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GaussianNB.ts
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GaussianNB.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'
/**
Gaussian Naive Bayes (GaussianNB).
Can perform online updates to model parameters via [`partial\_fit`](#sklearn.naive_bayes.GaussianNB.partial_fit "sklearn.naive_bayes.GaussianNB.partial_fit"). For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
[Python Reference](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html)
*/
export class GaussianNB {
id: string
opts: any
_py: PythonBridge
_isInitialized: boolean = false
_isDisposed: boolean = false
constructor(opts?: {
/**
Prior probabilities of the classes. If specified, the priors are not adjusted according to the data.
*/
priors?: ArrayLike
/**
Portion of the largest variance of all features that is added to variances for calculation stability.
@defaultValue `1e-9`
*/
var_smoothing?: number
}) {
this.id = `GaussianNB${crypto.randomUUID().split('-')[0]}`
this.opts = opts || {}
}
get py(): PythonBridge {
return this._py
}
set py(pythonBridge: PythonBridge) {
this._py = pythonBridge
}
/**
Initializes the underlying Python resources.
This instance is not usable until the `Promise` returned by `init()` resolves.
*/
async init(py: PythonBridge): Promise<void> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (this._isInitialized) {
return
}
if (!py) {
throw new Error('GaussianNB.init requires a PythonBridge instance')
}
this._py = py
await this._py.ex`
import numpy as np
from sklearn.naive_bayes import GaussianNB
try: bridgeGaussianNB
except NameError: bridgeGaussianNB = {}
`
// set up constructor params
await this._py.ex`ctor_GaussianNB = {'priors': np.array(${
this.opts['priors'] ?? undefined
}) if ${this.opts['priors'] !== undefined} else None, 'var_smoothing': ${
this.opts['var_smoothing'] ?? undefined
}}
ctor_GaussianNB = {k: v for k, v in ctor_GaussianNB.items() if v is not None}`
await this._py
.ex`bridgeGaussianNB[${this.id}] = GaussianNB(**ctor_GaussianNB)`
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 bridgeGaussianNB[${this.id}]`
this._isDisposed = true
}
/**
Fit Gaussian Naive Bayes according to X, y.
*/
async fit(opts: {
/**
Training vectors, where `n\_samples` is the number of samples and `n\_features` is the number of features.
*/
X?: ArrayLike[]
/**
Target values.
*/
y?: ArrayLike
/**
Weights applied to individual samples (1. for unweighted).
*/
sample_weight?: ArrayLike
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before fit()')
}
// set up method params
await this._py.ex`pms_GaussianNB_fit = {'X': np.array(${
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 ${opts['sample_weight'] !== undefined} else None}
pms_GaussianNB_fit = {k: v for k, v in pms_GaussianNB_fit.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_fit = bridgeGaussianNB[${this.id}].fit(**pms_GaussianNB_fit)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_fit.tolist() if hasattr(res_GaussianNB_fit, 'tolist') else res_GaussianNB_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 GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'GaussianNB must call init() before get_metadata_routing()'
)
}
// set up method params
await this._py.ex`pms_GaussianNB_get_metadata_routing = {'routing': ${
opts['routing'] ?? undefined
}}
pms_GaussianNB_get_metadata_routing = {k: v for k, v in pms_GaussianNB_get_metadata_routing.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_get_metadata_routing = bridgeGaussianNB[${this.id}].get_metadata_routing(**pms_GaussianNB_get_metadata_routing)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_get_metadata_routing.tolist() if hasattr(res_GaussianNB_get_metadata_routing, 'tolist') else res_GaussianNB_get_metadata_routing`
}
/**
Incremental fit on a batch of samples.
This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.
This is especially useful when the whole dataset is too big to fit in memory at once.
This method has some performance and numerical stability overhead, hence it is better to call partial\_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.
*/
async partial_fit(opts: {
/**
Training vectors, where `n\_samples` is the number of samples and `n\_features` is the number of features.
*/
X?: ArrayLike[]
/**
Target values.
*/
y?: ArrayLike
/**
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial\_fit, can be omitted in subsequent calls.
*/
classes?: ArrayLike
/**
Weights applied to individual samples (1. for unweighted).
