"nsgaiii",
)
_OPTUNA_HPO_SAMPLERS: Final[tuple[OptunaSampler, ...]] = _OPTUNA_SAMPLERS[:2]
+ _OPTUNA_HPO_SAMPLERS_SET: Final[frozenset[OptunaSampler]] = frozenset(
+ _OPTUNA_HPO_SAMPLERS
+ )
_OPTUNA_LABEL_SAMPLERS: Final[tuple[OptunaSampler, ...]] = (
_OPTUNA_SAMPLERS[1], # "auto"
_OPTUNA_SAMPLERS[0], # "tpe"
_OPTUNA_SAMPLERS[2], # "nsgaii"
_OPTUNA_SAMPLERS[3], # "nsgaiii"
)
+ _OPTUNA_LABEL_SAMPLERS_SET: Final[frozenset[OptunaSampler]] = frozenset(
+ _OPTUNA_LABEL_SAMPLERS
+ )
_SCALER_TYPES: Final[tuple[ScalerType, ...]] = (
"minmax",
"standard",
"robust",
)
+ _SCALER_TYPES_SET: Final[frozenset[ScalerType]] = frozenset(_SCALER_TYPES)
SCALER_DEFAULT: Final[ScalerType] = _SCALER_TYPES[0] # "minmax"
RANGE_DEFAULT: Final[tuple[float, float]] = (-1.0, 1.0)
"compromise_programming",
"topsis",
)
+ _DISTANCE_METHODS_SET: Final[frozenset[DistanceMethod]] = frozenset(
+ _DISTANCE_METHODS
+ )
_CLUSTER_METHODS: Final[tuple[ClusterMethod, ...]] = (
"kmeans",
"kmeans2",
*_CLUSTER_METHODS,
*_DENSITY_METHODS,
)
+ _SELECTION_METHODS_SET: Final[frozenset[SelectionMethod]] = frozenset(
+ _SELECTION_METHODS
+ )
_DISTANCE_METRICS: Final[tuple[str, ...]] = (
"euclidean",
"power_mean",
"weighted_sum",
)
+ _DISTANCE_METRICS_SET: Final[frozenset[str]] = frozenset(_DISTANCE_METRICS)
+ # SciPy-compatible distance metrics: the first 8 entries of
+ # ``_DISTANCE_METRICS`` route to ``scipy.spatial.distance.cdist``.
+ _SCIPY_METRICS_SET: Final[frozenset[str]] = frozenset(_DISTANCE_METRICS[:8])
_UNSUPPORTED_WEIGHTS_METRICS: Final[tuple[str, ...]] = (
_DISTANCE_METRICS[6], # "mahalanobis"
_DISTANCE_METRICS[5], # "seuclidean"
_DISTANCE_METRICS[7], # "jensenshannon"
)
+ _UNSUPPORTED_WEIGHTS_METRICS_SET: Final[frozenset[str]] = frozenset(
+ _UNSUPPORTED_WEIGHTS_METRICS
+ )
_PROBABILITY_DISTANCE_METRICS: Final[tuple[str, ...]] = (
"jensenshannon",
"hellinger",
"shellinger",
)
+ _PROBABILITY_DISTANCE_METRICS_SET: Final[frozenset[str]] = frozenset(
+ _PROBABILITY_DISTANCE_METRICS
+ )
+ _LABEL_SELECTION_DISTANCE_METRICS_SET: Final[frozenset[str]] = (
+ _DISTANCE_METRICS_SET - _PROBABILITY_DISTANCE_METRICS_SET
+ )
+ # Aggregate metrics: distance metrics computed by reduction over
+ # objective coordinates rather than SciPy/sklearn pairwise routines
+ # (``harmonic_mean``, ``geometric_mean``, ``arithmetic_mean``,
+ # ``quadratic_mean``, ``cubic_mean``, ``power_mean``, ``weighted_sum``).
+ # Accepted by ``compromise_programming``/``topsis`` via
+ # ``_calculate_trial_distance_to_ideal``; rejected by cluster/density
+ # categories that route to ``pairwise_distances``/``KMeans``/
+ # ``KMedoids``/``NearestNeighbors``.
