Class: StandardScaler
Standardize features by removing the mean and scaling to unit variance.
The standard score of a sample x is calculated as:
Constructors
new StandardScaler()
new StandardScaler(
opts?):StandardScaler
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy? | boolean | If false, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned. |
opts.with_mean? | boolean | If true, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory. |
opts.with_std? | boolean | If true, scale the data to unit variance (or equivalently, unit standard deviation). |
Returns StandardScaler
Defined in generated/preprocessing/StandardScaler.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/preprocessing/StandardScaler.ts:21 |
_isInitialized | boolean | false | generated/preprocessing/StandardScaler.ts:20 |
_py | PythonBridge | undefined | generated/preprocessing/StandardScaler.ts:19 |
id | string | undefined | generated/preprocessing/StandardScaler.ts:16 |
opts | any | undefined | generated/preprocessing/StandardScaler.ts:17 |
Accessors
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/preprocessing/StandardScaler.ts:676
mean_
Get Signature
get mean_():
Promise<ArrayLike>
The mean value for each feature in the training set. Equal to undefined when with_mean=False and with_std=False.
Returns Promise<ArrayLike>
Defined in generated/preprocessing/StandardScaler.ts:605
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/preprocessing/StandardScaler.ts:651
n_samples_seen_
Get Signature
get n_samples_seen_():
Promise<number|ArrayLike>
The number of samples processed by the estimator for each feature. If there are no missing samples, the n_samples_seen will be an integer, otherwise it will be an array of dtype int. If sample_weights are used it will be a float (if no missing data) or an array of dtype float that sums the weights seen so far. Will be reset on new calls to fit, but increments across partial_fit calls.
Returns Promise<number | ArrayLike>
Defined in generated/preprocessing/StandardScaler.ts:701
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/preprocessing/StandardScaler.ts:49
scale_
Get Signature
get scale_():
Promise<ArrayLike>
Per feature relative scaling of the data to achieve zero mean and unit variance. Generally this is calculated using np.sqrt(var_). If a variance is zero, we can’t achieve unit variance, and the data is left as-is, giving a scaling factor of 1. scale_ is equal to undefined when with_std=False.
Returns Promise<ArrayLike>
Defined in generated/preprocessing/StandardScaler.ts:582
var_
Get Signature
get var_():
Promise<ArrayLike>
The variance for each feature in the training set. Used to compute scale_. Equal to undefined when with_mean=False and with_std=False.
Returns Promise<ArrayLike>
Defined in generated/preprocessing/StandardScaler.ts:628
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/preprocessing/StandardScaler.ts:101
fit()
fit(
opts):Promise<any>
Compute the mean and std to be used for later scaling.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Individual weights for each sample. |
opts.X? | ArrayLike | The data used to compute the mean and standard deviation used for later scaling along the features axis. |
opts.y? | any | Ignored. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:118
fit_transform()
fit_transform(
opts):Promise<any[]>
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Additional fit parameters. |
opts.X? | ArrayLike[] | Input samples. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any[]>
Defined in generated/preprocessing/StandardScaler.ts:162
get_feature_names_out()
get_feature_names_out(
opts):Promise<any>
Get output feature names for transformation.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:204
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:240
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/preprocessing/StandardScaler.ts:62
inverse_transform()
inverse_transform(
opts):Promise<ArrayLike>
Scale back the data to the original representation.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.copy? | boolean | Copy the input X or not. |
opts.X? | ArrayLike | The data used to scale along the features axis. |
Returns Promise<ArrayLike>
Defined in generated/preprocessing/StandardScaler.ts:274
partial_fit()
partial_fit(
opts):Promise<any>
Online computation of mean and std on X for later scaling.
All of X is processed as a single batch. This is intended for cases when fit is not feasible due to very large number of n_samples or because X is read from a continuous stream.
The algorithm for incremental mean and std is given in Equation 1.5a,b in Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. “Algorithms for computing the sample variance: Analysis and recommendations.” The American Statistician 37.3 (1983): 242-247:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Individual weights for each sample. |
opts.X? | ArrayLike | The data used to compute the mean and standard deviation used for later scaling along the features axis. |
opts.y? | any | Ignored. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:317
set_fit_request()
set_fit_request(
opts):Promise<any>
Request metadata passed to the fit method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:363
set_inverse_transform_request()
set_inverse_transform_request(
opts):Promise<any>
Request metadata passed to the inverse_transform method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.copy? | string | boolean | Metadata routing for copy parameter in inverse_transform. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:401
set_output()
set_output(
opts):Promise<any>
Set output container.
See Introducing the set_output API for an example on how to use the API.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | "polars" | Configure output of transform and fit_transform. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:437
set_partial_fit_request()
set_partial_fit_request(
opts):Promise<any>
Request metadata passed to the partial_fit method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in partial_fit. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:473
set_transform_request()
set_transform_request(
opts):Promise<any>
Request metadata passed to the transform method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.copy? | string | boolean | Metadata routing for copy parameter in transform. |
Returns Promise<any>
Defined in generated/preprocessing/StandardScaler.ts:511
transform()
transform(
opts):Promise<ArrayLike>
Perform standardization by centering and scaling.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.copy? | boolean | Copy the input X or not. |
opts.X? | any[] | The data used to scale along the features axis. |
Returns Promise<ArrayLike>