LinearSVC
Linear Support Vector Classification.
Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.
The main differences between LinearSVC and SVC lie in the loss function used by default, and in the handling of intercept regularization between those two implementations.
This class supports both dense and sparse input and the multiclass support is handled according to a one-vs-the-rest scheme.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LinearSVC(opts?: object): LinearSVC;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.C? | number | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. Default Value 1 |
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 in the input data as n\_samples / (n\_classes \* np.bincount(y)). |
opts.dual? | boolean | "auto" | Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=false when n_samples > n_features. dual="auto" will choose the value of the parameter automatically, based on the values of n\_samples, n\_features, loss, multi\_class and penalty. If n\_samples < n\_features and optimizer supports chosen loss, multi\_class and penalty, then dual will be set to true, otherwise it will be set to false. Default Value true |
opts.fit_intercept? | boolean | Whether or not to fit an intercept. If set to true, the feature vector is extended to include an intercept term: \[x\_1, ..., x\_n, 1\], where 1 corresponds to the intercept. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered). Default Value true |
opts.intercept_scaling? | number | When fit\_intercept is true, the instance vector x becomes \[x\_1, ..., x\_n, intercept\_scaling\], i.e. a “synthetic” feature with a constant value equal to intercept\_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight. Note that liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the intercept\_scaling parameter can be set to a value greater than 1; the higher the value of intercept\_scaling, the lower the impact of regularization on it. Then, the weights become \[w\_x\_1, ..., w\_x\_n, w\_intercept\*intercept\_scaling\], where w\_x\_1, ..., w\_x\_n represent the feature weights and the intercept weight is scaled by intercept\_scaling. This scaling allows the intercept term to have a different regularization behavior compared to the other features. Default Value 1 |
opts.loss? | "hinge" | "squared_hinge" | Specifies the loss function. ‘hinge’ is the standard SVM loss (used e.g. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss. The combination of penalty='l1' and loss='hinge' is not supported. Default Value 'squared_hinge' |
opts.max_iter? | number | The maximum number of iterations to be run. Default Value 1000 |
opts.multi_class? | "ovr" | "crammer_singer" | Determines the multi-class strategy if y contains more than two classes. "ovr" trains n_classes one-vs-rest classifiers, while "crammer\_singer" optimizes a joint objective over all classes. While crammer\_singer is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If "crammer\_singer" is chosen, the options loss, penalty and dual will be ignored. Default Value 'ovr' |
opts.penalty? | "l1" | "l2" | Specifies the norm used in the penalization. The ‘l2’ penalty is the standard used in SVC. The ‘l1’ leads to coef\_ vectors that are sparse. Default Value 'l2' |
opts.random_state? | number | Controls the pseudo random number generation for shuffling the data for the dual coordinate descent (if dual=True). When dual=False the underlying implementation of LinearSVC is not random and random\_state has no effect on the results. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.tol? | number | Tolerance for stopping criteria. Default Value 0.0001 |
opts.verbose? | number | Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context. Default Value 0 |
Returns
Defined in: generated/svm/LinearSVC.ts:29 (opens in a new tab)
Methods
decision_function()
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data matrix for which we want to get the confidence scores. |
Returns
Promise<ArrayLike>
Defined in: generated/svm/LinearSVC.ts:199 (opens in a new tab)
densify()
Convert coefficient matrix to dense array format.
Converts the coef\_ member (back) to a numpy.ndarray. This is the default format of coef\_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.
Signature
densify(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/svm/LinearSVC.ts:234 (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/LinearSVC.ts:180 (opens in a new tab)
fit()
Fit the model according to the given training data.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.sample_weight? | ArrayLike | Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. |
opts.y? | ArrayLike | Target vector relative to X. |
Returns
Promise<any>
Defined in: generated/svm/LinearSVC.ts:260 (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/LinearSVC.ts:309 (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/LinearSVC.ts:127 (opens in a new tab)
predict()
Predict class labels for samples in X.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data matrix for which we want to get the predictions. |
Returns
Promise<ArrayLike>
Defined in: generated/svm/LinearSVC.ts:344 (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/LinearSVC.ts:379 (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/LinearSVC.ts:430 (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/LinearSVC.ts:467 (opens in a new tab)
sparsify()
Convert coefficient matrix to sparse format.
Converts the coef\_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept\_ member is not converted.
Signature
sparsify(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/svm/LinearSVC.ts:504 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/svm/LinearSVC.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/svm/LinearSVC.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/svm/LinearSVC.ts:25 (opens in a new tab)
id
string
Defined in: generated/svm/LinearSVC.ts:22 (opens in a new tab)
opts
any
Defined in: generated/svm/LinearSVC.ts:23 (opens in a new tab)
Accessors
classes_
The unique classes labels.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/svm/LinearSVC.ts:578 (opens in a new tab)
coef_
Weights assigned to the features (coefficients in the primal problem).
coef\_ is a readonly property derived from raw\_coef\_ that follows the internal memory layout of liblinear.
Signature
coef_(): Promise<ArrayLike[][]>;Returns
Promise<ArrayLike[][]>
Defined in: generated/svm/LinearSVC.ts:532 (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/LinearSVC.ts:626 (opens in a new tab)
intercept_
Constants in decision function.
Signature
intercept_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/svm/LinearSVC.ts:555 (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/LinearSVC.ts:601 (opens in a new tab)
n_iter_
Maximum number of iterations run across all classes.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/svm/LinearSVC.ts:651 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/svm/LinearSVC.ts:114 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/svm/LinearSVC.ts:118 (opens in a new tab)