+ _AGGREGATE_DISTANCE_METRICS_SET: Final[frozenset[str]] = (
+ _DISTANCE_METRICS_SET - _SCIPY_METRICS_SET - _PROBABILITY_DISTANCE_METRICS_SET
+ )
+ # SciPy-compatible non-probability metrics. Accepted by cluster/density
+ # categories that route to ``pairwise_distances``/``KMeans``/
+ # ``KMedoids``/``NearestNeighbors``; rejected by the aggregate set and
+ # by probability metrics.
+ _CLUSTER_DENSITY_DISTANCE_METRICS_SET: Final[frozenset[str]] = (
+ _SCIPY_METRICS_SET - _PROBABILITY_DISTANCE_METRICS_SET
+ )
# Absolute tolerance (rtol=0) for constant-column detection in
# `_non_constant_objective_indices`; valid on the [0,1]-normalized
"min",
"max",
)
+ _DENSITY_AGGREGATIONS_SET: Final[frozenset[DensityAggregation]] = frozenset(
+ _DENSITY_AGGREGATIONS
+ )
_POWER_MEAN_MAP: Final[dict[str, float]] = {
"harmonic_mean": -1.0,
"quadratic_mean": 2.0,
"cubic_mean": 3.0,
}
+ _POWER_MEAN_METRICS_SET: Final[frozenset[str]] = frozenset(_POWER_MEAN_MAP)
FIT_LIVE_PREDICTIONS_CANDLES_DEFAULT: Final[int] = (
DEFAULT_FIT_LIVE_PREDICTIONS_CANDLES
TIMESERIES_GAP_DEFAULT: Final[int] = 0
TIMESERIES_MAX_TRAIN_SIZE_DEFAULT: Final[int | None] = None
- @staticmethod
- @lru_cache(maxsize=None)
- def _extrema_selection_methods_set() -> set[ExtremaSelectionMethod]:
- return set(EXTREMA_SELECTION_METHODS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _custom_threshold_methods_set() -> set[CustomThresholdMethod]:
- return set(CUSTOM_THRESHOLD_METHODS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _skimage_threshold_methods_set() -> set[SkimageThresholdMethod]:
- return set(SKIMAGE_THRESHOLD_METHODS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _threshold_methods_set() -> set[ThresholdMethod]:
- return set(THRESHOLD_METHODS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _optuna_namespaces_set() -> set[OptunaNamespace]:
- return set(_OPTUNA_NAMESPACES)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _optuna_hpo_samplers_set() -> set[OptunaSampler]:
- return set(QuickAdapterRegressorV3._OPTUNA_HPO_SAMPLERS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _optuna_label_samplers_set() -> set[OptunaSampler]:
- return set(QuickAdapterRegressorV3._OPTUNA_LABEL_SAMPLERS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _scaler_types_set() -> set[ScalerType]:
- return set(QuickAdapterRegressorV3._SCALER_TYPES)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _scipy_metrics_set() -> set[str]:
- return set(QuickAdapterRegressorV3._DISTANCE_METRICS[:8])
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _unsupported_weights_metrics_set() -> set[str]:
- return set(QuickAdapterRegressorV3._UNSUPPORTED_WEIGHTS_METRICS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _probability_distance_metrics_set() -> set[str]:
- return set(QuickAdapterRegressorV3._PROBABILITY_DISTANCE_METRICS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _label_selection_distance_metrics_set() -> set[str]:
- return (
- QuickAdapterRegressorV3._distance_metrics_set()
- - QuickAdapterRegressorV3._probability_distance_metrics_set()
- )
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _distance_methods_set() -> set[DistanceMethod]:
- return set(QuickAdapterRegressorV3._DISTANCE_METHODS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _selection_methods_set() -> set[str]:
- return set(QuickAdapterRegressorV3._SELECTION_METHODS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _distance_metrics_set() -> set[str]:
- return set(QuickAdapterRegressorV3._DISTANCE_METRICS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _density_aggregations_set() -> set[str]:
- return set(QuickAdapterRegressorV3._DENSITY_AGGREGATIONS)
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _power_mean_metrics_set() -> set[str]:
- return set(QuickAdapterRegressorV3._POWER_MEAN_MAP.keys())
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _aggregate_distance_metrics_set() -> set[str]:
- """Aggregate metrics: distance metrics computed by reduction over
- objective coordinates rather than SciPy/sklearn pairwise routines.
