repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/dcrnn_train_pytorch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
import setproctitle
setproctitle.setproctitle("stgcn@lifuxian")
def main(args):
... | 1,455 | 38.351351 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/run_demo_pytorch.py | import argparse
import numpy as np
import os
import sys
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config[... | 1,264 | 36.205882 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/dcrnn_train.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import tensorflow as tf
import yaml
from lib.utils import load_graph_data
from model.tf.dcrnn_supervisor import DCRNNSupervisor
def main(args):
with open(args.config_filename) as f:
... | 1,240 | 32.540541 | 104 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/run_demo.py | import argparse
import numpy as np
import os
import sys
import tensorflow as tf
import yaml
from lib.utils import load_graph_data
from model.tf.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
config = yaml.load(f)
tf_config = tf.ConfigProto()
if ... | 1,433 | 36.736842 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/scripts/generate_training_data.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import os
import pandas as pd
def generate_graph_seq2seq_io_data(
df, x_offsets, y_offsets, add_time_in_day=True, add_day_in_... | 3,904 | 30.491935 | 103 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/scripts/gen_adj_mx.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import pandas as pd
import pickle
def get_adjacency_matrix(distance_df, sensor_ids, normalized_k=0.1):
"""
:param distance_df: data frame with three columns: [from,... | 2,790 | 42.609375 | 125 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/scripts/eval_baseline_methods.py | import argparse
import numpy as np
import pandas as pd
from statsmodels.tsa.vector_ar.var_model import VAR
from lib import utils
from lib.metrics import masked_rmse_np, masked_mape_np, masked_mae_np
from lib.utils import StandardScaler
def historical_average_predict(df, period=12 * 24 * 7, test_ratio=0.2, null_val=... | 5,893 | 40.507042 | 116 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/scripts/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/dcrnn_model.py | import numpy as np
import torch
import torch.nn as nn
from model.pytorch.dcrnn_cell import DCGRUCell
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class Seq2SeqAttrs:
def __init__(self... | 13,218 | 41.779935 | 119 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/dcrnn_cell.py | import numpy as np
import torch
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._biases_dict = {}
... | 6,939 | 41.576687 | 105 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/utils.py | import torch
import numpy as np
def masked_mae_loss(y_pred, y_true):
mask = (y_true != 0).float()
mask /= mask.mean()
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
loss[loss != loss] = 0
return loss... | 3,175 | 30.76 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/dcrnn_supervisor.py | import os
import time
import numpy as np
import torch
import torch.nn as nn
# from torch.utils.tensorboard import SummaryWriter
from lib import utils
# from model.pytorch.dcrnn_model import DCRNNModel
from model.pytorch.dcrnn_model import STGCN
from model.pytorch.utils import masked_mae_loss, metric, get_normalized_a... | 17,411 | 41.8867 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/tf/dcrnn_model.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib import legacy_seq2seq
from model.tf.dcrnn_cell import DCGRUCell
class DCRNNModel(object):
def __init__(self, is_training, batch_size, scaler, adj_mx, **mo... | 4,940 | 41.594828 | 119 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/tf/dcrnn_cell.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn import RNNCell
from lib import utils
class DCGRUCell(RNNCell):
"""Graph Convolution Gated Recurrent Unit cell.
"""
def cal... | 8,023 | 42.372973 | 105 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/tf/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/tf/dcrnn_supervisor.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
import sys
import tensorflow as tf
import time
import yaml
from lib import utils, metrics
from lib.AMSGrad import AMSGrad
from lib.metrics import masked_mae_loss
from model.tf.dcr... | 13,531 | 41.420063 | 115 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/lib/utils.py | import logging
import numpy as np
import os
import pickle
import scipy.sparse as sp
import sys
# import tensorflow as tf
from scipy.sparse import linalg
class DataLoader(object):
def __init__(self, xs, ys, batch_size, pad_with_last_sample=True, shuffle=False):
"""
:param xs:
:param ys:
... | 7,499 | 33.562212 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/lib/metrics_test.py | import unittest
import numpy as np
import tensorflow as tf
from lib import metrics
class MyTestCase(unittest.TestCase):
def test_masked_mape_np(self):
preds = np.array([
[1, 2, 2],
[3, 4, 5],
], dtype=np.float32)
labels = np.array([
[1, 2, 2],
... | 6,135 | 29.834171 | 80 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/lib/AMSGrad.py | """AMSGrad for TensorFlow.
