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>
74 行
2.8 KiB
Python
74 行
2.8 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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This file re-uses implementation from https://github.com/yl-1993/learn-to-cluster
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"""
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import numpy as np
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from tqdm import tqdm
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from itertools import groupby
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import torch
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__all__ = ['density_estimation', 'density_to_peaks', 'density_to_peaks_vectorize']
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def density_estimation(dists, nbrs, labels, **kwargs):
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''' use supervised density defined on neigborhood
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'''
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num, k_knn = dists.shape
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conf = np.ones((num, ), dtype=np.float32)
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ind_array = labels[nbrs] == np.expand_dims(labels, 1).repeat(k_knn, 1)
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pos = ((1-dists[:,1:]) * ind_array[:,1:]).sum(1)
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neg = ((1-dists[:,1:]) * (1-ind_array[:,1:])).sum(1)
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conf = (pos - neg) * conf
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conf /= (k_knn - 1)
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return conf
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def density_to_peaks_vectorize(dists, nbrs, density, max_conn=1, name = ''):
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# just calculate 1 connectivity
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assert dists.shape[0] == density.shape[0]
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assert dists.shape == nbrs.shape
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num, k = dists.shape
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if name == 'gcn_feat':
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include_mask = nbrs != np.arange(0, num).reshape(-1, 1)
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secondary_mask = np.sum(include_mask, axis = 1) == k # TODO: the condition == k should not happen as distance to the node self should be smallest, check for numerical stability; TODO: make top M instead of only supporting top 1
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include_mask[secondary_mask, -1] = False
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nbrs_exclude_self = nbrs[include_mask].reshape(-1, k-1) # (V, 79)
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dists_exclude_self = dists[include_mask].reshape(-1, k-1) # (V, 79)
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else:
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include_mask = nbrs != np.arange(0, num).reshape(-1, 1)
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nbrs_exclude_self = nbrs[include_mask].reshape(-1, k-1) # (V, 79)
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dists_exclude_self = dists[include_mask].reshape(-1, k-1) # (V, 79)
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compare_map = density[nbrs_exclude_self] > density.reshape(-1, 1)
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peak_index = np.argmax(np.where(compare_map, 1, 0), axis = 1) # (V,)
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compare_map_sum = np.sum(compare_map.cpu().data.numpy(), axis=1) # (V,)
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dist2peak = {i: [] if compare_map_sum[i] == 0 else [dists_exclude_self[i, peak_index[i]]] for i in range(num)}
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peaks = {i: [] if compare_map_sum[i] == 0 else [nbrs_exclude_self[i, peak_index[i]]] for i in range(num)}
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return dist2peak, peaks
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def density_to_peaks(dists, nbrs, density, max_conn=1, sort='dist'):
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# Note that dists has been sorted in ascending order
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assert dists.shape[0] == density.shape[0]
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assert dists.shape == nbrs.shape
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num, _ = dists.shape
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dist2peak = {i: [] for i in range(num)}
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peaks = {i: [] for i in range(num)}
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for i, nbr in tqdm(enumerate(nbrs)):
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nbr_conf = density[nbr]
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for j, c in enumerate(nbr_conf):
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nbr_idx = nbr[j]
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if i == nbr_idx or c <= density[i]:
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continue
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dist2peak[i].append(dists[i, j])
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peaks[i].append(nbr_idx)
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if len(dist2peak[i]) >= max_conn:
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break
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return dist2peak, peaks
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