-
- Computed as the complement of SciPy and probability metrics over
- ``_DISTANCE_METRICS``: ``harmonic_mean``, ``geometric_mean``,
- ``arithmetic_mean``, ``quadratic_mean``, ``cubic_mean``,
- ``power_mean``, ``weighted_sum``. Accepted by
- ``compromise_programming``/``topsis`` via
- ``_calculate_trial_distance_to_ideal``; rejected by
- cluster/density categories that route to
- ``pairwise_distances``/``KMeans``/``KMedoids``/``NearestNeighbors``.
- """
- return (
- QuickAdapterRegressorV3._distance_metrics_set()
- - QuickAdapterRegressorV3._scipy_metrics_set()
- - QuickAdapterRegressorV3._probability_distance_metrics_set()
- )
-
- @staticmethod
- @lru_cache(maxsize=None)
- def _cluster_density_distance_metrics_set() -> set[str]:
- """SciPy-compatible non-probability metrics.
-
- Accepted by cluster/density categories that route to
- ``pairwise_distances``/``KMeans``/``KMedoids``/``NearestNeighbors``;
- rejected by the aggregate set and by probability metrics.
- """
- return (
- QuickAdapterRegressorV3._scipy_metrics_set()
- - QuickAdapterRegressorV3._probability_distance_metrics_set()
- )
+ _EXTREMA_SELECTION_METHODS_SET: Final[frozenset[ExtremaSelectionMethod]] = (
+ frozenset(EXTREMA_SELECTION_METHODS)
+ )
+ _CUSTOM_THRESHOLD_METHODS_SET: Final[frozenset[CustomThresholdMethod]] = frozenset(
+ CUSTOM_THRESHOLD_METHODS
+ )
+ _SKIMAGE_THRESHOLD_METHODS_SET: Final[frozenset[SkimageThresholdMethod]] = (
+ frozenset(SKIMAGE_THRESHOLD_METHODS)
+ )
+ _THRESHOLD_METHODS_SET: Final[frozenset[ThresholdMethod]] = frozenset(
+ THRESHOLD_METHODS
+ )
+ _OPTUNA_NAMESPACES_SET: Final[frozenset[OptunaNamespace]] = frozenset(
+ _OPTUNA_NAMESPACES
+ )
@staticmethod
def _coerce_int(value: Any, name: str, *, minimum: int) -> int:
def _validate_metric_weights_support(
metric: str, *, ctx: str, mode: ValidationMode = "warn"
) -> Optional[str]:
- if metric not in QuickAdapterRegressorV3._unsupported_weights_metrics_set():
+ if metric not in QuickAdapterRegressorV3._UNSUPPORTED_WEIGHTS_METRICS_SET:
return metric
if mode == "none":
) -> str:
if aggregate_allowed:
valid_metrics = (
- QuickAdapterRegressorV3._label_selection_distance_metrics_set()
+ QuickAdapterRegressorV3._LABEL_SELECTION_DISTANCE_METRICS_SET
)
else:
