synthcity.plugins.core.plugin module

class Plugin(sampling_patience: int = 500, strict: bool = True, device: Any = device(type='cpu'), random_state: int = 0, workspace: pathlib.Path = PosixPath('workspace'), compress_dataset: bool = False, sampling_strategy: str = 'marginal')

Bases: synthcity.plugins.core.serializable.Serializable

Base class for all plugins.

Each derived class must implement the following methods:

type() - a static method that returns the type of the plugin. e.g., debug, generative, bayesian, etc. name() - a static method that returns the name of the plugin. e.g., ctgan, random_noise, etc. hyperparameter_space() - a static method that returns the hyperparameters that can be tuned during AutoML. _fit() - internal method, called by fit on each training set. _generate() - internal method, called by generate.

If any method implementation is missing, the class constructor will fail.

Parameters

• strict – bool. Default = True If True, is raises an exception if the generated data is not following the requested constraints. If False, it returns only the rows that match the constraints.

• workspace – Path Path for caching intermediary results

• compress_dataset – bool. Default = False Drop redundant features before training the generator.

• device – PyTorch device: cpu or cuda.

• random_state – int Random seed

• sampling_patience – int. Max inference iterations to wait for the generated data to match the training schema.

• sampling_strategy – str Internal parameter for schema. marginal or uniform.

class Config

Bases: object

arbitrary_types_allowed = True

validate_assignment = True

fit(X: Union[synthcity.plugins.core.dataloader.DataLoader, pandas.core.frame.DataFrame], *args: Any, **kwargs: Any) → Any

Training method the synthetic data plugin.

Parameters

• X – DataLoader. The reference dataset.

• cond –

  Optional, Union[pd.DataFrame, pd.Series, np.ndarray] Optional Training Conditional. The training conditional can be used to control to output of some models, like GANs or VAEs. The content can be anything, as long as it maps to the training dataset X. Usage example:

  >>> from sklearn.datasets import load_iris
  >>> from synthcity.plugins.core.dataloader import GenericDataLoader
  >>> from synthcity.plugins.core.constraints import Constraints
  >>>
  >>> # Load in `test_plugin` the generative model of choice
  >>> # ....
  >>>
  >>> X, y = load_iris(as_frame=True, return_X_y=True)
  >>> X["target"] = y
  >>>
  >>> X = GenericDataLoader(X)
  >>> test_plugin.fit(X, cond=y)
  >>>
  >>> count = 10
  >>> X_gen = test_plugin.generate(count, cond=np.ones(count))
  >>>
  >>> # The Conditional only optimizes the output generation
  >>> # for GANs and VAEs, but does NOT guarantee the samples
  >>> # are only from that condition.
  >>> # If you want to guarantee that output contains only
  >>> # "target" == 1 samples, use Constraints.
  >>>
  >>> constraints = Constraints(
  >>>     rules=[
  >>>         ("target", "==", 1),
  >>>     ]
  >>> )
  >>> X_gen = test_plugin.generate(count,
  >>>         cond=np.ones(count),
  >>>         constraints=constraints
  >>>        )
  >>> assert (X_gen["target"] == 1).all()
  

Returns

self

classmethod fqdn() → str

The Fully-Qualified name of the plugin.

generate(count: Optional[int] = None, constraints: Optional[synthcity.plugins.core.constraints.Constraints] = None, random_state: Optional[int] = None, **kwargs: Any) → synthcity.plugins.core.dataloader.DataLoader

Synthetic data generation method.

Parameters

• count – optional int. The number of samples to generate. If None, it generated len(reference_dataset) samples.

• cond – Optional, Union[pd.DataFrame, pd.Series, np.ndarray]. Optional Generation Conditional. The conditional can be used only if the model was trained using a conditional too. If provided, it must have count length. Not all models support conditionals. The conditionals can be used in VAEs or GANs to speed-up the generation under some constraints. For model agnostic solutions, check out the constraints parameter.

• constraints –

  optional Constraints. Optional constraints to apply on the generated data. If none, the reference schema constraints are applied. The constraints are model agnostic, and will filter the output of the generative model. The constraints are a list of rules. Each rule is a tuple of the form (<feature>, <operation>, <value>).

  Valid Operations:

  • ”<”, “lt” : less than <value>

  • ”<=”, “le”: less or equal with <value>

  • ”>”, “gt” : greater than <value>

  • ”>=”, “ge”: greater or equal with <value>

  • ”==”, “eq”: equal with <value>

  • ”in”: valid for categorical features, and <value> must be array. for example, (“target”, “in”, [0, 1])

  • ”dtype”: <value> can be a data type. For example, (“target”, “dtype”, “int”)

  Usage example:

  >>> from synthcity.plugins.core.constraints import Constraints
  >>> constraints = Constraints(
  >>>   rules=[
  >>>             ("InterestingFeature", "==", 0),
  >>>         ]
  >>>     )
  >>>
  >>> syn_data = syn_model.generate(
          count=count,
          constraints=constraints
      ).dataframe()
  >>>
  >>> assert (syn_data["InterestingFeature"] == 0).all()
  

• random_state – optional int. Optional random seed to use.

Returns

<count> synthetic samples

abstract static hyperparameter_space(**kwargs: Any) → List[synthcity.plugins.core.distribution.Distribution]

Returns the hyperparameter space for the derived plugin.

static load(buff: bytes) → Any

static load_dict(representation: dict) → Any

abstract static name() → str

The name of the plugin.

plot(plt: Any, X: synthcity.plugins.core.dataloader.DataLoader, count: Optional[int] = None, plots: list = ['marginal', 'associations', 'tsne'], **kwargs: Any) → Any

Plot the real-synthetic distributions.

Parameters

• plt – output

• X – DataLoader. The reference dataset.

Returns

self

classmethod sample_hyperparameters(*args: Any, **kwargs: Any) → Dict[str, Any]

Sample value from the hyperparameter space for the current plugin.

classmethod sample_hyperparameters_optuna(trial: Any, *args: Any, **kwargs: Any) → Dict[str, Any]

save() → bytes

save_dict() → dict

save_to_file(path: pathlib.Path) → bytes

schema() → synthcity.plugins.core.schema.Schema

The reference schema

schema_includes(other: Union[synthcity.plugins.core.dataloader.DataLoader, pandas.core.frame.DataFrame]) → bool

Helper method to test if the reference schema includes a Dataset

Parameters

other – DataLoader. The dataset to test

Returns

bool, if the schema includes the dataset or not.

training_schema() → synthcity.plugins.core.schema.Schema

The internal schema

abstract static type() → str

The type of the plugin.

static version() → str

API version

class PluginLoader(plugins: list, expected_type: Type, categories: list)

Bases: object

Plugin loading utility class. Used to load the plugins from the current folder.

add(name: str, cls: Type) → synthcity.plugins.core.plugin.PluginLoader

Add a new plugin

get(name: str, *args: Any, **kwargs: Any) → Any

Create a new object from a plugin. :param name: str. The name of the plugin :param &args: :param **kwargs. Plugin specific arguments:

Returns

The new object

get_type(name: str) → Type

Get the class type of a plugin. :param name: str. The name of the plugin

Returns

The class of the plugin

list() → List[str]

Get all the available plugins.

load(buff: bytes) → Any

Load serialized plugin

reload() → synthcity.plugins.core.plugin.PluginLoader

types() → List[Type]

Get the loaded plugins types