MonthlyFourier

class pymc_marketing.mmm.fourier.MonthlyFourier(**data)

Monthly fourier seasonality.

(Source code, png, hires.png, pdf)

n_orderint

Number of fourier modes to use.

prefixstr, optional

Alternative prefix for the fourier seasonality, by default None or “fourier”

priorPrior | VariableFactory, optional

Prior distribution or VariableFactory for the fourier seasonality beta parameters, by default Prior("Laplace", mu=0, b=1)

namestr, optional

Name of the variable that multiplies the fourier modes, by default None

variable_namestr, optional

Name of the variable that multiplies the fourier modes, by default None

Methods

MonthlyFourier.__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
MonthlyFourier.apply(dayofperiod[, ...])	Apply fourier seasonality to day of year.
MonthlyFourier.construct([_fields_set])
MonthlyFourier.copy(*[, include, exclude, ...])	Returns a copy of the model.
MonthlyFourier.dict(*[, include, exclude, ...])
MonthlyFourier.from_dict(data)	Deserialize the Fourier seasonality.
MonthlyFourier.from_orm(obj)
MonthlyFourier.get_default_start_date([...])	Get the start date for the Fourier curve.
MonthlyFourier.json(*[, include, exclude, ...])
MonthlyFourier.model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
MonthlyFourier.model_copy(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
MonthlyFourier.model_dump(*[, mode, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
MonthlyFourier.model_dump_json(*[, indent, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
MonthlyFourier.model_json_schema([by_alias, ...])	Generates a JSON schema for a model class.
MonthlyFourier.model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
MonthlyFourier.model_post_init(...)	Model post initialization for a Pydantic model.
MonthlyFourier.model_rebuild(*[, force, ...])	Try to rebuild the pydantic-core schema for the model.
MonthlyFourier.model_validate(obj, *[, ...])	Validate a pydantic model instance.
MonthlyFourier.model_validate_json(json_data, *)	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
MonthlyFourier.model_validate_strings(obj, *)	Validate the given object with string data against the Pydantic model.
MonthlyFourier.parse_file(path, *[, ...])
MonthlyFourier.parse_obj(obj)
MonthlyFourier.parse_raw(b, *[, ...])
MonthlyFourier.plot_curve(curve[, ...])	Plot the seasonality for one full period.
MonthlyFourier.plot_curve_hdi(curve[, ...])	Plot full period of the fourier seasonality.
MonthlyFourier.plot_curve_samples(curve[, ...])	Plot samples from the curve.
MonthlyFourier.sample_curve(parameters[, ...])	Create full period of the Fourier seasonality.
MonthlyFourier.sample_prior([coords])	Sample the prior distributions.
MonthlyFourier.schema([by_alias, ref_template])
MonthlyFourier.schema_json(*[, by_alias, ...])
MonthlyFourier.serialize_prior()	Serialize the prior distribution.
MonthlyFourier.to_dict()	Serialize the Fourier seasonality.
MonthlyFourier.update_forward_refs(**localns)
MonthlyFourier.validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
nodes	Fourier node names for model coordinates.
days_in_period
n_order
prefix
prior
variable_name