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 (opens in a new tab)
Constructors
constructor()
Signature
new OrdinalEncoder(opts?: object): OrdinalEncoder;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.categories? | "auto" | Categories (unique values) per feature: Default Value 'auto' |
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. Default Value 'error' |
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
Defined in: generated/preprocessing/OrdinalEncoder.ts:25 (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/OrdinalEncoder.ts:132 (opens in a new tab)
fit()
Fit the OrdinalEncoder to X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:149 (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/OrdinalEncoder.ts:189 (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/OrdinalEncoder.ts:236 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/preprocessing/OrdinalEncoder.ts:274 (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/OrdinalEncoder.ts:84 (opens in a new tab)
inverse_transform()
Convert the data back to the original representation.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The transformed data. |
Returns
Promise<ArrayLike[]>
Defined in: generated/preprocessing/OrdinalEncoder.ts:309 (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/OrdinalEncoder.ts:346 (opens in a new tab)
transform()
Transform X to ordinal codes.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data to encode. |
Returns
Promise<ArrayLike[]>
Defined in: generated/preprocessing/OrdinalEncoder.ts:379 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/preprocessing/OrdinalEncoder.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/preprocessing/OrdinalEncoder.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/preprocessing/OrdinalEncoder.ts:21 (opens in a new tab)
id
string
Defined in: generated/preprocessing/OrdinalEncoder.ts:18 (opens in a new tab)
opts
any
Defined in: generated/preprocessing/OrdinalEncoder.ts:19 (opens in a new tab)
Accessors
categories_
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.
Signature
categories_(): Promise<any>;Returns
Promise<any>
Defined in: generated/preprocessing/OrdinalEncoder.ts:412 (opens in a new tab)
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/OrdinalEncoder.ts:462 (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/OrdinalEncoder.ts:437 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/preprocessing/OrdinalEncoder.ts:71 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/preprocessing/OrdinalEncoder.ts:75 (opens in a new tab)