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MinMaxScaler.ts
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MinMaxScaler.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'
/**
Transform features by scaling each feature to a given range.
This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.
The transformation is given by:
[Python Reference](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
*/
export class MinMaxScaler {
id: string
opts: any
_py: PythonBridge
_isInitialized: boolean = false
_isDisposed: boolean = false
constructor(opts?: {
/**
Desired range of transformed data.
*/
feature_range?: any
/**
Set to `false` to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
@defaultValue `true`
*/
copy?: boolean
/**
Set to `true` to clip transformed values of held-out data to provided `feature range`.
@defaultValue `false`
*/
clip?: boolean
}) {
this.id = `MinMaxScaler${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 MinMaxScaler instance has already been disposed')
}
if (this._isInitialized) {
return
}
if (!py) {
throw new Error('MinMaxScaler.init requires a PythonBridge instance')
}
this._py = py
await this._py.ex`
import numpy as np
from sklearn.preprocessing import MinMaxScaler
try: bridgeMinMaxScaler
except NameError: bridgeMinMaxScaler = {}
`
// set up constructor params
await this._py.ex`ctor_MinMaxScaler = {'feature_range': ${
this.opts['feature_range'] ?? undefined
}, 'copy': ${this.opts['copy'] ?? undefined}, 'clip': ${
this.opts['clip'] ?? undefined
}}
ctor_MinMaxScaler = {k: v for k, v in ctor_MinMaxScaler.items() if v is not None}`
await this._py
.ex`bridgeMinMaxScaler[${this.id}] = MinMaxScaler(**ctor_MinMaxScaler)`
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 bridgeMinMaxScaler[${this.id}]`
this._isDisposed = true
}
/**
Compute the minimum and maximum to be used for later scaling.
*/
async fit(opts: {
/**
The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
*/
X?: ArrayLike[]
/**
Ignored.
*/
y?: any
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('MinMaxScaler must call init() before fit()')
}
// set up method params
await this._py.ex`pms_MinMaxScaler_fit = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': ${opts['y'] ?? undefined}}
pms_MinMaxScaler_fit = {k: v for k, v in pms_MinMaxScaler_fit.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_fit = bridgeMinMaxScaler[${this.id}].fit(**pms_MinMaxScaler_fit)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_fit.tolist() if hasattr(res_MinMaxScaler_fit, 'tolist') else res_MinMaxScaler_fit`
}
/**
Fit to data, then transform it.
Fits transformer to `X` and `y` with optional parameters `fit\_params` and returns a transformed version of `X`.
*/
async fit_transform(opts: {
/**
Input samples.
*/
X?: ArrayLike[]
/**
Target values (`undefined` for unsupervised transformations).
*/
y?: ArrayLike
/**
Additional fit parameters.
*/
fit_params?: any
}): Promise<any[]> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('MinMaxScaler must call init() before fit_transform()')
}
// set up method params
await this._py.ex`pms_MinMaxScaler_fit_transform = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': np.array(${
opts['y'] ?? undefined
}) if ${opts['y'] !== undefined} else None, 'fit_params': ${
opts['fit_params'] ?? undefined
}}
pms_MinMaxScaler_fit_transform = {k: v for k, v in pms_MinMaxScaler_fit_transform.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_fit_transform = bridgeMinMaxScaler[${this.id}].fit_transform(**pms_MinMaxScaler_fit_transform)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_fit_transform.tolist() if hasattr(res_MinMaxScaler_fit_transform, 'tolist') else res_MinMaxScaler_fit_transform`
}
/**
Get output feature names for transformation.
*/
async get_feature_names_out(opts: {
/**
Input features.