From: https://github.com/taki0112/AMSGrad-Tensorflow
"""
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_o... | 7,695 | 44.538462 | 115 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/lib/metrics.py | import numpy as np
import tensorflow as tf
def masked_mse_tf(preds, labels, null_val=np.nan):
"""
Accuracy with masking.
:param preds:
:param labels:
:param null_val:
:return:
"""
if np.isnan(null_val):
mask = ~tf.is_nan(labels)
else:
mask = tf.not_equal(labels, nul... | 4,371 | 29.361111 | 99 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/lib/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/layer.py | from __future__ import division
import torch
import torch.nn as nn
from torch.nn import init
import numbers
import torch.nn.functional as F
from collections import OrderedDict
class gconv_RNN(nn.Module):
def __init__(self):
super(gconv_RNN, self).__init__()
def forward(self, x, A):
x = torch... | 2,007 | 27.28169 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/net.py | import torch.utils.data as utils
import torch.nn.functional as F
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import numpy as np
import pandas as pd
import math
import time
from layer import *
import sys
from collections import OrderedDict
class DGCRN... | 10,196 | 36.215328 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/util.py | import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
from torch.autograd import Variable
def normal_std(x):
return x.std() * np.sqrt((len(x) - 1.) / (len(x)))
class DataLoaderS(object):
def __init__(self,
file_name,
... | 12,210 | 31.562667 | 112 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/train.py | import torch
import numpy as np
import argparse
import time
from util import *
from trainer import Trainer
from net import DGCRN
import setproctitle
import os
setproctitle.setproctitle("DGCRN@lifuxian")
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() in {'false', 'f... | 15,124 | 35.184211 | 186 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/trainer.py | import torch.optim as optim
import math
from net import *
import util
class Trainer():
def __init__(self,
model,
lrate,
wdecay,
clip,
step_size,
seq_out_len,
scaler,
device,
... | 3,313 | 33.520833 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/PeMS/tf_utils.py | # import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def conv2d(x, output_dims, kernel_size, stride = [1, 1],
padding = 'SAME', use_bias = True, activation = tf.nn.relu,
bn = False, bn_decay = None, is_training = None):
input_dims = x.get_shape()[-1].value
... | 2,424 | 38.754098 | 77 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/PeMS/test.py | import math
import argparse
import utils
import time
import numpy as np
import tensorflow as tf
parser = argparse.ArgumentParser()
parser.add_argument('--P', type = int, default = 12,
help = 'history steps')
parser.add_argument('--Q', type = int, default = 12,
help = 'prediction... | 5,723 | 43.030769 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/PeMS/utils.py | import numpy as np
import pandas as pd
# log string
def log_string(log, string):
log.write(string + '\n')
log.flush()
print(string)
# metric
def metric(pred, label):
with np.errstate(divide = 'ignore', invalid = 'ignore'):
mask = np.not_equal(label, 0)
mask = mask.astype(np.float32)
... | 3,345 | 33.142857 | 77 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/PeMS/model.py | import tf_utils
# import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def placeholder(P, Q, N):
X = tf.compat.v1.placeholder(shape = (None, P, N), dtype = tf.float32)
TE = tf.compat.v1.placeholder(shape = (None, P + Q, 2), dtype = tf.int32)
label = tf.compat.v1.placeholder(sh... | 10,625 | 37.781022 | 80 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/PeMS/train.py | import math
import argparse
import utils, model
import time, datetime
import numpy as np
# import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
parser = argparse.ArgumentParser()
parser.add_argument('--time_slot', type = int, default = 5,
help = 'a time step is 5 mins'... | 9,998 | 40.318182 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/PeMS/node2vec/node2vec.py | '''
Aditya Grover and Jure Leskovec. node2vec: Scalable Feature Learning for Networks. In KDD, 2016.
