All Configs are derived from rastervision.pipeline.config.Config, which itself is a pydantic Model.

pydantic model RegressionDataConfig[source]#

Show JSON schema
   "title": "RegressionDataConfig",
   "description": "Base class that can be extended to provide custom configurations.\n\nThis adds some extra methods to Pydantic BaseModel.\nSee\n\nThe general idea is that configuration schemas can be defined by\nsubclassing this and adding class attributes with types and\ndefault values for each field. Configs can be defined hierarchically,\nie. a Config can have fields which are of type Config.\nValidation, serialization, deserialization, and IDE support is\nprovided automatically based on this schema.",
   "type": "object",
   "properties": {
      "pos_class_names": {
         "title": "Pos Class Names",
         "default": [],
         "type": "array",
         "items": {
            "type": "string"
      "prob_class_names": {
         "title": "Prob Class Names",
         "default": [],
         "type": "array",
         "items": {
            "type": "string"
      "type_hint": {
         "title": "Type Hint",
         "default": "regression_data",
         "enum": [
         "type": "string"
   "additionalProperties": false

  • extra: str = forbid

  • validate_assignment: bool = True

field pos_class_names: List[str] = []#
field prob_class_names: List[str] = []#
field type_hint: Literal['regression_data'] = 'regression_data'#

Build an instance of the corresponding type of object using this config.

For example, BackendConfig will build a Backend object. The arguments to this method will vary depending on the type of Config.


Recursively validate hierarchies of Configs.

This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.


Re-validate an instantiated Config.

Runs all Pydantic validators plus self.validate_config().

Adapted from:

update(*args, **kwargs)#

Update any fields before validation.

Subclasses should override this to provide complex default behavior, for example, setting default values as a function of the values of other fields. The arguments to this method will vary depending on the type of Config.


Validate fields that should be checked after update is called.

This is to complement the builtin validation that Pydantic performs at the time of object construction.

validate_list(field: str, valid_options: List[str])#

Validate a list field.

  • field (str) – name of field to validate

  • valid_options (List[str]) – values that field is allowed to take


ConfigError – if field is invalid