import time
import argparse
import numpy as np
# from train import train
import pickle as pkl
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
# from args import get_text_args
from args import get_citation_args
from utils import *
from train import train_linear, adj, sp_adj, label_dict, index_dict
import torch.nn.functional as F
from models import get_model
from math import log
# args = get_text_args()
args = get_citation_args()
set_seed(args.seed, args.cuda)
adj_dense = sparse_to_torch_dense(sp_adj, device='cpu')
feat_dict, precompute_time = sgc_precompute(adj, adj_dense, args.degree - 1,
index_dict)
if args.dataset == "mr": nclass = 1
else: nclass = label_dict["train"].max().item() + 1
def linear_objective(space):
model = get_model(args.model,
nfeat=feat_dict["train"].size(1),
nclass=nclass,
nhid=0,
dropout=0,
def main():
args = get_citation_args()
n_way = args.n_way
train_shot = args.train_shot
test_shot = args.test_shot
step = args.step
node_num = args.node_num
iteration = args.iteration
accuracy_meta_test = []
total_accuracy_meta_test = []
set_seed(args.seed, args.cuda)
adj, features, labels = load_citation(args.dataset, args.normalization,
args.cuda)
if args.dataset == 'cora':
class_label = [0, 1, 2, 3, 4, 5, 6]
combination = list(combinations(class_label, n_way))
elif args.dataset == 'citeseer':
node_num = 3327
iteration = 15
class_label = [0, 1, 2, 3, 4, 5]
combination = list(combinations(class_label, n_way))
if args.model == 'SGC':
features = sgc_precompute(features, adj, args.degree)
for i in range(len(combination)):
print('Cross_Validation: ', i + 1)
test_label = list(combination[i])
train_label = [n for n in class_label if n not in test_label]
print('Cross_Validation {} Train_Label_List {}: '.format(
i + 1, train_label))
print('Cross_Validation {} Test_Label_List {}: '.format(
i + 1, test_label))
model = get_model(args.model, features.size(1), n_way, args.cuda)
for j in range(iteration):
labels_local = labels.clone().detach()
select_class = random.sample(train_label, n_way)
print('Cross_Validation {} ITERATION {} Train_Label: {}'.format(
i + 1, j + 1, select_class))
class1_idx = []
class2_idx = []
for k in range(node_num):
if (labels_local[k] == select_class[0]):
class1_idx.append(k)
labels_local[k] = 0
elif (labels_local[k] == select_class[1]):
class2_idx.append(k)
labels_local[k] = 1
for m in range(step):
class1_train = random.sample(class1_idx, train_shot)
class2_train = random.sample(class2_idx, train_shot)
class1_test = [
n1 for n1 in class1_idx if n1 not in class1_train
]
class2_test = [
n2 for n2 in class2_idx if n2 not in class2_train
]
train_idx = class1_train + class2_train
random.shuffle(train_idx)
test_idx = class1_test + class2_test
random.shuffle(test_idx)
model = train_regression(model, features[train_idx],
labels_local[train_idx], args.epochs,
args.weight_decay, args.lr)
acc_query = test_regression(model, features[test_idx],
labels_local[test_idx])
reset_array()
torch.save(model.state_dict(), 'model.pkl')
labels_local = labels.clone().detach()
select_class = random.sample(test_label, 2)
class1_idx = []
class2_idx = []
reset_array()
print('Cross_Validation {} Test_Label {}: '.format(
i + 1, select_class))
for k in range(node_num):
if (labels_local[k] == select_class[0]):
class1_idx.append(k)
labels_local[k] = 0
elif (labels_local[k] == select_class[1]):
class2_idx.append(k)
labels_local[k] = 1
for m in range(step):
class1_train = random.sample(class1_idx, test_shot)
class2_train = random.sample(class2_idx, test_shot)
class1_test = [n1 for n1 in class1_idx if n1 not in class1_train]
class2_test = [n2 for n2 in class2_idx if n2 not in class2_train]
train_idx = class1_train + class2_train
random.shuffle(train_idx)
test_idx = class1_test + class2_test
random.shuffle(test_idx)
model_meta_trained = get_model(args.model, features.size(1), n_way,
args.cuda).cuda()
model_meta_trained.load_state_dict(torch.load('model.pkl'))
model_meta_trained = train_regression(model_meta_trained,
features[train_idx],
labels_local[train_idx],
args.epochs,
args.weight_decay, args.lr)
acc_test = test_regression(model_meta_trained, features[test_idx],
labels_local[test_idx])
accuracy_meta_test.append(acc_test)
total_accuracy_meta_test.append(acc_test)
reset_array()
if args.dataset == 'cora':
with open('cora.txt', 'a') as f:
f.write('Cross_Validation: {} Meta-Test_Accuracy: {}'.format(
i + 1,
torch.tensor(accuracy_meta_test).numpy().mean()))
f.write('\n')
elif args.dataset == 'citeseer':
with open('citeseer.txt', 'a') as f:
f.write('Cross_Validation: {} Meta-Test_Accuracy: {}'.format(
i + 1,
torch.tensor(accuracy_meta_test).numpy().mean()))
f.write('\n')
accuracy_meta_test = []
if args.dataset == 'cora':
with open('cora.txt', 'a') as f:
f.write('Dataset: {}, Train_Shot: {}, Test_Shot: {}'.format(
args.dataset, train_shot, test_shot))
f.write('\n')
f.write('Total_Meta-Test_Accuracy: {}'.format(
torch.tensor(total_accuracy_meta_test).numpy().mean()))
f.write('\n')
f.write('\n\n\n')
elif args.dataset == 'citeseer':
with open('citeseer.txt', 'a') as f:
f.write('Dataset: {}, Train_Shot: {}, Test_Shot: {}'.format(
args.dataset, train_shot, test_shot))
f.write('\n')
f.write('Total_Meta-Test_Accuracy: {}'.format(
torch.tensor(total_accuracy_meta_test).numpy().mean()))
f.write('\n')
f.write('\n\n\n')