https://github.com/aditya-grover/node2vec
'''
import numpy as np
import networkx as nx
import random
class Graph():
def __init__(self, nx_G, is_directed, p, q):
self.G = nx_G
self.is_directed = is_directed
sel... | 3,855 | 23.877419 | 120 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/PeMS/node2vec/generateSE.py | import node2vec
import numpy as np
import networkx as nx
from gensim.models import Word2Vec
is_directed = True
p = 2
q = 1
num_walks = 100
walk_length = 80
dimensions = 64
window_size = 10
iter = 1000
Adj_file = '../data/Adj.txt'
SE_file = '../data/SE.txt'
def read_graph(edgelist):
G = nx.read_edgelist(
e... | 911 | 23 | 65 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/BJ500/tf_utils.py | # import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def conv2d(x, output_dims, kernel_size, stride = [1, 1],
padding = 'SAME', use_bias = True, activation = tf.nn.relu,
bn = False, bn_decay = None, is_training = None):
input_dims = x.get_shape()[-1].value
... | 2,424 | 38.754098 | 77 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/BJ500/test.py | import math
import argparse
import utils
import time
import numpy as np
import tensorflow as tf
parser = argparse.ArgumentParser()
parser.add_argument('--P', type = int, default = 12,
help = 'history steps')
parser.add_argument('--Q', type = int, default = 12,
help = 'prediction... | 5,723 | 43.030769 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/BJ500/utils.py | import numpy as np
import pandas as pd
# log string
def log_string(log, string):
log.write(string + '\n')
log.flush()
print(string)
# metric
def metric(pred, label):
with np.errstate(divide = 'ignore', invalid = 'ignore'):
mask = np.not_equal(label, 0)
mask = mask.astype(np.float32)
... | 3,346 | 33.153061 | 77 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/BJ500/model.py | import tf_utils
# import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def placeholder(P, Q, N):
X = tf.compat.v1.placeholder(shape = (None, P, N), dtype = tf.float32)
TE = tf.compat.v1.placeholder(shape = (None, P + Q, 2), dtype = tf.int32)
label = tf.compat.v1.placeholder(sh... | 10,625 | 37.781022 | 80 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/BJ500/train.py | import math
import argparse
import utils, model
import time, datetime
import numpy as np
# import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
parser = argparse.ArgumentParser()
parser.add_argument('--time_slot', type = int, default = 5,
help = 'a time step is 5 mins'... | 10,002 | 40.334711 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/BJ500/node2vec/node2vec.py | '''
Aditya Grover and Jure Leskovec. node2vec: Scalable Feature Learning for Networks. In KDD, 2016.
https://github.com/aditya-grover/node2vec
'''
import numpy as np
import networkx as nx
import random
class Graph():
def __init__(self, nx_G, is_directed, p, q):
self.G = nx_G
self.is_directed = is_directed
sel... | 3,855 | 23.877419 | 120 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/BJ500/node2vec/generateSE.py | import node2vec
import numpy as np
import networkx as nx
from gensim.models import Word2Vec
is_directed = False #此处改为False
p = 2
q = 1
num_walks = 100
walk_length = 80
dimensions = 64
window_size = 10
iter = 1000
Adj_file = '../data/Adj(BJ500).txt'
SE_file = '../data/SE(BJ500).txt'
def read_graph(edgelist):
G = n... | 937 | 23.684211 | 65 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/METR/tf_utils.py | # import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def conv2d(x, output_dims, kernel_size, stride = [1, 1],
padding = 'SAME', use_bias = True, activation = tf.nn.relu,
bn = False, bn_decay = None, is_training = None):
input_dims = x.get_shape()[-1].value
... | 2,424 | 38.754098 | 77 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/METR/test.py | import math
import argparse
import utils
import time
import numpy as np
import tensorflow as tf
parser = argparse.ArgumentParser()
parser.add_argument('--P', type = int, default = 12,
help = 'history steps')
parser.add_argument('--Q', type = int, default = 12,
help = 'prediction... | 5,654 | 42.5 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/METR/utils.py | import numpy as np
import pandas as pd
# log string
def log_string(log, string):
log.write(string + '\n')
log.flush()
print(string)
# metric
def metric(pred, label):
with np.errstate(divide = 'ignore', invalid = 'ignore'):
mask = np.not_equal(label, 0)
mask = mask.astype(np.float32)
... | 3,331 | 33.708333 | 77 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/METR/model.py | import tf_utils
# import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def placeholder(P, Q, N):
X = tf.placeholder(
shape = (None, P, N), dtype = tf.float32, name = 'X')
TE = tf.placeholder(
shape = (None, P + Q, 2), dtype = tf.int32, name = 'TE')
label = tf.... | 10,872 | 37.556738 | 87 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/METR/train.py | import math
import argparse
import utils, model
import time, datetime
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
parser = argparse.ArgumentParser()
parser.add_argument('--time_slot', type = int, default = 5,
help = 'a time step is 5 mins')
parser.add_argument('--... | 9,863 | 39.760331 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/METR/node2vec/node2vec.py | '''
Aditya Grover and Jure Leskovec. node2vec: Scalable Feature Learning for Networks. In KDD, 2016.
