LogisticRegression#

class skfda.ml.classification.LogisticRegression(max_features=5, penalty=None, C=1, solver='lbfgs', max_iter=100)[source]#

Logistic Regression classifier for functional data.

This class implements the sequential “greedy” algorithm for functional logistic regression proposed in Berrendero, Bueno-Larraz, and Cuevas[1].

Warning

For now, only binary classification for functional data with one dimensional domains is supported.

Parameters:

  • n_features_to_select – Number of points (and coefficients) to be selected by the algorithm.

  • penalty (Literal['l1', 'l2', 'elasticnet', None]) – Penalty to use in the multivariate logistic regresion optimization problem. For more info check the parameter “penalty” in sklearn.linear_model.LogisticRegression.

  • C (float) – Inverse of the regularization parameter in the multivariate logistic regresion optimization problem. For more info check the parameter “C” in sklearn.linear_model.LogisticRegression.

  • solver (Solver) – Algorithm to use in the multivariate logistic regresion optimization problem. For more info check the parameter “solver” in sklearn.linear_model.LogisticRegression.

  • max_iter (int) – Maximum number of iterations taken for the solver to converge.

  • max_features (int)

Attributes:

  • classes_ – A list containing the name of the classes

  • points_ – A list containing the selected points.

  • coef_ – A list containing the coefficient for each selected point.

  • intercept_ – Independent term.

Examples

>>> import skfda
>>> from skfda.datasets import make_gaussian_process
>>> from skfda.ml.classification import LogisticRegression
>>>
>>> n_samples = 2000
>>> n_features = 101
>>>
>>> def mean_1(t):
...     return (np.abs(t - 0.25)
...             - 2 * np.abs(t - 0.5)
...             + np.abs(t - 0.75))
>>>
>>> X_0 = make_gaussian_process(
...     n_samples=n_samples // 2,
...     n_features=n_features,
...     random_state=0,
... )
>>> X_1 = make_gaussian_process(
...     n_samples=n_samples // 2,
...     n_features=n_features,
...     mean=mean_1,
...     random_state=1,
... )
>>> X = skfda.concatenate((X_0, X_1))
>>>
>>> y = np.zeros(n_samples)
>>> y [n_samples // 2:] = 1
>>> lr = LogisticRegression(max_features=3)
>>> _ = lr.fit(X[::2], y[::2])
>>> np.allclose(sorted(lr.points_), [0.25, 0.5, 0.75], rtol=1e-2)
True
>>> lr.score(X[1::2],y[1::2])
0.768
References:

Methods

fit(X, y)

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

predict_log_proba(X)

predict_proba(X)

score(X, y[, sample_weight])

Return accuracy on provided data and labels.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the score method.
fit(X, y)[source]#
Parameters:
Return type:

LogisticRegression

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

predict(X)[source]#
Parameters:

X (FDataGrid)

Return type:

ndarray[tuple[Any, …], dtype[integer[Any]]]

predict_log_proba(X)[source]#
Parameters:

X (FDataGrid)

Return type:

ndarray[tuple[Any, …], dtype[integer[Any]]]

predict_proba(X)[source]#
Parameters:

X (FDataGrid)

Return type:

ndarray[tuple[Any, …], dtype[integer[Any]]]

score(X, y, sample_weight=None)[source]#

Return accuracy on provided data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

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

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – Mean accuracy of self.predict(X) w.r.t. y.

Return type:

float

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

set_score_request(*, sample_weight='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

  • self (LogisticRegression)

Returns:

self – The updated object.

Return type:

object