*/
input_features?: any
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before get_feature_names_out()'
)
}
// set up method params
await this._py
.ex`pms_MinMaxScaler_get_feature_names_out = {'input_features': ${
opts['input_features'] ?? undefined
}}
pms_MinMaxScaler_get_feature_names_out = {k: v for k, v in pms_MinMaxScaler_get_feature_names_out.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_get_feature_names_out = bridgeMinMaxScaler[${this.id}].get_feature_names_out(**pms_MinMaxScaler_get_feature_names_out)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_get_feature_names_out.tolist() if hasattr(res_MinMaxScaler_get_feature_names_out, 'tolist') else res_MinMaxScaler_get_feature_names_out`
}
/**
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 MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before get_metadata_routing()'
)
}
// set up method params
await this._py.ex`pms_MinMaxScaler_get_metadata_routing = {'routing': ${
opts['routing'] ?? undefined
}}
pms_MinMaxScaler_get_metadata_routing = {k: v for k, v in pms_MinMaxScaler_get_metadata_routing.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_get_metadata_routing = bridgeMinMaxScaler[${this.id}].get_metadata_routing(**pms_MinMaxScaler_get_metadata_routing)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_get_metadata_routing.tolist() if hasattr(res_MinMaxScaler_get_metadata_routing, 'tolist') else res_MinMaxScaler_get_metadata_routing`
}
/**
Undo the scaling of X according to feature\_range.
*/
async inverse_transform(opts: {
/**
Input data that will be transformed. It cannot be sparse.
*/
X?: ArrayLike[]
}): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before inverse_transform()'
)
}
// set up method params
await this._py.ex`pms_MinMaxScaler_inverse_transform = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_MinMaxScaler_inverse_transform = {k: v for k, v in pms_MinMaxScaler_inverse_transform.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_inverse_transform = bridgeMinMaxScaler[${this.id}].inverse_transform(**pms_MinMaxScaler_inverse_transform)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_inverse_transform.tolist() if hasattr(res_MinMaxScaler_inverse_transform, 'tolist') else res_MinMaxScaler_inverse_transform`
}
/**
Online computation of min and max on X for later scaling.
All of X is processed as a single batch. This is intended for cases when [`fit`](#sklearn.preprocessing.MinMaxScaler.fit "sklearn.preprocessing.MinMaxScaler.fit") is not feasible due to very large number of `n\_samples` or because X is read from a continuous stream.
*/
async partial_fit(opts: {
/**
The data used to compute the mean and standard deviation used for later scaling along the features axis.
*/
X?: ArrayLike[]
/**
Ignored.
*/
y?: any
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('MinMaxScaler must call init() before partial_fit()')
}
// set up method params
await this._py.ex`pms_MinMaxScaler_partial_fit = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None, 'y': ${opts['y'] ?? undefined}}
pms_MinMaxScaler_partial_fit = {k: v for k, v in pms_MinMaxScaler_partial_fit.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_partial_fit = bridgeMinMaxScaler[${this.id}].partial_fit(**pms_MinMaxScaler_partial_fit)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_partial_fit.tolist() if hasattr(res_MinMaxScaler_partial_fit, 'tolist') else res_MinMaxScaler_partial_fit`
}
/**
Set output container.
See [Introducing the set\_output API](../../auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py) for an example on how to use the API.
*/
async set_output(opts: {
/**
Configure output of `transform` and `fit\_transform`.
*/
transform?: 'default' | 'pandas'
}): Promise<any> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('MinMaxScaler must call init() before set_output()')
}
// set up method params
await this._py.ex`pms_MinMaxScaler_set_output = {'transform': ${
opts['transform'] ?? undefined
}}
pms_MinMaxScaler_set_output = {k: v for k, v in pms_MinMaxScaler_set_output.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_set_output = bridgeMinMaxScaler[${this.id}].set_output(**pms_MinMaxScaler_set_output)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_set_output.tolist() if hasattr(res_MinMaxScaler_set_output, 'tolist') else res_MinMaxScaler_set_output`
}
/**
Scale features of X according to feature\_range.
*/
async transform(opts: {
/**
Input data that will be transformed.