https://github.com/aditya-grover/node2vec
'''
import numpy as np
import networkx as nx
import random
class Graph():
def __init__(self, nx_G, is_directed, p, q):
self.G = nx_G
self.is_directed = is_directed
sel... | 3,855 | 23.877419 | 120 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/GMAN/METR/node2vec/generateSE.py | import node2vec
import numpy as np
import networkx as nx
from gensim.models import Word2Vec
is_directed = True
p = 2
q = 1
num_walks = 100
walk_length = 80
dimensions = 64
window_size = 10
iter = 1000
Adj_file = '../data/Adj.txt'
SE_file = '../data/SE.txt'
def read_graph(edgelist):
G = nx.read_edgelist(
e... | 911 | 23 | 65 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCRN.py | import torch
import torch.nn as nn
from model.AGCRNCell import AGCRNCell
class AVWDCRNN(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim, num_layers=1):
super(AVWDCRNN, self).__init__()
assert num_layers >= 1, 'At least one DCRNN layer in the Encoder.'
self.node_n... | 3,454 | 44.460526 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/Run_PEMS-BAY.py | import os
import sys
file_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(file_dir)
sys.path.append(file_dir)
import torch
import numpy as np
import torch.nn as nn
import argparse
import configparser
from datetime import datetime
from model.AGCRN import AGCRN as Network
from model.BasicTrainer... | 6,953 | 32.921951 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCRN_debug.py | import torch
import torch.nn as nn
from model.AGCRNCell import AGCRNCell
class AVWDCRNN(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim, num_layers=1):
super(AVWDCRNN, self).__init__()
assert num_layers >= 1, 'At least one DCRNN layer in the Encoder.'
self.node_n... | 4,677 | 41.917431 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCN.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AVWGCN(nn.Module):
def __init__(self, dim_in, dim_out, cheb_k, embed_dim):
super(AVWGCN, self).__init__()
self.cheb_k = cheb_k
self.weights_pool = nn.Parameter(torch.FloatTensor(embed_dim, cheb_k, dim_in, dim_out))
... | 1,453 | 54.923077 | 112 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCRNCell.py | import torch
import torch.nn as nn
from model.AGCN import AVWGCN
class AGCRNCell(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim):
super(AGCRNCell, self).__init__()
self.node_num = node_num
self.hidden_dim = dim_out
self.gate = AVWGCN(dim_in+self.hidden_d... | 1,065 | 40 | 80 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/Run_METR-LA.py | import os
import sys
file_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(file_dir)
sys.path.append(file_dir)
import torch
import numpy as np
import torch.nn as nn
import argparse
import configparser
from datetime import datetime
from model.AGCRN import AGCRN as Network
from model.BasicTraine... | 6,953 | 32.757282 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/BasicTrainer.py | import torch
import math
import os
import time
import copy
import numpy as np
from lib.logger import get_logger
from lib.metrics import All_Metrics
class Trainer(object):
def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader,
scaler, args, lr_scheduler=None):
sup... | 9,286 | 42.600939 | 148 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/Run_BJ.py | import os
import sys
file_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(file_dir)
sys.path.append(file_dir)
import torch
import numpy as np
import torch.nn as nn
import argparse
import configparser
from datetime import datetime
from model.AGCRN import AGCRN as Network
from model.BasicTrainer... | 6,947 | 32.892683 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/load_dataset.py | import os
import numpy as np
def load_st_dataset(dataset):
#output B, N, D
if dataset == 'PEMSD4':
data_path = os.path.join('../data/PeMSD4/pems04.npz')
data = np.load(data_path)['data'][:, :, 0] #onley the first dimension, traffic flow data
elif dataset == 'PEMSD8':
data_path = os... | 697 | 37.777778 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/TrainInits.py | import torch
import random
import numpy as np
def init_seed(seed):
'''
Disable cudnn to maximize reproducibility
'''
torch.cuda.cudnn_enabled = False
torch.backends.cudnn.deterministic = True
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)... | 1,818 | 33.980769 | 120 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/dataloader.py | import torch
import numpy as np
import torch.utils.data
from lib.add_window import Add_Window_Horizon
from lib.load_dataset import load_st_dataset
from lib.normalization import NScaler, MinMax01Scaler, MinMax11Scaler, StandardScaler, ColumnMinMaxScaler
def normalize_dataset(data, normalizer, column_wise=False):
if... | 9,437 | 45.492611 | 208 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/logger.py | import os
import logging
from datetime import datetime
def get_logger(root, name=None, debug=True):
#when debug is true, show DEBUG and INFO in screen
#when debug is false, show DEBUG in file and info in both screen&file
#INFO will always be in screen
# create a logger
logger = logging.getLogger(na... | 1,641 | 35.488889 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/add_window.py | import numpy as np
def Add_Window_Horizon(data, window=3, horizon=1, single=False):
'''
:param data: shape [B, ...]
