transforms

Classes

AsSet()	The .as_set(["x", "y"]) transform indicates that both x and y are treated as sets.
AsTimeSeries()	The .as_time_series transform can be used to indicate that variables shall be treated as time series.
Broadcast(keys, *, to[, expand, exclude, ...])	Broadcasts arrays or scalars to the shape of a given other array.
Concatenate(keys, *, into[, axis, _indices])	Concatenate multiple arrays into a new key.
Constrain(*[, lower, upper, method, ...])	Constrains neural network predictions of a data variable to specified bounds.
ConvertDType(from_dtype, to_dtype)	Default transform used to convert all floats from float64 to float32 to be in line with keras framework.
Drop(keys)	Transform to drop variables from further calculation.
ElementwiseTransform()	Base class on which other transforms are based
ExpandDims(*, axis)	Expand the shape of an array.
FilterTransform(*, transform_constructor[, ...])	Implements a transform that applies a different transform on a subset of the data.
Keep(keys)	Name the data parameters that should be kept for futher calculation.
Log(*[, p1])	Log transforms a variable.
MapTransform(transform_map)	Implements a transform that applies a set of elementwise transforms to a subset of the data as given by a mapping.
NumpyTransform(forward[, inverse])	A class to apply element-wise transformations using plain NumPy functions.
OneHot(num_classes)	Changes data to be one-hot encoded.
Rename(from_key, to_key)	Transform to rename keys in data dictionary.
Sqrt()	Square-root transform a variable.
Standardize([mean, std, axis, momentum])	Transform that when applied standardizes data using typical z-score standardization i.e. for some unstandardized data x the standardized version z would be.
ToArray()	Checks provided data for any non-arrays and converts them to numpy arrays.
Transform()	Base class on which other transforms are based