ExtremaSelectionMethod = Literal["rank_extrema", "rank_peaks", "partition"]
OptunaNamespace = Literal["hp", "train", "label"]
OptunaSampler = Literal["tpe", "auto", "nsgaii", "nsgaiii"]
-ClusterSelectionMethod = Literal["medoid", "min"]
+ClusterSelectionMethod = Literal["medoid", "min", "topsis"]
CustomThresholdMethod = Literal["median", "soft_extremum"]
SkimageThresholdMethod = Literal[
"mean", "isodata", "li", "minimum", "otsu", "triangle", "yen"
_CLUSTER_SELECTION_METHODS: Final[tuple[ClusterSelectionMethod, ...]] = (
"medoid",
"min",
+ "topsis",
)
_OPTUNA_LABEL_N_OBJECTIVES: Final[int] = 7
return scores
+ def _select_best_trial_from_cluster(
+ self,
+ selection_method: ClusterSelectionMethod,
+ best_cluster_indices: NDArray[np.intp],
+ normalized_matrix: NDArray[np.floating],
+ ideal_point_2d: NDArray[np.floating],
+ metric: str,
+ cdist_kwargs: dict[str, Any],
+ np_weights: Optional[NDArray[np.floating]],
+ *,
+ known_medoid_index: Optional[int] = None,
+ known_medoid_distance: Optional[float] = None,
+ ) -> tuple[int, float]:
+ """Select one trial from a cluster.
+
+ Args:
+ selection_method: Cluster selection method ("medoid", "min", "topsis").
+ best_cluster_indices: 1D array of trial indices belonging to the cluster.
+ normalized_matrix: Normalized objective matrix, shape (n_trials, n_objectives).
+ ideal_point_2d: Ideal objective point, shape (1, n_objectives).
+ metric: Distance metric used for scoring (scipy.cdist/pdist).
+ cdist_kwargs: Optional metric parameters for distance scoring (e.g., Minkowski p).
+ np_weights: Optional objective weights (used for weighted distances and TOPSIS).
+ known_medoid_index: Optional precomputed cluster medoid index.
+ known_medoid_distance: Optional precomputed medoid distance to the ideal point.
+
+ Returns:
+ (trial_index, distance_to_ideal) for the selected trial.
+ """
+ local_cdist_kwargs = dict(cdist_kwargs)
+ if np_weights is not None:
+ local_cdist_kwargs["w"] = np_weights
+
+ if best_cluster_indices.size == 1:
+ best_trial_index = best_cluster_indices[0]
+ if known_medoid_distance is not None:
+ return best_trial_index, known_medoid_distance
+ best_trial_distance = sp.spatial.distance.cdist(
+ normalized_matrix[[best_trial_index]],
+ ideal_point_2d,
+ metric=metric,
+ **local_cdist_kwargs,
+ ).item()
+
+ return best_trial_index, best_trial_distance
+
+ if (
+ selection_method
+ == QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS[0] # "medoid"
+ ):
+ if known_medoid_index is not None and known_medoid_distance is not None:
+ return known_medoid_index, known_medoid_distance
+ p = cdist_kwargs.get("p")
+ best_medoid_position = np.nanargmin(
+ self._pairwise_distance_sums(
+ normalized_matrix[best_cluster_indices],
+ metric,
+ weights=np_weights,
+ p=p,
+ )
+ )
+ best_trial_index = best_cluster_indices[best_medoid_position]
+ best_trial_distance = sp.spatial.distance.cdist(
+ normalized_matrix[[best_trial_index]],
+ ideal_point_2d,
+ metric=metric,
+ **cdist_kwargs,
+ ).item()
+ return best_trial_index, best_trial_distance
+
+ if (
+ selection_method
+ == QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS[1] # "min"
+ ):
+ best_cluster_distances = sp.spatial.distance.cdist(
+ normalized_matrix[best_cluster_indices],
+ ideal_point_2d,
+ metric=metric,
+ **local_cdist_kwargs,
+ ).flatten()
+ min_distance_position = np.nanargmin(best_cluster_distances)
+ best_trial_index = best_cluster_indices[min_distance_position]
+ return best_trial_index, best_cluster_distances[min_distance_position]
+
+ if (
+ selection_method
+ == QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS[2] # "topsis"
+ ):
+ topsis_scores = QuickAdapterRegressorV3._topsis_scores(
+ normalized_matrix[best_cluster_indices],
+ metric,
+ weights=np_weights,
+ p=cdist_kwargs.get("p"),
+ )
+ min_score_position = np.nanargmin(topsis_scores)
+ best_trial_index = best_cluster_indices[min_score_position]
+ best_trial_distance = sp.spatial.distance.cdist(
+ normalized_matrix[[best_trial_index]],
+ ideal_point_2d,
+ metric=metric,
+ **cdist_kwargs,
+ ).item()
+ return best_trial_index, best_trial_distance
+
+ raise ValueError(
+ f"Invalid selection_method {selection_method!r}. "