:param window:
:param horizon:
:return: X is [B, W, ...], Y is [B, H, ...]
'''
length = len(data)
end_index = length - horizon - window + 1
X = [] #windows
Y ... | 1,053 | 26.736842 | 71 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/metrics.py | '''
Always evaluate the model with MAE, RMSE, MAPE, RRSE, PNBI, and oPNBI.
Why add mask to MAE and RMSE?
Filter the 0 that may be caused by error (such as loop sensor)
Why add mask to MAPE and MARE?
Ignore very small values (e.g., 0.5/0.5=100%)
'''
import numpy as np
import torch
def MAE_torch(pred, true, mask... | 7,947 | 34.641256 | 103 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/normalization.py | import numpy as np
import torch
class NScaler(object):
def transform(self, data):
return data
def inverse_transform(self, data):
return data
class StandardScaler:
"""
Standard the input
"""
def __init__(self, mean, std):
self.mean = mean
self.std = std
de... | 4,047 | 30.138462 | 93 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/layer.py | from __future__ import division
import torch
import torch.nn as nn
from torch.nn import init
import numbers
import torch.nn.functional as F
from collections import OrderedDict
class gconv_RNN(nn.Module):
def __init__(self):
super(gconv_RNN, self).__init__()
def forward(self, x, A):
x = torch... | 2,008 | 26.902778 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/net.py | import torch.utils.data as utils
import torch.nn.functional as F
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import numpy as np
import pandas as pd
import math
import time
from layer import *
import random
import sys
from collections import OrderedDict... | 9,248 | 33.901887 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/util.py | import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
from torch.autograd import Variable
def normal_std(x):
return x.std() * np.sqrt((len(x) - 1.) / (len(x)))
class DataLoaderS(object):
def __init__(self,
file_name,
... | 12,198 | 31.530667 | 112 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/train.py | import torch
import numpy as np
import argparse
import time
from util import *
from trainer import Trainer
from net import DGCRN
import setproctitle
import os
import random
setproctitle.setproctitle("DGCRN@lifuxian")
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() i... | 15,842 | 34.76298 | 186 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/trainer.py | import torch.optim as optim
import math
from net import *
import util
class Trainer():
def __init__(self,
model,
lrate,
wdecay,
clip,
step_size,
seq_out_len,
scaler,
device,
... | 3,313 | 33.520833 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/scripts/generate_training_data_BJ.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import os
import pandas as pd
def generate_graph_seq2seq_io_data(
df, x_offsets, y_offsets, add_time_in_day=True, add_day_in_... | 4,417 | 31.970149 | 398 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/scripts/gen_adj_mx_BJ.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import pandas as pd
import pickle
def get_adjacency_matrix(distance_df, sensor_ids, normalized_k=0.1):
"""
:param distance_df: data frame with three columns: [from,... | 2,790 | 42.609375 | 125 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/train_MSTGCN_r.py | #!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import os
from time import time
import shutil
import argparse
import configparser
from model.MSTGCN_r import make_model
from lib.utils import load_graphdata_channel1, get_adjacency_matrix, evaluate_on... | 6,970 | 31.423256 | 150 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/train_ASTGCN_r.py | #!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import os
from time import time
import shutil
import argparse
import configparser
from model.ASTGCN_r import make_model
