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import numpy as np
import os
import pickle
import scipy.sparse as sp
import sys
import tensorflow as tf
import random
from scipy.sparse import linalg
class DataLoader(object):
def __init__(
self, xs, ys, x0s, batch_size, pad_with_last_sample=True, shuffle=False
):
"""
:param xs:
:param ys:
:param x0s: (starting point)
:param batch_size:
:param pad_with_last_sample: pad with the last sample to make number of samples divisible to batch_size.
"""
self.batch_size = batch_size
self.current_ind = 0
if pad_with_last_sample:
num_padding = (batch_size - (len(xs) % batch_size)) % batch_size
x_padding = np.repeat(xs[-1:], num_padding, axis=0)
y_padding = np.repeat(ys[-1:], num_padding, axis=0)
x0_padding = np.repeat(x0s[-1:], num_padding, axis=0)
xs = np.concatenate([xs, x_padding], axis=0)
ys = np.concatenate([ys, y_padding], axis=0)
x0s = np.concatenate([x0s, x0_padding], axis=0)
self.size = len(xs)
self.num_batch = int(self.size // self.batch_size)
if shuffle:
permutation = np.random.permutation(self.size)
xs, ys, x0s = xs[permutation], ys[permutation], x0s[permutation]
self.xs = xs
self.ys = ys
self.x0s = x0s
def get_iterator(self):
self.current_ind = 0
def _wrapper():
while self.current_ind < self.num_batch:
start_ind = self.batch_size * self.current_ind
end_ind = min(self.size, self.batch_size * (self.current_ind + 1))
x_i = self.xs[start_ind:end_ind, ...]
y_i = self.ys[start_ind:end_ind, ...]
x0_i = self.x0s[start_ind:end_ind, ...]
yield (x_i, y_i, x0_i)
self.current_ind += 1
return _wrapper()
def add_simple_summary(writer, names, values, global_step):
"""
Writes summary for a list of scalars.
:param writer:
:param names:
:param values:
:param global_step:
:return:
"""
for name, value in zip(names, values):
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value
summary_value.tag = name
writer.add_summary(summary, global_step)
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)] = 0.0
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)] = 0.0
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)
logger.handlers = []
# 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 generate_new_trainset(
selected_data,
previous_data,
dataset_dir,
batch_size,
test_batch_size=None,
**kwargs
):
selected_x = np.concatenate(selected_data["x"], 0)
selected_y = np.concatenate(selected_data["y"], 0)
data1 = previous_data
data1["x_train"] = np.concatenate([previous_data["x_train"], selected_x], 0)
data1["y_train"] = np.concatenate([previous_data["y_train"], selected_y[:, 1:]], 0)
data1["x0_train"] = np.concatenate([previous_data["x0_train"], selected_y[:, 0]], 0)
data1["train_loader"] = DataLoader(
data1["x_train"], data1["y_train"], data1["x0_train"], batch_size, shuffle=True
)
return data1
def load_dataset(dataset_dir, batch_size, test_batch_size=None, **kwargs):
data_list = []
data = {}
for category in ["train", "val", "test"]:
cat_data = np.load(os.path.join(dataset_dir, category + ".npz"))
data["x_" + category] = cat_data["x"]
# normalize data
data["y_" + category] = np.log(cat_data["y"] + 1.0)
# # load scenario index
# scenario_array = np.load(os.path.join(dataset_dir, 'train_scenario_array.npy'),allow_pickle=True)
# x_scenario_list = []
# y_scenario_list = []
# data['x_train'] = data['x_train'].reshape(9,1848,28, 58, 10)
# data['y_train'] = data['y_train'].reshape(9,1848, 29, 24)
# for i in range(len(scenario_array)):
# indices = np.array(scenario_array[i])
# scenario_x = data['x_train'][:,indices]
# scenario_y = data['y_train'][:,indices]
# x_scenario_list.append(scenario_x)
# y_scenario_list.append(scenario_y)
# # delet original train data
# del data['x_train']
# del data['y_train']
# # generate training data for initial case
# data['x_train'] = np.concatenate([x_scenario_list[12].reshape(-1,28, 58, 10),x_scenario_list[16].reshape(-1,28, 58, 10),
# x_scenario_list[20].reshape(-1,28, 58, 10),x_scenario_list[24].reshape(-1,28, 58, 10),x_scenario_list[28].reshape(-1,28, 58, 10)],0)
# data['y_train'] = np.concatenate([y_scenario_list[12].reshape(-1,29, 24),y_scenario_list[16].reshape(-1,29, 24),
# y_scenario_list[20].reshape(-1,29, 24),y_scenario_list[24].reshape(-1,29, 24),y_scenario_list[28].reshape(-1,29, 24)],0)
# # search data
# # search_data_list_x = list(x_scenario_list[:23] + x_scenario_list[24:])
# # search_data_list_y = list(y_scenario_list[:23] + y_scenario_list[24:])
# search_data_list_x = list(x_scenario_list[:12] + x_scenario_list[13:16] +
# x_scenario_list[17:20] + x_scenario_list[21:24] + x_scenario_list[25:28] + x_scenario_list[29:])
# search_data_list_y = list(y_scenario_list[:12] + y_scenario_list[13:16] +
# y_scenario_list[17:20] + y_scenario_list[21:24] + y_scenario_list[25:28] + y_scenario_list[29:])
# # flatten list
# search_data_x = [item for sublist in search_data_list_x for item in sublist]
# search_data_y = [item for sublist in search_data_list_y for item in sublist]
# Data format (train data modified)
data1 = {}
for category in ["train", "val", "test"]:
data1["x_" + category] = data["x_" + category]
data1["y_" + category] = data["y_" + category][:, 1:]
data1["x0_" + category] = data["y_" + category][:, 0]
data1["train_loader"] = DataLoader(
data1["x_train"], data1["y_train"], data1["x0_train"], batch_size, shuffle=True
)
data1["val_loader"] = DataLoader(
data1["x_val"], data1["y_val"], data1["x0_val"], test_batch_size, shuffle=False
)
data1["test_loader"] = DataLoader(
data1["x_test"],
data1["y_test"],
data1["x0_test"],
test_batch_size,
shuffle=False,
)
return data1
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
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