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NuSVR.ts
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/* eslint-disable */
/* NOTE: This file is auto-generated. Do not edit it directly. */
import crypto from 'node:crypto'
import { PythonBridge, NDArray, ArrayLike, SparseMatrix } from '@/sklearn/types'
/**
Nu Support Vector Regression.
Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-SVR.
The implementation is based on libsvm.
Read more in the [User Guide](../svm.html#svm-regression).
[Python Reference](https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html)
*/
export class NuSVR {
id: string
opts: any
_py: PythonBridge
_isInitialized: boolean = false
_isDisposed: boolean = false
constructor(opts?: {
/**
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.
@defaultValue `0.5`
*/
nu?: number
/**
Penalty parameter C of the error term.
@defaultValue `1`
*/
C?: number
/**
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.
@defaultValue `'rbf'`
*/
kernel?: 'linear' | 'poly' | 'rbf' | 'sigmoid' | 'precomputed'
/**
Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels.
@defaultValue `3`
*/
degree?: number
/**
Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.
@defaultValue `'scale'`
*/
gamma?: 'scale' | 'auto' | number
/**
Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
@defaultValue `0`
*/
coef0?: number
/**
Whether to use the shrinking heuristic. See the [User Guide](../svm.html#shrinking-svm).
@defaultValue `true`
*/
shrinking?: boolean
/**
Tolerance for stopping criterion.
@defaultValue `0.001`
*/
tol?: number
/**
Specify the size of the kernel cache (in MB).
@defaultValue `200`
*/
cache_size?: number
/**
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.
@defaultValue `false`
*/
verbose?: boolean
/**
Hard limit on iterations within solver, or -1 for no limit.
@defaultValue `-1`
*/
max_iter?: number
}) {
this.id = `NuSVR${crypto.randomUUID().split('-')[0]}`
this.opts = opts || {}
}
get py(): PythonBridge {
return this._py
}
set py(pythonBridge: PythonBridge) {
this._py = pythonBridge
}
/**
Initializes the underlying Python resources.
This instance is not usable until the `Promise` returned by `init()` resolves.
*/
async init(py: PythonBridge): Promise<void> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (this._isInitialized) {
return
}
if (!py) {
throw new Error('NuSVR.init requires a PythonBridge instance')
}
this._py = py
await this._py.ex`
import numpy as np
from sklearn.svm import NuSVR
try: bridgeNuSVR
except NameError: bridgeNuSVR = {}
`
// set up constructor params
await this._py.ex`ctor_NuSVR = {'nu': ${
this.opts['nu'] ?? undefined
}, 'C': ${this.opts['C'] ?? undefined}, 'kernel': ${
this.opts['kernel'] ?? undefined
}, 'degree': ${this.opts['degree'] ?? undefined}, 'gamma': ${
this.opts['gamma'] ?? undefined
}, 'coef0': ${this.opts['coef0'] ?? undefined}, 'shrinking': ${
this.opts['shrinking'] ?? undefined
}, 'tol': ${this.opts['tol'] ?? undefined}, 'cache_size': ${
this.opts['cache_size'] ?? undefined
}, 'verbose': ${this.opts['verbose'] ?? undefined}, 'max_iter': ${
this.opts['max_iter'] ?? undefined
}}
ctor_NuSVR = {k: v for k, v in ctor_NuSVR.items() if v is not None}`
await this._py.ex`bridgeNuSVR[${this.id}] = NuSVR(**ctor_NuSVR)`
this._isInitialized = true
}
/**
Disposes of the underlying Python resources.
Once `dispose()` is called, the instance is no longer usable.
*/
async dispose() {
if (this._isDisposed) {
return
}
if (!this._isInitialized) {
return
}
await this._py.ex`del bridgeNuSVR[${this.id}]`
this._isDisposed = true
}
/**
Fit the SVM model according to the given training data.
*/
async fit(opts: {
/**
Training vectors, where `n\_samples` is the number of samples and `n\_features` is the number of features. For kernel=”precomputed”, the expected shape of X is (n\_samples, n\_samples).
*/
X?: ArrayLike | SparseMatrix[]
/**
Target values (class labels in classification, real numbers in regression).
*/
y?: ArrayLike
/**
Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
*/
sample_weight?: ArrayLike
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before fit()')
}
// set up method params
await this._py.ex`pms_NuSVR_fit = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': np.array(${
opts['y'] ?? undefined
}) if ${opts['y'] !== undefined} else None, 'sample_weight': np.array(${
opts['sample_weight'] ?? undefined
}) if ${opts['sample_weight'] !== undefined} else None}
pms_NuSVR_fit = {k: v for k, v in pms_NuSVR_fit.items() if v is not None}`
// invoke method
await this._py
.ex`res_NuSVR_fit = bridgeNuSVR[${this.id}].fit(**pms_NuSVR_fit)`
// convert the result from python to node.js
return this
._py`res_NuSVR_fit.tolist() if hasattr(res_NuSVR_fit, 'tolist') else res_NuSVR_fit`
}
/**
Get metadata routing of this object.
