首次提交本地代码

evaluation/f1pa.py

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+import numpy as np
+from sklearn.metrics import f1_score, precision_score, recall_score
+
+
+def adjust_predicts(score, label,
+                    threshold=None,
+                    pred=None,
+                    calc_latency=False):
+    """
+    该函数是point-adjust官方源码
+    Calculate adjusted predict labels using given `score`, `threshold` (or given `pred`) and `label`.
+    Args:
+        score =: The anomaly score
+        label : The ground-truth label
+        threshold (float): The threshold of anomaly score.
+            A point is labeled as "anomaly" if its score is lower than the threshold.
+        pred : if not None, adjust `pred` and ignore `score` and `threshold`,
+        calc_latency (bool):
+    Returns:
+        np.ndarray: predict labels
+    """
+    if len(score) != len(label):
+        raise ValueError("score and label must have the same length")
+    score = np.asarray(score)
+    label = np.asarray(label)
+    latency = 0
+    if pred is None:
+        predict = score < threshold
+    else:
+        predict = pred
+    actual = label > 0.1
+    anomaly_state = False
+    anomaly_count = 0
+    for i in range(len(score)):
+        if actual[i] and predict[i] and not anomaly_state:
+                anomaly_state = True
+                anomaly_count += 1
+                for j in range(i, 0, -1):
+                    if not actual[j]:
+                        break
+                    else:
+                        if not predict[j]:
+                            predict[j] = True
+                            latency += 1
+        elif not actual[i]:
+            anomaly_state = False
+        if anomaly_state:
+            predict[i] = True
+    if calc_latency:
+        return predict, latency / (anomaly_count + 1e-4)
+    else:
+        return predict
+
+
+def evaluate(y_true: list, y_pred: list) -> float:
+    """
+    F1PA评估方法，经过point adjust调整标签后再用F1评分
+    :param y_true: 真实标签
+    :param y_pred: 检测标签
+    :return: 经过pa调整后的f1、recall、precision
+    """
+    y_true, y_pred = y_true.copy(), y_pred.copy()
+    adjust_y_pred = adjust_predicts(score=np.array([0] * len(y_true)), label=y_true, pred=y_pred)
+    f1 = f1_score(y_true, adjust_y_pred)
+    return f1
+