imputer#

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

Module Contents#

Classes Summary#

Imputer

Imputes missing data according to a specified imputation strategy.

Contents#

class evalml.pipelines.components.transformers.imputers.imputer.Imputer(categorical_impute_strategy='most_frequent', categorical_fill_value=None, numeric_impute_strategy='mean', numeric_fill_value=None, boolean_impute_strategy='most_frequent', boolean_fill_value=None, random_seed=0, **kwargs)[source]#

Imputes missing data according to a specified imputation strategy.

Parameters
  • categorical_impute_strategy (string) – Impute strategy to use for string, object, boolean, categorical dtypes. Valid values include “most_frequent” and “constant”.

  • numeric_impute_strategy (string) – Impute strategy to use for numeric columns. Valid values include “mean”, “median”, “most_frequent”, and “constant”.

  • boolean_impute_strategy (string) – Impute strategy to use for boolean columns. Valid values include “most_frequent” and “constant”.

  • categorical_fill_value (string) – When categorical_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with the string “missing_value”.

  • numeric_fill_value (int, float) – When numeric_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with 0.

  • boolean_fill_value (bool) – When boolean_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with True.

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

Attributes

hyperparameter_ranges

{ “categorical_impute_strategy”: [“most_frequent”], “numeric_impute_strategy”: [“mean”, “median”, “most_frequent”, “knn”], “boolean_impute_strategy”: [“most_frequent”]}

modifies_features

True

modifies_target

False

name

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 data X by imputing missing values.

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, 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) – Target data.

Returns

Transformed X.

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.

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 data X by imputing missing values.

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.