How to Plot the Results of ETTh1 dataset

I'm trying to run the Code for the ETTh1 dataset using the following run command: !python run_ETTh_10.py --data ETTh1 --features S --seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 3 --lr 3e-3 --batch_size 8 --dropout 0.5 --model_name etth1_M_I48_O24_lr3e-3_bs8_dp0.5_h4_s1l3 and it runs successfully before early stopping at epoch 17 and i get the MAE and MSE values for both normalized and de-normalized data. `Args in experiment: Namespace(INN=1, RIN=False, batch_size=8, c_out=1, checkpoints='exp/ETT_checkpoints/', cols=None, concat_len=0, data='ETTh1', data_path='ETTh1.csv', dec_in=1, detail_freq='h', devices='0', dilation=1, dropout=0.5, embed='timeF', enc_in=1, evaluate=False, features='S', freq='h', gpu=0, groups=1, hidden_size=4.0, inverse=False, itr=0, kernel=5, label_len=48, lastWeight=1.0, levels=3, loss='mae', lr=0.003, lradj=1, model='SCINet', model_name='etth1_M_I48_O24_lr3e-3_bs8_dp0.5_h4_s1l3', num_decoder_layer=1, num_workers=0, patience=5, positionalEcoding=False, pred_len=48, resume=False, root_path='./datasets/', save=False, seq_len=96, single_step=0, single_step_output_One=0, stacks=1, target='OT', train_epochs=100, use_amp=False, use_gpu=True, use_multi_gpu=False, window_size=12) SCINet( (blocks1): EncoderTree( (SCINet_Tree): SCINet_Tree( (workingblock): LevelSCINet( (interact): InteractorLevel( (level): Interactor( (split): Splitting() (phi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (psi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (P): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (U): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) ) ) ) (SCINet_Tree_odd): SCINet_Tree( (workingblock): LevelSCINet( (interact): InteractorLevel( (level): Interactor( (split): Splitting() (phi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (psi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (P): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (U): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) ) ) ) (SCINet_Tree_odd): SCINet_Tree( (workingblock): LevelSCINet( (interact): InteractorLevel( (level): Interactor( (split): Splitting() (phi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (psi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (P): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (U): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) ) ) ) ) (SCINet_Tree_even): SCINet_Tree( (workingblock): LevelSCINet( (interact): InteractorLevel( (level): Interactor( (split): Splitting() (phi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (psi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (P): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (U): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) ) ) ) ) ) (SCINet_Tree_even): SCINet_Tree( (workingblock): LevelSCINet( (interact): InteractorLevel( (level): Interactor( (split): Splitting() (phi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (psi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (P): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (U): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) ) ) ) (SCINet_Tree_odd): SCINet_Tree( (workingblock): LevelSCINet( (interact): InteractorLevel( (level): Interactor( (split): Splitting() (phi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (psi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (P): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (U): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) ) ) ) ) (SCINet_Tree_even): SCINet_Tree( (workingblock): LevelSCINet( (interact): InteractorLevel( (level): Interactor( (split): Splitting() (phi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (psi): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (P): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) (U): Sequential( (0): ReplicationPad1d((3, 