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KNNImputer

Converter
DashAI.back.converters.scikit_learn.KNNImputer

Fill missing values by averaging over the K nearest complete neighbours.

For each sample that contains missing entries, the n_neighbors nearest complete samples (those without any missing value in the imputed feature) are located using the nan_euclidean distance, which handles partially observed vectors by ignoring the missing dimensions during distance computation. The imputed value is then either the uniform mean or the distance-weighted mean of those neighbours, depending on the weights parameter.

Unlike SimpleImputer, which computes a single global statistic per column, KNN imputation is instance-based and preserves the local correlation structure of the data. All output columns are typed as Float64 in DashAI.

Wraps sklearn.impute.KNNImputer.

References​

Parameters​

n_neighbors : integer, default=5
The number of nearest neighbors to use for imputation.
weights : string, default=uniform
The weight function to use for imputation.
metric : string, default=nan_euclidean
The metric to use for imputation.
use_copy : boolean, default=True
If True, a copy of X will be created.
add_indicator : boolean, default=False
If True, a MissingIndicator transform will stack onto output.
keep_empty_features : boolean, default=False
If True, empty features will be kept.

Methods​

get_output_type(self, column_name: str = None) -> DashAI.back.types.dashai_data_type.DashAIDataType

Defined on KNNImputer

Return the DashAI data type produced by this converter for a column.

Parameters

column_name : str, optional
Not used; all output columns share the same type. Defaults to None.

Returns

DashAIDataType
A Float type backed by pyarrow.float64().

changes_row_count(self) -> 'bool'

Defined on BaseConverter

Indicate whether this converter changes the number of dataset rows.

Returns

bool
True if the converter may add or remove rows, False otherwise.

fit(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> DashAI.back.converters.base_converter.BaseConverter

Defined on SklearnWrapper

Fit the scikit-learn transformer to the data.

Parameters

x : DashAIDataset
The input dataset to fit the transformer on.
y : DashAIDataset, optional
Target values for supervised transformers. Defaults to None.

Returns

BaseConverter
The fitted transformer instance (self).

get_metadata(cls) -> 'Dict[str, Any]'

Defined on BaseConverter

Get metadata for the converter, used by the DashAI frontend.

Parameters

cls : type
The converter class (injected automatically by Python for classmethods).

Returns

Dict[str, Any]
Dictionary containing display name, short description, image preview path, category, icon, color, and whether the converter is supervised.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

transform(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'DashAIDataset'

Defined on SklearnWrapper

Transform the data using the fitted scikit-learn transformer.

Parameters

x : DashAIDataset
The input dataset to transform.
y : DashAIDataset, optional
Not used. Present for API consistency. Defaults to None.

Returns

DashAIDataset
The transformed dataset with updated DashAI column types.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict
A dictionary with the data provided by the user to initialize the model.

Returns

dict
A validated dictionary with the necessary objects.