler.scaler.Scaler(name='Scaler', offset=0.0, coefficient=1.0)[source]¶

Bases: Transformer

Data scaler.

Parameters:

name (str) –
A name for this transformer.

By default it is set to “Scaler”.
offset (float | ndarray) –
The offset of the linear transformation.

By default it is set to 0.0.
coefficient (float | ndarray) –
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 description is missing.
**kwargs (Any) – The description is missing.

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 description is missing.
**kwargs (Any) – The description is missing.

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 (Union[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 (Union[float, int, str]) –

Returns: