_OPTUNA_SAMPLERS: Final[tuple[str, ...]] = ("tpe", "auto")
_OPTUNA_NAMESPACES: Final[tuple[OptunaNamespace, ...]] = ("hp", "train", "label")
+ _SCIPY_METRICS: Final[tuple[str, ...]] = (
+ # "braycurtis",
+ # "canberra",
+ "chebyshev",
+ "cityblock",
+ # "correlation",
+ # "cosine",
+ # "dice",
+ "euclidean",
+ # "hamming",
+ # "jaccard",
+ "jensenshannon",
+ # "kulczynski1", # Deprecated in SciPy ≥ 1.15.0; do not use.
+ "mahalanobis",
+ # "matching",
+ "minkowski",
+ # "rogerstanimoto",
+ # "russellrao",
+ "seuclidean",
+ # "sokalmichener", # Deprecated in SciPy ≥ 1.15.0; do not use.
+ # "sokalsneath",
+ "sqeuclidean",
+ # "yule",
+ )
+
+ _CUSTOM_METRICS: Final[tuple[str, ...]] = (
+ "hellinger",
+ "shellinger",
+ "harmonic_mean",
+ "geometric_mean",
+ "arithmetic_mean",
+ "quadratic_mean",
+ "cubic_mean",
+ "power_mean",
+ "weighted_sum",
+ "kmeans",
+ "kmeans2",
+ "kmedoids",
+ "knn_power_mean",
+ "knn_percentile",
+ "knn_min",
+ "knn_max",
+ "medoid",
+ )
+
+ _METRICS: Final[tuple[str, ...]] = (
+ *_SCIPY_METRICS,
+ *_CUSTOM_METRICS,
+ )
+
@staticmethod
def _extrema_selection_methods_set() -> set[ExtremaSelectionMethod]:
return set(QuickAdapterRegressorV3._EXTREMA_SELECTION_METHODS)
def _optuna_namespaces_set() -> set[OptunaNamespace]:
return set(QuickAdapterRegressorV3._OPTUNA_NAMESPACES)
+ @staticmethod
+ def _scipy_metrics_set() -> set[str]:
+ return set(QuickAdapterRegressorV3._SCIPY_METRICS)
+
+ @staticmethod
+ def _custom_metrics_set() -> set[str]:
+ return set(QuickAdapterRegressorV3._CUSTOM_METRICS)
+
+ @staticmethod
+ def _metrics_set() -> set[str]:
+ return set(QuickAdapterRegressorV3._METRICS)
+
@cached_property
def _optuna_config(self) -> dict[str, Any]:
optuna_default_config = {
)
if not np.all(np.isfinite(weights)) or np.any(weights < 0):
raise ValueError("weights must be finite and non-negative")
- if metric in {"mahalanobis", "seuclidean", "jensenshannon"}:
+ if metric in {
+ QuickAdapterRegressorV3._SCIPY_METRICS[4], # "mahalanobis"
+ QuickAdapterRegressorV3._SCIPY_METRICS[6], # "seuclidean"
+ QuickAdapterRegressorV3._SCIPY_METRICS[3], # "jensenshannon"
+ }:
raise ValueError(f"weights not supported for metric '{metric}'")
matrix = np.asarray(matrix, dtype=np.float64)
pdist_kwargs = {}
if weights is not None:
pdist_kwargs["w"] = weights
- if metric == "minkowski" and p is not None and np.isfinite(p):
+ if (
+ metric == QuickAdapterRegressorV3._SCIPY_METRICS[5] # "minkowski"
+ and p is not None
+ and np.isfinite(p)
+ ):
pdist_kwargs["p"] = p
pairwise_distances_vector = sp.spatial.distance.pdist(
return np.array([])
if n_samples == 1:
if metric in {
- "medoid",
- "kmeans",
- "kmeans2",
- "kmedoids",
- "knn_power_mean",
- "knn_percentile",
- "knn_min",
- "knn_max",
+ QuickAdapterRegressorV3._CUSTOM_METRICS[16], # "medoid"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[9], # "kmeans"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[10], # "kmeans2"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[11], # "kmedoids"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[12], # "knn_power_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[13], # "knn_percentile"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[14], # "knn_min"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[15], # "knn_max"
}:
return np.array([0.0])
- if metric in {
- # "braycurtis",
- # "canberra",
- "chebyshev",
- "cityblock",
- # "correlation",
- # "cosine",
- # "dice",
- "euclidean",
- # "hamming",
- # "jaccard",
- "jensenshannon",
- # "kulczynski1", # Deprecated in SciPy ≥ 1.15.0; do not use.
