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
value |
|---|---|---|---|---|---|---|
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/scripts/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/model/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/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... | 29,634 | 40.331939 | 128 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/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/ST-MetaNet/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... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/model/pytorch/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/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 STMetaNet
from model.pytorch.utils import masked_mae_loss, metric, get_normaliz... | 17,117 | 40.853301 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/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/ST-MetaNet/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/ST-MetaNet/model/tf/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/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/ST-MetaNet/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:
... | 7,286 | 33.211268 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/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/ST-MetaNet/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/ST-MetaNet/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/ST-MetaNet/lib/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/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("dcrnn@lifuxian")
def main(args):
... | 1,455 | 38.351351 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/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/DCRNN/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/DCRNN/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/DCRNN/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/DCRNN/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/DCRNN/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/DCRNN/scripts/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/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... | 7,642 | 44.494048 | 119 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/loss.py | import torch
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.mean()
| 309 | 24.833333 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/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/DCRNN/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... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/dcrnn_supervisor.py | import os
import time
import numpy as np
import torch
# from torch.utils.tensorboard import SummaryWriter
from lib import utils
from model.pytorch.dcrnn_model import DCRNNModel
from model.pytorch.utils import masked_mae_loss, metric, get_normalized_adj
device = torch.device("cuda" if torch.cuda.is_available() else "... | 14,986 | 39.287634 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/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/DCRNN/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/DCRNN/model/tf/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/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/DCRNN/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:
... | 7,284 | 33.201878 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/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/DCRNN/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/DCRNN/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/DCRNN/lib/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/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_... | 4,051 | 35.836364 | 116 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/engine.py | import torch.optim as optim
from model import *
import util
class trainer():
def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device, supports, gcn_bool, addaptadj, aptinit):
self.model = gwnet(device, num_nodes, dropout, supports=supports, gcn_bool=gcn_bool, addaptad... | 1,963 | 43.636364 | 261 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/test.py | import util
import argparse
from model import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
parser = argparse.ArgumentParser()
parser.add_argument('--device',type=str,default='cuda:3',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path'... | 4,230 | 36.776786 | 142 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/train_demo.py | import torch
import numpy as np
import argparse
import time
import util
import matplotlib.pyplot as plt
from engine import trainer
parser = argparse.ArgumentParser()
parser.add_argument('--device',type=str,default='cuda:3',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
parser.a... | 9,623 | 37.650602 | 186 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
class nconv(nn.Module):
def __init__(self):
super(nconv,self).__init__()
def forward(self,x, A):
x = torch.einsum('ncvl,vw->ncwl',(x,A))
return x.contiguous()
class linea... | 7,730 | 35.466981 | 245 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/util.py | import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
class DataLoader(object):
def __init__(self, xs, ys, batch_size, pad_with_last_sample=True):
"""
:param xs:
:param ys:
:param batch_size:
:param pad_with_last_s... | 7,185 | 32.896226 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/train.py | import torch
import numpy as np
import argparse
import time
import util
import matplotlib.pyplot as plt
from engine import trainer
parser = argparse.ArgumentParser()
parser.add_argument('--device',type=str,default='cuda:3',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
parser.a... | 8,970 | 38.346491 | 184 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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("stmetanet@lifuxian")
def main(args):... | 1,459 | 38.459459 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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/FNN/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/FNN/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/FNN/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/FNN/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/FNN/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/FNN/scripts/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/model/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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... | 30,485 | 40.933975 | 223 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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/FNN/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... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/model/pytorch/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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 STMetaNet
from model.pytorch.utils import masked_mae_loss, metric, get_normaliz... | 17,117 | 40.853301 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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/FNN/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/FNN/model/tf/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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/FNN/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:
... | 7,286 | 33.211268 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/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/FNN/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/FNN/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/FNN/lib/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/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
class nconv(nn.Module):
def __init__(self):
super(nconv,self).__init__()
def forward(self,x, A):
x = torch.einsum('ncvl,vw->ncwl',(x,A))
return x... | 10,549 | 31.164634 | 114 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/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/MTGNN/train_single_step.py | import argparse
import math
import time
import torch
import torch.nn as nn
from net import gtnet
import numpy as np
import importlib
from util import *
from trainer import Optim
def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size):
model.eval()
total_loss = 0
total_loss_l1 = 0
n_sampl... | 10,199 | 42.220339 | 146 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/net.py | from layer import *
class gtnet(nn.Module):
def __init__(self, gcn_true, buildA_true, gcn_depth, num_nodes, device, predefined_A=None, static_feat=None, dropout=0.3, subgraph_size=20, node_dim=40, dilation_exponential=1, conv_channels=32, residual_channels=32, skip_channels=64, end_channels=128, seq_length=12, in... | 6,760 | 47.292857 | 358 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/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):
# train and valid is the ratio of training set and validation set... | 10,951 | 34.102564 | 116 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/train_multi_step.py | import torch
import numpy as np
import argparse
import time
from util import *
from trainer import Trainer
from net import gtnet
import setproctitle
setproctitle.setproctitle("MTGNN@lifuxian")
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() in {'false', 'f', '0', 'no', ... | 11,855 | 38.52 | 178 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/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, cl=True):
self.scaler = scaler
self.model = model
self.model.to(device)
self.optimizer = optim.Adam(self.model... | 4,312 | 34.644628 | 102 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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("stmetanet@lifuxian")
def main(args):... | 1,459 | 38.459459 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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/LSTM/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/LSTM/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/LSTM/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/LSTM/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/LSTM/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/LSTM/scripts/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/model/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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... | 29,536 | 40.31049 | 124 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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/LSTM/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... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/model/pytorch/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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 STMetaNet
from model.pytorch.utils import masked_mae_loss, metric, get_normaliz... | 16,974 | 40.605392 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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/LSTM/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/LSTM/model/tf/__init__.py | 0 | 0 | 0 | py | |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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/LSTM/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:
... | 7,286 | 33.211268 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/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/LSTM/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/LSTM/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/LSTM/lib/__init__.py | 0 | 0 | 0 | py |
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