Within a mlm:input definition, when a norm_type is specified, it is expected that the corresponding statistics details are provided for the relevant normalization technique.
For example, a z-score needs the corresponding mean and stddev (minimally), while min-max would require the minimum and maximum statistics.
For the JSON schema, the dependencies property could be used to check that when norm_type is equal to some constant, the subset of corresponding statistics are defined + minItems: 1 or similar. In the stac_model definition, a @model_validator(mode="after") on the ModelInput could be applied.
:sound: Motivation
Ensure that the intended definition is properly specified.
If the user forgot to apply some parameters unintentionally, this check would ensure the error is caught early, increasing model reusability. Not all cases can necessarily be covered, but most common normalization techniques could be handled.
:rocket: Feature Request
Within a
mlm:inputdefinition, when anorm_typeis specified, it is expected that the correspondingstatisticsdetails are provided for the relevant normalization technique.For example, a
z-scoreneeds the correspondingmeanandstddev(minimally), whilemin-maxwould require theminimumandmaximumstatistics.For the JSON schema, the
dependenciesproperty could be used to check that whennorm_typeis equal to some constant, the subset of correspondingstatisticsare defined +minItems: 1or similar. In thestac_modeldefinition, a@model_validator(mode="after")on theModelInputcould be applied.:sound: Motivation
Ensure that the intended definition is properly specified. If the user forgot to apply some parameters unintentionally, this check would ensure the error is caught early, increasing model reusability. Not all cases can necessarily be covered, but most common normalization techniques could be handled.
:satellite: Alternatives
There are no explicit validation currently.
:paperclip: Additional context
n/a