Code Documentation: Preprocessing

mastml.preprocessing Module

This module contains methods to perform data preprocessing, such as various standardization/normalization methods

BasePreprocessor:

Base class that adds some MAST-ML type functionality to other preprocessors. Other preprocessor classes all inherit this base class

SklearnPreprocessor:

Class that wraps any preprocessor method from scikit-learn (e.g. StandardScaler) to have MAST-ML type functionality

NoPreprocessor:

Class that performs no preprocessing. A preprocessor is needed in the MAST-ML evaluation of data splits. If no preprocessing is desired, then this NoPreprocessor class is invoked by default

MeanStdevScaler:

Preprocessor class which extends scikit-learn’s StandardScaler to scale the dataset to a particular user-specified mean and standard deviation value

Classes

BaseEstimator()	Base class for all estimators in scikit-learn.
BasePreprocessor(preprocessor[, as_frame])	Base class to provide new methods beyond sklearn fit_transform, such as dataframe support and directory management
MeanStdevScaler([mean, stdev, as_frame])	Class designed to normalize input data to a specified mean and standard deviation
NoPreprocessor([preprocessor, as_frame])	Class for having a "null" transform where the output is the same as the input.
SklearnPreprocessor(preprocessor[, as_frame])	Class to wrap any scikit-learn preprocessor, e.g.
TransformerMixin()	Mixin class for all transformers in scikit-learn.
datetime(year, month, day[, hour[, minute[, ...)	The year, month and day arguments are required.

Class Inheritance Diagram