| """
|
| Evaluation metrics for anomaly detection performance.
|
|
|
| Computes accuracy, precision, recall, F1-score, and AUC-ROC.
|
| """
|
|
|
| import numpy as np
|
| from typing import Optional, List
|
| from sklearn.metrics import (
|
| accuracy_score,
|
| precision_score,
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| recall_score,
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| f1_score,
|
| roc_auc_score,
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| confusion_matrix,
|
| classification_report,
|
| )
|
|
|
|
|
| class MetricsCalculator:
|
| """Calculates all evaluation metrics for binary classification."""
|
|
|
| @staticmethod
|
| def compute_all(
|
| y_true: list | np.ndarray,
|
| y_pred: list | np.ndarray,
|
| y_scores: Optional[list | np.ndarray] = None,
|
| ) -> dict:
|
| """
|
| Compute all metrics.
|
|
|
| Args:
|
| y_true: Ground-truth labels (0 = normal, 1 = abnormal).
|
| y_pred: Predicted labels.
|
| y_scores: Predicted probabilities for the positive class
|
| (required for AUC-ROC).
|
|
|
| Returns:
|
| Dictionary of metric_name → value.
|
| """
|
| y_true = np.asarray(y_true)
|
| y_pred = np.asarray(y_pred)
|
|
|
| metrics = {
|
| "accuracy": accuracy_score(y_true, y_pred),
|
| "precision": precision_score(y_true, y_pred, zero_division=0),
|
| "recall": recall_score(y_true, y_pred, zero_division=0),
|
| "f1_score": f1_score(y_true, y_pred, zero_division=0),
|
| }
|
|
|
| if y_scores is not None and len(np.unique(y_true)) > 1:
|
| metrics["auc_roc"] = roc_auc_score(y_true, y_scores)
|
|
|
| return metrics
|
|
|
| @staticmethod
|
| def get_confusion_matrix(
|
| y_true: list | np.ndarray,
|
| y_pred: list | np.ndarray,
|
| ) -> np.ndarray:
|
| """Return confusion matrix as a 2×2 numpy array."""
|
| return confusion_matrix(y_true, y_pred)
|
|
|
| @staticmethod
|
| def get_classification_report(
|
| y_true: list | np.ndarray,
|
| y_pred: list | np.ndarray,
|
| target_names: list = None,
|
| ) -> str:
|
| """Return a formatted classification report."""
|
| if target_names is None:
|
| target_names = ["Normal", "Abnormal"]
|
| return classification_report(
|
| y_true, y_pred, target_names=target_names, zero_division=0
|
| )
|
|
|
| @staticmethod
|
| def format_metrics(metrics: dict) -> str:
|
| """Pretty-print metrics to a formatted string."""
|
| lines = []
|
| for name, value in metrics.items():
|
| if isinstance(value, float):
|
| lines.append(f" {name:>15s}: {value:.4f}")
|
| else:
|
| lines.append(f" {name:>15s}: {value}")
|
| return "\n".join(lines)
|
|
|
