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RidgeCV.ts
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RidgeCV.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'
/**
Ridge regression with built-in cross-validation.
See glossary entry for [cross-validation estimator](../../glossary.html#term-cross-validation-estimator).
By default, it performs efficient Leave-One-Out Cross-Validation.
Read more in the [User Guide](../linear_model.html#ridge-regression).
[Python Reference](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html)
*/
export class RidgeCV {
id: string
opts: any
_py: PythonBridge
_isInitialized: boolean = false
_isDisposed: boolean = false
constructor(opts?: {
/**
Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to `1 / (2C)` in other linear models such as [`LogisticRegression`](sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression "sklearn.linear_model.LogisticRegression") or [`LinearSVC`](sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC "sklearn.svm.LinearSVC"). If using Leave-One-Out cross-validation, alphas must be positive.
*/
alphas?: ArrayLike
/**
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
@defaultValue `true`
*/
fit_intercept?: boolean
/**
A string (see model evaluation documentation) or a scorer callable object / function with signature `scorer(estimator, X, y)`. If `undefined`, the negative mean squared error if cv is ‘auto’ or `undefined` (i.e. when using leave-one-out cross-validation), and r2 score otherwise.
*/
scoring?: string
/**
Determines the cross-validation splitting strategy. Possible inputs for cv are:
*/
cv?: number
/**
Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are:
@defaultValue `'auto'`
*/
gcv_mode?: 'auto' | 'svd' | 'eigen'
/**
Flag indicating if the cross-validation values corresponding to each alpha should be stored in the `cv\_values\_` attribute (see below). This flag is only compatible with `cv=None` (i.e. using Leave-One-Out Cross-Validation).
@defaultValue `false`
*/
store_cv_values?: boolean
/**
Flag indicating whether to optimize the alpha value (picked from the `alphas` parameter list) for each target separately (for multi-output settings: multiple prediction targets). When set to `true`, after fitting, the `alpha\_` attribute will contain a value for each target. When set to `false`, a single alpha is used for all targets.
@defaultValue `false`
*/
alpha_per_target?: boolean
}) {
this.id = `RidgeCV${crypto.randomUUID().split('-')[0]}`
this.opts = opts || {}
}
get py(): PythonBridge {
return this._py
}
set py(pythonBridge: PythonBridge) {
this._py = pythonBridge
}
/**
Initializes the underlying Python resources.
This instance is not usable until the `Promise` returned by `init()` resolves.
*/
async init(py: PythonBridge): Promise<void> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (this._isInitialized) {
return
}
if (!py) {
throw new Error('RidgeCV.init requires a PythonBridge instance')
}
this._py = py
await this._py.ex`
import numpy as np
from sklearn.linear_model import RidgeCV
try: bridgeRidgeCV
except NameError: bridgeRidgeCV = {}
`
// set up constructor params
await this._py.ex`ctor_RidgeCV = {'alphas': np.array(${
this.opts['alphas'] ?? undefined
}) if ${this.opts['alphas'] !== undefined} else None, 'fit_intercept': ${
this.opts['fit_intercept'] ?? undefined
}, 'scoring': ${this.opts['scoring'] ?? undefined}, 'cv': ${
this.opts['cv'] ?? undefined
}, 'gcv_mode': ${this.opts['gcv_mode'] ?? undefined}, 'store_cv_values': ${
this.opts['store_cv_values'] ?? undefined
}, 'alpha_per_target': ${this.opts['alpha_per_target'] ?? undefined}}
ctor_RidgeCV = {k: v for k, v in ctor_RidgeCV.items() if v is not None}`
await this._py.ex`bridgeRidgeCV[${this.id}] = RidgeCV(**ctor_RidgeCV)`
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 bridgeRidgeCV[${this.id}]`
this._isDisposed = true
}
/**
Fit Ridge regression model with cv.
*/
async fit(opts: {
/**
Training data. If using GCV, will be cast to float64 if necessary.