*/
X?: ArrayLike[]
}): Promise<NDArray[]> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('MinMaxScaler must call init() before transform()')
}
// set up method params
await this._py.ex`pms_MinMaxScaler_transform = {'X': np.array(${
opts['X'] ?? undefined
}) if ${opts['X'] !== undefined} else None}
pms_MinMaxScaler_transform = {k: v for k, v in pms_MinMaxScaler_transform.items() if v is not None}`
// invoke method
await this._py
.ex`res_MinMaxScaler_transform = bridgeMinMaxScaler[${this.id}].transform(**pms_MinMaxScaler_transform)`
// convert the result from python to node.js
return this
._py`res_MinMaxScaler_transform.tolist() if hasattr(res_MinMaxScaler_transform, 'tolist') else res_MinMaxScaler_transform`
}
/**
Per feature adjustment for minimum. Equivalent to `min \- X.min(axis=0) \* self.scale\_`
*/
get min_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('MinMaxScaler must call init() before accessing min_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_min_ = bridgeMinMaxScaler[${this.id}].min_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_min_.tolist() if hasattr(attr_MinMaxScaler_min_, 'tolist') else attr_MinMaxScaler_min_`
})()
}
/**
Per feature relative scaling of the data. Equivalent to `(max \- min) / (X.max(axis=0) \- X.min(axis=0))`
*/
get scale_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error('MinMaxScaler must call init() before accessing scale_')
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_scale_ = bridgeMinMaxScaler[${this.id}].scale_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_scale_.tolist() if hasattr(attr_MinMaxScaler_scale_, 'tolist') else attr_MinMaxScaler_scale_`
})()
}
/**
Per feature minimum seen in the data
*/
get data_min_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before accessing data_min_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_data_min_ = bridgeMinMaxScaler[${this.id}].data_min_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_data_min_.tolist() if hasattr(attr_MinMaxScaler_data_min_, 'tolist') else attr_MinMaxScaler_data_min_`
})()
}
/**
Per feature maximum seen in the data
*/
get data_max_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before accessing data_max_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_data_max_ = bridgeMinMaxScaler[${this.id}].data_max_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_data_max_.tolist() if hasattr(attr_MinMaxScaler_data_max_, 'tolist') else attr_MinMaxScaler_data_max_`
})()
}
/**
Per feature range `(data\_max\_ \- data\_min\_)` seen in the data
*/
get data_range_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before accessing data_range_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_data_range_ = bridgeMinMaxScaler[${this.id}].data_range_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_data_range_.tolist() if hasattr(attr_MinMaxScaler_data_range_, 'tolist') else attr_MinMaxScaler_data_range_`
})()
}
/**
Number of features seen during [fit](../../glossary.html#term-fit).
*/
get n_features_in_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before accessing n_features_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_n_features_in_ = bridgeMinMaxScaler[${this.id}].n_features_in_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_n_features_in_.tolist() if hasattr(attr_MinMaxScaler_n_features_in_, 'tolist') else attr_MinMaxScaler_n_features_in_`
})()
}
/**
The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across `partial\_fit` calls.
*/
get n_samples_seen_(): Promise<number> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before accessing n_samples_seen_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_n_samples_seen_ = bridgeMinMaxScaler[${this.id}].n_samples_seen_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_n_samples_seen_.tolist() if hasattr(attr_MinMaxScaler_n_samples_seen_, 'tolist') else attr_MinMaxScaler_n_samples_seen_`
})()
}
/**
Names of features seen during [fit](../../glossary.html#term-fit). Defined only when `X` has feature names that are all strings.
*/
get feature_names_in_(): Promise<NDArray> {
if (this._isDisposed) {
throw new Error('This MinMaxScaler instance has already been disposed')
}
if (!this._isInitialized) {
throw new Error(
'MinMaxScaler must call init() before accessing feature_names_in_'
)
}
return (async () => {
// invoke accessor
await this._py
.ex`attr_MinMaxScaler_feature_names_in_ = bridgeMinMaxScaler[${this.id}].feature_names_in_`
// convert the result from python to node.js
return this
._py`attr_MinMaxScaler_feature_names_in_.tolist() if hasattr(attr_MinMaxScaler_feature_names_in_, 'tolist') else attr_MinMaxScaler_feature_names_in_`
})()
}
}