Class: MaxAbsScaler

Scale each feature by its maximum absolute value.

This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

This scaler can also be applied to sparse CSR or CSC matrices.

MaxAbsScaler doesn’t reduce the effect of outliers; it only linearly scales them down. For an example visualization, refer to Compare MaxAbsScaler with other scalers.

Python Reference

Constructors

new MaxAbsScaler()

new MaxAbsScaler(opts?): MaxAbsScaler

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.copy`?	`boolean`	Set to `false` to perform inplace scaling and avoid a copy (if the input is already a numpy array).

Returns MaxAbsScaler

Defined in generated/preprocessing/MaxAbsScaler.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/preprocessing/MaxAbsScaler.ts:25
`_isInitialized`	`boolean`	`false`	generated/preprocessing/MaxAbsScaler.ts:24
`_py`	`PythonBridge`	`undefined`	generated/preprocessing/MaxAbsScaler.ts:23
`id`	`string`	`undefined`	generated/preprocessing/MaxAbsScaler.ts:20
`opts`	`any`	`undefined`	generated/preprocessing/MaxAbsScaler.ts:21

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/MaxAbsScaler.ts:469

max_abs_

Get Signature

get max_abs_(): Promise<ArrayLike>

Per feature maximum absolute value.

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MaxAbsScaler.ts:421

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/preprocessing/MaxAbsScaler.ts:444

n_samples_seen_

Get Signature

get n_samples_seen_(): Promise<number>

The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across partial_fit calls.

Returns Promise<number>

Defined in generated/preprocessing/MaxAbsScaler.ts:494

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/MaxAbsScaler.ts:39

scale_

Get Signature

get scale_(): Promise<ArrayLike>

Per feature relative scaling of the data.

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MaxAbsScaler.ts:398

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/MaxAbsScaler.ts:91

fit()

fit(opts): Promise<any>

Compute the maximum absolute value to be used for later scaling.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
`opts.y`?	`any`	Ignored.

Returns Promise<any>

Defined in generated/preprocessing/MaxAbsScaler.ts:108

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/MaxAbsScaler.ts:147

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/MaxAbsScaler.ts:189

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/MaxAbsScaler.ts:225

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/MaxAbsScaler.ts:52

inverse_transform()

inverse_transform(opts): Promise<ArrayLike>

Scale back the data to the original representation.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	The data that should be transformed back.

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MaxAbsScaler.ts:259

partial_fit()

partial_fit(opts): Promise<any>

Online computation of max absolute value of 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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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.