*/
X?: NDArray[]
/**
Target values. Will be cast to X’s dtype if necessary.
*/
y?: NDArray
/**
Individual weights for each sample. If given a float, every sample will have the same weight.
*/
sample_weight?: number | NDArray
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before fit()')
}
// set up method params
await this._py.ex`pms_RidgeCV_fit = {'X': np.array(${
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 ${opts['sample_weight'] !== undefined} else None}
pms_RidgeCV_fit = {k: v for k, v in pms_RidgeCV_fit.items() if v is not None}`
// invoke method
await this._py
.ex`res_RidgeCV_fit = bridgeRidgeCV[${this.id}].fit(**pms_RidgeCV_fit)`
// convert the result from python to node.js
return this
._py`res_RidgeCV_fit.tolist() if hasattr(res_RidgeCV_fit, 'tolist') else res_RidgeCV_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 RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before get_metadata_routing()')
}
// set up method params
await this._py.ex`pms_RidgeCV_get_metadata_routing = {'routing': ${
opts['routing'] ?? undefined
}}
pms_RidgeCV_get_metadata_routing = {k: v for k, v in pms_RidgeCV_get_metadata_routing.items() if v is not None}`
// invoke method
await this._py
.ex`res_RidgeCV_get_metadata_routing = bridgeRidgeCV[${this.id}].get_metadata_routing(**pms_RidgeCV_get_metadata_routing)`
// convert the result from python to node.js
return this
._py`res_RidgeCV_get_metadata_routing.tolist() if hasattr(res_RidgeCV_get_metadata_routing, 'tolist') else res_RidgeCV_get_metadata_routing`
}
/**
Predict using the linear model.
*/
async predict(opts: {
/**
Samples.
*/
X?: ArrayLike | SparseMatrix
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before predict()')
}
// set up method params
await this._py.ex`pms_RidgeCV_predict = {'X': ${opts['X'] ?? undefined}}
pms_RidgeCV_predict = {k: v for k, v in pms_RidgeCV_predict.items() if v is not None}`
// invoke method
await this._py
.ex`res_RidgeCV_predict = bridgeRidgeCV[${this.id}].predict(**pms_RidgeCV_predict)`
// convert the result from python to node.js
return this
._py`res_RidgeCV_predict.tolist() if hasattr(res_RidgeCV_predict, 'tolist') else res_RidgeCV_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 RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before score()')
}
// set up method params
await this._py.ex`pms_RidgeCV_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 ${opts['sample_weight'] !== undefined} else None}
pms_RidgeCV_score = {k: v for k, v in pms_RidgeCV_score.items() if v is not None}`
// invoke method
await this._py
.ex`res_RidgeCV_score = bridgeRidgeCV[${this.id}].score(**pms_RidgeCV_score)`
// convert the result from python to node.js
return this
._py`res_RidgeCV_score.tolist() if hasattr(res_RidgeCV_score, 'tolist') else res_RidgeCV_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 RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before set_fit_request()')
}
// set up method params
await this._py.ex`pms_RidgeCV_set_fit_request = {'sample_weight': ${
opts['sample_weight'] ?? undefined
}}
pms_RidgeCV_set_fit_request = {k: v for k, v in pms_RidgeCV_set_fit_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_RidgeCV_set_fit_request = bridgeRidgeCV[${this.id}].set_fit_request(**pms_RidgeCV_set_fit_request)`
// convert the result from python to node.js
return this
._py`res_RidgeCV_set_fit_request.tolist() if hasattr(res_RidgeCV_set_fit_request, 'tolist') else res_RidgeCV_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 RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before set_score_request()')
}
// set up method params
await this._py.ex`pms_RidgeCV_set_score_request = {'sample_weight': ${
opts['sample_weight'] ?? undefined
}}
pms_RidgeCV_set_score_request = {k: v for k, v in pms_RidgeCV_set_score_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_RidgeCV_set_score_request = bridgeRidgeCV[${this.id}].set_score_request(**pms_RidgeCV_set_score_request)`
// convert the result from python to node.js
return this
._py`res_RidgeCV_set_score_request.tolist() if hasattr(res_RidgeCV_set_score_request, 'tolist') else res_RidgeCV_set_score_request`
}
/**
Cross-validation values for each alpha (only available if `store\_cv\_values=True` and `cv=None`). After `fit()` has been called, this attribute will contain the mean squared errors if `scoring is None` otherwise it will contain standardized per point prediction values.