*/
sample_weight?: ArrayLike
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before partial_fit()')
}
// set up method params
await this._py.ex`pms_GaussianNB_partial_fit = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': np.array(${
opts['y'] ?? undefined
}) if ${opts['y'] !== undefined} else None, 'classes': np.array(${
opts['classes'] ?? undefined
}) if ${
opts['classes'] !== undefined
} else None, 'sample_weight': np.array(${
opts['sample_weight'] ?? undefined
}) if ${opts['sample_weight'] !== undefined} else None}
pms_GaussianNB_partial_fit = {k: v for k, v in pms_GaussianNB_partial_fit.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_partial_fit = bridgeGaussianNB[${this.id}].partial_fit(**pms_GaussianNB_partial_fit)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_partial_fit.tolist() if hasattr(res_GaussianNB_partial_fit, 'tolist') else res_GaussianNB_partial_fit`
}
/**
Perform classification on an array of test vectors X.
*/
async predict(opts: {
/**
The input samples.
*/
X?: ArrayLike[]
}): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before predict()')
}
// set up method params
await this._py.ex`pms_GaussianNB_predict = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_GaussianNB_predict = {k: v for k, v in pms_GaussianNB_predict.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_predict = bridgeGaussianNB[${this.id}].predict(**pms_GaussianNB_predict)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_predict.tolist() if hasattr(res_GaussianNB_predict, 'tolist') else res_GaussianNB_predict`
}
/**
Return joint log probability estimates for the test vector X.
For each row x of X and class y, the joint log probability is given by `log P(x, y) \= log P(y) + log P(x|y),` where `log P(y)` is the class prior probability and `log P(x|y)` is the class-conditional probability.
*/
async predict_joint_log_proba(opts: {
/**
The input samples.
*/
X?: ArrayLike[]
}): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'GaussianNB must call init() before predict_joint_log_proba()'
)
}
// set up method params
await this._py.ex`pms_GaussianNB_predict_joint_log_proba = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_GaussianNB_predict_joint_log_proba = {k: v for k, v in pms_GaussianNB_predict_joint_log_proba.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_predict_joint_log_proba = bridgeGaussianNB[${this.id}].predict_joint_log_proba(**pms_GaussianNB_predict_joint_log_proba)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_predict_joint_log_proba.tolist() if hasattr(res_GaussianNB_predict_joint_log_proba, 'tolist') else res_GaussianNB_predict_joint_log_proba`
}
/**
Return log-probability estimates for the test vector X.
*/
async predict_log_proba(opts: {
/**
The input samples.
*/
X?: ArrayLike[]
}): Promise<ArrayLike[]> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before predict_log_proba()')
}
// set up method params
await this._py.ex`pms_GaussianNB_predict_log_proba = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_GaussianNB_predict_log_proba = {k: v for k, v in pms_GaussianNB_predict_log_proba.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_predict_log_proba = bridgeGaussianNB[${this.id}].predict_log_proba(**pms_GaussianNB_predict_log_proba)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_predict_log_proba.tolist() if hasattr(res_GaussianNB_predict_log_proba, 'tolist') else res_GaussianNB_predict_log_proba`
}
/**
Return probability estimates for the test vector X.
*/
async predict_proba(opts: {
/**
The input samples.
*/
X?: ArrayLike[]
}): Promise<ArrayLike[]> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before predict_proba()')
}
// set up method params
await this._py.ex`pms_GaussianNB_predict_proba = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_GaussianNB_predict_proba = {k: v for k, v in pms_GaussianNB_predict_proba.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_predict_proba = bridgeGaussianNB[${this.id}].predict_proba(**pms_GaussianNB_predict_proba)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_predict_proba.tolist() if hasattr(res_GaussianNB_predict_proba, 'tolist') else res_GaussianNB_predict_proba`
}
/**
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.
*/
async score(opts: {
/**
Test samples.
*/
X?: ArrayLike[]
/**
True labels for `X`.
*/
y?: ArrayLike
/**
Sample weights.
*/
sample_weight?: ArrayLike
}): Promise<number> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before score()')
}
// set up method params
await this._py.ex`pms_GaussianNB_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 ${opts['sample_weight'] !== undefined} else None}
pms_GaussianNB_score = {k: v for k, v in pms_GaussianNB_score.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_score = bridgeGaussianNB[${this.id}].score(**pms_GaussianNB_score)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_score.tolist() if hasattr(res_GaussianNB_score, 'tolist') else res_GaussianNB_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 GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before set_fit_request()')
}
// set up method params
await this._py.ex`pms_GaussianNB_set_fit_request = {'sample_weight': ${
opts['sample_weight'] ?? undefined
}}
pms_GaussianNB_set_fit_request = {k: v for k, v in pms_GaussianNB_set_fit_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_set_fit_request = bridgeGaussianNB[${this.id}].set_fit_request(**pms_GaussianNB_set_fit_request)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_set_fit_request.tolist() if hasattr(res_GaussianNB_set_fit_request, 'tolist') else res_GaussianNB_set_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`](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_partial_fit_request(opts: {
/**
Metadata routing for `classes` parameter in `partial\_fit`.
