imputers

Components that impute missing values in the input data.

Submodules

• imputer
• per_column_imputer
• simple_imputer
• target_imputer

Package Contents

Classes Summary

Imputer	Imputes missing data according to a specified imputation strategy.
PerColumnImputer	Imputes missing data according to a specified imputation strategy per column.
SimpleImputer	Imputes missing data according to a specified imputation strategy.
TargetImputer	Imputes missing target data according to a specified imputation strategy.

Contents

class evalml.pipelines.components.transformers.imputers.Imputer(categorical_impute_strategy='most_frequent', categorical_fill_value=None, numeric_impute_strategy='mean', numeric_fill_value=None, random_seed=0, **kwargs)

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”.

• 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.

• 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”],}
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. ‘None’ values are converted to np.nan before imputation and 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

Returns dictionary if return_dict is True, else None.

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, 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)

Transforms data X by imputing missing values. ‘None’ values are converted to np.nan before imputation and are treated as the same.

Parameters

• X (pd.DataFrame) – Data to transform

• y (pd.Series, optional) – Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.imputers.PerColumnImputer(impute_strategies=None, default_impute_strategy='most_frequent', impute_all=True, random_seed=0, **kwargs)

Imputes missing data according to a specified imputation strategy per column.

Parameters

• impute_strategies (dict) – Column and {“impute_strategy”: strategy, “fill_value”:value} pairings. Valid values for impute strategy include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types. Defaults to None, which uses “most_frequent” for all columns. When impute_strategy == “constant”, fill_value is used to replace missing data. When None, uses 0 when imputing numerical data and “missing_value” for strings or object data types.

• default_impute_strategy (str) – Impute strategy to fall back on when none is provided for a certain column. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types. Defaults to “most_frequent”.

• impute_all (bool) – Whether or not to impute all columns or just the columns that are specified in impute_strategies. If True, columns will be imputed using the strategy in the impute_strategies dictionary if specified or using the default_impute_strategy. If False, only columns specified as keys in the impute_strategies dictionary are imputed. If False and impute_strategies is None, no columns will be imputed. Defaults to True.

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

Attributes

hyperparameter_ranges	{}
modifies_features	True
modifies_target	False
name	Per Column 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 imputers on input data.
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 data by imputing missing values.

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)

Fits imputers on input data.

Parameters

• X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features] to fit.

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

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)

Transforms input data by imputing missing values.

Parameters

• X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features] to transform.

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

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.imputers.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”]}
modifies_features	True
modifies_target	False
name	Simple 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.

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)

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.

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)

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

class evalml.pipelines.components.transformers.imputers.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”]}
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.

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)

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)

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)

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)

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