3)) (1): Conv1d(1, 4, kernel_size=(5,), stride=(1,)) (2): LeakyReLU(negative_slope=0.01, inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Conv1d(4, 1, kernel_size=(3,), stride=(1,)) (5): Tanh() ) ) ) ) ) ) ) ) (projection1): Conv1d(96, 48, kernel_size=(1,), stride=(1,), bias=False) (div_projection): ModuleList() )

start training : SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0>>>>>>>>>>>>>>>>>>>>>>>>>> train 8497 val 2833 test 2833 exp/ETT_checkpoints/SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0 iters: 100, epoch: 1 | loss: 0.2635144 speed: 0.0918s/iter; left time: 9735.2018s iters: 200, epoch: 1 | loss: 0.2746293 speed: 0.0640s/iter; left time: 6782.9779s iters: 300, epoch: 1 | loss: 0.2532458 speed: 0.0641s/iter; left time: 6787.8207s iters: 400, epoch: 1 | loss: 0.2308514 speed: 0.0644s/iter; left time: 6817.4635s iters: 500, epoch: 1 | loss: 0.3040747 speed: 0.0651s/iter; left time: 6883.0310s iters: 600, epoch: 1 | loss: 0.2578846 speed: 0.0644s/iter; left time: 6798.4408s iters: 700, epoch: 1 | loss: 0.2459396 speed: 0.0634s/iter; left time: 6689.3999s iters: 800, epoch: 1 | loss: 0.2914965 speed: 0.0653s/iter; left time: 6883.0213s iters: 900, epoch: 1 | loss: 0.2554513 speed: 0.0641s/iter; left time: 6750.0606s iters: 1000, epoch: 1 | loss: 0.2524573 speed: 0.0650s/iter; left time: 6838.8867s Epoch: 1 cost time: 71.33614897727966 --------start to validate----------- normed mse:0.0814, mae:0.2147, rmse:0.2852, mape:1.3623, mspe:25.9642, corr:0.8572 denormed mse:6.8514, mae:1.9706, rmse:2.6175, mape:0.1642, mspe:0.0813, corr:0.8572 --------start to test----------- normed mse:0.0892, mae:0.2317, rmse:0.2987, mape:0.1735, mspe:0.0476, corr:0.8131 denormed mse:7.5120, mae:2.1258, rmse:2.7408, mape:inf, mspe:inf, corr:0.8131 Epoch: 1, Steps: 1062 | Train Loss: 0.2954810 valid Loss: 0.2147395 Test Loss: 0.2316557 Validation loss decreased (inf --> 0.214739). Saving model ... Updating learning rate to 0.00285 iters: 100, epoch: 2 | loss: 0.2651560 speed: 0.2249s/iter; left time: 23618.5159s iters: 200, epoch: 2 | loss: 0.3282328 speed: 0.0653s/iter; left time: 6857.5758s iters: 300, epoch: 2 | loss: 0.2604796 speed: 0.0648s/iter; left time: 6791.7008s iters: 400, epoch: 2 | loss: 0.2489426 speed: 0.0648s/iter; left time: 6790.2499s iters: 500, epoch: 2 | loss: 0.3080887 speed: 0.0640s/iter; left time: 6695.5776s iters: 600, epoch: 2 | loss: 0.2923984 speed: 0.0637s/iter; left time: 6657.3868s iters: 700, epoch: 2 | loss: 0.3410122 speed: 0.0639s/iter; left time: 6673.1482s iters: 800, epoch: 2 | loss: 0.2606724 speed: 0.0651s/iter; left time: 6792.7796s iters: 900, epoch: 2 | loss: 0.2952042 speed: 0.0646s/iter; left time: 6730.5264s iters: 1000, epoch: 2 | loss: 0.1823040 speed: 0.0639s/iter; left time: 6656.2354s Epoch: 2 cost time: 68.4526846408844 --------start to validate----------- normed