tubular.imputers.NearestMeanResponseImputer

class tubular.imputers.NearestMeanResponseImputer(columns=None, **kwds)

Bases: tubular.imputers.BaseImputer

Class to impute missing values with; the value for which the average response is closest to the average response for the unknown levels.

Parameters

columns (None or str or list, default = None) – Columns to impute, if the default of None is supplied all columns in X are used when the transform method is called.

__init__(columns=None, **kwds) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([columns])	Initialize self.
check_is_fitted(attribute)	Check if particular attributes are on the object.
check_weights_column(X, weights_column)	Helper method for validating weights column in dataframe.
classname()	Method that returns the name of the current class when called.
columns_check(X)	Method to check that the columns attribute is set and all values are present in X.
columns_set_or_check(X)	Function to check or set columns attribute.
fit(X, y)	Calculate mean values to impute with.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Impute missing values with median values calculated from fit method.

check_is_fitted(attribute)

Check if particular attributes are on the object. This is useful to do before running transform to avoid trying to transform data without first running the fit method.

Wrapper for utils.validation.check_is_fitted function.

Parameters

attributes (List) – List of str values giving names of attribute to check exist on self.

static check_weights_column(X, weights_column)

Helper method for validating weights column in dataframe.

X (pd.DataFrame): df containing weight column weights_column (str): name of weight column

classname()

Method that returns the name of the current class when called.

columns_check(X)

Method to check that the columns attribute is set and all values are present in X.

Parameters

X (pd.DataFrame) – Data to check columns are in.

columns_set_or_check(X)

Function to check or set columns attribute.

If the columns attribute is None then set it to all columns in X. Otherwise run the columns_check method.

Parameters

X (pd.DataFrame) – Data to check columns are in.

fit(X, y)

Calculate mean values to impute with.

Parameters

• X (pd.DataFrame) – Data to fit the transformer on.

• y (pd.Series) – Response column used to determine the value to impute with. The average response for each level of every column is calculated. The level which has the closest average response to the average response of the unknown levels is selected as the imputation value.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

transform(X)

Impute missing values with median values calculated from fit method.

Parameters

X (pd.DataFrame) – Data to impute.

Returns

X – Transformed input X with nulls imputed with the median value for the specified columns.

Return type

pd.DataFrame