+ f"Supported: {', '.join(QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS)}"
+ )
+
@staticmethod
def _normalize_objective_values(
objective_values_matrix: NDArray[np.floating],
break
trial_distances = np.full(n_samples, np.inf)
if best_cluster_indices is not None and best_cluster_indices.size > 0:
- if (
- label_kmeans_selection
- == QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS[0] # "medoid"
- ):
- p = None
- if (
- label_kmeans_metric
- == QuickAdapterRegressorV3._SCIPY_METRICS[5] # "minkowski"
- ):
- p = (
- label_p_order
- if label_p_order is not None and np.isfinite(label_p_order)
- else self._get_label_p_order_default(label_kmeans_metric)
- )
- best_medoid_position = np.nanargmin(
- self._pairwise_distance_sums(
- normalized_matrix[best_cluster_indices],
- label_kmeans_metric,
- p=p,
- )
- )
- best_trial_index = best_cluster_indices[best_medoid_position]
- best_trial_distance = sp.spatial.distance.cdist(
- normalized_matrix[[best_trial_index]],
- ideal_point_2d,
- metric=label_kmeans_metric,
- **cdist_kwargs,
- ).item()
- trial_distances[best_trial_index] = best_trial_distance
- elif (
- label_kmeans_selection
- == QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS[1] # "min"
- ):
- best_cluster_distances = sp.spatial.distance.cdist(
- normalized_matrix[best_cluster_indices],
+ best_trial_index, best_trial_distance = (
+ self._select_best_trial_from_cluster(
+ label_kmeans_selection,
+ best_cluster_indices,
+ normalized_matrix,
ideal_point_2d,
- metric=label_kmeans_metric,
- **cdist_kwargs,
- ).flatten()
- min_distance_position = np.nanargmin(best_cluster_distances)
- best_trial_index = best_cluster_indices[min_distance_position]
- trial_distances[best_trial_index] = best_cluster_distances[
- min_distance_position
- ]
- else:
- raise ValueError(
- f"Invalid label_kmeans_selection {label_kmeans_selection!r}. "
- f"Supported: {', '.join(QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS)}"
+ label_kmeans_metric,
+ cdist_kwargs,
+ np_weights,
)
+ )
+ trial_distances[best_trial_index] = best_trial_distance
return trial_distances
elif metric == QuickAdapterRegressorV3._CUSTOM_METRICS[11]: # "kmedoids"
n_clusters = QuickAdapterRegressorV3._get_n_clusters(normalized_matrix)
cluster_index = cluster_labels[best_medoid_index]
best_cluster_indices = np.flatnonzero(cluster_labels == cluster_index)
trial_distances = np.full(n_samples, np.inf)
- if best_cluster_indices.size > 0:
- if (
- label_kmedoids_selection
- == QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS[0] # "medoid"
- ):
- trial_distances[best_medoid_index] = medoid_distances_to_ideal[
- best_medoid_distance_position
- ]
- elif (
- label_kmedoids_selection
- == QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS[1] # "min"
- ):
- if best_cluster_indices.size == 1:
- best_trial_index = best_cluster_indices[0]
- trial_distances[best_trial_index] = medoid_distances_to_ideal[
+ if best_cluster_indices is not None and best_cluster_indices.size > 0:
+ best_trial_index, best_trial_distance = (
+ self._select_best_trial_from_cluster(
+ label_kmedoids_selection,
+ best_cluster_indices,
+ normalized_matrix,
+ ideal_point_2d,
+ label_kmedoids_metric,
+ cdist_kwargs,
+ np_weights,
+ known_medoid_index=best_medoid_index,
+ known_medoid_distance=medoid_distances_to_ideal[
best_medoid_distance_position
- ]
- else:
- best_cluster_distances = sp.spatial.distance.cdist(
- normalized_matrix[best_cluster_indices],
- ideal_point_2d,
- metric=label_kmedoids_metric,
- **cdist_kwargs,
- ).flatten()
- min_distance_position = np.nanargmin(best_cluster_distances)
- best_trial_index = best_cluster_indices[min_distance_position]
- trial_distances[best_trial_index] = best_cluster_distances[
- min_distance_position
- ]
- else:
- raise ValueError(
- f"Invalid label_kmedoids_selection {label_kmedoids_selection!r}. "
- f"Supported: {', '.join(QuickAdapterRegressorV3._CLUSTER_SELECTION_METHODS)}"
+ ],
)
+ )
+ trial_distances[best_trial_index] = best_trial_distance
return trial_distances
elif metric in {
QuickAdapterRegressorV3._CUSTOM_METRICS[12], # "knn_power_mean"
repositories / freqai-strategies.git / commitdiff
