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)[source]#

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.
`update_parameters`	Updates the parameter dictionary of the component.

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)[source]#

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)[source]#

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)[source]#

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

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.