wehub-resource-sync
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
from collections import defaultdict
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import numpy as np
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import torch
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def remove_unseen_classes_from_training(train_mask, labels, removed_class):
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"""Remove the unseen classes (the first three classes by default) to get the zero-shot (i.e., completely imbalanced) label setting
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Input: train_mask, labels, removed_class
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Output: train_mask_zs: the bool list only containing seen classes
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"""
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train_mask_zs = train_mask.clone()
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for i in range(train_mask_zs.numel()):
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if train_mask_zs[i] == 1 and (labels[i].item() in removed_class):
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train_mask_zs[i] = 0
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return train_mask_zs
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def get_class_set(labels):
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"""Get the class set.
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Input: labels [l, [c1, c2, ..]]
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Output：the labeled class set dict_keys([k1, k2, ..])
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"""
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mydict = {}
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for y in labels:
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for label in y:
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mydict[int(label)] = 1
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return mydict.keys()
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def get_label_attributes(train_mask_zs, nodeids, labellist, features):
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"""Get the class-center (semanic knowledge) of each seen class.
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Suppose a node i is labeled as c, then attribute[c] += node_i_attribute, finally mean(attribute[c])
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Input: train_mask_zs, nodeids, labellist, features
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Output: label_attribute{}: label -> average_labeled_node_features (class centers)
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"""
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_, feat_num = features.shape
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labels = get_class_set(labellist)
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label_attribute_nodes = defaultdict(list)
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for nodeid, labels in zip(nodeids, labellist):
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for label in labels:
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label_attribute_nodes[int(label)].append(int(nodeid))
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label_attribute = {}
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for label in label_attribute_nodes.keys():
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nodes = label_attribute_nodes[int(label)]
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selected_features = features[nodes, :]
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label_attribute[int(label)] = np.mean(selected_features, axis=0)
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return label_attribute
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def get_labeled_nodes_label_attribute(train_mask_zs, labels, features, cuda):
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"""Replace the original labels by their class-centers.
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For each label c in the training set, the following operations will be performed:
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Get label_attribute{} through function get_label_attributes, then res[i, :] = label_attribute[c]
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Input: train_mask_zs, labels, features
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Output: Y_{semantic} [l, ft]: tensor
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"""
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X = torch.LongTensor(range(features.shape[0]))
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nodeids = []
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labellist = []
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for i in X[train_mask_zs].numpy().tolist():
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nodeids.append(str(i))
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for i in labels[train_mask_zs].cpu().numpy().tolist():
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labellist.append([str(i)])
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# 1. get the semantic knowledge (class centers) of all seen classes
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label_attribute = get_label_attributes(
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train_mask_zs=train_mask_zs,
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nodeids=nodeids,
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labellist=labellist,
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features=features.cpu().numpy(),
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)
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# 2. replace original labels by their class centers (semantic knowledge)
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res = np.zeros([len(nodeids), features.shape[1]])
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for i, labels in enumerate(labellist):
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# support mutiple labels
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c = len(labels)
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temp = np.zeros([c, features.shape[1]])
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for ii, label in enumerate(labels):
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temp[ii, :] = label_attribute[int(label)]
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temp = np.mean(temp, axis=0)
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res[i, :] = temp
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if cuda:
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res = torch.FloatTensor(res).cuda()
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else:
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res = torch.FloatTensor(res)
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return res
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