*/
classes?: string | boolean
/**
Metadata routing for `sample\_weight` parameter in `partial\_fit`.
*/
sample_weight?: string | boolean
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'GaussianNB must call init() before set_partial_fit_request()'
)
}
// set up method params
await this._py.ex`pms_GaussianNB_set_partial_fit_request = {'classes': ${
opts['classes'] ?? undefined
}, 'sample_weight': ${opts['sample_weight'] ?? undefined}}
pms_GaussianNB_set_partial_fit_request = {k: v for k, v in pms_GaussianNB_set_partial_fit_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_set_partial_fit_request = bridgeGaussianNB[${this.id}].set_partial_fit_request(**pms_GaussianNB_set_partial_fit_request)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_set_partial_fit_request.tolist() if hasattr(res_GaussianNB_set_partial_fit_request, 'tolist') else res_GaussianNB_set_partial_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 GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before set_score_request()')
}
// set up method params
await this._py.ex`pms_GaussianNB_set_score_request = {'sample_weight': ${
opts['sample_weight'] ?? undefined
}}
pms_GaussianNB_set_score_request = {k: v for k, v in pms_GaussianNB_set_score_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_GaussianNB_set_score_request = bridgeGaussianNB[${this.id}].set_score_request(**pms_GaussianNB_set_score_request)`
// convert the result from python to node.js
return this
._py`res_GaussianNB_set_score_request.tolist() if hasattr(res_GaussianNB_set_score_request, 'tolist') else res_GaussianNB_set_score_request`
}
/**
number of training samples observed in each class.
*/
get class_count_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'GaussianNB must call init() before accessing class_count_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_class_count_ = bridgeGaussianNB[${this.id}].class_count_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_class_count_.tolist() if hasattr(attr_GaussianNB_class_count_, 'tolist') else attr_GaussianNB_class_count_`
})()
}
/**
probability of each class.
*/
get class_prior_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'GaussianNB must call init() before accessing class_prior_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_class_prior_ = bridgeGaussianNB[${this.id}].class_prior_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_class_prior_.tolist() if hasattr(attr_GaussianNB_class_prior_, 'tolist') else attr_GaussianNB_class_prior_`
})()
}
/**
class labels known to the classifier.
*/
get classes_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before accessing classes_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_classes_ = bridgeGaussianNB[${this.id}].classes_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_classes_.tolist() if hasattr(attr_GaussianNB_classes_, 'tolist') else attr_GaussianNB_classes_`
})()
}
/**
absolute additive value to variances.
*/
get epsilon_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before accessing epsilon_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_epsilon_ = bridgeGaussianNB[${this.id}].epsilon_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_epsilon_.tolist() if hasattr(attr_GaussianNB_epsilon_, 'tolist') else attr_GaussianNB_epsilon_`
})()
}
/**
Number of features seen during [fit](../../glossary.html#term-fit).
*/
get n_features_in_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'GaussianNB must call init() before accessing n_features_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_n_features_in_ = bridgeGaussianNB[${this.id}].n_features_in_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_n_features_in_.tolist() if hasattr(attr_GaussianNB_n_features_in_, 'tolist') else attr_GaussianNB_n_features_in_`
})()
}
/**
Names of features seen during [fit](../../glossary.html#term-fit). Defined only when `X` has feature names that are all strings.
*/
get feature_names_in_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'GaussianNB must call init() before accessing feature_names_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_feature_names_in_ = bridgeGaussianNB[${this.id}].feature_names_in_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_feature_names_in_.tolist() if hasattr(attr_GaussianNB_feature_names_in_, 'tolist') else attr_GaussianNB_feature_names_in_`
})()
}
/**
Variance of each feature per class.
*/
get var_(): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before accessing var_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_var_ = bridgeGaussianNB[${this.id}].var_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_var_.tolist() if hasattr(attr_GaussianNB_var_, 'tolist') else attr_GaussianNB_var_`
})()
}
/**
mean of each feature per class.
*/
get theta_(): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This GaussianNB instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('GaussianNB must call init() before accessing theta_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_GaussianNB_theta_ = bridgeGaussianNB[${this.id}].theta_`
// convert the result from python to node.js
return this
._py`attr_GaussianNB_theta_.tolist() if hasattr(attr_GaussianNB_theta_, 'tolist') else attr_GaussianNB_theta_`
})()
}
}