Available transform

PTR

class optunaz.utils.preprocessing.transform.PTRTransform(name='PTRTransform', parameters=PTRTransform.Parameters(threshold=None, std=None))

Transform model input/output with PTR

class Parameters(threshold=None, std=None)

Parameters:

• threshold (float) – The decision boundary for discretising active or inactive classes used by PTR. - title: PTR Threshold

• std (float) – The standard deviation used by PTR, e.g. experimental reproducibility/uncertainty - title: PTR standard deviation

ModelDataTransform

class optunaz.utils.preprocessing.transform.ModelDataTransform(name='ModelDataTransform', parameters=ModelDataTransform.Parameters(base=None, negation=None, conversion=None))

Data transformer that applies and reverses logarithmic functions to user data

class Parameters(base=None, negation=None, conversion=None)

Parameters:

• base (LogBase) – The log, log2 or log10 base to use in log transformation - title: Base

• negation (LogNegative) – Whether or not to make the log transform performed negated (-) - title: Negation

• conversion (Optional) – The conversion power applied in the log transformation - title: Conversion power

VectorFromColumn

class optunaz.utils.preprocessing.transform.VectorFromColumn(name='VectorFromColumn', parameters=VectorFromColumn.Parameters(delimiter=','))

Vector from column

Splits delimited values from in inputs into usable vectors

class Parameters(delimiter=',')

Parameters:

delimiter (str) – String used to split the auxiliary column into a vector - title: Delimiter

ZScales

class optunaz.utils.preprocessing.transform.ZScales(name='ZScales', parameters=ZScales.Parameters())

Z-scales from column

Calculates Z-scores for sequences or a predefined list of peptide/protein targets

class Parameters