parameters

parameters

Classes:

Name	Description
SafeSynthesizerParameters	Main configuration class for the Safe Synthesizer pipeline.

SafeSynthesizerParameters pydantic-model

Bases: Parameters

Main configuration class for the Safe Synthesizer pipeline.

This is the top-level configuration class that orchestrates all aspects of synthetic data generation including training, generation, privacy, evaluation, and data handling. It provides validation to ensure parameter compatibility.

Fields:

• data (DataParameters)
• evaluation (EvaluationParameters)
• training (TrainingHyperparams)
• generation (GenerateParameters)
• privacy (DifferentialPrivacyHyperparams | None)
• time_series (TimeSeriesParameters)
• replace_pii (PiiReplacerConfig | None)
• preflight (PreflightParameters)
• emit_telemetry (bool)

Validators:

• _validate_and_resolve_data_params
• check_timeseries_group_column

data pydantic-field

Configuration controlling how input data is grouped and split for training and evaluation.

evaluation pydantic-field

Parameters for evaluating the quality of generated synthetic data.

training pydantic-field

Hyperparameters for model training such as learning rate, batch size, and LoRA adapter settings.

generation pydantic-field

Parameters governing synthetic data generation including temperature, top-p, and number of records to produce.

privacy pydantic-field

Differential-privacy hyperparameters. When None, differential privacy is disabled entirely.

time_series pydantic-field

Configuration for time-series mode. Time-series pipeline is currently experimental.

replace_pii pydantic-field

PII replacement configuration. When None, PII replacement is skipped.

preflight pydantic-field

Preflight validation overrides, including checks to skip via disabled_checks.

emit_telemetry pydantic-field

Whether to emit anonymous Safe Synthesizer telemetry events. Defaults from NEMO_TELEMETRY_ENABLED when unset.

from_params(**kwargs) classmethod

Convert singular, flat parameters to nested structure.

Takes a flat dictionary of parameters, where keys correspond to attributes of the nested parameter classes, and constructs a SafeSynthesizerParameters instance with the appropriate nested structure, using default values for each subgroup that are not explicitly provided.

Args: **kwargs: Flat key-value pairs that map to attributes of the nested parameter classes (e.g., TrainingHyperparams, GenerateParameters).

Returns: A fully initialized SafeSynthesizerParameters instance with nested sub-configurations populated from the provided values.

Example

from nemo_safe_synthesizer.config import SafeSynthesizerParameters SafeSynthesizerParameters.from_params(use_structured_generation=True)

Source code in src/nemo_safe_synthesizer/config/parameters.py

@classmethod
def from_params(cls, **kwargs) -> "SafeSynthesizerParameters":
    """Convert singular, flat parameters to nested structure.

      Takes a flat dictionary of parameters, where keys correspond to
      attributes of the nested parameter classes, and constructs a
      ``SafeSynthesizerParameters`` instance with the appropriate nested
      structure, using default values for each subgroup that are not
      explicitly provided.

      Args:
          **kwargs: Flat key-value pairs that map to attributes of the
              nested parameter classes (e.g., ``TrainingHyperparams``,
              ``GenerateParameters``).

      Returns:
          A fully initialized ``SafeSynthesizerParameters`` instance with
          nested sub-configurations populated from the provided values.

    Example:
        >>> from nemo_safe_synthesizer.config import SafeSynthesizerParameters
        >>> SafeSynthesizerParameters.from_params(use_structured_generation=True)
    """
    thp = TrainingHyperparams().model_copy(update=kwargs)
    gp = GenerateParameters().model_copy(update=kwargs)
    ep = EvaluationParameters().model_copy(update=kwargs)
    pp = DifferentialPrivacyHyperparams().model_copy(update=kwargs)
    dp = DataParameters().model_copy(update=kwargs)
    tsp = TimeSeriesParameters().model_copy(update=kwargs)

    extra: dict[str, Any] = {
        "training": thp,
        "generation": gp,
        "evaluation": ep,
        "privacy": pp,
        "data": dp,
        "time_series": tsp,
    }
    if "replace_pii" in kwargs:
        extra["replace_pii"] = kwargs["replace_pii"]
    if "preflight" in kwargs:
        extra["preflight"] = kwargs["preflight"]
    if "emit_telemetry" in kwargs:
        extra["emit_telemetry"] = kwargs["emit_telemetry"]
    return cls(**extra)