Please check [User Guide](../../metadata_routing.html#metadata-routing) on how the routing mechanism works.
*/
async get_metadata_routing(opts: {
/**
A [`MetadataRequest`](sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest "sklearn.utils.metadata_routing.MetadataRequest") encapsulating routing information.
*/
routing?: any
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before get_metadata_routing()')
}
// set up method params
await this._py.ex`pms_NuSVR_get_metadata_routing = {'routing': ${
opts['routing'] ?? undefined
}}
pms_NuSVR_get_metadata_routing = {k: v for k, v in pms_NuSVR_get_metadata_routing.items() if v is not None}`
// invoke method
await this._py
.ex`res_NuSVR_get_metadata_routing = bridgeNuSVR[${this.id}].get_metadata_routing(**pms_NuSVR_get_metadata_routing)`
// convert the result from python to node.js
return this
._py`res_NuSVR_get_metadata_routing.tolist() if hasattr(res_NuSVR_get_metadata_routing, 'tolist') else res_NuSVR_get_metadata_routing`
}
/**
Perform regression on samples in X.
For an one-class model, +1 (inlier) or -1 (outlier) is returned.
*/
async predict(opts: {
/**
For kernel=”precomputed”, the expected shape of X is (n\_samples\_test, n\_samples\_train).
*/
X?: ArrayLike | SparseMatrix[]
}): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before predict()')
}
// set up method params
await this._py.ex`pms_NuSVR_predict = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_NuSVR_predict = {k: v for k, v in pms_NuSVR_predict.items() if v is not None}`
// invoke method
await this._py
.ex`res_NuSVR_predict = bridgeNuSVR[${this.id}].predict(**pms_NuSVR_predict)`
// convert the result from python to node.js
return this
._py`res_NuSVR_predict.tolist() if hasattr(res_NuSVR_predict, 'tolist') else res_NuSVR_predict`
}
/**
Return the coefficient of determination of the prediction.
The coefficient of determination \\(R^2\\) is defined as \\((1 - \\frac{u}{v})\\), where \\(u\\) is the residual sum of squares `((y\_true \- y\_pred)\*\* 2).sum()` and \\(v\\) is the total sum of squares `((y\_true \- y\_true.mean()) \*\* 2).sum()`. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a \\(R^2\\) score of 0.0.
*/
async score(opts: {
/**
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n\_samples, n\_samples\_fitted)`, where `n\_samples\_fitted` is the number of samples used in the fitting for the estimator.
*/
X?: ArrayLike[]
/**
True values for `X`.
*/
y?: ArrayLike
/**
Sample weights.
*/
sample_weight?: ArrayLike
}): Promise<number> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before score()')
}
// set up method params
await this._py.ex`pms_NuSVR_score = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': np.array(${
opts['y'] ?? undefined
}) if ${opts['y'] !== undefined} else None, 'sample_weight': np.array(${
opts['sample_weight'] ?? undefined
}) if ${opts['sample_weight'] !== undefined} else None}
pms_NuSVR_score = {k: v for k, v in pms_NuSVR_score.items() if v is not None}`
// invoke method
await this._py
.ex`res_NuSVR_score = bridgeNuSVR[${this.id}].score(**pms_NuSVR_score)`
// convert the result from python to node.js
return this
._py`res_NuSVR_score.tolist() if hasattr(res_NuSVR_score, 'tolist') else res_NuSVR_score`
}
/**
Request metadata passed to the `fit` method.
Note that this method is only relevant if `enable\_metadata\_routing=True` (see [`sklearn.set\_config`](sklearn.set_config.html#sklearn.set_config "sklearn.set_config")). Please see [User Guide](../../metadata_routing.html#metadata-routing) on how the routing mechanism works.
The options for each parameter are:
*/
async set_fit_request(opts: {
/**
Metadata routing for `sample\_weight` parameter in `fit`.
*/
sample_weight?: string | boolean
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before set_fit_request()')
}
// set up method params
await this._py.ex`pms_NuSVR_set_fit_request = {'sample_weight': ${
opts['sample_weight'] ?? undefined
}}
pms_NuSVR_set_fit_request = {k: v for k, v in pms_NuSVR_set_fit_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_NuSVR_set_fit_request = bridgeNuSVR[${this.id}].set_fit_request(**pms_NuSVR_set_fit_request)`
// convert the result from python to node.js
return this
._py`res_NuSVR_set_fit_request.tolist() if hasattr(res_NuSVR_set_fit_request, 'tolist') else res_NuSVR_set_fit_request`
}
/**
Request metadata passed to the `score` method.
