Class: OneClassSVM
Unsupervised Outlier Detection.
Estimate the support of a high-dimensional distribution.
The implementation is based on libsvm.
Read more in the User Guide.
Constructors
new OneClassSVM()
new OneClassSVM(
opts?):OneClassSVM
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.cache_size? | number | Specify the size of the kernel cache (in MB). |
opts.coef0? | number | Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’. |
opts.degree? | number | Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels. |
opts.gamma? | number | "auto" | "scale" | Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. |
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. |
opts.max_iter? | number | Hard limit on iterations within solver, or -1 for no limit. |
opts.nu? | number | 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. |
opts.shrinking? | boolean | Whether to use the shrinking heuristic. See the User Guide. |
opts.tol? | number | Tolerance for stopping criterion. |
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. |
Returns OneClassSVM
Defined in generated/svm/OneClassSVM.ts:27
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/svm/OneClassSVM.ts:25 |
_isInitialized | boolean | false | generated/svm/OneClassSVM.ts:24 |
_py | PythonBridge | undefined | generated/svm/OneClassSVM.ts:23 |
id | string | undefined | generated/svm/OneClassSVM.ts:20 |
opts | any | undefined | generated/svm/OneClassSVM.ts:21 |
Accessors
dual_coef_
Get Signature
get dual_coef_():
Promise<ArrayLike[]>
Coefficients of the support vectors in the decision function.
Returns Promise<ArrayLike[]>
Defined in generated/svm/OneClassSVM.ts:429
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/OneClassSVM.ts:529
fit_status_
Get Signature
get fit_status_():
Promise<number>
0 if correctly fitted, 1 otherwise (will raise warning)
Returns Promise<number>
Defined in generated/svm/OneClassSVM.ts:454
intercept_
Get Signature
get intercept_():
Promise<ArrayLike>
Constant in the decision function.
Returns Promise<ArrayLike>
Defined in generated/svm/OneClassSVM.ts:479
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/svm/OneClassSVM.ts:504
n_iter_
Get Signature
get n_iter_():
Promise<number>
Number of iterations run by the optimization routine to fit the model.
Returns Promise<number>
Defined in generated/svm/OneClassSVM.ts:554
offset_
Get Signature
get offset_():
Promise<number>
Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset_. The offset is the opposite of intercept_ and is provided for consistency with other outlier detection algorithms.
Returns Promise<number>
Defined in generated/svm/OneClassSVM.ts:577
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/OneClassSVM.ts:102
shape_fit_
Get Signature
get shape_fit_():
Promise<any[]>
Array dimensions of training vector X.
Returns Promise<any[]>
Defined in generated/svm/OneClassSVM.ts:600
support_
Get Signature
get support_():
Promise<ArrayLike>
Indices of support vectors.
Returns Promise<ArrayLike>
Defined in generated/svm/OneClassSVM.ts:625
support_vectors_
Get Signature
get support_vectors_():
Promise<ArrayLike[]>
Support vectors.
Returns Promise<ArrayLike[]>
Defined in generated/svm/OneClassSVM.ts:648
Methods
decision_function()
decision_function(
opts):Promise<ArrayLike>
Signed distance to the separating hyperplane.
Signed distance is positive for an inlier and negative for an outlier.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns Promise<ArrayLike>
Defined in generated/svm/OneClassSVM.ts:173
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/OneClassSVM.ts:154
fit()
fit(
opts):Promise<any>
Detect the soft boundary of the set of samples X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points. |
opts.X? | ArrayLike | Set of samples, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns Promise<any>
Defined in generated/svm/OneClassSVM.ts:205
fit_predict()
fit_predict(
opts):Promise<ArrayLike>
Perform fit on X and returns labels for X.
Returns -1 for outliers and 1 for inliers.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.kwargs? | any | Arguments to be passed to fit. |
opts.X? | ArrayLike | The input samples. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns Promise<ArrayLike>
Defined in generated/svm/OneClassSVM.ts:249
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/OneClassSVM.ts:293
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/OneClassSVM.ts:115
predict()
predict(
opts):Promise<ArrayLike>
Perform classification on samples in X.
For a one-class model, +1 or -1 is returned.
Parameters
| Parameter | 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/OneClassSVM.ts:329
score_samples()
score_samples(
opts):Promise<ArrayLike>
Raw scoring function of the samples.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns Promise<ArrayLike>
Defined in generated/svm/OneClassSVM.ts:361
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/OneClassSVM.ts:397