# Cluster/density paths route the metric to SciPy/sklearn APIs
# (pairwise_distances, KMeans, KMedoids, NearestNeighbors) which
- # reject `_aggregate_distance_metrics_set()`; restrict the
+ # reject `_AGGREGATE_DISTANCE_METRICS_SET`; restrict the
# valid set to SciPy-compatible non-probability metrics.
valid_metrics = (
- QuickAdapterRegressorV3._cluster_density_distance_metrics_set()
+ QuickAdapterRegressorV3._CLUSTER_DENSITY_DISTANCE_METRICS_SET
)
valid_options = tuple(
candidate
def _resolve_label_method_config(self, label_method: str) -> dict[str, Any]:
QuickAdapterRegressorV3._validate_enum_value(
label_method,
- QuickAdapterRegressorV3._selection_methods_set(),
+ QuickAdapterRegressorV3._SELECTION_METHODS_SET,
QuickAdapterRegressorV3._SELECTION_METHODS,
ctx="label_method",
)
)
QuickAdapterRegressorV3._validate_enum_value(
selection_method,
- QuickAdapterRegressorV3._distance_methods_set(),
+ QuickAdapterRegressorV3._DISTANCE_METHODS_SET,
QuickAdapterRegressorV3._DISTANCE_METHODS,
ctx="label_cluster_selection_method",
)
)
QuickAdapterRegressorV3._validate_enum_value(
trial_selection_method,
- QuickAdapterRegressorV3._distance_methods_set(),
+ QuickAdapterRegressorV3._DISTANCE_METHODS_SET,
QuickAdapterRegressorV3._DISTANCE_METHODS,
ctx="label_cluster_trial_selection_method",
)
)
QuickAdapterRegressorV3._validate_enum_value(
aggregation,
- QuickAdapterRegressorV3._density_aggregations_set(),
+ QuickAdapterRegressorV3._DENSITY_AGGREGATIONS_SET,
QuickAdapterRegressorV3._DENSITY_AGGREGATIONS,
ctx="label_density_aggregation",
)
QuickAdapterRegressorV3._validate_enum_value(
scaler,
- QuickAdapterRegressorV3._scaler_types_set(),
+ QuickAdapterRegressorV3._SCALER_TYPES_SET,
QuickAdapterRegressorV3._SCALER_TYPES,
ctx="scaler",
)
keep_fraction,
)
elif (
- threshold_method in QuickAdapterRegressorV3._skimage_threshold_methods_set()
+ threshold_method in QuickAdapterRegressorV3._SKIMAGE_THRESHOLD_METHODS_SET
):
return QuickAdapterRegressorV3.skimage_min_max(
pred_label,
) -> NDArray[np.floating]:
power = (
QuickAdapterRegressorV3._POWER_MEAN_MAP[distance_metric]
- if distance_metric in QuickAdapterRegressorV3._power_mean_metrics_set()
+ if distance_metric in QuickAdapterRegressorV3._POWER_MEAN_METRICS_SET
else (
QuickAdapterRegressorV3._validate_power_mean_p(p, ctx=p_ctx, mode=mode)
or 1.0
ideal_point = np.ones(n_objectives)
- if distance_metric in QuickAdapterRegressorV3._scipy_metrics_set():
+ if distance_metric in QuickAdapterRegressorV3._SCIPY_METRICS_SET:
return sp.spatial.distance.cdist(
normalized_matrix,
ideal_point.reshape(1, -1),
)
if distance_metric in (
- QuickAdapterRegressorV3._power_mean_metrics_set()
+ QuickAdapterRegressorV3._POWER_MEAN_METRICS_SET
| {QuickAdapterRegressorV3._DISTANCE_METRICS[15]} # "power_mean"
):
return QuickAdapterRegressorV3._power_mean_distance(
ideal_point = np.ones(n_objectives)
anti_ideal_point = np.zeros(n_objectives)
- if distance_metric in QuickAdapterRegressorV3._scipy_metrics_set():
+ if distance_metric in QuickAdapterRegressorV3._SCIPY_METRICS_SET:
cdist_kwargs = QuickAdapterRegressorV3._prepare_distance_kwargs(
distance_metric=distance_metric,
weights=weights,
),
)
elif distance_metric in (
- QuickAdapterRegressorV3._power_mean_metrics_set()
+ QuickAdapterRegressorV3._POWER_MEAN_METRICS_SET
| {QuickAdapterRegressorV3._DISTANCE_METRICS[15]} # "power_mean"
):
dist_to_ideal = np.abs(
knn_aggregation = cast(DensityAggregation, label_config["aggregation"])
if (
knn_aggregation
- not in QuickAdapterRegressorV3._density_aggregations_set()
+ not in QuickAdapterRegressorV3._DENSITY_AGGREGATIONS_SET
):
raise ValueError(
f"Invalid aggregation value in label_config {knn_aggregation!r}: "
@lru_cache(maxsize=8)
def optuna_samplers_by_namespace(
self, namespace: OptunaNamespace
- ) -> tuple[set[OptunaSampler], OptunaSampler]:
+ ) -> tuple[frozenset[OptunaSampler], OptunaSampler]:
if namespace == _OPTUNA_NAMESPACES.hp:
return (
- QuickAdapterRegressorV3._optuna_hpo_samplers_set(),
+ QuickAdapterRegressorV3._OPTUNA_HPO_SAMPLERS_SET,
self._optuna_config.get(
"sampler", QuickAdapterRegressorV3._OPTUNA_HPO_SAMPLERS[0]
),
)
elif namespace == _OPTUNA_NAMESPACES.label:
return (
- QuickAdapterRegressorV3._optuna_label_samplers_set(),
+ QuickAdapterRegressorV3._OPTUNA_LABEL_SAMPLERS_SET,
self._optuna_config.get(
"label_sampler", QuickAdapterRegressorV3._OPTUNA_LABEL_SAMPLERS[0]
),
repositories / freqai-strategies.git / commitdiff
