OneHotEncoder
Encode categorical features as a one-hot numeric 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 encoded using a one-hot (aka ‘one-of-K’ or ‘dummy’) encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the sparse\_output parameter)
By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the categories manually.
This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels.
Note: a one-hot encoding of y labels should use a LabelBinarizer instead.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new OneHotEncoder(opts?: object): OneHotEncoder;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.categories? | "auto" | Categories (unique values) per feature: Default Value 'auto' |
opts.drop? | any[] | "first" | "if_binary" | Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where perfectly collinear features cause problems, such as when feeding the resulting data into an unregularized linear regression model. However, dropping one category breaks the symmetry of the original representation and can therefore induce a bias in downstream models, for instance for penalized linear classification or regression models. |
opts.dtype? | any | Desired dtype of output. |
opts.handle_unknown? | "ignore" | "error" | "infrequent_if_exist" | Specifies the way unknown categories are handled during transform. Default Value 'error' |
opts.max_categories? | number | Specifies an upper limit to the number of output features 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. |
opts.min_frequency? | number | Specifies the minimum frequency below which a category will be considered infrequent. |
opts.sparse? | boolean | Will return sparse matrix if set true else will return an array. Default Value true |
opts.sparse_output? | boolean | Will return sparse matrix if set true else will return an array. Default Value true |
Returns
Defined in: generated/preprocessing/OneHotEncoder.ts:31 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/preprocessing/OneHotEncoder.ts:29 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/preprocessing/OneHotEncoder.ts:28 (opens in a new tab)
_py
PythonBridge
Defined in: generated/preprocessing/OneHotEncoder.ts:27 (opens in a new tab)
id
string
Defined in: generated/preprocessing/OneHotEncoder.ts:24 (opens in a new tab)
opts
any
Defined in: generated/preprocessing/OneHotEncoder.ts:25 (opens in a new tab)
Accessors
categories_
The categories of each feature determined during fitting (in order of the features in X and corresponding with the output of transform). This includes the category specified in drop (if any).
Signature
categories_(): Promise<any>;Returns
Promise<any>
Defined in: generated/preprocessing/OneHotEncoder.ts:398 (opens in a new tab)
drop_idx_
drop\_idx\_\[i\] is the index in categories\_\[i\] of the category to be dropped for each feature.
Signature
drop_idx_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/preprocessing/OneHotEncoder.ts:423 (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/OneHotEncoder.ts:473 (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/OneHotEncoder.ts:448 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/preprocessing/OneHotEncoder.ts:86 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/preprocessing/OneHotEncoder.ts:90 (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/OneHotEncoder.ts:149 (opens in a new tab)
fit()
Fit OneHotEncoder 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/OneHotEncoder.ts:166 (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/OneHotEncoder.ts:206 (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/OneHotEncoder.ts:253 (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/OneHotEncoder.ts:99 (opens in a new tab)
inverse_transform()
Convert the data back to the original representation.
When unknown categories are encountered (all zeros in the one-hot encoding), undefined is used to represent this category. If the feature with the unknown category has a dropped category, the dropped category will be its inverse.
For a given input feature, if there is an infrequent category, ‘infrequent_sklearn’ will be used to represent the infrequent category.
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/OneHotEncoder.ts:293 (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/OneHotEncoder.ts:330 (opens in a new tab)
transform()
Transform X using one-hot encoding.
If there are infrequent categories for a feature, the infrequent categories will be grouped into a single category.
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/OneHotEncoder.ts:365 (opens in a new tab)