VariableSchema

class pymc_marketing.data.idata.schema.VariableSchema(**data)

Schema for a single variable in InferenceData.

Validates the structure (dimensions and dtype) of xarray.DataArray variables within InferenceData groups.

Parameters:

namestr

Variable name

requiredbool, default True

Whether this variable must be present

dimstuple of str or “*”

Expected dimension names. Use “*” to accept any dimensions.

dtypestr, tuple of str, or None

Expected numpy dtype(s) (e.g., “float64”, “int64”, or (“float64”, “int64”)). Use None to skip dtype validation.

descriptionstr, default “”

Human-readable description of this variable

Examples

>>> schema = VariableSchema(
...     name="channel_contribution",
...     dims=("date", "channel"),
...     dtype="float64",
...     required=True,
... )
>>> data_array = xr.DataArray(...)
>>> errors = schema.validate_variable(data_array)
>>> if errors:
...     print("Validation errors:", errors)

Methods

VariableSchema.__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
VariableSchema.construct([_fields_set])
VariableSchema.copy(*[, include, exclude, ...])	Returns a copy of the model.
VariableSchema.dict(*[, include, exclude, ...])
VariableSchema.from_orm(obj)
VariableSchema.json(*[, include, exclude, ...])
VariableSchema.model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
VariableSchema.model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
VariableSchema.model_dump(*[, mode, ...])	!!! abstract "Usage Documentation"
VariableSchema.model_dump_json(*[, indent, ...])	!!! abstract "Usage Documentation"
VariableSchema.model_json_schema([by_alias, ...])	Generates a JSON schema for a model class.
VariableSchema.model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
VariableSchema.model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
VariableSchema.model_rebuild(*[, force, ...])	Try to rebuild the pydantic-core schema for the model.
VariableSchema.model_validate(obj, *[, ...])	Validate a pydantic model instance.
VariableSchema.model_validate_json(json_data, *)	!!! abstract "Usage Documentation"
VariableSchema.model_validate_strings(obj, *)	Validate the given object with string data against the Pydantic model.
VariableSchema.parse_file(path, *[, ...])
VariableSchema.parse_obj(obj)
VariableSchema.parse_raw(b, *[, ...])
VariableSchema.schema([by_alias, ref_template])
VariableSchema.schema_json(*[, by_alias, ...])
VariableSchema.update_forward_refs(**localns)
VariableSchema.validate(value)
VariableSchema.validate_variable(data_array)	Validate variable structure.

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.
name
required
dims
dtype
description