"""Thresholding primitives for converting scores to labels."""
from dataclasses import dataclass
from typing import Literal
import numpy as np
from anomsmith.objects.views import LabelView, ScoreView
[docs]
@dataclass(frozen=True)
class ThresholdRule:
"""Rule for thresholding anomaly scores.
Attributes:
method: 'absolute' (use value directly) or 'quantile' (use quantile)
value: Threshold value (absolute) or quantile (0-1)
quantile: If method is 'quantile', this is the quantile to use
"""
method: Literal["absolute", "quantile"]
value: float
quantile: float | None = None
def __post_init__(self) -> None:
"""Validate inputs after initialization."""
if self.method == "quantile":
if self.quantile is None:
raise ValueError("quantile must be provided when method is 'quantile'")
if not 0 <= self.quantile <= 1:
raise ValueError(f"quantile must be in [0, 1], got {self.quantile}")
elif self.method == "absolute":
if self.quantile is not None:
raise ValueError(
"quantile should not be provided when method is 'absolute'"
)
else:
raise ValueError(
f"method must be 'absolute' or 'quantile', got {self.method}"
)
[docs]
def apply_threshold(score_view: ScoreView, rule: ThresholdRule) -> LabelView:
"""Apply threshold rule to scores to produce binary labels.
Args:
score_view: ScoreView with anomaly scores
rule: ThresholdRule to apply
Returns:
LabelView with binary labels (1 = anomaly, 0 = normal)
"""
scores = score_view.scores
if rule.method == "absolute":
threshold = rule.value
elif rule.method == "quantile":
if rule.quantile is None:
raise ValueError("quantile must be provided when method is 'quantile'")
threshold = np.quantile(scores, rule.quantile)
else:
raise ValueError(f"Unknown method: {rule.method}")
labels = (scores >= threshold).astype(int)
return LabelView(index=score_view.index, labels=labels)