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KernelDensity.ts
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KernelDensity.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'
/**
Kernel Density Estimation.
Read more in the [User Guide](../density.html#kernel-density).
[Python Reference](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html)
*/
export class KernelDensity {
id: string
opts: any
_py: PythonBridge
_isInitialized: boolean = false
_isDisposed: boolean = false
constructor(opts?: {
/**
The bandwidth of the kernel. If bandwidth is a float, it defines the bandwidth of the kernel. If bandwidth is a string, one of the estimation methods is implemented.
@defaultValue `1`
*/
bandwidth?: number | 'scott' | 'silverman'
/**
The tree algorithm to use.
@defaultValue `'auto'`
*/
algorithm?: 'kd_tree' | 'ball_tree' | 'auto'
/**
The kernel to use.
@defaultValue `'gaussian'`
*/
kernel?:
| 'gaussian'
| 'tophat'
| 'epanechnikov'
| 'exponential'
| 'linear'
| 'cosine'
/**
Metric to use for distance computation. See the documentation of [scipy.spatial.distance](https://docs.scipy.org/doc/scipy/reference/spatial.distance.html) and the metrics listed in [`distance\_metrics`](sklearn.metrics.pairwise.distance_metrics.html#sklearn.metrics.pairwise.distance_metrics "sklearn.metrics.pairwise.distance_metrics") for valid metric values.
Not all metrics are valid with all algorithms: refer to the documentation of [`BallTree`](sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree "sklearn.neighbors.BallTree") and [`KDTree`](sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree "sklearn.neighbors.KDTree"). Note that the normalization of the density output is correct only for the Euclidean distance metric.
@defaultValue `'euclidean'`
*/
metric?: string
/**
The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution.
@defaultValue `0`
*/
atol?: number
/**
The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution.
@defaultValue `0`
*/
rtol?: number
/**
If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach.
@defaultValue `true`
*/
breadth_first?: boolean
/**
Specify the leaf size of the underlying tree. See [`BallTree`](sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree "sklearn.neighbors.BallTree") or [`KDTree`](sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree "sklearn.neighbors.KDTree") for details.
@defaultValue `40`
*/
leaf_size?: number
/**
Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of [`BallTree`](sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree "sklearn.neighbors.BallTree") or [`KDTree`](sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree "sklearn.neighbors.KDTree").
*/
metric_params?: any
}) {
this.id = `KernelDensity${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 KernelDensity instance has already been disposed')
}
if (this._isInitialized) {
return
}
if (!py) {
throw new Error('KernelDensity.init requires a PythonBridge instance')
}
this._py = py
await this._py.ex`
import numpy as np
from sklearn.neighbors import KernelDensity
try: bridgeKernelDensity
except NameError: bridgeKernelDensity = {}
`
// set up constructor params
await this._py.ex`ctor_KernelDensity = {'bandwidth': ${
this.opts['bandwidth'] ?? undefined
}, 'algorithm': ${this.opts['algorithm'] ?? undefined}, 'kernel': ${
this.opts['kernel'] ?? undefined
}, 'metric': ${this.opts['metric'] ?? undefined}, 'atol': ${
this.opts['atol'] ?? undefined
}, 'rtol': ${this.opts['rtol'] ?? undefined}, 'breadth_first': ${
this.opts['breadth_first'] ?? undefined
}, 'leaf_size': ${this.opts['leaf_size'] ?? undefined}, 'metric_params': ${
this.opts['metric_params'] ?? undefined
}}
ctor_KernelDensity = {k: v for k, v in ctor_KernelDensity.items() if v is not None}`
await this._py
.ex`bridgeKernelDensity[${this.id}] = KernelDensity(**ctor_KernelDensity)`
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 bridgeKernelDensity[${this.id}]`
this._isDisposed = true
}
/**
Fit the Kernel Density model on the data.
*/
async fit(opts: {
/**
List of n\_features-dimensional data points. Each row corresponds to a single data point.
*/
X?: ArrayLike[]
/**
Ignored. This parameter exists only for compatibility with [`Pipeline`](sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline "sklearn.pipeline.Pipeline").
*/
y?: any
/**
List of sample weights attached to the data X.
*/
sample_weight?: ArrayLike
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('KernelDensity must call init() before fit()')
}
// set up method params
await this._py.ex`pms_KernelDensity_fit = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': ${
opts['y'] ?? undefined
}, 'sample_weight': np.array(${opts['sample_weight'] ?? undefined}) if ${
opts['sample_weight'] !== undefined
} else None}
pms_KernelDensity_fit = {k: v for k, v in pms_KernelDensity_fit.items() if v is not None}`
// invoke method
await this._py
.ex`res_KernelDensity_fit = bridgeKernelDensity[${this.id}].fit(**pms_KernelDensity_fit)`
// convert the result from python to node.js
return this
._py`res_KernelDensity_fit.tolist() if hasattr(res_KernelDensity_fit, 'tolist') else res_KernelDensity_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 KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'KernelDensity must call init() before get_metadata_routing()'
)
}
// set up method params
await this._py.ex`pms_KernelDensity_get_metadata_routing = {'routing': ${
opts['routing'] ?? undefined
}}
pms_KernelDensity_get_metadata_routing = {k: v for k, v in pms_KernelDensity_get_metadata_routing.items() if v is not None}`
// invoke method
await this._py
.ex`res_KernelDensity_get_metadata_routing = bridgeKernelDensity[${this.id}].get_metadata_routing(**pms_KernelDensity_get_metadata_routing)`
// convert the result from python to node.js
return this
._py`res_KernelDensity_get_metadata_routing.tolist() if hasattr(res_KernelDensity_get_metadata_routing, 'tolist') else res_KernelDensity_get_metadata_routing`
}
/**
Generate random samples from the model.
