VariableScaling#
- class pymc_marketing.mmm.scaling.VariableScaling(**data)[source]#
How to scale a variable.
The scaling through the dimension of ‘date’ is assumed and doesn’t need to be specified.
Methods
VariableScaling.__init__(**data)Create a new model by parsing and validating input data from keyword arguments.
VariableScaling.construct([_fields_set])VariableScaling.copy(*[, include, exclude, ...])Returns a copy of the model.
VariableScaling.dict(*[, include, exclude, ...])VariableScaling.json(*[, include, exclude, ...])VariableScaling.model_construct([_fields_set])Creates a new instance of the
Modelclass with validated data.VariableScaling.model_copy(*[, update, deep])!!! abstract "Usage Documentation"
VariableScaling.model_dump(*[, mode, ...])!!! abstract "Usage Documentation"
VariableScaling.model_dump_json(*[, indent, ...])!!! abstract "Usage Documentation"
Generates a JSON schema for a model class.
Compute the class name for parametrizations of generic classes.
VariableScaling.model_post_init(context, /)Override this method to perform additional initialization after
__init__andmodel_construct.VariableScaling.model_rebuild(*[, force, ...])Try to rebuild the pydantic-core schema for the model.
VariableScaling.model_validate(obj, *[, ...])Validate a pydantic model instance.
VariableScaling.model_validate_json(json_data, *)!!! abstract "Usage Documentation"
Validate the given object with string data against the Pydantic model.
VariableScaling.parse_file(path, *[, ...])VariableScaling.parse_raw(b, *[, ...])VariableScaling.schema([by_alias, ref_template])VariableScaling.schema_json(*[, by_alias, ...])VariableScaling.update_forward_refs(**localns)VariableScaling.validate(value)Attributes
model_computed_fieldsmodel_configConfiguration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].model_extraGet extra fields set during validation.
model_fieldsmodel_fields_setReturns the set of fields that have been explicitly set on this model instance.
methoddims