NuSVC
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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new NuSVC(opts?: object): NuSVC;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.break_ties? | boolean | 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. Default Value false |
opts.cache_size? | number | Specify the size of the kernel cache (in MB). Default Value 200 |
opts.class_weight? | any | 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)). |
opts.coef0? | number | Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’. Default Value 0 |
opts.decision_function_shape? | "ovr" | "ovo" | 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). However, one-vs-one (‘ovo’) is always used as multi-class strategy. The parameter is ignored for binary classification. Default Value 'ovr' |
opts.degree? | number | Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels. Default Value 3 |
opts.gamma? | number | "auto" | "scale" | Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. Default Value 'scale' |
opts.kernel? | "sigmoid" | "precomputed" | "linear" | "poly" | "rbf" | Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix. For an intuitive visualization of different kernel types see Plot classification boundaries with different SVM Kernels. Default Value 'rbf' |
opts.max_iter? | number | Hard limit on iterations within solver, or -1 for no limit. Default Value -1 |
opts.nu? | number | An upper bound on the fraction of margin errors (see User Guide) and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. Default Value 0.5 |
opts.probability? | boolean | 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. Default Value false |
opts.random_state? | number | Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is false. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.shrinking? | boolean | Whether to use the shrinking heuristic. See the User Guide. Default Value true |
opts.tol? | number | Tolerance for stopping criterion. Default Value 0.001 |
opts.verbose? | boolean | 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. Default Value false |
Returns
Defined in: generated/svm/NuSVC.ts:27 (opens in a new tab)
Methods
decision_function()
Evaluate the decision function for the samples in X.
Signature
decision_function(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns
Promise<ArrayLike[]>
Defined in: generated/svm/NuSVC.ts:218 (opens in a new tab)
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/svm/NuSVC.ts:201 (opens in a new tab)
fit()
Fit the SVM model according to the given training data.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | 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). |
opts.sample_weight? | ArrayLike | Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points. |
opts.y? | ArrayLike | Target values (class labels in classification, real numbers in regression). |
Returns
Promise<any>
Defined in: generated/svm/NuSVC.ts:251 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/svm/NuSVC.ts:300 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/svm/NuSVC.ts:146 (opens in a new tab)
predict()
Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train). |
Returns
Promise<ArrayLike>
Defined in: generated/svm/NuSVC.ts:335 (opens in a new tab)
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.
Signature
predict_log_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train). |
Returns
Promise<ArrayLike[]>
Defined in: generated/svm/NuSVC.ts:370 (opens in a new tab)
predict_proba()
Compute probabilities of possible outcomes for samples in X.
The model needs to have probability information computed at training time: fit with attribute probability set to true.
Signature
predict_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train). |
Returns
Promise<ArrayLike[]>
Defined in: generated/svm/NuSVC.ts:405 (opens in a new tab)
score()
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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/svm/NuSVC.ts:440 (opens in a new tab)
set_fit_request()
Request metadata passed to the fit 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:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/svm/NuSVC.ts:491 (opens in a new tab)
set_score_request()
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:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in score. |
Returns
Promise<any>
Defined in: generated/svm/NuSVC.ts:528 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/svm/NuSVC.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/svm/NuSVC.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/svm/NuSVC.ts:23 (opens in a new tab)
id
string
Defined in: generated/svm/NuSVC.ts:20 (opens in a new tab)
opts
any
Defined in: generated/svm/NuSVC.ts:21 (opens in a new tab)
Accessors
class_weight_
Multipliers of parameter C of each class. Computed based on the class\_weight parameter.
Signature
class_weight_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/svm/NuSVC.ts:561 (opens in a new tab)
classes_
The unique classes labels.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/svm/NuSVC.ts:584 (opens in a new tab)
dual_coef_
Dual coefficients of the support vector in the decision function (see Mathematical formulation), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the multi-class section of the User Guide for details.
Signature
dual_coef_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/svm/NuSVC.ts:606 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/svm/NuSVC.ts:698 (opens in a new tab)
fit_status_
0 if correctly fitted, 1 if the algorithm did not converge.
Signature
fit_status_(): Promise<number>;Returns
Promise<number>
Defined in: generated/svm/NuSVC.ts:629 (opens in a new tab)
intercept_
Constants in decision function.
Signature
intercept_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/svm/NuSVC.ts:652 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/svm/NuSVC.ts:675 (opens in a new tab)
n_iter_
Number of iterations run by the optimization routine to fit the model. The shape of this attribute depends on the number of models optimized which in turn depends on the number of classes.
Signature
n_iter_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/svm/NuSVC.ts:723 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/svm/NuSVC.ts:133 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/svm/NuSVC.ts:137 (opens in a new tab)
shape_fit_
Array dimensions of training vector X.
Signature
shape_fit_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/svm/NuSVC.ts:792 (opens in a new tab)
support_
Indices of support vectors.
Signature
support_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/svm/NuSVC.ts:745 (opens in a new tab)
support_vectors_
Support vectors.
Signature
support_vectors_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/svm/NuSVC.ts:767 (opens in a new tab)