target_imputer

Module Contents

Classes Summary

TargetImputer	Imputes missing target data according to a specified imputation strategy.
TargetImputerMeta	A version of the ComponentBaseMeta class which handles when input features is None

Contents

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

Imputes missing target 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. Defaults to “most_frequent”.

• fill_value (string) – When impute_strategy == “constant”, fill_value is used to replace missing data. Defaults to None which uses 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	False
modifies_target	True
name	Target 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 target data. ‘None’ values are converted to np.nan before imputation and are
fit_transform	Fits on and transforms the input target data.
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 target data 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)

Fits imputer to target 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]. Ignored.

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

Returns

self

fit_transform(self, X, y)

Fits on and transforms the input target data.

Parameters

• X (pd.DataFrame) – Features. Ignored.

• y (pd.Series) – Target data to impute.

Returns

The original X, transformed y

Return type

(pd.DataFrame, pd.Series)

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)

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

Parameters

• X (pd.DataFrame) – Features. Ignored.

• y (pd.Series) – Target data to impute.

Returns

The original X, transformed y

Return type

(pd.DataFrame, pd.Series)

class evalml.pipelines.components.transformers.imputers.target_imputer.TargetImputerMeta

A version of the ComponentBaseMeta class which handles when input features is None

Attributes

FIT_METHODS	[‘fit’, ‘fit_transform’]
METHODS_TO_CHECK	[‘predict’, ‘predict_proba’, ‘transform’, ‘inverse_transform’]
PROPERTIES_TO_CHECK	[‘feature_importance’]

Methods

check_for_fit	check_for_fit wraps a method that validates if self._is_fitted is True.
register	Register a virtual subclass of an ABC.
set_fit

classmethod check_for_fit(cls, method)

check_for_fit wraps a method that validates if self._is_fitted is True. It raises an exception if False and calls and returns the wrapped method if True.

register(cls, subclass)

Register a virtual subclass of an ABC.

Returns the subclass, to allow usage as a class decorator.

classmethod set_fit(cls, method)

simple_imputer preprocessing