Class: KNNImputer

Imputation for completing missing values using k-Nearest Neighbors.

Each sample’s missing values are imputed using the mean value from n_neighbors nearest neighbors found in the training set. Two samples are close if the features that neither is missing are close.

Constructors

new KNNImputer()

new KNNImputer(opts?): KNNImputer

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.add_indicator`?	`boolean`	If `true`, a `MissingIndicator` transform will stack onto the output of the imputer’s transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won’t appear on the missing indicator even if there are missing values at transform/test time.
`opts.copy`?	`boolean`	If `true`, a copy of X will be created. If `false`, imputation will be done in-place whenever possible.
`opts.keep_empty_features`?	`boolean`	If `true`, features that consist exclusively of missing values when `fit` is called are returned in results when `transform` is called. The imputed value is always `0`.
`opts.metric`?	`"nan_euclidean"`	Distance metric for searching neighbors. Possible values:
`opts.missing_values`?	`string` \| `number`	The placeholder for the missing values. All occurrences of `missing_values` will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values, `missing_values` should be set to np.nan, since `pd.NA` will be converted to np.nan.
`opts.n_neighbors`?	`number`	Number of neighboring samples to use for imputation.
`opts.weights`?	`"uniform"` \| `"distance"`	Weight function used in prediction. Possible values:

Returns KNNImputer

Defined in generated/impute/KNNImputer.ts:25

Properties

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

Accessors

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/impute/KNNImputer.ts:411

indicator_

Get Signature

get indicator_(): Promise<any>

Indicator used to add binary indicators for missing values. undefined if add_indicator is false.

Returns Promise<any>

Defined in generated/impute/KNNImputer.ts:363

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/impute/KNNImputer.ts:386

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/impute/KNNImputer.ts:77

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/impute/KNNImputer.ts:129

fit()

fit(opts): Promise<any>

Fit the imputer on X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Input data, where `n_samples` is the number of samples and `n_features` is the number of features.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<any>

Defined in generated/impute/KNNImputer.ts:146

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).