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loadData.py
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loadData.py
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import os
import numpy as np
import pandas as pd
import os
import torch
import sys
sys.path.append("..")
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import StandardScaler,MinMaxScaler
from utlize import time_features,moving_average
import matplotlib.pyplot as plt
import warnings
import time
import datetime
warnings.filterwarnings('ignore')
import tsaug
from einops import rearrange
import emd
def concentration(dataX):
data=np.copy(dataX.flatten())
data=np.abs(StandardScaler().fit_transform(data.reshape(-1, 1) ))
concentration3=np.sum(data<3)/data.shape[0]
concentration4=np.sum(data<4)/data.shape[0]
concentration5=np.sum(data<5)/data.shape[0]
concentration6=np.sum(data<6)/data.shape[0]
concentration7=np.sum(data<7)/data.shape[0]
concentration10=np.sum(data<10)/data.shape[0]
print('concentration 3 4 5 6 7 10',
concentration3,concentration4,concentration5,concentration6,concentration7,concentration10 )
class Dataset_VR(Dataset):
def __init__(self, args):
pass
def featureNumberDict(self,dataPath):
return {
'exchange_rate.csv':8,
'ETTm2.csv':7,
'ETTm1.csv':7,
'ETTh1.csv':7,
'ETTh2.csv':7,
'electricity.csv':321,
'traffic.csv':862,
'weather.csv':21,
'national_illness.csv':7,
}[dataPath]
def __prepareD__(self):
self.taskType = 'regression'
self.D = np.zeros([self.h, self.h])
for i in range(self.h):
self.D[i, :i] = np.arange(1, i + 1)[::-1]
self.D[i, i:] = np.arange(0, self.h - i)
self.D = self.D ** self.Norm
try:
self.Norm_Insequence = self.args.Norm_Insequence
except:
self.Norm_Insequence=False
def data2Pixel(self, dataXIn, dataYIN):
'''
:param dataX: tin,pX dataY: whole,pY
:return: imgX, (w,h,px)*C imgY, w,h,pY
'''
t1 = datetime.datetime.now()
dataX = np.copy(dataXIn.T)
dataY = np.copy(dataYIN.T)
dataX[dataX > self.maxScal] = self.maxScal
dataX[dataX < -self.maxScal] = -self.maxScal
dataY[dataY > self.maxScal] = self.maxScal
dataY[dataY < -self.maxScal] = -self.maxScal
px = dataX.shape[0]
py = dataY.shape[0]
TY = dataY.shape[1]
TX = dataX.shape[1]
imgY0 = np.zeros([py, TY, self.h])
maxstd = self.maxScal
resolution = maxstd * 2 / (self.h - 1)
indY = np.floor((dataY + maxstd) / resolution).astype('int16')
aY = imgY0
aY =aY.reshape(-1, self.h)
aY[np.arange(TY * py), indY.astype('int16').flatten()] = 1
imgY0= aY.reshape(py, TY, self.h)
d= self.D[list(indY), :]
imgX0=np.copy(imgY0)
imgX0[:,TX:,:]=0
return imgX0, imgY0, d##c,w,h d c,w,h
def Pixel2data(self, imgX0, method='max'):
if len(imgX0.shape) == 3:
imgX0 = imgX0.unsqueeze(0)
# bs,c,w,h imgX0
bs, ch, w, h = imgX0.shape
# res=np.zeros([bs,w,ch])
try:
imgX0 = imgX0.cpu().detach().numpy()
except:
pass
if method == 'max':
indx = np.argmax(imgX0, axis=-1)
elif method == 'expection':
imgX0 = imgX0 / np.sum(imgX0, axis=-1, keepdims=True)
indNumber = np.arange(0, h)
imgX0 *= indNumber
indx = np.sum(imgX0, axis=-1)
maxstd = self.maxScal
resolution = maxstd * 2 / (self.h - 1)
res = np.transpose(indx, (0, 2, 1)) * resolution - maxstd
return res
def __getitem__(self, index):
s_begin = index
s_end = s_begin + self.seq_len
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
seq_xO = np.copy(self.data_x[s_begin:s_end])##no use
seq_yO = np.copy(self.data_y[s_begin:r_end])
std=np.std(seq_xO,axis=0).reshape(1,-1)+1e-7
mu=np.mean(seq_xO,axis=0).reshape(1,-1)
seq_x=(seq_xO-mu)/std
seq_y=(seq_yO-mu)/std
if self.flag=='train':
if np.random.rand()<self.TAP:
seq_y+=np.random.rand(1,seq_y.shape[1])-0.5
seq_y+=np.random.randn(seq_y.shape[0],seq_y.shape[1])*0.05*6
if np.random.rand()<0.5:
seq_y=seq_y[::-1,:]
x,y,d=self.data2Pixel(seq_x, seq_y)#c,w,h
if 'train' not in self.flag:
return torch.from_numpy(x).float(), torch.from_numpy(y).float(), \
torch.from_numpy(d).float(), torch.from_numpy(seq_xO).float(), \
torch.from_numpy(seq_yO).float(),\
torch.from_numpy(mu).float(), torch.from_numpy(std).float(),
else:
return torch.from_numpy(x).float(), torch.from_numpy(y).float(), torch.from_numpy(d).float()
def __len__(self):
return len(self.data_x) - self.seq_len - self.pred_len + 1
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)
class Dataset_CustomVR(Dataset_VR):
