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
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy? | boolean | Set 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? | number | Feature 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
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
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data. |
opts.y? | any | Ignored. |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target 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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input 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
| Name | Type |
|---|---|
py | PythonBridge |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy. |
opts.copy? | boolean | Copy the input X or not. |
Returns
Promise<ArrayLike>
Defined in: generated/preprocessing/Binarizer.ts:277 (opens in a new tab)