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 of this object.
get_params
([deep])Get parameters for this estimator.
predict
(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.- 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.
- 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:
- 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
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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 inscore
.self (LogisticRegression) –
- Returns:
self – The updated object.
- Return type: