def run_expreminent(look_forward, hidden_size, batch_size, epochs, dropout,
dataset):
x, y = get_sentiments_prices(dataset['twitter_sentiments'],
dataset["reddit_sentiments"],
dataset["coin_price"], look_forward)
for i in range(x.shape[1]):
x[:, i] = normalize_array(x[:, i])
# split into train and test sets
train_x, test_x = split(x)
train_y, test_y = split(y)
train_x = np.reshape(train_x,
(train_x.shape[0], look_forward, train_x.shape[1]))
test_x = np.reshape(test_x,
(test_x.shape[0], look_forward, test_x.shape[1]))
model = create_model(hidden_size=hidden_size,
look_forward=look_forward,
dropout=dropout)
model = train(model,
train_x,
train_y,
batch_size=batch_size,
epochs=epochs)
y_pred = test(model, test_x)
score = evaluate(test_y, y_pred)
print('Test Score: %.2f RMSE' % score)
return score
def train_hypergcn(args):
if args.dataset_name in ['citeseer', 'cora']:
if args.do_svd:
data_path = 'data/citeseer.pt' if args.dataset_name == 'citeseer' else 'data/cora_author_10cls300.pt'
else:
data_path = 'data/citeseer6cls3703.pt' if args.dataset_name == 'citeseer' else 'data/cora_author_10cls1000.pt'
args = hypergraph.gen_data_cora(args,
data_path=data_path,
flip_edge_node=False)
elif args.dataset_name in ['dblp', 'pubmed']:
data_path = 'data/pubmed_data.pt' if args.dataset_name == 'pubmed' else 'data/dblp_data.pt'
args = hypergraph.gen_data_dblp(args, data_path=data_path)
else:
raise Exception('dataset {} not supported'.format(args.dataset_name))
#data_path = 'data/citeseer.pt' if args.dataset_name == 'citeseer' else 'data/cora_author.pt'
#args = hypergraph.gen_data_cora(args, data_path=data_path)
#dict_keys(['hypergraph', 'features', 'labels', 'n'])
if args.predict_edge:
hyp_struct = create_hypergcn_struct(args)
n_edge = len(args.edge_X)
n_node = len(args.all_labels)
labels = torch.cat([args.all_labels, args.edge_classes], 0)
X = torch.cat([args.v, args.edge_X], 0)
#dict_keys(['hypergraph', 'features', 'labels', 'n'])
#pdb.set_trace()
datadict = {
'hypergraph': hyp_struct,
'features': args.v,
'labels': labels,
'n': len(args.v) + n_edge
}
n_labels = max(
1, math.ceil(n_edge * utils.get_label_percent(args.dataset_name)))
train_idx = torch.from_numpy(
np.random.choice(n_edge, size=(n_labels, ),
replace=False)).to(torch.int64)
all_idx = torch.LongTensor(list(range(n_edge)))
all_idx[train_idx] = -1
test_idx = all_idx[all_idx > -1]
train_idx += n_node
test_idx += n_node #len(args.all_labels)
hg_args = hgcn_config.parse()
else:
hyp_struct = create_hypergcn_struct(args)
#dict_keys(['hypergraph', 'features', 'labels', 'n'])
#pdb.set_trace()
datadict = {
'hypergraph': hyp_struct,
'features': args.v,
'labels': args.all_labels,
'n': len(args.v)
}
train_idx = args.label_idx
all_idx = torch.LongTensor(list(range(len(args.v))))
all_idx[train_idx] = -1
test_idx = all_idx[all_idx > -1]
hg_args = hgcn_config.parse()
'''
HyperGCN = model.initialise(dataset, args)
# train and test HyperGCN
HyperGCN = model.train(HyperGCN, dataset, train, args)
'''
hg_args.n_cls = int(args.all_labels.max() + 1)
hg_args.fast = args.fast
save_data = False #True
if save_data:
full_datadict = dict(datadict)
full_datadict.update({
'train_idx': train_idx,
'test_idx': test_idx,
'n_cls': hg_args.n_cls
})
torch.save(full_datadict,
'../hypergcn/data/{}_torch.pt'.format(args.dataset_name))
pdb.set_trace()
hg = hgcn_model.initialise(datadict, hg_args)
time0 = time.time()
hg = hgcn_model.train(hg, datadict, train_idx, hg_args)
dur = time.time() - time0
hg_acc = hgcn_model.test(hg, datadict, test_idx, hg_args)
print('hg acc ', hg_acc)
#pdb.set_trace()
return hg_acc, dur
import argparse
import os
import tensorflow as tf
from model import model
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint_dir', type=str, default='checkpoint')
parser.add_argument('--results_dir', type=str, default='results')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--c_dim', type=int, default=3)
parser.add_argument('--num_shots', type=int, default=3)
parser.add_argument('--dataset', type=str, default='dataset/tf_record/train.tfrecords')
parser.add_argument('--test_h', type=int, default=800)
parser.add_argument('--test_w', type=int, default=1200)
parser.add_argument('--save_freq', type=int, default=0)
args = parser.parse_args()
assert(os.path.exists(args.checkpoint_dir))
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = model(sess, args, train=False)
model.test(args)
import tensorflow as tf
from model import model
from data import data
def parse():
parser = argparse.ArgumentParser()
parser.add_argument('--action', required=True)
parser.add_argument('--datatype')
parser.add_argument('--load', action='store_true')
args = parser.parse_args()
return args
args = parse()
if args.action == 'data':
if args.datatype != 'gigaword' and args.datatype != 'reuters' and args.datatype != 'cnn':
print('Invalid data type.')
