lightning.classification.KernelSVC¶

class lightning.classification.KernelSVC(alpha=1.0, solver='cg', max_iter=50, tol=0.001, kernel='linear', gamma=0.1, coef0=1, degree=4, random_state=None, verbose=0, n_jobs=1)[source]¶

Estimator for learning kernel SVMs by Newton’s method.

Parameters

alpha (float) – Weight of the penalty term.
solver (str, 'cg', 'dense') –
max_iter (int) – Maximum number of iterations to perform.
tol (float) – Tolerance of the stopping criterion.
kernel ("linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed") – Kernel to use. Default: “linear”
degree (int, default=3) – Degree for poly, rbf and sigmoid kernels. Ignored by other kernels.
gamma (float, optional) – Kernel coefficient for rbf and poly kernels. Default: 1/n_features. Ignored by other kernels.
coef0 (float, optional) – Independent term in poly and sigmoid kernels. Ignored by other kernels.
random_state (RandomState or int) – The seed of the pseudo random number generator to use.
verbose (int) – Verbosity level.
n_jobs (int) – Number of jobs to use to compute the kernel matrix.

Examples

>>> from sklearn.datasets import make_classification
>>> from lightning.classification import KernelSVC
>>> X, y = make_classification()
>>> clf = KernelSVC().fit(X, y)
>>> accuracy = clf.score(X, y)

decision_function(X)[source]¶

Return the decision function for test vectors X.

Parameters: X (array-like, shape = [n_samples, n_features]) –
Returns: P – Decision function for X
Return type: array, shape = [n_classes, n_samples]

fit(X, y)[source]¶

Fit model according to X and y.

Parameters

X (array-like, shape = [n_samples, n_features]) – Training vectors, where n_samples is the number of samples and n_features is the number of features.
y (array-like, shape = [n_samples]) – Target values.

Returns

self – Returns self.

Return type

classifier

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

n_nonzero(percentage=False)¶

predict(X)¶

property predict_proba¶

score(X, y, sample_weight=None)¶

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) wrt. 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