Class: OrdinalEncoder

Encode categorical features as an integer array.

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are converted to ordinal integers. This results in a single column of integers (0 to n_categories - 1) per feature.

Read more in the User Guide. For a comparison of different encoders, refer to: Comparing Target Encoder with Other Encoders.

Python Reference

Constructors

new OrdinalEncoder()

new OrdinalEncoder(opts?): OrdinalEncoder

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.categories`?	`"auto"`	Categories (unique values) per feature:
`opts.dtype`?	`any`	Desired dtype of output.
`opts.encoded_missing_value`?	`number`	Encoded value of missing categories. If set to `np.nan`, then the `dtype` parameter must be a float dtype.
`opts.handle_unknown`?	`"error"` \| `"use_encoded_value"`	When set to ‘error’ an error will be raised in case an unknown categorical feature is present during transform. When set to ‘use_encoded_value’, the encoded value of unknown categories will be set to the value given for the parameter `unknown_value`. In `inverse_transform`, an unknown category will be denoted as `undefined`.
`opts.max_categories`?	`number`	Specifies an upper limit to the number of output categories for each input feature when considering infrequent categories. If there are infrequent categories, `max_categories` includes the category representing the infrequent categories along with the frequent categories. If `undefined`, there is no limit to the number of output features. `max_categories` do not take into account missing or unknown categories. Setting `unknown_value` or `encoded_missing_value` to an integer will increase the number of unique integer codes by one each. This can result in up to `max_categories + 2` integer codes.
`opts.min_frequency`?	`number`	Specifies the minimum frequency below which a category will be considered infrequent.
`opts.unknown_value`?	`number`	When the parameter handle_unknown is set to ‘use_encoded_value’, this parameter is required and will set the encoded value of unknown categories. It has to be distinct from the values used to encode any of the categories in `fit`. If set to np.nan, the `dtype` parameter must be a float dtype.

Returns OrdinalEncoder

Defined in generated/preprocessing/OrdinalEncoder.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/preprocessing/OrdinalEncoder.ts:23
`_isInitialized`	`boolean`	`false`	generated/preprocessing/OrdinalEncoder.ts:22
`_py`	`PythonBridge`	`undefined`	generated/preprocessing/OrdinalEncoder.ts:21
`id`	`string`	`undefined`	generated/preprocessing/OrdinalEncoder.ts:18
`opts`	`any`	`undefined`	generated/preprocessing/OrdinalEncoder.ts:19

Accessors

categories_

Get Signature

get categories_(): Promise<any>

The categories of each feature determined during fit (in order of the features in X and corresponding with the output of transform). This does not include categories that weren’t seen during fit.

Returns Promise<any>

Defined in generated/preprocessing/OrdinalEncoder.ts:391

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/OrdinalEncoder.ts:441

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/preprocessing/OrdinalEncoder.ts:416

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/OrdinalEncoder.ts:71

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/OrdinalEncoder.ts:123

fit()

fit(opts): Promise<any>

Fit the OrdinalEncoder to X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The data to determine the categories of each feature.
`opts.y`?	`any`	Ignored. This parameter exists only for compatibility with `Pipeline`.

Returns Promise<any>

Defined in generated/preprocessing/OrdinalEncoder.ts:140

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).