synthcity.plugins.core.constraints module

class Constraints(*, rules: list = [])

Bases: pydantic.main.BaseModel

Constraints on data.

The Constraints class allows users to specify constraints on the features. Examples include the feature value range, allowed item set, and data type. These constraints can be used to filter out invalid values in synthetic datasets.

Constructor Args:

rules: List[Tuple]

Each tuple in the list specifies a constraint on a feature. The tuple has the form of (feature, op, thresh), where feature is the feature name to apply constraint on, op takes values in [

“<”, “>=”, “<=”, “>”, “==”, “lt”, “le”, “gt”, “ge”, “eq”, “in”, “dtype”,

],

and thresh is the threshold or data type.

Config

alias of pydantic.config.BaseConfig

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

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.

extend(other: synthcity.plugins.core.constraints.Constraints) → synthcity.plugins.core.constraints.Constraints

Extend the local constraints with more constraints.

Parameters

other – The new constraints to add.

Returns

self with the updated constraints.

feature_constraints(ref_feature: str) → List

Get constraints for a given feature

Parameters

ref_feature – str The name of the feature of interest.

Returns

[(‘le’, 3.), (‘gt’, 1.)]

If ref_feature has no constraint, None will be returned.

Return type

A list of tuples of (op, threshold) For example

feature_params(feature: str) → Tuple

Provide the parameters of Distribution from the Constraint

This is to be used with the constraint_to_distribution function in distribution module.

Parameters

feature – str The name of the feature of interest.

Returns

str

The type of inferred distribution from (“categorical”, “float”, “integer”)

dist_args: Dict

The arguments to the constructor of the Distribution.

Return type

dist_template

features() → List

Return list of feature names in an undefined order

filter(X: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Apply the constraints to a DataFrame X.

Parameters

X – DataFrame. The dataset to apply the constraints on.

Returns

pandas.Index which matches all the constraints

classmethod from_orm(obj: Any) → Model

is_valid(X: pandas.core.frame.DataFrame) → bool

Checks if all the rows in X meet the constraints.

Parameters

X – DataFrame. The dataset to apply the constraints on.

Returns

True if all rows match the constraints, False otherwise

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().

match(X: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Apply the constraints to a DataFrame X and return the filtered dataset.

Parameters

X – DataFrame. The dataset to apply the constraints on.

Returns

The filtered Dataframe

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

rules: list

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