ConversionConfig

@dataclass
class blackbox2c.ConversionConfig

Dataclass holding all conversion parameters. Pass to Converter(config) or as **kwargs to convert().

Parameters

Parameter	Type	Default	Description
max_depth	int	5	Max depth of the surrogate decision tree. Range: 1-10. Higher = more accurate but larger code.
optimize_rules	str	'medium'	Rule optimization level: 'low' (light pruning), 'medium' (pruning), 'high' (pruning + leaf merging).
use_fixed_point	bool	False	Use integer arithmetic instead of float. Useful for MCUs without FPU.
precision	int	8	Bit width for fixed-point scaling: 8, 16, or 32. Ignored if use_fixed_point=False.
function_name	str	'predict'	Name of the generated C/C++ function.
n_samples	int	10000	Number of synthetic samples generated for surrogate training.
feature_threshold	int	None	If set, automatically selects the N most important features before conversion.
memory_budget_kb	float	None	Target memory budget in KB. Auto-adjusts max_depth, precision, and use_fixed_point.
random_state	int	42	Random seed for reproducibility.
include_probabilities	bool	False	Not yet implemented. Will emit a warning if set to True.

Memory Budget Auto-tuning

When memory_budget_kb is set, parameters are adjusted automatically:

Budget	Effect
< 1 KB	max_depth <= 3, precision=8, use_fixed_point=True
1-2 KB	max_depth <= 4, precision=8
2-4 KB	max_depth <= 6

Example

from blackbox2c import ConversionConfig

# For a very constrained MCU (ATmega328P, 2KB RAM)
config = ConversionConfig(
    max_depth=3,
    use_fixed_point=True,
    precision=8,
    optimize_rules='high',
)

# Auto-tune for 1KB budget
config = ConversionConfig(memory_budget_kb=1.0)

# Feature selection: keep only 3 most important features
config = ConversionConfig(feature_threshold=3, max_depth=4)