min_max_scaler module¶

Scaling a variable with a geometrical linear transformation.

The MinMaxScaler class implements the MinMax scaling method applying to some parameter \(z\):

\[\bar{z} := \text{offset} + \text{coefficient}\times z = \frac{z-\text{min}(z)}{(\text{max}(z)-\text{min}(z))},\]

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.

Warning

When \(\text{min}(z)=\text{max}(z)\), we use \(\bar{z}=\frac{z}{\text{min}(z)}-0.5\).

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

Bases: Scaler

Min-max scaler.

Parameters:

name (str) –
A name for this transformer.

By default it is set to “MinMaxScaler”.
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 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:

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 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

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 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

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

Examples using MinMaxScaler¶

Quality measure for surrogate model comparison

Mixture of experts

Scaler example