137 lines
4.9 KiB
Python
137 lines
4.9 KiB
Python
import logging
|
|
import numpy as np
|
|
import os
|
|
import pickle
|
|
import scipy.sparse as sp
|
|
import sys
|
|
|
|
from scipy.sparse import linalg
|
|
def calculate_normalized_laplacian(adj):
|
|
"""
|
|
# L = D^-1/2 (D-A) D^-1/2 = I - D^-1/2 A D^-1/2
|
|
# D = diag(A 1)
|
|
:param adj:
|
|
:return:
|
|
"""
|
|
adj = sp.coo_matrix(adj)
|
|
d = np.array(adj.sum(1))
|
|
d_inv_sqrt = np.power(d, -0.5).flatten()
|
|
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
|
|
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
|
|
normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()
|
|
return normalized_laplacian
|
|
|
|
|
|
def calculate_random_walk_matrix(adj_mx):
|
|
adj_mx = sp.coo_matrix(adj_mx)
|
|
d = np.array(adj_mx.sum(1))
|
|
d_inv = np.power(d, -1).flatten()
|
|
d_inv[np.isinf(d_inv)] = 0.
|
|
d_mat_inv = sp.diags(d_inv)
|
|
random_walk_mx = d_mat_inv.dot(adj_mx).tocoo()
|
|
return random_walk_mx
|
|
|
|
|
|
def calculate_reverse_random_walk_matrix(adj_mx):
|
|
return calculate_random_walk_matrix(np.transpose(adj_mx))
|
|
|
|
|
|
def calculate_scaled_laplacian(adj_mx, lambda_max=2, undirected=True):
|
|
if undirected:
|
|
adj_mx = np.maximum.reduce([adj_mx, adj_mx.T])
|
|
L = calculate_normalized_laplacian(adj_mx)
|
|
if lambda_max is None:
|
|
lambda_max, _ = linalg.eigsh(L, 1, which='LM')
|
|
lambda_max = lambda_max[0]
|
|
L = sp.csr_matrix(L)
|
|
M, _ = L.shape
|
|
I = sp.identity(M, format='csr', dtype=L.dtype)
|
|
L = (2 / lambda_max * L) - I
|
|
return L.astype(np.float32)
|
|
|
|
|
|
def config_logging(log_dir, log_filename='info.log', level=logging.INFO):
|
|
# Add file handler and stdout handler
|
|
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
|
# Create the log directory if necessary.
|
|
try:
|
|
os.makedirs(log_dir)
|
|
except OSError:
|
|
pass
|
|
file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
|
|
file_handler.setFormatter(formatter)
|
|
file_handler.setLevel(level=level)
|
|
# Add console handler.
|
|
console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
|
|
console_handler = logging.StreamHandler(sys.stdout)
|
|
console_handler.setFormatter(console_formatter)
|
|
console_handler.setLevel(level=level)
|
|
logging.basicConfig(handlers=[file_handler, console_handler], level=level)
|
|
|
|
|
|
def get_logger(log_dir, name, log_filename='info.log', level=logging.INFO):
|
|
logger = logging.getLogger(name)
|
|
logger.setLevel(level)
|
|
# Add file handler and stdout handler
|
|
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
|
file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
|
|
file_handler.setFormatter(formatter)
|
|
# Add console handler.
|
|
console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
|
|
console_handler = logging.StreamHandler(sys.stdout)
|
|
console_handler.setFormatter(console_formatter)
|
|
logger.addHandler(file_handler)
|
|
logger.addHandler(console_handler)
|
|
# Add google cloud log handler
|
|
logger.info('Log directory: %s', log_dir)
|
|
return logger
|
|
|
|
|
|
def get_total_trainable_parameter_size():
|
|
"""
|
|
Calculates the total number of trainable parameters in the current graph.
|
|
:return:
|
|
"""
|
|
total_parameters = 0
|
|
for variable in tf.trainable_variables():
|
|
# shape is an array of tf.Dimension
|
|
total_parameters += np.product([x.value for x in variable.get_shape()])
|
|
return total_parameters
|
|
|
|
|
|
def load_dataset(dataset_dir, batch_size, test_batch_size=None, **kwargs):
|
|
data = {}
|
|
for category in ['train', 'val', 'test']:
|
|
cat_data = np.load(os.path.join(dataset_dir, category + '.npz'))
|
|
data['x_' + category] = cat_data['x']
|
|
data['y_' + category] = cat_data['y']
|
|
scaler = StandardScaler(mean=data['x_train'][..., 0].mean(), std=data['x_train'][..., 0].std())
|
|
# Data format
|
|
for category in ['train', 'val', 'test']:
|
|
data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
|
|
data['y_' + category][..., 0] = scaler.transform(data['y_' + category][..., 0])
|
|
data['train_loader'] = DataLoader(data['x_train'], data['y_train'], batch_size, shuffle=True)
|
|
data['val_loader'] = DataLoader(data['x_val'], data['y_val'], test_batch_size, shuffle=False)
|
|
data['test_loader'] = DataLoader(data['x_test'], data['y_test'], test_batch_size, shuffle=False)
|
|
data['scaler'] = scaler
|
|
|
|
return data
|
|
|
|
|
|
def load_graph_data(pkl_filename):
|
|
sensor_ids, sensor_id_to_ind, adj_mx = load_pickle(pkl_filename)
|
|
return sensor_ids, sensor_id_to_ind, adj_mx
|
|
|
|
|
|
def load_pickle(pickle_file):
|
|
try:
|
|
with open(pickle_file, 'rb') as f:
|
|
pickle_data = pickle.load(f)
|
|
except UnicodeDecodeError as e:
|
|
with open(pickle_file, 'rb') as f:
|
|
pickle_data = pickle.load(f, encoding='latin1')
|
|
except Exception as e:
|
|
print('Unable to load data ', pickle_file, ':', e)
|
|
raise
|
|
return pickle_data
|