quick_anomaly_detector.models.ImputerNa

class quick_anomaly_detector.models.ImputerNa(strategy='mean', fill_values=None)

A custom imputer transformer that extends scikit-learn’s SimpleImputer while preserving column names after imputation.

Parameters strategy : {‘mean’, ‘median’, ‘most_frequent’, ‘constant’}, default=’mean’

The imputation strategy.

fill_valuestr, int, or float, optional

The constant value to fill missing values when strategy=’constant’.

Attributes strategy : str

The imputation strategy.

fill_valuestr, int, or float

The constant value to fill missing values when strategy=’constant’.

Methods fit(X, y=None)

Fit the imputer to the data.

transform(X, y=None)

Transform the data by imputing missing values and preserving column names.

Examples

from sklearn.pipeline import Pipeline quick_anomaly_detector.data_process import CustomImputer

fill_values = {

‘column1’: 0, ‘column2’: ‘’

} pipeline = Pipeline([

(‘imputer’, CustomImputer(strategy=’mean’)), (‘fillna’, CustomImputer(strategy=’constant’, fill_value=fill_values)),

]) X_train_imputed = pipeline.fit_transform(X_train)

__init__(strategy='mean', fill_values=None)

Methods

__init__([strategy, fill_values])
fit(X[, y])
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X[, y])