Ctrl+K

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 the mean accuracy on the given test data and labels.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata 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[Any, dtype[int64]]

predict_log_proba(X)[source]#
Parameters:

X (FDataGrid) –

Return type:

ndarray[Any, dtype[int64]]

predict_proba(X)[source]#
Parameters:

X (FDataGrid) –

Return type:

ndarray[Any, dtype[int64]]

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

Return the mean accuracy on the given test 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='$UNCHANGED$')#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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

On this page
Edit on GitHub
Show Source