dmlc--dgl
Tong He
9c41c22d7b
* add hilander model implementation draft * use focal loss * fix * change data root * add necessary scripts * update download links * update * update example table * fix * update readme with numbers * add empty folder * only eval at the end * set up hilander * inform results may fluctuate * address comments Co-authored-by: sneakerkg <xiaotj1990327@gmail.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-19-212.us-east-2.compute.internal>
67 行
2.4 KiB
Python
67 行
2.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import inspect
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import argparse
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import numpy as np
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from utils import Timer, TextColors, metrics
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from clustering_benchmark import ClusteringBenchmark
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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('#inst: gt({}) vs pred({})'.format(len(gt_labels),
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len(pred_labels)))
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print('#cls: gt({}) vs pred({})'.format(len(gt_lb_set),
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len(pred_lb_set)))
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metric_func = metrics.__dict__[metric]
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with Timer('evaluate with {}{}{}'.format(TextColors.FATAL, metric,
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TextColors.ENDC)):
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result = metric_func(gt_labels, pred_labels)
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if isinstance(result, np.float):
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print('{}{}: {:.4f}{}'.format(TextColors.OKGREEN, metric, result,
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TextColors.ENDC))
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else:
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ave_pre, ave_rec, fscore = result
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print('{}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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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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