Normalizers

These are pipeline components that transform the input format or content while preserving their original domain semantics. Think of normalizers as necessary pre-processors that standardize data for downstream steps. Some types of normalizers include:

Type	Description
scalers	adjust values within a target range ie: MinMaxScaler, ImageCropper
converters	modify values to a common range ie: LanguageTranslator, CurrencyConverter
formatters	change the structure of the object ie: PDFConverter
enrichers	add context or details to the objects ie: NameEntityTagger, Q&ALLM

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scikitlab.normalizers.sparsity.DenseTransformer

DenseTransformer()

Converts a sparse matrix into a dense format. This may consume lots of memory.

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scikitlab.normalizers.sparsity.SparseTransformer

SparseTransformer()

Converts a dense matrix of data into a compressed sparse row format to preserve memory.