mse:0.0799, mae:0.2147, rmse:0.2827, mape:1.4790, mspe:30.6527, corr:0.8621 denormed mse:6.7300, mae:1.9705, rmse:2.5942, mape:0.1594, mspe:0.0701, corr:0.8621 --------start to test----------- normed mse:0.0586, mae:0.1883, rmse:0.2420, mape:0.1493, mspe:0.0399, corr:0.8178 denormed mse:4.9326, mae:1.7278, rmse:2.2210, mape:inf, mspe:inf, corr:0.8178 Epoch: 2, Steps: 1062 | Train Loss: 0.2776401 valid Loss: 0.2147304 Test Loss: 0.1882900 Validation loss decreased (0.214739 --> 0.214730). Saving model ... Updating learning rate to 0.0027075 iters: 100, epoch: 3 | loss: 0.3098924 speed: 0.2221s/iter; left time: 23097.3052s iters: 200, epoch: 3 | loss: 0.2770404 speed: 0.0643s/iter; left time: 6683.5361s iters: 300, epoch: 3 | loss: 0.3261764 speed: 0.0639s/iter; left time: 6627.7702s iters: 400, epoch: 3 | loss: 0.3463621 speed: 0.0645s/iter; left time: 6690.5621s iters: 500, epoch: 3 | loss: 0.2016839 speed: 0.0669s/iter; left time: 6931.5046s iters: 600, epoch: 3 | loss: 0.2773947 speed: 0.0660s/iter; left time: 6829.7108s iters: 700, epoch: 3 | loss: 0.2436916 speed: 0.0645s/iter; left time: 6672.7252s iters: 800, epoch: 3 | loss: 0.3100113 speed: 0.0636s/iter; left time: 6570.5793s iters: 900, epoch: 3 | loss: 0.2406910 speed: 0.0637s/iter; left time: 6570.7141s iters: 1000, epoch: 3 | loss: 0.2994752 speed: 0.0644s/iter; left time: 6641.0536s Epoch: 3 cost time: 68.57215976715088 --------start to validate----------- normed mse:0.0780, mae:0.2130, rmse:0.2793, mape:1.4954, mspe:33.6005, corr:0.8610 denormed mse:6.5708, mae:1.9548, rmse:2.5634, mape:0.1579, mspe:0.0679, corr:0.8610 --------start to test----------- normed mse:0.0572, mae:0.1855, rmse:0.2392, mape:0.1437, mspe:0.0355, corr:0.8176 denormed mse:4.8174, mae:1.7019, rmse:2.1949, mape:inf, mspe:inf, corr:0.8176 Epoch: 3, Steps: 1062 | Train Loss: 0.2742680 valid Loss: 0.2130265 Test Loss: 0.1854673 Validation loss decreased (0.214730 --> 0.213026). Saving model ... Updating learning rate to 0.0025721249999999998 iters: 100, epoch: 4 | loss: 0.3014244 speed: 0.2245s/iter; left time: 23101.4108s iters: 200, epoch: 4 | loss: 0.2271728 speed: 0.0643s/iter; left time: 6606.3076s iters: 300, epoch: 4 | loss: 0.3784584 speed: 0.0640s/iter; left time: 6569.4517s iters: 400, epoch: 4 | loss: 0.2752601 speed: 0.0653s/iter; left time: 6696.5213s iters: 500, epoch: 4 | loss: 0.3025605 speed: 0.0638s/iter; left time: 6536.2859s iters: 600, epoch: 4 | loss: 0.2795481 speed: 0.0638s/iter; left time: 6538.9267s iters: 700, epoch: 4 | loss: 0.2788646 speed: 0.0632s/iter; left time: 6465.8545s iters: 800, epoch: 4 | loss: 0.2323274 speed: 0.0640s/iter; left time: 6545.1154s iters: 900, epoch: 4 | loss: 0.2965076 speed: 0.0648s/iter; left time: 6620.8814s iters: 1000, epoch: 4 | loss: 0.2785395 speed: 0.0643s/iter; left time: 6555.9024s Epoch: 4 cost time: 68.27407765388489 --------start to validate----------- normed mse:0.0787, mae:0.2138, rmse:0.2805, mape:1.5257, mspe:33.8453, corr:0.8624 denormed mse:6.6266, mae:1.9617, rmse:2.5742, mape:0.1578, mspe:0.0676, corr:0.8624 --------start to test----------- normed mse:0.0564, mae:0.1850, rmse:0.2375, mape:0.1471, mspe:0.0391, corr:0.8203 denormed mse:4.7516, mae:1.6981, rmse:2.1798, mape:inf, mspe:inf, corr:0.8203 Epoch: 4, Steps: 1062 | Train Loss: 0.2719553 valid Loss: 0.2137705 Test Loss: 0.1850464 EarlyStopping counter: 1 out of 5 Updating