gemseo / mlearning / transformers / scaler

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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 \(\text{min}(z)=\text{max}(z)\neq 0\), we use \(\bar{z}=\frac{z}{\text{min}(z)}-0.5\). When \(\text{min}(z)=\text{max}(z)=0\), we use \(\bar{z}=z+0.5\).

class gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler(name='', offset=0.0, coefficient=1.0)

Bases: Scaler

Min-max 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:

BaseTransformer

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: RealArray

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: RealArray

The scaling offset.

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

The parameters of the transformer.

Examples using MinMaxScaler

Scalers

Scalers