Preprocessor

Preprocessor Interface

Constructor

Preprocessor

Implements an ML preprocessing operation.

Fit/Transform APIs

fit	Fit this Preprocessor to the Dataset.
fit_transform	Fit this Preprocessor to the Dataset and then transform the Dataset.
transform	Transform the given dataset.
transform_batch	Transform a single batch of data.
PreprocessorNotFittedException	Error raised when the preprocessor needs to be fitted first.

Generic Preprocessors

Concatenator	Combine numeric columns into a column of type TensorDtype.
SimpleImputer	Replace missing values with imputed values.
Chain	Combine multiple preprocessors into a single Preprocessor.

Categorical Encoders

Categorizer	Convert columns to pd.CategoricalDtype.
LabelEncoder	Encode labels as integer targets.
MultiHotEncoder	Multi-hot encode categorical data.
OneHotEncoder	One-hot encode categorical data.
OrdinalEncoder	Encode values within columns as ordered integer values.

Feature Scalers

MaxAbsScaler	Scale each column by its absolute max value.
MinMaxScaler	Scale each column by its range.
Normalizer	Scales each sample to have unit norm.
PowerTransformer	Apply a power transform to make your data more normally distributed.
RobustScaler	Scale and translate each column using approximate quantiles.
StandardScaler	Translate and scale each column by its mean and standard deviation, respectively.

K-Bins Discretizers

CustomKBinsDiscretizer	Bin values into discrete intervals using custom bin edges.
UniformKBinsDiscretizer	Bin values into discrete intervals (bins) of uniform width.

Feature Hashers and Vectorizers

FeatureHasher	Apply the hashing trick to a table that describes token frequencies.
CountVectorizer	Count the frequency of tokens in a column of strings.
HashingVectorizer	Count the frequency of tokens using the hashing trick.

Specialized Preprocessors

TorchVisionPreprocessor

Apply a TorchVision transform to image columns.