learning rate to 0.0024435187499999996 iters: 100, epoch: 5 | loss: 0.3289642 speed: 0.2193s/iter; left time: 22341.4123s iters: 200, epoch: 5 | loss: 0.2475609 speed: 0.0641s/iter; left time: 6520.0001s iters: 300, epoch: 5 | loss: 0.2934369 speed: 0.0640s/iter; left time: 6501.5043s iters: 400, epoch: 5 | loss: 0.3514774 speed: 0.0639s/iter; left time: 6488.8877s iters: 500, epoch: 5 | loss: 0.3289756 speed: 0.0650s/iter; left time: 6592.7983s iters: 600, epoch: 5 | loss: 0.3147124 speed: 0.0660s/iter; left time: 6684.8574s iters: 700, epoch: 5 | loss: 0.2444675 speed: 0.0656s/iter; left time: 6642.7295s iters: 800, epoch: 5 | loss: 0.2227931 speed: 0.0648s/iter; left time: 6558.8881s iters: 900, epoch: 5 | loss: 0.2905650 speed: 0.0645s/iter; left time: 6519.5313s iters: 1000, epoch: 5 | loss: 0.2011140 speed: 0.0643s/iter; left time: 6490.7155s Epoch: 5 cost time: 68.49428033828735 --------start to validate----------- normed mse:0.0795, mae:0.2143, rmse:0.2820, mape:1.5023, mspe:32.9689, corr:0.8631 denormed mse:6.6963, mae:1.9665, rmse:2.5877, mape:0.1583, mspe:0.0665, corr:0.8631 --------start to test----------- normed mse:0.0568, mae:0.1897, rmse:0.2384, mape:0.1513, mspe:0.0402, corr:0.8214 denormed mse:4.7869, mae:1.7409, rmse:2.1879, mape:inf, mspe:inf, corr:0.8214 Epoch: 5, Steps: 1062 | Train Loss: 0.2680931 valid Loss: 0.2143007 Test Loss: 0.1897096 EarlyStopping counter: 2 out of 5 Updating learning rate to 0.0023213428124999992 iters: 100, epoch: 6 | loss: 0.2878237 speed: 0.2231s/iter; left time: 22487.2134s iters: 200, epoch: 6 | loss: 0.3053960 speed: 0.0642s/iter; left time: 6463.6262s iters: 300, epoch: 6 | loss: 0.2794231 speed: 0.0657s/iter; left time: 6608.5483s iters: 400, epoch: 6 | loss: 0.1824071 speed: 0.0658s/iter; left time: 6613.3665s iters: 500, epoch: 6 | loss: 0.3717845 speed: 0.0657s/iter; left time: 6599.7808s iters: 600, epoch: 6 | loss: 0.2623390 speed: 0.0657s/iter; left time: 6589.1822s iters: 700, epoch: 6 | loss: 0.2274510 speed: 0.0651s/iter; left time: 6523.5771s iters: 800, epoch: 6 | loss: 0.2571564 speed: 0.0665s/iter; left time: 6657.4378s iters: 900, epoch: 6 | loss: 0.2891446 speed: 0.0667s/iter; left time: 6670.0139s iters: 1000, epoch: 6 | loss: 0.3868507 speed: 0.0665s/iter; left time: 6639.8010s Epoch: 6 cost time: 69.7518322467804 --------start to validate----------- normed mse:0.0777, mae:0.2116, rmse:0.2788, mape:1.5445, mspe:35.4777, corr:0.8636 denormed mse:6.5435, mae:1.9422, rmse:2.5580, mape:0.1556, mspe:0.0658, corr:0.8636 --------start to test----------- normed mse:0.0489, mae:0.1677, rmse:0.2211, mape:0.1310, mspe:0.0322, corr:0.8212 denormed mse:4.1181, mae:1.5390, rmse:2.0293, mape:inf, mspe:inf, corr:0.8212 Epoch: 6, Steps: 1062 | Train Loss: 0.2668546 valid Loss: 0.2116495 Test Loss: 0.1677159 Validation loss decreased (0.213026 --> 0.211650). Saving model ... Updating learning rate to 0.0022052756718749992 iters: 100, epoch: 7 | loss: 0.2596520 speed: 0.2240s/iter; left time: 22334.3117s iters: 200, epoch: 7 | loss: 0.2324577 speed: 0.0640s/iter; left time: 6381.1203s iters: 300, epoch: 7 | loss: 0.2214808 speed: 0.0651s/iter; left time: 6474.6273s iters: 400, epoch: 7 | loss: 0.2045112 speed: 0.0632s/iter; left time: 6281.8838s iters: 500, epoch: 7 | loss: 0.2396872 speed: 0.0636s/iter; left time: 6316.9631s iters: 600, epoch: 7 | loss: 0.1907633 speed: 