sklearn.svm.NuSVC¶

class sklearn.svm.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)[source]¶

Nu-Support Vector Classification.

Similar to SVC but uses a parameter to control the number of support vectors.

The implementation is based on libsvm.

Read more in the User Guide.

Parameters

nufloat, optional (default=0.5)

An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1].

kernelstring, optional (default=’rbf’)

Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.

degreeint, optional (default=3)

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma{‘scale’, ‘auto’} or float, optional (default=’scale’)

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

• if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

• if ‘auto’, uses 1 / n_features.

Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.

coef0float, optional (default=0.0)

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

shrinkingboolean, optional (default=True)

Whether to use the shrinking heuristic.

probabilityboolean, optional (default=False)

Whether to enable probability estimates. This must be enabled prior to calling fit, will slow down that method as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict. Read more in the User Guide.

tolfloat, optional (default=1e-3)

Tolerance for stopping criterion.

cache_sizefloat, optional

Specify the size of the kernel cache (in MB).

class_weight{dict, ‘balanced’}, optional

Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies as n_samples / (n_classes * np.bincount(y))

verbosebool, default: False

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

max_iterint, optional (default=-1)

Hard limit on iterations within solver, or -1 for no limit.

decision_function_shape‘ovo’, ‘ovr’, default=’ovr’

Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2).

Changed in version 0.19: decision_function_shape is ‘ovr’ by default.

New in version 0.17: decision_function_shape=’ovr’ is recommended.

Changed in version 0.17: Deprecated decision_function_shape=’ovo’ and None.

break_tiesbool, optional (default=False)

If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.

New in version 0.22.

random_stateint, RandomState instance or None, optional (default=None)

The seed of the pseudo random number generator used when shuffling the data for probability estimates. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes

support_array-like, shape = [n_SV]

Indices of support vectors.

support_vectors_array-like, shape = [n_SV, n_features]

Support vectors.

n_support_array-like, dtype=int32, shape = [n_class]

Number of support vectors for each class.

dual_coef_array, shape = [n_class-1, n_SV]

Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the section about multi-class classification in the SVM section of the User Guide for details.

coef_array, shape = [n_class * (n_class-1) / 2, n_features]

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.

coef_ is readonly property derived from dual_coef_ and support_vectors_.

intercept_array, shape = [n_class * (n_class-1) / 2]

Constants in decision function.

classes_array of shape = (n_classes,)

The unique classes labels.

fit_status_int

0 if correctly fitted, 1 if the algorithm did not converge.

probA_ndarray, shape of (n_class * (n_class-1) / 2,)

probB_ndarray of shape (n_class * (n_class-1) / 2,)

If probability=True, it corresponds to the parameters learned in Platt scaling to produce probability estimates from decision values. If probability=False, it’s an empty array. Platt scaling uses the logistic function 1 / (1 + exp(decision_value * probA_ + probB_)) where probA_ and probB_ are learned from the dataset [R9709ce4a60d3-2]. For more information on the multiclass case and training procedure see section 8 of [R9709ce4a60d3-1].

class_weight_ndarray of shape (n_class,)

Multipliers of parameter C of each class. Computed based on the class_weight parameter.

shape_fit_tuple of int of shape (n_dimensions_of_X,)

Array dimensions of training vector X.

See also

SVC

Support Vector Machine for classification using libsvm.

LinearSVC

Scalable linear Support Vector Machine for classification using liblinear.

References

R9709ce4a60d3-1

LIBSVM: A Library for Support Vector Machines

R9709ce4a60d3-2

Platt, John (1999). “Probabilistic outputs for support vector machines and comparison to regularizedlikelihood methods.”

Examples

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y)
NuSVC()
>>> print(clf.predict([[-0.8, -1]]))
[1]

Methods

decision_function(self, X)	Evaluates the decision function for the samples in X.
fit(self, X, y[, sample_weight])	Fit the SVM model according to the given training data.
get_params(self[, deep])	Get parameters for this estimator.
predict(self, X)	Perform classification on samples in X.
score(self, X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(self, \*\*params)	Set the parameters of this estimator.

__init__(self, nu=0.5, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)[source]¶

Initialize self. See help(type(self)) for accurate signature.

decision_function(self, X)[source]¶

Evaluates the decision function for the samples in X.

Parameters

Xarray-like, shape (n_samples, n_features)

Returns

Xarray-like, shape (n_samples, n_classes * (n_classes-1) / 2)

Returns the decision function of the sample for each class in the model. If decision_function_shape=’ovr’, the shape is (n_samples, n_classes).

Notes

If decision_function_shape=’ovo’, the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (coef_). See also this question for further details. If decision_function_shape=’ovr’, the decision function is a monotonic transformation of ovo decision function.

fit(self, X, y, sample_weight=None)[source]¶

Fit the SVM model according to the given training data.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).

yarray-like, shape (n_samples,)

Target values (class labels in classification, real numbers in regression)

sample_weightarray-like, shape (n_samples,)

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns

selfobject

Notes

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

predict(self, X)[source]¶

Perform classification on samples in X.

For an one-class model, +1 or -1 is returned.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features)

For kernel=”precomputed”, the expected shape of X is [n_samples_test, n_samples_train]

Returns

y_predarray, shape (n_samples,)

Class labels for samples in X.

property predict_log_proba¶

Compute log probabilities of possible outcomes for samples in X.

The model need to have probability information computed at training time: fit with attribute probability set to True.

Parameters

Xarray-like, shape (n_samples, n_features)

For kernel=”precomputed”, the expected shape of X is [n_samples_test, n_samples_train]

Returns

Tarray-like, shape (n_samples, n_classes)

Returns the log-probabilities of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

property predict_proba¶

Compute probabilities of possible outcomes for samples in X.

The model need to have probability information computed at training time: fit with attribute probability set to True.

Parameters

Xarray-like, shape (n_samples, n_features)

For kernel=”precomputed”, the expected shape of X is [n_samples_test, n_samples_train]

Returns

Tarray-like, shape (n_samples, n_classes)

Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

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

Returns 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

Xarray-like, shape = (n_samples, n_features)

Test samples.

yarray-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like, shape = [n_samples], optional

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(self, **params)[source]¶

Set the parameters of this estimator.

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

Returns

self