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
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Base class for all estimators in scikit-learn. |
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Base class to provide new methods beyond sklearn fit_transform, such as dataframe support and directory management |
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Class designed to normalize input data to a specified mean and standard deviation |
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Class for having a "null" transform where the output is the same as the input. |
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Class to wrap any scikit-learn preprocessor, e.g. StandardScaler. |
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Mixin class for all transformers in scikit-learn. |
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The year, month and day arguments are required. |