knn_imputer

Component that imputes missing data according to a specified imputation strategy.

Module Contents

Classes Summary

KNNImputer

Imputes missing data using KNN according to a specified number of neighbors. Natural language columns are ignored.

Contents

class evalml.pipelines.components.transformers.imputers.knn_imputer.KNNImputer(number_neighbors=3, random_seed=0, **kwargs)

Imputes missing data using KNN according to a specified number of neighbors. Natural language columns are ignored.

Parameters

• number_neighbors (int) – Number of nearest neighbors for KNN to search for. Defaults to 3.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

modifies_features	True
modifies_target	False
name	KNN Imputer
training_only	False

Methods

clone	Constructs a new component with the same parameters and random state.
default_parameters	Returns the default parameters for this component.
describe	Describe a component and its parameters.
fit	Fits imputer to data. 'None' values are converted to np.nan before imputation and are treated as the same.
fit_transform	Fits on X and transforms X.
load	Loads component at file path.
needs_fitting	Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
parameters	Returns the parameters which were used to initialize the component.
save	Saves component at file path.
transform	Transforms input by imputing missing values. 'None' and np.nan values are treated as the same.

clone(self)

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)

Describe a component and its parameters.

Parameters

• print_name (bool, optional) – whether to print name of component

• return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)

Fits imputer to data. ‘None’ values are converted to np.nan before imputation and are treated as the same.

Parameters

• X (pd.DataFrame or np.ndarray) – the input training data of shape [n_samples, n_features]

• y (pd.Series, optional) – the target training data of length [n_samples]

Returns

self

Raises

ValueError – if the KNNImputer receives a dataframe with both Boolean and Categorical data.

fit_transform(self, X, y=None)

Fits on X and transforms X.

Parameters

• X (pd.DataFrame) – Data to fit and transform

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

static load(file_path)

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

needs_fitting(self)

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)

Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)

Saves component at file path.

Parameters

• file_path (str) – Location to save file.

• pickle_protocol (int) – The pickle data stream format.

transform(self, X, y=None)

Transforms input by imputing missing values. ‘None’ and np.nan values are treated as the same.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Ignored.

Returns

Transformed X

Return type

pd.DataFrame