from lib.utils import load_graphdata_channel1, get_adjacency_matrix, compute_val... | 6,972 | 30.840183 | 150 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/prepareData.py | import os
import numpy as np
import argparse
import configparser
def search_data(sequence_length, num_of_depend, label_start_idx,
num_for_predict, units, points_per_hour):
'''
Parameters
----------
sequence_length: int, length of all history data
num_of_depend: int,
label_start... | 11,903 | 38.157895 | 147 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/model/ASTGCN_r.py | # -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.utils import scaled_Laplacian, cheb_polynomial
class Spatial_Attention_layer(nn.Module):
'''
compute spatial attention scores
'''
def __init__(self, DEVICE, in_channels, num_of_vertices, num_of_timesteps... | 10,548 | 36.275618 | 194 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/model/MSTGCN_r.py | # -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.utils import scaled_Laplacian, cheb_polynomial
class cheb_conv(nn.Module):
'''
K-order chebyshev graph convolution
'''
def __init__(self, K, cheb_polynomials, in_channels, out_channels):
'''
... | 5,014 | 32.885135 | 154 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/lib/utils.py | import os
import numpy as np
import torch
import torch.utils.data
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from .metrics import masked_mape_np
from scipy.sparse.linalg import eigs
import pickle
def load_pickle(pickle_file):
try:
with open(pickle_file,... | 18,213 | 34.996047 | 208 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/lib/metrics.py | # -*- coding:utf-8 -*-
import numpy as np
def masked_mape_np(y_true, y_pred, null_val=np.nan):
with np.errstate(divide='ignore', invalid='ignore'):
if np.isnan(null_val):
mask = ~np.isnan(y_true)
else:
mask = np.not_equal(y_true, null_val)
mask = mask.astype('float... | 541 | 30.882353 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/main.py | # -*- coding:utf-8 -*-
import setproctitle
setproctitle.setproctitle("STSGCN@lifuxian")
import time
import json
import argparse
import numpy as np
import mxnet as mx
from utils import (construct_model, generate_data,
masked_mae_np, masked_mape_np, masked_mse_np)
parser = argparse.ArgumentParser()
... | 6,547 | 32.238579 | 830 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/utils.py |
import os
import numpy as np
import mxnet as mx
import pickle
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='l... | 7,732 | 27.747212 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/load_params.py | # -*- coding:utf-8 -*-
import mxnet as mx
sym, arg_params, aux_params = mx.model.load_checkpoint('STSGCN', 200)
print(type(arg_params), type(aux_params))
| 157 | 18.75 | 69 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/models/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/models/stsgcn.py | # -*- coding:utf-8 -*-
import mxnet as mx
def position_embedding(data,
input_length, num_of_vertices, embedding_size,
temporal=True, spatial=True,
init=mx.init.Xavier(magnitude=0.0003), prefix=""):
'''
Parameters
----------
data: mx... | 12,140 | 23.137177 | 76 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/test/test_stsgcn.py | # -*- coding:utf-8 -*-
import sys
import mxnet as mx
sys.path.append('.')
num_of_vertices = 358
batch_size = 16
filter_ = [3, 3, 3]
filter_list = [[3, 3, 3], [6, 6, 6], [9, 9, 9]]
predict_length = 12
data = mx.sym.var('data')
adj = mx.sym.var('adj')
label = mx.sym.var('label')
def test_position_embedding():
fro... | 4,947 | 34.342857 | 75 | py |
AGC | AGC-master-master/test.py | import scipy.io as sio
import time
import tensorflow as tf
import numpy as np
import scipy.sparse as sp
from sklearn.cluster import KMeans
from metrics import clustering_metrics
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.preprocessi... | 4,987 | 26.711111 | 104 | py |
