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