Documentation
Classes
Binarizer

Binarizer

Binarize data (set feature values to 0 or 1) according to a threshold.

Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1.

Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences for instance.

It can also be used as a pre-processing step for estimators that consider boolean random variables (e.g. modelled using the Bernoulli distribution in a Bayesian setting).

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new Binarizer(opts?: object): Binarizer;

Parameters

NameTypeDescription
opts?object-
opts.copy?booleanSet to false to perform inplace binarization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix). Default Value true
opts.threshold?numberFeature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices. Default Value 0

Returns

Binarizer

Defined in: generated/preprocessing/Binarizer.ts:29 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/preprocessing/Binarizer.ts:27 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/preprocessing/Binarizer.ts:26 (opens in a new tab)

_py

PythonBridge

Defined in: generated/preprocessing/Binarizer.ts:25 (opens in a new tab)

id

string

Defined in: generated/preprocessing/Binarizer.ts:22 (opens in a new tab)

opts

any

Defined in: generated/preprocessing/Binarizer.ts:23 (opens in a new tab)

Accessors

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/preprocessing/Binarizer.ts:342 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/preprocessing/Binarizer.ts:317 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/preprocessing/Binarizer.ts:48 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/preprocessing/Binarizer.ts:52 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/preprocessing/Binarizer.ts:100 (opens in a new tab)

fit()

Only validates estimator’s parameters.

This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe data.
opts.y?anyIgnored.

Returns

Promise<any>

Defined in: generated/preprocessing/Binarizer.ts:119 (opens in a new tab)

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/preprocessing/Binarizer.ts:159 (opens in a new tab)

get_feature_names_out()

Get output feature names for transformation.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.input_features?anyInput features.

Returns

Promise<any>

Defined in: generated/preprocessing/Binarizer.ts:206 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/preprocessing/Binarizer.ts:61 (opens in a new tab)

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/preprocessing/Binarizer.ts:244 (opens in a new tab)

transform()

Binarize each element of X.

Signature

transform(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe data to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.
opts.copy?booleanCopy the input X or not.

Returns

Promise<ArrayLike>

Defined in: generated/preprocessing/Binarizer.ts:277 (opens in a new tab)