Currently, this is implemented only for gaussian and tophat kernels.
*/
async sample(opts: {
/**
Number of samples to generate.
@defaultValue `1`
*/
n_samples?: number
/**
Determines random number generation used to generate random samples. Pass an int for reproducible results across multiple function calls. See [Glossary](../../glossary.html#term-random_state).
*/
random_state?: number
}): Promise<ArrayLike[]> {
if (this._isDisposed) {
throw new Error('This KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('KernelDensity must call init() before sample()')
}
// set up method params
await this._py.ex`pms_KernelDensity_sample = {'n_samples': ${
opts['n_samples'] ?? undefined
}, 'random_state': ${opts['random_state'] ?? undefined}}
pms_KernelDensity_sample = {k: v for k, v in pms_KernelDensity_sample.items() if v is not None}`
// invoke method
await this._py
.ex`res_KernelDensity_sample = bridgeKernelDensity[${this.id}].sample(**pms_KernelDensity_sample)`
// convert the result from python to node.js
return this
._py`res_KernelDensity_sample.tolist() if hasattr(res_KernelDensity_sample, 'tolist') else res_KernelDensity_sample`
}
/**
Compute the total log-likelihood under the model.
*/
async score(opts: {
/**
List of n\_features-dimensional data points. Each row corresponds to a single data point.
*/
X?: ArrayLike[]
/**
Ignored. This parameter exists only for compatibility with [`Pipeline`](sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline "sklearn.pipeline.Pipeline").
*/
y?: any
}): Promise<number> {
if (this._isDisposed) {
throw new Error('This KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('KernelDensity must call init() before score()')
}
// set up method params
await this._py.ex`pms_KernelDensity_score = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': ${opts['y'] ?? undefined}}
pms_KernelDensity_score = {k: v for k, v in pms_KernelDensity_score.items() if v is not None}`
// invoke method
await this._py
.ex`res_KernelDensity_score = bridgeKernelDensity[${this.id}].score(**pms_KernelDensity_score)`
// convert the result from python to node.js
return this
._py`res_KernelDensity_score.tolist() if hasattr(res_KernelDensity_score, 'tolist') else res_KernelDensity_score`
}
/**
Compute the log-likelihood of each sample under the model.
*/
async score_samples(opts: {
/**
An array of points to query. Last dimension should match dimension of training data (n\_features).
*/
X?: ArrayLike[]
}): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('KernelDensity must call init() before score_samples()')
}
// set up method params
await this._py.ex`pms_KernelDensity_score_samples = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_KernelDensity_score_samples = {k: v for k, v in pms_KernelDensity_score_samples.items() if v is not None}`
// invoke method
await this._py
.ex`res_KernelDensity_score_samples = bridgeKernelDensity[${this.id}].score_samples(**pms_KernelDensity_score_samples)`
// convert the result from python to node.js
return this
._py`res_KernelDensity_score_samples.tolist() if hasattr(res_KernelDensity_score_samples, 'tolist') else res_KernelDensity_score_samples`
}
/**
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 KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('KernelDensity must call init() before set_fit_request()')
}
// set up method params
await this._py.ex`pms_KernelDensity_set_fit_request = {'sample_weight': ${
opts['sample_weight'] ?? undefined
}}
pms_KernelDensity_set_fit_request = {k: v for k, v in pms_KernelDensity_set_fit_request.items() if v is not None}`
// invoke method
await this._py
.ex`res_KernelDensity_set_fit_request = bridgeKernelDensity[${this.id}].set_fit_request(**pms_KernelDensity_set_fit_request)`
// convert the result from python to node.js
return this
._py`res_KernelDensity_set_fit_request.tolist() if hasattr(res_KernelDensity_set_fit_request, 'tolist') else res_KernelDensity_set_fit_request`
}
/**
Number of features seen during [fit](../../glossary.html#term-fit).
*/
get n_features_in_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'KernelDensity must call init() before accessing n_features_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_KernelDensity_n_features_in_ = bridgeKernelDensity[${this.id}].n_features_in_`
// convert the result from python to node.js
return this
._py`attr_KernelDensity_n_features_in_.tolist() if hasattr(attr_KernelDensity_n_features_in_, 'tolist') else attr_KernelDensity_n_features_in_`
})()
}
/**
The tree algorithm for fast generalized N-point problems.
*/
get tree_(): Promise<any> {
if (this._isDisposed) {
throw new Error('This KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('KernelDensity must call init() before accessing tree_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_KernelDensity_tree_ = bridgeKernelDensity[${this.id}].tree_`
// convert the result from python to node.js
return this
._py`attr_KernelDensity_tree_.tolist() if hasattr(attr_KernelDensity_tree_, 'tolist') else attr_KernelDensity_tree_`
})()
}
/**
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 KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'KernelDensity must call init() before accessing feature_names_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_KernelDensity_feature_names_in_ = bridgeKernelDensity[${this.id}].feature_names_in_`
// convert the result from python to node.js
return this
._py`attr_KernelDensity_feature_names_in_.tolist() if hasattr(attr_KernelDensity_feature_names_in_, 'tolist') else attr_KernelDensity_feature_names_in_`
})()
}
/**
Value of the bandwidth, given directly by the bandwidth parameter or estimated using the ‘scott’ or ‘silverman’ method.
*/
get bandwidth_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This KernelDensity instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'KernelDensity must call init() before accessing bandwidth_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_KernelDensity_bandwidth_ = bridgeKernelDensity[${this.id}].bandwidth_`
// convert the result from python to node.js
return this
._py`attr_KernelDensity_bandwidth_.tolist() if hasattr(attr_KernelDensity_bandwidth_, 'tolist') else attr_KernelDensity_bandwidth_`
})()
}
}