AGC | AGC-master-master/munkres.py | #!/usr/bin/env python
# -*- coding: iso-8859-1 -*-
# Documentation is intended to be processed by Epydoc.
"""
Introduction
============
The Munkres module provides an implementation of the Munkres algorithm
(also called the Hungarian algorithm or the Kuhn-Munkres algorithm),
useful for solving the Assignment Problem... | 27,084 | 30.275982 | 82 | py |
AGC | AGC-master-master/metrics.py | from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import average_precision_score
from sklearn import metrics
from munkres import Munkres, print_matrix
import numpy as np
class linkpred_metrics():
def __init__(self, edges_pos, edges_neg):
self.edges_pos = ed... | 4,397 | 39.348624 | 259 | py |
FL-MRCM | FL-MRCM-main/main_fl_mr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import copy
import numpy as np
import torch
import os
from utils.options import args_parser
from models.recon_Update import LocalUpdate
from models.Fed import FedAvg
from models.test import evaluator_normal as evaluator
from data.mri_data import Slice... | 5,441 | 37.595745 | 148 | py |
FL-MRCM | FL-MRCM-main/main_test.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import torch
import os
from utils.options import args_parser
from models.test import test_save_result, test_save_vector
from data.mri_data import SliceData, DataTransform
from data.subsample import create_mask_for_mask_type
from models.unet_model impo... | 2,799 | 32.73494 | 127 | py |
FL-MRCM | FL-MRCM-main/main_fl_mrcm.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
# import matplotlib
# matplotlib.use('Agg')
# import matplotlib.pyplot as plt
import copy
import numpy as np
import torch
import os
from utils.options import args_parser
from models.recon_Update import LocalUpdate_ad_da
from models.Fed import FedAvg
f... | 8,221 | 43.443243 | 167 | py |
FL-MRCM | FL-MRCM-main/models/test.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @python: 3.6
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from collections import defaultdict
import numpy as np
from utils import evaluate
import h5py
from tqdm import tqdm
def test_save_result(net_g, datatest, args):
net_g.... | 9,725 | 44.877358 | 91 | py |
FL-MRCM | FL-MRCM-main/models/unet_model.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class ConvBlock(nn.Module):
"""
A Convolutional Block that co... | 10,036 | 35.234657 | 98 | py |
FL-MRCM | FL-MRCM-main/models/Fed.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import copy
import torch
from torch import nn
def FedAvg(w):
w_avg = copy.deepcopy(w[0])
for k in w_avg.keys():
for i in range(1, len(w)):
w_avg[k] += w[i][k]
w_avg[k] = torch.div(w_avg[k], len(w))
return w_av... | 322 | 18 | 46 | py |
FL-MRCM | FL-MRCM-main/models/recon_Update.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
import time
import numpy as np
from torch.autograd import Variable
from torch.nn import functional as F
class LocalUpdate(object):
def __init__(self, args, device... | 8,565 | 46.588889 | 131 | py |
FL-MRCM | FL-MRCM-main/models/__init__.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @python: 3.6
| 61 | 14.5 | 23 | py |
FL-MRCM | FL-MRCM-main/utils/evaluate.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import argparse
import pathlib
from argparse import ArgumentParser
import h5py
import numpy as np
from runstats import Statistics
from skima... | 3,937 | 31.816667 | 96 | py |
FL-MRCM | FL-MRCM-main/utils/preprocess_datasets_brats.py |
import os
import h5py
import pathlib
from data import transforms
import numpy as np
import torch
import nibabel as nib
from tqdm import tqdm
def mkdir(folder):
if not os.path.exists(folder):
os.makedirs(folder)
def main():
root_dir ='path to /MICCAI_BraTS2020_ValidationData'
root_out_dir = 'path... | 2,678 | 35.202703 | 97 | py |
FL-MRCM | FL-MRCM-main/utils/sampling.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import numpy as np
from torchvision import datasets, transforms
from collections import OrderedDict
def mnist_iid(dataset, num_users):
"""
Sample I.I.D. client data from MNIST dataset
:param dataset:
:param num_users:
:return: dict... | 3,579 | 31.844037 | 106 | py |
FL-MRCM | FL-MRCM-main/utils/options.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import argparse
import pathlib
def args_parser():
parser = argparse.ArgumentParser()
# federated arguments
parser.add_argument('--epochs', type=int, default=50, help="rounds of training")
parser.add_argument('--num_users', type=int, d... | 2,977 | 61.041667 | 105 | py |
FL-MRCM | FL-MRCM-main/utils/__init__.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @python: 3.6
| 61 | 14.5 | 23 | py |
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