per_column_imputer

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

Module Contents

Classes Summary

PerColumnImputer

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

Contents

class evalml.pipelines.components.transformers.imputers.per_column_imputer.PerColumnImputer(impute_strategies=None, random_seed=0, **kwargs)[source]

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.

  • 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)[source]

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)[source]

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