wehub-resource-sync
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import argparse
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import inspect
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import numpy as np
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from clustering_benchmark import ClusteringBenchmark
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from utils import metrics, TextColors, Timer
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def _read_meta(fn):
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labels = list()
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lb_set = set()
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with open(fn) as f:
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for lb in f.readlines():
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lb = int(lb.strip())
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labels.append(lb)
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lb_set.add(lb)
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return np.array(labels), lb_set
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def evaluate(gt_labels, pred_labels, metric="pairwise"):
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if isinstance(gt_labels, str) and isinstance(pred_labels, str):
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print("[gt_labels] {}".format(gt_labels))
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print("[pred_labels] {}".format(pred_labels))
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gt_labels, gt_lb_set = _read_meta(gt_labels)
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pred_labels, pred_lb_set = _read_meta(pred_labels)
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print(
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"#inst: gt({}) vs pred({})".format(len(gt_labels), len(pred_labels))
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)
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print(
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"#cls: gt({}) vs pred({})".format(len(gt_lb_set), len(pred_lb_set))
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)
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metric_func = metrics.__dict__[metric]
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with Timer(
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"evaluate with {}{}{}".format(TextColors.FATAL, metric, TextColors.ENDC)
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):
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result = metric_func(gt_labels, pred_labels)
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if isinstance(result, float):
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print(
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"{}{}: {:.4f}{}".format(
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TextColors.OKGREEN, metric, result, TextColors.ENDC
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)
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)
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else:
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ave_pre, ave_rec, fscore = result
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print(
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"{}ave_pre: {:.4f}, ave_rec: {:.4f}, fscore: {:.4f}{}".format(
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TextColors.OKGREEN, ave_pre, ave_rec, fscore, TextColors.ENDC
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)
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)
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def evaluation(pred_labels, labels, metrics):
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print("==> evaluation")
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# pred_labels = g.ndata['pred_labels'].cpu().numpy()
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max_cluster = np.max(pred_labels)
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# gt_labels_all = g.ndata['labels'].cpu().numpy()
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gt_labels_all = labels
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pred_labels_all = pred_labels
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metric_list = metrics.split(",")
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for metric in metric_list:
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evaluate(gt_labels_all, pred_labels_all, metric)
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# H and C-scores
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gt_dict = {}
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pred_dict = {}
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for i in range(len(gt_labels_all)):
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gt_dict[str(i)] = gt_labels_all[i]
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pred_dict[str(i)] = pred_labels_all[i]
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bm = ClusteringBenchmark(gt_dict)
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scores = bm.evaluate_vmeasure(pred_dict)
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fmi_scores = bm.evaluate_fowlkes_mallows_score(pred_dict)
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print(scores)
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