Informer时序模型(自定义项目)

开源项目

说明

setting = 'informer_power data_ftMS_sl96_ll48_pl24_dm512_nh8_el2_dl1_df2048_atprob_fc5_ebtimeF_dtTrue_mxTrue_exp_0'
pred = np.load('./results/'+setting+'/pred.npy')
true = np.load('./results/'+setting+'/true.npy')
print(pred.shape)
print(true.shape)

import matplotlib.pyplot as plt
plt.figure()
plt.plot(true[0,:,-1], label='GroundTruth')
plt.plot(pred[0,:,-1], label='Prediction')
plt.legend()
plt.show()

输出：

第2种：在模型训练完以后（在jupyter notebook）中使用exp.predict(setting, True)得到预测值
输出：

在Kaggle上使用

下载项目文件

!git clone https://github.com/zhouhaoyi/Informer2020.git
!git clone https://github.com/zhouhaoyi/ETDataset.git
!ls

导入包

from utils.tools import dotdict
import matplotlib.pyplot as plt
from exp.exp_informer import Exp_Informer
import torch

参数传导

args = dotdict()

args.model = 'informer' # model of experiment, options: [informer, informerstack, informerlight(TBD)]

args.data = 'ETTh1' # data
args.root_path = './ETDataset/ETT-small/' # root path of data file
args.data_path = 'ETTh1.csv' # data file
args.features = 'M' # forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate
args.target = 'OT' # target feature in S or MS task
args.freq = 'h' # freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h
args.checkpoints = './informer_checkpoints' # location of model checkpoints

args.seq_len = 96 # input sequence length of Informer encoder
args.label_len = 48 # start token length of Informer decoder
args.pred_len = 24 # prediction sequence length
# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]

args.enc_in = 7 # encoder input size
args.dec_in = 7 # decoder input size
args.c_out = 7 # output size
args.factor = 5 # probsparse attn factor
args.d_model = 512 # dimension of model
args.n_heads = 8 # num of heads
args.e_layers = 2 # num of encoder layers
args.d_layers = 1 # num of decoder layers
args.d_ff = 2048 # dimension of fcn in model
args.dropout = 0.05 # dropout
args.attn = 'prob' # attention used in encoder, options:[prob, full]
args.embed = 'timeF' # time features encoding, options:[timeF, fixed, learned]
args.activation = 'gelu' # activation
args.distil = True # whether to use distilling in encoder
args.output_attention = False # whether to output attention in ecoder
args.mix = True
args.padding = 0
args.freq = 'h'

args.batch_size = 32 
args.learning_rate = 0.0001
args.loss = 'mse'
args.lradj = 'type1'
args.use_amp = False # whether to use automatic mixed precision training

args.num_workers = 0
args.itr = 1
args.train_epochs = 6
args.patience = 3
args.des = 'exp'

args.use_gpu = True if torch.cuda.is_available() else False
args.gpu = 0

args.use_multi_gpu = False
args.devices = '0,1,2,3'

检查GPU

args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False

if args.use_gpu and args.use_multi_gpu:
    args.devices = args.devices.replace(' ','')
    device_ids = args.devices.split(',')
    args.device_ids = [int(id_) for id_ in device_ids]
    args.gpu = args.device_ids[0]

定义数据加载

# Set augments by using data name
data_parser = {
    'ETTh1':{'data':'ETTh1.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
    'ETTh2':{'data':'ETTh2.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
    'ETTm1':{'data':'ETTm1.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
    'ETTm2':{'data':'ETTm2.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
}
if args.data in data_parser.keys():
    data_info = data_parser[args.data]
    args.data_path = data_info['data']
    args.target = data_info['T']
    args.enc_in, args.dec_in, args.c_out = data_info[args.features]

训练模型

args.detail_freq = args.freq
args.freq = args.freq[-1:]
Exp = Exp_Informer

for ii in range(args.itr):
    # setting record of experiments
    setting = '{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_at{}_fc{}_eb{}_dt{}_mx{}_{}_{}'.format(args.model, args.data, args.features, 
                args.seq_len, args.label_len, args.pred_len,
                args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.attn, args.factor, args.embed, args.distil, args.mix, args.des, ii)

    # set experiments
    exp = Exp(args)
    
    # train
    print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
    exp.train(setting)
    
    # test
    print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
    exp.test(setting)

    torch.cuda.empty_cache()

预测

import os
setting = 'informer_ETTh1_ftM_sl96_ll48_pl24_dm512_nh8_el2_dl1_df2048_atprob_fc5_ebtimeF_dtTrue_mxTrue_exp_0'
exp = Exp(args)
exp.predict(setting, True)

prediction = np.load('./results/'+setting+'/real_prediction.npy')
plt.figure()
plt.plot(prediction[0,:,-1], label='Prediction')
plt.legend()
plt.show()

自定义项目

数据处理

parser.add_argument('--data', type=str, default='power data')
parser.add_argument('--root_path', type=str, default='./data/')
parser.add_argument('--data_path', type=str, default='power data.csv')

parser.add_argument('--features', type=str, default='MS')
parser.add_argument('--target', type=str, default='总有功功率')
parser.add_argument('--freq', type=str, default='t')

parser.add_argument('--seq_len', type=int, default=192)
parser.add_argument('--label_len', type=int, default=96)
parser.add_argument('--pred_len', type=int, default=48)

data_parser = {'power data':{'data':'power data.csv','T':'总有功功率','M':[5,5,5],'S':[1,1,1],'MS':[5,5,1]},
}

data_dict = {'power data':Dataset_Custom,}

模型参数

parser.add_argument('--do_predict', action='store_false')

在Kaggle上使用