Transformers cheat sheets

dunders glossary

Type	Dunder	Meaning	sktime class
compose	*	chaining/pipeline - also works with other estimator types	type dependent
compose	**	chaining to secondary input of another estimator	type dependent
compose	+	feature union	FeatureUnion
interface	~	invert	InvertTransform
structural	¦	multiplexing (“switch”)	type dependent
structural	-	optional passthrough (“on/off”)	OptionalPassthrough

selected useful transformers, compositors, adapters

• delay fitting to transform via sktime.transformations.compose.FitInTransform

• any pandas method via sktime.transformations.compose.adapt.PandasTransformAdaptor

• date/time features via sktime.transformations.series.date.DateTimeFeatures

• lags via transformations.series.lag.Lag

• differences, first and n-th, via transformations.series.difference.Differencer

• scaled logit via transformations.series.scaledlogit.ScaledLogitTransform

Transformer type glossary

Common types of transformation in sktime:

from	to	base class	examples (sci)	examples (sktime)
time series	scalar features	BaseTransformer (Primitives output)	tsfresh, or 7-number-summary	Catch22, SummaryTransformer
time series	time series	BaseTransformer (Series, instancewise)	detrending, smoothing, filtering, lagging	Detrender, Differencer, Lag, Filter
time series panel	also a panel	BaseTransformer (Series output)	principal component projection	PCATransformer, PaddingTransformer
two feature vectors	a scalar	BasePairwiseTransformer	Euclidean distance, L1 distance	ScipyDist, AggrDist, FlatDist
two time series	a scalar	BasePairwiseTransformerPanel	DTW distance, alignment kernel	DtwDist, EditDist

first three = “time series transformers”, or, simply, “transformers”

all “transformers” follow the same base interface.

“pairwise transformers” have separate base interface (due to two inputs)

include distances and kernels between time series or feature vectors

all inherit BaseObject and follow unified skbase interface with get_params, get_fitted_params, etc

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