def __init__(self, args):
# size [seq_len, label_len, pred_len]
# info
try:
self.anomalyFlitter = args.anomalyFlitter
except:
self.anomalyFlitter=False
size=args.size
self.args=args
flag=args.flag
self.flag=args.flag
h=args.h
data_path=args.data_path
features=args.features
self.maxScal=args.maxScal
target=args.target
scale = True
timeenc = 1
freq = 'h'
self.TA=args.TA
self.TAP=args.TAP
self.args=args
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val']
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = features
self.target = target
self.scale = scale
self.timeenc = timeenc
self.freq = freq
self.h = h
self.Norm = args.dNorm
self.data_path = data_path
self.__read_data__()
self.__prepareD__()
def __read_data__(self):
df_raw = pd.read_csv(os.path.join('dataset',
self.data_path))
self.scalerStand = StandardScaler()
self.scaler = StandardScaler()
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
# print(cols)
num_train = int(len(df_raw) * 0.7)
num_test = int(len(df_raw) * 0.2)
num_vali = len(df_raw) - num_train - num_test
border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len]
border2s = [num_train, num_train + num_vali, len(df_raw)]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
self.featureNumber = len(cols_data)
elif self.features == 'S':
df_data = df_raw[[self.target]]
self.featureNumber=1
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
self.scalerStand.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
self.data_x = data[border1:border2]
self.data_y = data[border1:border2]
class Dataset_ETTminVR(Dataset_VR):
def __init__(self, args):
# size [seq_len, label_len, pred_len]
# info
size = args.size
self.args = args
self.anomalyFlitter = False
flag = args.flag
self.flag = args.flag
h = args.h
data_path = args.data_path
self.TA=args.TA
self.TAP=args.TAP
target = args.target
self.maxScal = args.maxScal
target = args.target
scale = True
timeenc = 1
freq = 't'
self.args=args
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val']
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = args.features
self.target = target
self.scale = scale
self.timeenc = timeenc
self.freq = freq
self.h = h
self.Norm = args.dNorm
self.data_path = data_path
self.__read_data__()
self.__prepareD__()
def __read_data__(self):
df_raw = pd.read_csv(os.path.join(r'dataset',
self.data_path))
self.scalerStand = StandardScaler()
self.scaler = StandardScaler()
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
border1s = [0, 12 * 30 * 24 * 4 - self.seq_len, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len]
border2s = [12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
self.featureNumber = len(cols_data)
elif self.features == 'S':
df_data = df_raw[[self.target]]
self.featureNumber=1
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
self.scalerStand.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
self.data_x = data[border1:border2]
self.data_y = data[border1:border2]
class Dataset_ETThourVR(Dataset_VR):
def __init__(self, args):
# size [seq_len, label_len, pred_len]
# info
try:
self.anomalyFlitter = args.anomalyFlitter
except:
self.anomalyFlitter=False
self.TA=args.TA
self.TAP=args.TAP
size = args.size
self.args = args
flag = args.flag
self.flag = args.flag
h = args.h
data_path = args.data_path
features = 'S'
target = args.target
self.maxScal = args.maxScal
target = args.target
scale = True
timeenc = 1
freq = 'h'
self.args=args
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val', ]
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = args.features
self.target = target
self.scale = scale
self.timeenc = timeenc
self.freq = freq
self.h = h
self.Norm = args.dNorm
self.data_path = data_path
self.__read_data__()
self.__prepareD__()
def __read_data__(self):
df_raw = pd.read_csv(os.path.join('dataset',
self.data_path))
self.scalerStand = StandardScaler()
self.scaler = StandardScaler()
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
self.featureNumber = len(cols_data)
elif self.features == 'S':
df_data = df_raw[[self.target]]
self.featureNumber=1
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
self.scalerStand.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
self.data_x = data[border1:border2]
self.data_y = data[border1:border2]