- "mahalanobis",
- # "matching",
- "minkowski",
- # "rogerstanimoto",
- # "russellrao",
- "seuclidean",
- # "sokalmichener", # Deprecated in SciPy ≥ 1.15.0; do not use.
- # "sokalsneath",
- "sqeuclidean",
- # "yule",
- }:
+ if metric in QuickAdapterRegressorV3._scipy_metrics_set():
cdist_kwargs: dict[str, Any] = {}
- if metric not in {"mahalanobis", "seuclidean", "jensenshannon"}:
+ if metric not in {
+ QuickAdapterRegressorV3._SCIPY_METRICS[4], # "mahalanobis"
+ QuickAdapterRegressorV3._SCIPY_METRICS[6], # "seuclidean"
+ QuickAdapterRegressorV3._SCIPY_METRICS[3], # "jensenshannon"
+ }:
cdist_kwargs["w"] = np_weights
- if metric == "minkowski":
+ if metric == QuickAdapterRegressorV3._SCIPY_METRICS[5]: # "minkowski"
cdist_kwargs["p"] = (
label_p_order
if label_p_order is not None and np.isfinite(label_p_order)
metric=metric,
**cdist_kwargs,
).flatten()
- elif metric in {"hellinger", "shellinger"}:
+ elif metric in {
+ QuickAdapterRegressorV3._CUSTOM_METRICS[0], # "hellinger"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[1], # "shellinger"
+ }:
np_sqrt_normalized_matrix = np.sqrt(normalized_matrix)
- if metric == "shellinger":
+ if metric == QuickAdapterRegressorV3._CUSTOM_METRICS[1]: # "shellinger"
variances = np.var(np_sqrt_normalized_matrix, axis=0, ddof=1)
if np.any(variances <= 0):
raise ValueError(
/ QuickAdapterRegressorV3._SQRT_2
)
elif metric in {
- "harmonic_mean",
- "geometric_mean",
- "arithmetic_mean",
- "quadratic_mean",
- "cubic_mean",
- "power_mean",
+ QuickAdapterRegressorV3._CUSTOM_METRICS[2], # "harmonic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[3], # "geometric_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[4], # "arithmetic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[5], # "quadratic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[6], # "cubic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[7], # "power_mean"
}:
p = {
- "harmonic_mean": -1.0,
- "geometric_mean": 0.0,
- "arithmetic_mean": 1.0,
- "quadratic_mean": 2.0,
- "cubic_mean": 3.0,
- "power_mean": label_p_order
+ QuickAdapterRegressorV3._CUSTOM_METRICS[2]: -1.0, # "harmonic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[3]: 0.0, # "geometric_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[4]: 1.0, # "arithmetic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[5]: 2.0, # "quadratic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[6]: 3.0, # "cubic_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[
+ 7
+ ]: label_p_order # "power_mean"
if label_p_order is not None and np.isfinite(label_p_order)
else 1.0,
}[metric]
return sp.stats.pmean(
ideal_point, p=p, weights=np_weights
) - sp.stats.pmean(normalized_matrix, p=p, weights=np_weights, axis=1)
- elif metric == "weighted_sum":
+ elif metric == QuickAdapterRegressorV3._CUSTOM_METRICS[8]: # "weighted_sum"
return np.sum(np_weights * (ideal_point - normalized_matrix), axis=1)
- elif metric == "medoid":
- label_medoid_metric = self.ft_params.get("label_medoid_metric", "euclidean")
+ elif metric == QuickAdapterRegressorV3._CUSTOM_METRICS[16]: # "medoid"
+ label_medoid_metric = self.ft_params.get(
+ "label_medoid_metric",
+ QuickAdapterRegressorV3._SCIPY_METRICS[2], # "euclidean"
+ )
if label_medoid_metric in {
- "mahalanobis",
- "seuclidean",
- "jensenshannon",
+ QuickAdapterRegressorV3._SCIPY_METRICS[4], # "mahalanobis"
+ QuickAdapterRegressorV3._SCIPY_METRICS[6], # "seuclidean"
+ QuickAdapterRegressorV3._SCIPY_METRICS[3], # "jensenshannon"
}:
raise ValueError(
f"Unsupported label_medoid_metric: {label_medoid_metric}. Supported are euclidean/minkowski/cityblock/chebyshev/..."
