OccupationMeasureTransformer

class skfda.preprocessing.feature_construction.OccupationMeasureTransformer(intervals, *, n_points=None)

Transformer that works as an adapter for the occupation_measure function.

Parameters:

• intervals (Sequence[Tuple[float, float]]) – ndarray of tuples containing the intervals we want to consider. The shape should be (n_sequences, 2)

• n_points (Optional[int]) – Number of points to evaluate in the domain. By default will be used the points defined on the FDataGrid. On a FDataBasis this value should be specified.

Example

We will create the FDataGrid that we will use to extract the occupation measure >>> from skfda.representation import FDataGrid >>> import numpy as np >>> t = np.linspace(0, 10, 100) >>> fd_grid = FDataGrid( … data_matrix=[ … t, … 2 * t, … np.sin(t), … ], … grid_points=t, … )

Finally we call to the occupation measure function with the intervals that we want to consider. In our case (0.0, 1.0) and (2.0, 3.0). We need also to specify the number of points we want that the function takes into account to interpolate. We are going to use 501 points. >>> from skfda.preprocessing.feature_construction import ( … OccupationMeasureTransformer, … ) >>> occupation_measure = OccupationMeasureTransformer( … intervals=[(0.0, 1.0), (2.0, 3.0)], … n_points=501, … )

>>> np.around(occupation_measure.fit_transform(fd_grid), decimals=2)
array([[ 0.98,  1.  ],
       [ 0.5 ,  0.52],
       [ 6.28,  0.  ]])

Methods

fit(X[, y])
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X[, y])	Transform the provided data using the occupation_measure function.

fit(X, y=None)

Parameters:

• self (SelfType) –

• X (Input) –

• y (Target | None) –

Return type:

SelfType

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform ({"default", "pandas"}, default=None) –

Configure output of transform and fit_transform.

• ”default”: Default output format of a transformer

• ”pandas”: DataFrame output

• ”polars”: Polars output

• None: Transform configuration is unchanged

New in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

transform(X, y=None)

Transform the provided data using the occupation_measure function.

Parameters:

• X (FData) – FDataGrid or FDataBasis with the samples that are going to be transformed.

• y (object) –

Returns:

Array of shape (n_intervals, n_samples) including the transformed data.

Return type:

ndarray[Any, dtype[float64]]