chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
2172 changed files with 455595 additions and 0 deletions
@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import argparse
+import inspect
+
+import numpy as np
+from clustering_benchmark import ClusteringBenchmark
+from utils import metrics, TextColors, Timer
+
+
+def _read_meta(fn):
+    labels = list()
+    lb_set = set()
+    with open(fn) as f:
+        for lb in f.readlines():
+            lb = int(lb.strip())
+            labels.append(lb)
+            lb_set.add(lb)
+    return np.array(labels), lb_set
+
+
+def evaluate(gt_labels, pred_labels, metric="pairwise"):
+    if isinstance(gt_labels, str) and isinstance(pred_labels, str):
+        print("[gt_labels] {}".format(gt_labels))
+        print("[pred_labels] {}".format(pred_labels))
+        gt_labels, gt_lb_set = _read_meta(gt_labels)
+        pred_labels, pred_lb_set = _read_meta(pred_labels)
+
+        print(
+            "#inst: gt({}) vs pred({})".format(len(gt_labels), len(pred_labels))
+        )
+        print(
+            "#cls: gt({}) vs pred({})".format(len(gt_lb_set), len(pred_lb_set))
+        )
+
+    metric_func = metrics.__dict__[metric]
+
+    with Timer(
+        "evaluate with {}{}{}".format(TextColors.FATAL, metric, TextColors.ENDC)
+    ):
+        result = metric_func(gt_labels, pred_labels)
+    if isinstance(result, float):
+        print(
+            "{}{}: {:.4f}{}".format(
+                TextColors.OKGREEN, metric, result, TextColors.ENDC
+            )
+        )
+    else:
+        ave_pre, ave_rec, fscore = result
+        print(
+            "{}ave_pre: {:.4f}, ave_rec: {:.4f}, fscore: {:.4f}{}".format(
+                TextColors.OKGREEN, ave_pre, ave_rec, fscore, TextColors.ENDC
+            )
+        )
+
+
+def evaluation(pred_labels, labels, metrics):
+    print("==> evaluation")
+    # pred_labels = g.ndata['pred_labels'].cpu().numpy()
+    max_cluster = np.max(pred_labels)
+    # gt_labels_all = g.ndata['labels'].cpu().numpy()
+    gt_labels_all = labels
+    pred_labels_all = pred_labels
+    metric_list = metrics.split(",")
+    for metric in metric_list:
+        evaluate(gt_labels_all, pred_labels_all, metric)
+    # H and C-scores
+    gt_dict = {}
+    pred_dict = {}
+    for i in range(len(gt_labels_all)):
+        gt_dict[str(i)] = gt_labels_all[i]
+        pred_dict[str(i)] = pred_labels_all[i]
+    bm = ClusteringBenchmark(gt_dict)
+    scores = bm.evaluate_vmeasure(pred_dict)
+    fmi_scores = bm.evaluate_fowlkes_mallows_score(pred_dict)
+    print(scores)