)
p = None
- if label_medoid_metric == "minkowski":
+ if (
+ label_medoid_metric
+ == QuickAdapterRegressorV3._SCIPY_METRICS[5] # "minkowski"
+ ):
p = (
label_p_order
if label_p_order is not None and np.isfinite(label_p_order)
weights=np_weights,
p=p,
)
- elif metric in {"kmeans", "kmeans2"}:
+ elif metric in {
+ QuickAdapterRegressorV3._CUSTOM_METRICS[9], # "kmeans"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[10], # "kmeans2"
+ }:
n_clusters = QuickAdapterRegressorV3._get_n_clusters(normalized_matrix)
- if metric == "kmeans":
+ if metric == QuickAdapterRegressorV3._CUSTOM_METRICS[9]: # "kmeans"
kmeans = sklearn.cluster.KMeans(
n_clusters=n_clusters, random_state=42, n_init=10
)
cluster_labels = kmeans.fit_predict(normalized_matrix)
cluster_centers = kmeans.cluster_centers_
- elif metric == "kmeans2":
+ elif metric == QuickAdapterRegressorV3._CUSTOM_METRICS[10]: # "kmeans2"
cluster_centers, cluster_labels = sp.cluster.vq.kmeans2(
normalized_matrix, n_clusters, rng=42, minit="++"
)
- label_kmeans_metric = self.ft_params.get("label_kmeans_metric", "euclidean")
+ label_kmeans_metric = self.ft_params.get(
+ "label_kmeans_metric",
+ QuickAdapterRegressorV3._SCIPY_METRICS[2], # "euclidean"
+ )
if label_kmeans_metric in {
- "mahalanobis",
- "seuclidean",
- "jensenshannon",
+ QuickAdapterRegressorV3._SCIPY_METRICS[4], # "mahalanobis"
+ QuickAdapterRegressorV3._SCIPY_METRICS[6], # "seuclidean"
+ QuickAdapterRegressorV3._SCIPY_METRICS[3], # "jensenshannon"
}:
raise ValueError(
f"Unsupported label_kmeans_metric: {label_kmeans_metric}. Supported are euclidean/minkowski/cityblock/chebyshev/..."
)
cdist_kwargs: dict[str, Any] = {}
- if label_kmeans_metric == "minkowski":
+ if (
+ label_kmeans_metric
+ == QuickAdapterRegressorV3._SCIPY_METRICS[5] # "minkowski"
+ ):
cdist_kwargs["p"] = (
label_p_order
if label_p_order is not None and np.isfinite(label_p_order)
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 == "medoid":
+ if (
+ label_kmeans_selection
+ == QuickAdapterRegressorV3._CUSTOM_METRICS[16] # "medoid"
+ ):
p = None
- if label_kmeans_metric == "minkowski":
+ 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)
f"Unsupported label_kmeans_selection: {label_kmeans_selection}. Supported are medoid/min"
)
return trial_distances
- elif metric == "kmedoids":
+ elif metric == QuickAdapterRegressorV3._CUSTOM_METRICS[11]: # "kmedoids"
n_clusters = QuickAdapterRegressorV3._get_n_clusters(normalized_matrix)
label_kmedoids_metric = self.ft_params.get(
- "label_kmedoids_metric", "euclidean"
+ "label_kmedoids_metric",
+ QuickAdapterRegressorV3._SCIPY_METRICS[2], # "euclidean"
)
if label_kmedoids_metric in {
- "mahalanobis",
- "seuclidean",
- "jensenshannon",
+ QuickAdapterRegressorV3._SCIPY_METRICS[4], # "mahalanobis"
+ QuickAdapterRegressorV3._SCIPY_METRICS[6], # "seuclidean"
+ QuickAdapterRegressorV3._SCIPY_METRICS[3], # "jensenshannon"
}:
raise ValueError(
f"Unsupported label_kmedoids_metric: {label_kmedoids_metric}. Supported are euclidean/minkowski/cityblock/chebyshev/..."
