simple_imputer

Module Contents

Classes Summary

SimpleImputer

Imputes missing data according to a specified imputation strategy.

Contents

class evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer(impute_strategy='most_frequent', fill_value=None, random_seed=0, **kwargs)

Imputes missing data according to a specified imputation strategy.

Parameters

• impute_strategy (string) – Impute strategy to use. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types.

• fill_value (string) – When impute_strategy == “constant”, fill_value is used to replace missing data. Defaults to 0 when imputing numerical data and “missing_value” for strings or object data types.

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

Attributes

hyperparameter_ranges	{ “impute_strategy”: [“mean”, “median”, “most_frequent”]}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Simple Imputer

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
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
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

prints and returns dictionary

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

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.

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.

Returns

None

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

per_column_imputer target_imputer