Note that this method is only relevant if `enable\_metadata\_routing=True` (see [`sklearn.set\_config`](sklearn.set_config.html#sklearn.set_config "sklearn.set_config")). Please see [User Guide](../../metadata_routing.html#metadata-routing) on how the routing mechanism works.
The options for each parameter are:
*/
async set_score_request(opts: {
/**
Metadata routing for `sample\_weight` parameter in `score`.
*/
sample_weight?: string | boolean
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before set_score_request()')
}
// set up method params
await this._py.ex`pms_NuSVR_set_score_request = {'sample_weight': ${
opts['sample_weight'] ?? undefined
}}
pms_NuSVR_set_score_request = {k: v for k, v in pms_NuSVR_set_score_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_NuSVR_set_score_request = bridgeNuSVR[${this.id}].set_score_request(**pms_NuSVR_set_score_request)`
// convert the result from python to node.js
return this
._py`res_NuSVR_set_score_request.tolist() if hasattr(res_NuSVR_set_score_request, 'tolist') else res_NuSVR_set_score_request`
}
/**
Multipliers of parameter C for each class. Computed based on the `class\_weight` parameter.
*/
get class_weight_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing class_weight_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_class_weight_ = bridgeNuSVR[${this.id}].class_weight_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_class_weight_.tolist() if hasattr(attr_NuSVR_class_weight_, 'tolist') else attr_NuSVR_class_weight_`
})()
}
/**
Coefficients of the support vector in the decision function.
*/
get dual_coef_(): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing dual_coef_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_dual_coef_ = bridgeNuSVR[${this.id}].dual_coef_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_dual_coef_.tolist() if hasattr(attr_NuSVR_dual_coef_, 'tolist') else attr_NuSVR_dual_coef_`
})()
}
/**
0 if correctly fitted, 1 otherwise (will raise warning)
*/
get fit_status_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing fit_status_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_fit_status_ = bridgeNuSVR[${this.id}].fit_status_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_fit_status_.tolist() if hasattr(attr_NuSVR_fit_status_, 'tolist') else attr_NuSVR_fit_status_`
})()
}
/**
Constants in decision function.
*/
get intercept_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing intercept_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_intercept_ = bridgeNuSVR[${this.id}].intercept_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_intercept_.tolist() if hasattr(attr_NuSVR_intercept_, 'tolist') else attr_NuSVR_intercept_`
})()
}
/**
Number of features seen during [fit](../../glossary.html#term-fit).
*/
get n_features_in_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing n_features_in_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_n_features_in_ = bridgeNuSVR[${this.id}].n_features_in_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_n_features_in_.tolist() if hasattr(attr_NuSVR_n_features_in_, 'tolist') else attr_NuSVR_n_features_in_`
})()
}
/**
Names of features seen during [fit](../../glossary.html#term-fit). Defined only when `X` has feature names that are all strings.
*/
get feature_names_in_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'NuSVR must call init() before accessing feature_names_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_feature_names_in_ = bridgeNuSVR[${this.id}].feature_names_in_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_feature_names_in_.tolist() if hasattr(attr_NuSVR_feature_names_in_, 'tolist') else attr_NuSVR_feature_names_in_`
})()
}
/**
Number of iterations run by the optimization routine to fit the model.
*/
get n_iter_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing n_iter_')
}
return (async () => {
// invoke accessor
await this._py.ex`attr_NuSVR_n_iter_ = bridgeNuSVR[${this.id}].n_iter_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_n_iter_.tolist() if hasattr(attr_NuSVR_n_iter_, 'tolist') else attr_NuSVR_n_iter_`
})()
}
/**
Array dimensions of training vector `X`.
*/
get shape_fit_(): Promise<any[]> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing shape_fit_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_shape_fit_ = bridgeNuSVR[${this.id}].shape_fit_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_shape_fit_.tolist() if hasattr(attr_NuSVR_shape_fit_, 'tolist') else attr_NuSVR_shape_fit_`
})()
}
/**
Indices of support vectors.
*/
get support_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('NuSVR must call init() before accessing support_')
}
return (async () => {
// invoke accessor
await this._py.ex`attr_NuSVR_support_ = bridgeNuSVR[${this.id}].support_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_support_.tolist() if hasattr(attr_NuSVR_support_, 'tolist') else attr_NuSVR_support_`
})()
}
/**
Support vectors.
*/
get support_vectors_(): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This NuSVR instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'NuSVR must call init() before accessing support_vectors_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_NuSVR_support_vectors_ = bridgeNuSVR[${this.id}].support_vectors_`
// convert the result from python to node.js
return this
._py`attr_NuSVR_support_vectors_.tolist() if hasattr(attr_NuSVR_support_vectors_, 'tolist') else attr_NuSVR_support_vectors_`
})()
}
}