cluster_labels = kmedoids.fit_predict(normalized_matrix)
medoid_indices = kmedoids.medoid_indices_
cdist_kwargs: dict[str, Any] = {}
- if label_kmedoids_metric == "minkowski":
+ if (
+ label_kmedoids_metric
+ == QuickAdapterRegressorV3._SCIPY_METRICS[5] # "minkowski"
+ ):
cdist_kwargs["p"] = (
label_p_order
if label_p_order is not None and np.isfinite(label_p_order)
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 == "medoid":
+ if (
+ label_kmedoids_selection
+ == QuickAdapterRegressorV3._CUSTOM_METRICS[16] # "medoid"
+ ):
trial_distances[best_medoid_index] = medoid_distances_to_ideal[
best_medoid_distance_position
]
f"Unsupported label_kmedoids_selection: {label_kmedoids_selection}. Supported are medoid/min"
)
return trial_distances
- elif metric in {"knn_power_mean", "knn_percentile", "knn_min", "knn_max"}:
- label_knn_metric = self.ft_params.get("label_knn_metric", "minkowski")
+ elif metric in {
+ QuickAdapterRegressorV3._CUSTOM_METRICS[12], # "knn_power_mean"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[13], # "knn_percentile"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[14], # "knn_min"
+ QuickAdapterRegressorV3._CUSTOM_METRICS[15], # "knn_max"
+ }:
+ label_knn_metric = self.ft_params.get(
+ "label_knn_metric",
+ QuickAdapterRegressorV3._SCIPY_METRICS[5], # "minkowski"
+ )
knn_kwargs: dict[str, Any] = {}
- if label_knn_metric == "minkowski":
+ if (
+ label_knn_metric
+ == QuickAdapterRegressorV3._SCIPY_METRICS[5] # "minkowski"
+ ):
knn_kwargs["p"] = (
label_p_order
if label_p_order is not None and np.isfinite(label_p_order)
neighbor_distances = distances[:, 1:]
if neighbor_distances.shape[1] < 1:
return np.full(n_samples, np.inf)
- if metric == "knn_power_mean":
+ if (
+ metric == QuickAdapterRegressorV3._CUSTOM_METRICS[12]
+ ): # "knn_power_mean"
label_knn_p_order = (
label_knn_p_order
if label_knn_p_order is not None and np.isfinite(label_knn_p_order)
else 1.0
)
return sp.stats.pmean(neighbor_distances, p=label_knn_p_order, axis=1)
- elif metric == "knn_percentile":
+ elif (
+ metric == QuickAdapterRegressorV3._CUSTOM_METRICS[13]
+ ): # "knn_percentile"
label_knn_p_order = (
label_knn_p_order
if label_knn_p_order is not None and np.isfinite(label_knn_p_order)
else 50.0
)
return np.percentile(neighbor_distances, label_knn_p_order, axis=1)
- elif metric == "knn_min":
+ elif metric == QuickAdapterRegressorV3._CUSTOM_METRICS[14]: # "knn_min"
return np.min(neighbor_distances, axis=1)
- elif metric == "knn_max":
+ elif metric == QuickAdapterRegressorV3._CUSTOM_METRICS[15]: # "knn_max"
return np.max(neighbor_distances, axis=1)
else:
raise ValueError(
if not QuickAdapterRegressorV3.optuna_study_has_best_trials(study):
return None
- metrics = {
- # "braycurtis",
- # "canberra",
- "chebyshev",
- "cityblock",
- # "correlation",
- # "cosine",
- # "dice",
- "euclidean",
- # "hamming",
- # "jaccard",
- "jensenshannon",
- # "kulczynski1",
- "mahalanobis",
- # "matching",
- "minkowski",
- # "rogerstanimoto",
- # "russellrao",
- "seuclidean",
- # "sokalmichener",
- # "sokalsneath",
- "sqeuclidean",
- # "yule",
- "hellinger",
- "shellinger",
- "harmonic_mean",
- "geometric_mean",
- "arithmetic_mean",
- "quadratic_mean",
- "cubic_mean",
- "power_mean",
- "weighted_sum",
- "kmeans",
- "kmeans2",
- "kmedoids",
- "knn_power_mean",
- "knn_percentile",
- "knn_min",
- "knn_max",
- "medoid",
- }
- label_metric = self.ft_params.get("label_metric", "euclidean")
+ metrics = QuickAdapterRegressorV3._metrics_set()
+ label_metric = self.ft_params.get(
+ "label_metric", QuickAdapterRegressorV3._SCIPY_METRICS[2]
+ ) # "euclidean"
if label_metric not in metrics:
raise ValueError(
f"Unsupported label metric: {label_metric}. Supported metrics are {', '.join(metrics)}"
"values": self.get_optuna_values(pair, namespace),
**self.get_optuna_params(pair, namespace),
}
- metric_log_msg = (
- f" using {self.ft_params.get('label_metric', 'euclidean')} metric"
- )
+ metric_log_msg = f" using {self.ft_params.get('label_metric', QuickAdapterRegressorV3._SCIPY_METRICS[2])} metric"
logger.info(
f"Optuna {pair} {namespace} {objective_type} objective hyperopt done{metric_log_msg} ({time_spent:.2f} secs)"
)
repositories / freqai-strategies.git / commitdiff
