Classifier optimization

Classifier optimization is an optional post-classify stage for neural readout methods. The canonical import path is arcade.classifieroptimization. The module arcade.optimization is a compatibility shim that re-exports the same public API.

Enable the stage through YAML as shown in Enable optimization.

Public surface

Symbol	Role
BaseOptimizer	Optimizer contract
OptimizationResult	Structured result object
register_optimizer, get_optimizer, list_optimizers	Registry helpers
PruningOptimizer	Weight pruning under an error budget
DistillationOptimizer	Teacher to student distillation
NASOptimizer	Architecture search
HyperparameterTuner	Optuna or grid search via tune and maybe_tune
run_optimization_stage	Pipeline stage entry point
pareto_frontier, select_best	Multi-objective selection helpers

Quantization uses arcade.nn.quantization.quantize_model from the stage (no QuantizationOptimizer class).

Config models

The top-level optimization section maps to OptimizationConfig in arcade.config:

Field	Default	Notes
enabled	false	Master switch
target_classifiers	empty list	Empty means all eligible neural methods
error_budget	0.01	Allowed accuracy drop
pruning, distillation, quantization, nas	nested configs	Per-technique flags

Hyperparameter search uses the sibling tuning block as TuningConfig. It is not nested under optimization.

Example

optimization:
  enabled: true
  target_classifiers: [fnn]
  error_budget: 0.01
  pruning:
    enabled: true

tuning:
  enabled: false

from arcade.classifieroptimization import (
    list_optimizers,
    pareto_frontier,
    run_optimization_stage,
)

print(list_optimizers())

Autosummary pages live under API modules.