synthcity.plugins.core.schema module
- class Schema(*, sampling_strategy: str = 'marginal', protected_cols: List[str] = ['seq_id'], random_state: int = 0, data: Any = None, domain: Dict = {})
Bases:
pydantic.main.BaseModel
Utility class for defining the schema of a Dataset.
- Constructor Args:
- domain: Dict
A dictionary of feature_name: Distribution.
- sampling_strategy: str
Taking value of “marginal” (default) or “uniform” (for debugging).
- protected_cols: List[str]
List of columns that are exempt from distributional constraints (e.g. ID column)
- random_state: int
Random seed (default 0)
- data: Any
(Optional) the data set
- Config
alias of
pydantic.config.BaseConfig
- adapt_dtypes(X: pandas.core.frame.DataFrame) pandas.core.frame.DataFrame
Applying the data type to a new data frame
- Parameters
X – pd.DataFrame A new data frame to be adapted.
- Returns
A data frame whose data types are coerced to be the same with the Schema. If the data frame contains new features, these will be retained as is.
- as_constraints() synthcity.plugins.core.constraints.Constraints
Convert the schema to a list of Constraints.
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model
- Returns
new model instance
- data: Any
- dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- domain: Dict
- features() List
- classmethod from_constraints(constraints: synthcity.plugins.core.constraints.Constraints) synthcity.plugins.core.schema.Schema
Create a schema from a list of Constraints.
- classmethod from_orm(obj: Any) Model
- get(feature: str) synthcity.plugins.core.distribution.Distribution
Get the Distribution of a feature.
- Parameters
feature – str. the feature name
- Returns
The feature distribution
- includes(other: synthcity.plugins.core.schema.Schema) bool
Test if another schema is included in the local one.
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
- classmethod parse_obj(obj: Any) Model
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
- protected_cols: List[str]
- random_state: int
- sample(count: int) pandas.core.frame.DataFrame
- sampling_strategy: str
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
- classmethod update_forward_refs(**localns: Any) None
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model