Class: 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.

Constructors

new LinearSVC()

new LinearSVC(opts?): LinearSVC

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.C`?	`number`	Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. For an intuitive visualization of the effects of scaling the regularization parameter C, see Scaling the regularization parameter for SVCs.
`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`.
`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).
`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.
`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.
`opts.max_iter`?	`number`	The maximum number of iterations to be run.
`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.
`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.
`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.
`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.

Returns LinearSVC

Defined in generated/svm/LinearSVC.ts:29

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/svm/LinearSVC.ts:27
`_isInitialized`	`boolean`	`false`	generated/svm/LinearSVC.ts:26
`_py`	`PythonBridge`	`undefined`	generated/svm/LinearSVC.ts:25
`id`	`string`	`undefined`	generated/svm/LinearSVC.ts:22
`opts`	`any`	`undefined`	generated/svm/LinearSVC.ts:23

Accessors

classes_

Get Signature

get classes_(): Promise<ArrayLike>

The unique classes labels.

Returns Promise<ArrayLike>

Defined in generated/svm/LinearSVC.ts:548

coef_

Get Signature

get coef_(): Promise<ArrayLike[][]>

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.

Returns Promise<ArrayLike[][]>

Defined in generated/svm/LinearSVC.ts:502

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/svm/LinearSVC.ts:596

intercept_

Get Signature

get intercept_(): Promise<ArrayLike[]>

Constants in decision function.

Returns Promise<ArrayLike[]>

Defined in generated/svm/LinearSVC.ts:525

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/svm/LinearSVC.ts:571

n_iter_

Get Signature

get n_iter_(): Promise<number>

Maximum number of iterations run across all classes.

Returns Promise<number>

Defined in generated/svm/LinearSVC.ts:621

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/svm/LinearSVC.ts:114

Methods

decision_function()

decision_function(opts): Promise<ArrayLike>

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

Parameters

Parameter	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:184

densify()

densify(opts): Promise<any>

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.

Parameters

Parameter	Type
`opts`	`object`

Returns Promise<any>

Defined in generated/svm/LinearSVC.ts:218

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/svm/LinearSVC.ts:165

fit()

fit(opts): Promise<any>

Fit the model according to the given training data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
`opts.X`?	`ArrayLike`	Training vector, where `n_samples` is the number of samples and `n_features` is the number of features.
`opts.y`?	`ArrayLike`	Target vector relative to X.

Returns Promise<any>

Defined in generated/svm/LinearSVC.ts:244

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/svm/LinearSVC.ts:288

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/svm/LinearSVC.ts:127

predict()

predict(opts): Promise<ArrayLike>

Predict class labels for samples in X.

Parameters

Parameter	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:322

score()

score(opts): Promise<number>

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

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`[]	Test samples.
`opts.y`?	`ArrayLike`	True labels for `X`.

Returns Promise<number>

Defined in generated/svm/LinearSVC.ts:356

set_fit_request()

set_fit_request(opts): Promise<any>

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:

Parameters

Parameter	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:402

set_score_request()

set_score_request(opts): Promise<any>

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:

Parameters

Parameter	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:438

sparsify()

sparsify(opts): Promise<any>

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.

Parameters

Parameter	Type
`opts`	`object`

Returns Promise<any>

Defined in generated/svm/LinearSVC.ts:474

LinearRegression LinearSVR