SGDOneClassSVM
Solves linear One-Class SVM using Stochastic Gradient Descent.
This implementation is meant to be used with a kernel approximation technique (e.g. sklearn.kernel\_approximation.Nystroem) to obtain results similar to sklearn.svm.OneClassSVM which uses a Gaussian kernel by default.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new SGDOneClassSVM(opts?: object): SGDOneClassSVM;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.average? | number | boolean | When set to true, computes the averaged SGD weights and stores the result in the coef\_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples. Default Value false |
opts.eta0? | number | The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’. Default Value 0 |
opts.fit_intercept? | boolean | Whether the intercept should be estimated or not. Defaults to true. Default Value true |
opts.learning_rate? | "constant" | "optimal" | "invscaling" | "adaptive" | The learning rate schedule to use with fit. (If using partial\_fit, learning rate must be controlled directly). Default Value 'optimal' |
opts.max_iter? | number | The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial\_fit. Defaults to 1000. Default Value 1000 |
opts.nu? | number | The nu parameter of the One Class SVM: 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]. By default 0.5 will be taken. Default Value 0.5 |
opts.power_t? | number | The exponent for inverse scaling learning rate [default 0.5]. Default Value 0.5 |
opts.random_state? | number | The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If undefined, the random number generator is the RandomState instance used by np.random. |
opts.shuffle? | boolean | Whether or not the training data should be shuffled after each epoch. Defaults to true. Default Value true |
opts.tol? | number | The stopping criterion. If it is not undefined, the iterations will stop when (loss > previous_loss - tol). Defaults to 1e-3. Default Value 0.001 |
opts.verbose? | number | The verbosity level. Default Value 0 |
opts.warm_start? | boolean | When set to true, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. Repeatedly calling fit or partial_fit when warm_start is true can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling fit resets this counter, while partial\_fit will result in increasing the existing counter. Default Value false |
Returns
Defined in: generated/linear_model/SGDOneClassSVM.ts:25 (opens in a new tab)
Methods
decision_function()
Signed distance to the separating hyperplane.
Signed distance is positive for an inlier and negative for an outlier.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Testing data. |
Returns
Promise<ArrayLike>
Defined in: generated/linear_model/SGDOneClassSVM.ts:198 (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/linear_model/SGDOneClassSVM.ts:235 (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/linear_model/SGDOneClassSVM.ts:179 (opens in a new tab)
fit()
Fit linear One-Class SVM with Stochastic Gradient Descent.
This solves an equivalent optimization problem of the One-Class SVM primal optimization problem and returns a weight vector w and an offset rho such that the decision function is given by <w, x> - rho.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Training data. |
opts.coef_init? | any | The initial coefficients to warm-start the optimization. |
opts.offset_init? | any | The initial offset to warm-start the optimization. |
opts.sample_weight? | ArrayLike | Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/linear_model/SGDOneClassSVM.ts:263 (opens in a new tab)
fit_predict()
Perform fit on X and returns labels for X.
Returns -1 for outliers and 1 for inliers.
Signature
fit_predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input samples. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<ArrayLike>
Defined in: generated/linear_model/SGDOneClassSVM.ts:322 (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/linear_model/SGDOneClassSVM.ts:362 (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/linear_model/SGDOneClassSVM.ts:127 (opens in a new tab)
partial_fit()
Fit linear One-Class SVM with Stochastic Gradient Descent.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Subset of the training data. |
opts.sample_weight? | ArrayLike | Weights applied to individual samples. If not provided, uniform weights are assumed. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/linear_model/SGDOneClassSVM.ts:397 (opens in a new tab)
predict()
Return labels (1 inlier, -1 outlier) of the samples.
Signature
predict(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Testing data. |
Returns
Promise<any>
Defined in: generated/linear_model/SGDOneClassSVM.ts:442 (opens in a new tab)
score_samples()
Raw scoring function of the samples.
Signature
score_samples(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Testing data. |
Returns
Promise<ArrayLike>
Defined in: generated/linear_model/SGDOneClassSVM.ts:475 (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.coef_init? | string | boolean | Metadata routing for coef\_init parameter in fit. |
opts.offset_init? | string | boolean | Metadata routing for offset\_init parameter in fit. |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/linear_model/SGDOneClassSVM.ts:512 (opens in a new tab)
set_partial_fit_request()
Request metadata passed to the partial\_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_partial_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in partial\_fit. |
Returns
Promise<any>
Defined in: generated/linear_model/SGDOneClassSVM.ts:563 (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/linear_model/SGDOneClassSVM.ts:603 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/SGDOneClassSVM.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/SGDOneClassSVM.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/SGDOneClassSVM.ts:21 (opens in a new tab)
id
string
Defined in: generated/linear_model/SGDOneClassSVM.ts:18 (opens in a new tab)
opts
any
Defined in: generated/linear_model/SGDOneClassSVM.ts:19 (opens in a new tab)
Accessors
coef_
Weights assigned to the features.
Signature
coef_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/linear_model/SGDOneClassSVM.ts:629 (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/linear_model/SGDOneClassSVM.ts:772 (opens in a new tab)
loss_function_
Signature
loss_function_(): Promise<any>;Returns
Promise<any>
Defined in: generated/linear_model/SGDOneClassSVM.ts:722 (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/linear_model/SGDOneClassSVM.ts:747 (opens in a new tab)
n_iter_
The actual number of iterations to reach the stopping criterion.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/SGDOneClassSVM.ts:677 (opens in a new tab)
offset_
Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset.
Signature
offset_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/SGDOneClassSVM.ts:652 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/SGDOneClassSVM.ts:114 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/SGDOneClassSVM.ts:118 (opens in a new tab)
t_
Number of weight updates performed during training. Same as (n\_iter\_ \* n\_samples + 1).
Signature
t_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/SGDOneClassSVM.ts:702 (opens in a new tab)