target_imputer#

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

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)[source]#

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”]}
modifies_features	False
modifies_target	True
name	Target 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 target data. 'None' values are converted to np.nan before imputation and are treated as the same.
`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 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 target data 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)[source]#

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

Raises

TypeError – If target is filled with all null values.

fit_transform(self, X, y)[source]#

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.

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)[source]#

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)

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.

class evalml.pipelines.components.transformers.imputers.target_imputer.TargetImputerMeta[source]#

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’, ‘get_trend_dataframe’]
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`	Wrapper for the fit method.

classmethod check_for_fit(cls, method)[source]#

check_for_fit wraps a method that validates if self._is_fitted is True.

Parameters: method (callable) – Method to wrap.
Raises: ComponentNotYetFittedError – If component is not fitted.
Returns: The wrapped input method.

register(cls, subclass)#

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

classmethod set_fit(cls, method)#: Wrapper for the fit method.