pyanno4rt.learning_model.preprocessing
Data preprocessing module.
The module aims to provide methods and classes for data preprocessing, i.e., data cleaning, reduction, (re-)sampling and transformation.
Subpackages
Overview
Data preprocessing class. |
Classes
- class pyanno4rt.learning_model.preprocessing.DataPreprocessor(sequence, verbose=True)[source]
Data preprocessing class.
- Parameters:
sequence (list) – Labels for the preprocessing algorithm classes (sequential).
verbose (bool, default=True) – Indicator for the display of logging messages.
- steps
Dictionary with the preprocessing steps as pairs of labels and corresponding preprocessing algorithm classes.
- Type:
dict
Overview
Methods transform(features, labels)Transform the input features/labels.
fit_transform(features, labels)Fit the preprocessor and transform the input features/labels.
gradientize(features)Compute the preprocessing gradient w.r.t the input features.
Members
- transform(features, labels=None)[source]
Transform the input features/labels.
- Parameters:
features (ndarray) – Values of the input features.
labels (ndarray, default=None) – Values of the input labels.
- Returns:
ndarray – Transformed values of the input features.
None or ndarray – Transformed values of the input labels.
- fit_transform(features, labels=None)[source]
Fit the preprocessor and transform the input features/labels.
- Parameters:
features (ndarray) – Values of the input features.
labels (ndarray, default=None) – Values of the input labels.
- Returns:
ndarray – Transformed values of the input features.
None or ndarray – Transformed values of the input labels.