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'update Autoformer' #2

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6 changes: 1 addition & 5 deletions src/scalbo/scalbo/benchmark/autoformer/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -504,7 +504,7 @@ def __init__(self, adj, configs, DEVICE):

# define encoder
self.encoder_blocks = nn.Sequential( *(encoder_depth * [Block(encoder_width)]))
self.encoder = nn.Sequential(nn.Linear(1, encoder_width), nn.Tanh(), self.encoder_blocks,\
self.encoder = nn.Sequential(nn.Linear(3, encoder_width), nn.Tanh(), self.encoder_blocks,\
nn.Linear(encoder_width, configs.d_model))

# define the Autoformer
Expand All @@ -524,10 +524,6 @@ def forward(self, x_enc, t):
return: (B, T = num_pred_len * pred_len, N)
'''

# always use segment of length of pred_len to initialize the future
# return (B,pred_len,F)
x_enc = x_enc.unsqueeze(-1) # (B,T,N,1)

# apply encoder: (B,T,N,F)
x_enc = self.encoder(x_enc)
# permute the shape, return (B,N,T,F)
Expand Down
48 changes: 25 additions & 23 deletions src/scalbo/scalbo/benchmark/autoformer/utils/data.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,13 @@
import pickle
__all__ = ['trainingset_construct', 'load_graph_data', 'sparse_adj', 'scaled_Laplacian_list']

import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import torch
from scipy.sparse.linalg import eigs
from torch.utils.data import DataLoader, Dataset


def training_loader_construct(dataset, batch_num, Shuffle):
Expand Down Expand Up @@ -34,16 +36,17 @@ def __init__(self, traffic_data, args, num_data_limit, mean, std, transform=None
args:
'''
# extract information
assert args.pred_len % 12 == 0, 'this script is only for short-term traffic prediction'
pred_len = args.pred_len
input_length = args.time_steps
input_length = args.pred_len

PEMS = traffic_data # return (N,T)
print('traffic data shape:', PEMS.shape)

timestep_a_week = 7*24*12
timestep_a_day = 24*12
time_stamp_week = np.arange(timestep_a_week).repeat(15)
time_stamp_day = np.arange(timestep_a_day).repeat(15*7)
time_stamp_week = np.arange(timestep_a_week).repeat(30)
time_stamp_day = np.arange(timestep_a_day).repeat(30*7)
t = np.sin(time_stamp_week/timestep_a_week * 2*np.pi) + np.sin(time_stamp_day/timestep_a_day * 2*np.pi)

self.x = []
Expand All @@ -52,17 +55,25 @@ def __init__(self, traffic_data, args, num_data_limit, mean, std, transform=None
self.ty = []

sample_steps = 1
num_datapoints = int(np.floor((PEMS.shape[1] - input_length) / sample_steps))
num_datapoints = int(np.floor((PEMS.shape[1] - timestep_a_week) / sample_steps))
print('total number of datapoints:', num_datapoints)
starting_point = input_length
starting_point = timestep_a_week
endding_point = PEMS.shape[1] - pred_len

num_data = 0
for k in range(starting_point, endding_point, sample_steps):
if num_data < num_data_limit:
self.x.append((PEMS[:,k-input_length:k] - mean) / std)

input_data = []
# store weekly data
input_data.append(np.expand_dims(PEMS[:,k-timestep_a_week: k-timestep_a_week+pred_len], axis=-1))
input_data.append(np.expand_dims(PEMS[:,k-timestep_a_day: k-timestep_a_day+pred_len], axis=-1))
input_data.append(np.expand_dims(PEMS[:,k-pred_len: k], axis=-1))
input_data = np.concatenate(tuple(input_data), axis=-1) # return (N,T,F)

self.x.append((input_data - mean) / std)
self.y.append(np.array(PEMS[:, k : k + pred_len]))
self.tx.append(t[k-input_length:k])
self.tx.append(t[k-pred_len: k])
self.ty.append(t[k:k+pred_len])
num_data += 1

Expand Down Expand Up @@ -154,18 +165,15 @@ def sparse_adj():

return W

def scaled_Laplacian_list(W):
def scaled_Laplacian_list(W, device):
'''
compute \tilde{L}

Parameters
----------
W: np.ndarray, shape is (N, N), N is the num of vertices

Returns
----------
scaled_Laplacian: np.ndarray, shape (N, N)

'''

assert W.shape[0] == W.shape[1]
Expand All @@ -180,16 +188,10 @@ def scaled_Laplacian_list(W):

N = L_tilde.shape[0]

cheb_polynomials = [np.identity(N), L_tilde.copy()]
cheb_polynomials = [torch.eye(N).to(device), torch.from_numpy(L_tilde.copy()).to(device)]
L_tilde = torch.from_numpy(L_tilde).to(device)

for i in range(2, 3):
cheb_polynomials.append(2 * L_tilde * cheb_polynomials[i - 1] - cheb_polynomials[i - 2])

return cheb_polynomials







return cheb_polynomials
27 changes: 21 additions & 6 deletions src/scalbo/scalbo/benchmark/autoformer/utils/train_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
import torch
from tqdm import tqdm


# define training function
def train(loader, model, optimizer, criterion, device, mean, std, update_step):

Expand All @@ -21,15 +20,23 @@ def train(loader, model, optimizer, criterion, device, mean, std, update_step):

update_step = update_step

inputs = inputs.permute(0,2,1).to(device) # (B,T,N)
if inputs.dim() == 3:
inputs = inputs.permute(0,2,1).to(device) # (B,T,N)
if inputs.dim() == 4:
inputs = inputs.permute(0,2,1,3).to(device) # (B,T,N,F)

targets = targets.permute(0,2,1).to(device) # (B,T,N)

# create a mask for missing data
mask = torch.where(targets<1, torch.zeros_like(targets), torch.ones_like(targets))

tx = tx.to(device) # (B,T)
ty = ty.to(device) # (B,T)
t_stamp = torch.cat((tx,ty),1)
outputs = model.forward(inputs, t_stamp)[0] # (B,T,N)
outputs = outputs * std + mean

loss = criterion(outputs, targets) / update_step
loss = criterion(mask * outputs, mask * targets) / update_step
loss.backward()
if idx % update_step == 0:
optimizer.step()
Expand All @@ -49,17 +56,25 @@ def eval(loader, model, device, args, mean, std):
for idx, (inputs, targets, tx, ty) in enumerate(tqdm(loader)):
model.eval()

inputs = (inputs).permute(0,2,1).to(device) # (B,T,N)
if inputs.dim() == 3:
inputs = inputs.permute(0,2,1).to(device) # (B,T,N)
if inputs.dim() == 4:
inputs = inputs.permute(0,2,1,3).to(device) # (B,T,N,F)

targets = targets.permute(0,2,1).to(device) # (B,T,N)

# create a mask for missing data
mask = torch.where(targets<1, torch.zeros_like(targets), torch.ones_like(targets))

tx = tx.to(device) # (B,T)
ty = ty.to(device) # (B,T)
t_stamp = torch.cat((tx,ty),1)
outputs = model.forward(inputs, t_stamp)[0] # (B,T,N)

outputs = outputs * std + mean

out_unnorm = outputs.detach().cpu().numpy()
target_unnorm = (targets).detach().cpu().numpy()
out_unnorm = (mask * outputs).detach().cpu().numpy()
target_unnorm = (mask * targets).detach().cpu().numpy()

mae_loss = np.zeros(args.pred_len)
for k in range(out_unnorm.shape[1]):
Expand Down