dmlc--dgl
lt610
d70ca6eb7c
* graphsaint * graphsaint * graphsaint * graphsaint * fixed the model * fixed some bugs * fixed the computing of normalization and updated the results * fixed some bugs and updated the results * Update utils.py * Update train_sampling.py * Update train_sampling.py Co-authored-by: Mufei Li <mufeili1996@gmail.com> Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
106 行
3.3 KiB
Python
106 行
3.3 KiB
Python
import json
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import os
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from functools import namedtuple
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import scipy.sparse
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from sklearn.preprocessing import StandardScaler
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import dgl
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import numpy as np
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import torch
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from sklearn.metrics import f1_score
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class Logger(object):
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'''A custom logger to log stdout to a logging file.'''
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def __init__(self, path):
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"""Initialize the logger.
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Parameters
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---------
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path : str
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The file path to be stored in.
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"""
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self.path = path
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def write(self, s):
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with open(self.path, 'a') as f:
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f.write(str(s))
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print(s)
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return
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def save_log_dir(args):
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log_dir = './log/{}/{}'.format(args.dataset, args.log_dir)
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os.makedirs(log_dir, exist_ok=True)
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return log_dir
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def calc_f1(y_true, y_pred, multilabel):
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if multilabel:
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y_pred[y_pred > 0] = 1
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y_pred[y_pred <= 0] = 0
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else:
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y_pred = np.argmax(y_pred, axis=1)
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return f1_score(y_true, y_pred, average="micro"), \
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f1_score(y_true, y_pred, average="macro")
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def evaluate(model, g, labels, mask, multilabel=False):
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model.eval()
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with torch.no_grad():
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logits = model(g)
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logits = logits[mask]
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labels = labels[mask]
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f1_mic, f1_mac = calc_f1(labels.cpu().numpy(),
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logits.cpu().numpy(), multilabel)
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return f1_mic, f1_mac
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# load data of GraphSAINT and convert them to the format of dgl
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def load_data(args, multilabel):
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prefix = "data/{}".format(args.dataset)
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DataType = namedtuple('Dataset', ['num_classes', 'train_nid', 'g'])
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adj_full = scipy.sparse.load_npz('./{}/adj_full.npz'.format(prefix)).astype(np.bool)
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g = dgl.from_scipy(adj_full)
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num_nodes = g.num_nodes()
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adj_train = scipy.sparse.load_npz('./{}/adj_train.npz'.format(prefix)).astype(np.bool)
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train_nid = np.array(list(set(adj_train.nonzero()[0])))
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role = json.load(open('./{}/role.json'.format(prefix)))
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mask = np.zeros((num_nodes,), dtype=bool)
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train_mask = mask.copy()
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train_mask[role['tr']] = True
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val_mask = mask.copy()
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val_mask[role['va']] = True
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test_mask = mask.copy()
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test_mask[role['te']] = True
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feats = np.load('./{}/feats.npy'.format(prefix))
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scaler = StandardScaler()
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scaler.fit(feats[train_nid])
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feats = scaler.transform(feats)
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class_map = json.load(open('./{}/class_map.json'.format(prefix)))
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class_map = {int(k): v for k, v in class_map.items()}
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if multilabel:
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# Multi-label binary classification
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num_classes = len(list(class_map.values())[0])
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class_arr = np.zeros((num_nodes, num_classes))
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for k, v in class_map.items():
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class_arr[k] = v
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else:
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num_classes = max(class_map.values()) - min(class_map.values()) + 1
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class_arr = np.zeros((num_nodes,))
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for k, v in class_map.items():
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class_arr[k] = v
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g.ndata['feat'] = torch.tensor(feats, dtype=torch.float)
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g.ndata['label'] = torch.tensor(class_arr, dtype=torch.float if multilabel else torch.long)
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g.ndata['train_mask'] = torch.tensor(train_mask, dtype=torch.bool)
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g.ndata['val_mask'] = torch.tensor(val_mask, dtype=torch.bool)
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g.ndata['test_mask'] = torch.tensor(test_mask, dtype=torch.bool)
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data = DataType(g=g, num_classes=num_classes, train_nid=train_nid)
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return data
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