else:
data = data(args)
data.prepare_data(args)
else:
sess = tf.Session()
model = model(sess, args)
if(args.action == 'pretrain'):
model.pretrain()
elif(args.action == 'train'):
model.train()
elif(args.action == 'test'):
model.test()
elif(args.action == 'save'):
model.save()
from model import model
from dataset.mnist import load_mnist
import numpy as np
if __name__ == '__main__':
model = model()
_, testset = load_mnist(normalize=True, one_hot_label=True)
model.test(testset)
# In[ ]:
from data import data
dataset, train, test = data.load(args)
print("length of train is", len(train))
# # initialise HyperGCN
# In[ ]:
from model import model
HyperGCN = model.initialise(dataset, args)
# # train and test HyperGCN
# In[ ]:
HyperGCN = model.train(HyperGCN, dataset, train, args)
acc = model.test(HyperGCN, dataset, test, args)
print(float(acc))
# # store result
# In[ ]:
# # 'r': [run all cells](https://stackoverflow.com/questions/33143753/jupyter-ipython-notebooks-shortcut-for-run-all)
# In[ ]:
output = {}
for name in names:
temp = {}
name_idx = onehot_mapping[name]
temp["vvec"] = [float(x) for x in v_mat[name_idx, :]]
temp["uvec"] = [float(x) for x in u_mat[:, name_idx]]
temp["avg"] = [float(x) for x in npy.add(v_mat[name_idx, :], u_mat[:, name_idx]) * 0.5]
output[name] = temp
output["confusion_matrix"] = confusion_matrix.tolist()
with open("test.json", "w") as outfile:
json.dump(output, outfile)
if __name__ == "__main__":
fnames = preprocess.get_filenames()
onehot_mapping, data_mat, label_mat, weight_mat = preprocess.prepare_data(fnames)
# Test results
test_data, test_lab = preprocess.prepare_test_data(onehot_mapping)
model = model.Model(data_mat, label_mat, weight_mat)
model.train(ModelConfig().numEpochs)
conf_matrix = model.test(test_data, test_lab, species_clause=True)
output_results(model, onehot_mapping, conf_matrix)
model = model(num_classes=data_load.num_classes)
session_config = tf.ConfigProto(log_device_placement=False,
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
allow_soft_placement=True)
session_config.gpu_options.allow_growth = True
session_config.gpu_options.allocator_type = 'BFC'
with tf.Session(config=session_config) as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
best_dev_acc = 0
biggest_patient = 20
patient = 0
for i in range(100):
model.train(sess, data_load.train_datas, data_load.train_labels, 32)
dev_acc = model.test(sess, data_load.dev_datas, data_load.dev_labels,
32)
if dev_acc > best_dev_acc:
best_dev_acc = dev_acc
saver.save(sess, "./model/best_result.ckpt")
patient = 0
print("Epoch %d: best dev acc is updataed to %f" %
(i, best_dev_acc))
else:
patient += 1
print("Epoch %d: best acc is not updataed, the patient is %d" %
(i, patient))
if patient == biggest_patient:
print("Patient is achieve biggest patient, training finished")
saver.restore(sess, "./model/best_result.ckpt")
test_acc = model.test(sess, data_load.test_datas, data_load.test_labels,
32)