*/
get cv_values_(): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before accessing cv_values_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_RidgeCV_cv_values_ = bridgeRidgeCV[${this.id}].cv_values_`
// convert the result from python to node.js
return this
._py`attr_RidgeCV_cv_values_.tolist() if hasattr(attr_RidgeCV_cv_values_, 'tolist') else attr_RidgeCV_cv_values_`
})()
}
/**
Weight vector(s).
*/
get coef_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before accessing coef_')
}
return (async () => {
// invoke accessor
await this._py.ex`attr_RidgeCV_coef_ = bridgeRidgeCV[${this.id}].coef_`
// convert the result from python to node.js
return this
._py`attr_RidgeCV_coef_.tolist() if hasattr(attr_RidgeCV_coef_, 'tolist') else attr_RidgeCV_coef_`
})()
}
/**
Independent term in decision function. Set to 0.0 if `fit\_intercept \= False`.
*/
get intercept_(): Promise<number | NDArray> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before accessing intercept_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_RidgeCV_intercept_ = bridgeRidgeCV[${this.id}].intercept_`
// convert the result from python to node.js
return this
._py`attr_RidgeCV_intercept_.tolist() if hasattr(attr_RidgeCV_intercept_, 'tolist') else attr_RidgeCV_intercept_`
})()
}
/**
Estimated regularization parameter, or, if `alpha\_per\_target=True`, the estimated regularization parameter for each target.
*/
get alpha_(): Promise<number | NDArray> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before accessing alpha_')
}
return (async () => {
// invoke accessor
await this._py.ex`attr_RidgeCV_alpha_ = bridgeRidgeCV[${this.id}].alpha_`
// convert the result from python to node.js
return this
._py`attr_RidgeCV_alpha_.tolist() if hasattr(attr_RidgeCV_alpha_, 'tolist') else attr_RidgeCV_alpha_`
})()
}
/**
Score of base estimator with best alpha, or, if `alpha\_per\_target=True`, a score for each target.
*/
get best_score_(): Promise<number | NDArray> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('RidgeCV must call init() before accessing best_score_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_RidgeCV_best_score_ = bridgeRidgeCV[${this.id}].best_score_`
// convert the result from python to node.js
return this
._py`attr_RidgeCV_best_score_.tolist() if hasattr(attr_RidgeCV_best_score_, 'tolist') else attr_RidgeCV_best_score_`
})()
}
/**
Number of features seen during [fit](../../glossary.html#term-fit).
*/
get n_features_in_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'RidgeCV must call init() before accessing n_features_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_RidgeCV_n_features_in_ = bridgeRidgeCV[${this.id}].n_features_in_`
// convert the result from python to node.js
return this
._py`attr_RidgeCV_n_features_in_.tolist() if hasattr(attr_RidgeCV_n_features_in_, 'tolist') else attr_RidgeCV_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 RidgeCV instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'RidgeCV must call init() before accessing feature_names_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_RidgeCV_feature_names_in_ = bridgeRidgeCV[${this.id}].feature_names_in_`
// convert the result from python to node.js
return this
._py`attr_RidgeCV_feature_names_in_.tolist() if hasattr(attr_RidgeCV_feature_names_in_, 'tolist') else attr_RidgeCV_feature_names_in_`
})()
}
}