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. |