0.0644s/iter; left time: 6388.6488s iters: 700, epoch: 7 | loss: 0.2620018 speed: 0.0747s/iter; left time: 7404.8432s iters: 800, epoch: 7 | loss: 0.2821859 speed: 0.0652s/iter; left time: 6457.3174s iters: 900, epoch: 7 | loss: 0.2233998 speed: 0.0641s/iter; left time: 6339.8982s iters: 1000, epoch: 7 | loss: 0.2333842 speed: 0.0648s/iter; left time: 6402.4253s Epoch: 7 cost time: 69.41319704055786 --------start to validate----------- normed mse:0.0770, mae:0.2089, rmse:0.2776, mape:1.3294, mspe:25.3847, corr:0.8637 denormed mse:6.4876, mae:1.9170, rmse:2.5471, mape:0.1582, mspe:0.0734, corr:0.8637 --------start to test----------- normed mse:0.0935, mae:0.2430, rmse:0.3058, mape:0.1754, mspe:0.0452, corr:0.8126 denormed mse:7.8770, mae:2.2295, rmse:2.8066, mape:inf, mspe:inf, corr:0.8126 Epoch: 7, Steps: 1062 | Train Loss: 0.2652045 valid Loss: 0.2089042 Test Loss: 0.2429530 Validation loss decreased (0.211650 --> 0.208904). Saving model ... Updating learning rate to 0.0020950118882812493 iters: 100, epoch: 8 | loss: 0.2476663 speed: 0.2239s/iter; left time: 22095.3285s iters: 200, epoch: 8 | loss: 0.3140231 speed: 0.0660s/iter; left time: 6502.9781s iters: 300, epoch: 8 | loss: 0.2049506 speed: 0.0647s/iter; left time: 6368.1886s iters: 400, epoch: 8 | loss: 0.2751644 speed: 0.0648s/iter; left time: 6373.9229s iters: 500, epoch: 8 | loss: 0.3105533 speed: 0.0664s/iter; left time: 6520.4210s iters: 600, epoch: 8 | loss: 0.2195727 speed: 0.0640s/iter; left time: 6287.0444s iters: 700, epoch: 8 | loss: 0.2385361 speed: 0.0644s/iter; left time: 6313.8700s iters: 800, epoch: 8 | loss: 0.2514920 speed: 0.0661s/iter; left time: 6476.8801s iters: 900, epoch: 8 | loss: 0.2696154 speed: 0.0639s/iter; left time: 6250.8148s iters: 1000, epoch: 8 | loss: 0.2994070 speed: 0.0646s/iter; left time: 6314.7932s Epoch: 8 cost time: 69.0834846496582 --------start to validate----------- normed mse:0.0786, mae:0.2097, rmse:0.2803, mape:1.3397, mspe:24.8040, corr:0.8618 denormed mse:6.6175, mae:1.9241, rmse:2.5725, mape:0.1595, mspe:0.0748, corr:0.8618 --------start to test----------- normed mse:0.0803, mae:0.2185, rmse:0.2833, mape:0.1605, mspe:0.0409, corr:0.8191 denormed mse:6.7598, mae:2.0055, rmse:2.6000, mape:inf, mspe:inf, corr:0.8191 Epoch: 8, Steps: 1062 | Train Loss: 0.2626855 valid Loss: 0.2096790 Test Loss: 0.2185455 EarlyStopping counter: 1 out of 5 Updating learning rate to 0.0019902612938671868 iters: 100, epoch: 9 | loss: 0.2035707 speed: 0.2216s/iter; left time: 21633.7680s iters: 200, epoch: 9 | loss: 0.1929587 speed: 0.0643s/iter; left time: 6273.6812s iters: 300, epoch: 9 | loss: 0.2027658 speed: 0.0644s/iter; left time: 6275.3628s iters: 400, epoch: 9 | loss: 0.1670165 speed: 0.0655s/iter; left time: 6372.5070s iters: 500, epoch: 9 | loss: 0.3162191 speed: 0.0644s/iter; left time: 6264.6492s iters: 600, epoch: 9 | loss: 0.2530913 speed: 0.0642s/iter; left time: 6233.9174s iters: 700, epoch: 9 | loss: 0.2298067 speed: 0.0676s/iter; left time: 6557.6573s iters: 800, epoch: 9 | loss: 0.2281127 speed: 0.0661s/iter; left time: 6406.0165s iters: 900, epoch: 9 | loss: 0.2107817 speed: 0.0664s/iter; left time: 6426.1786s iters: 1000, epoch: 9 | loss: 0.2355524 speed: 0.0656s/iter; left time: 6345.4314s Epoch: 9 cost time: 69.29217648506165 --------start to validate----------- normed mse:0.0747, mae:0.2069, rmse:0.2732, mape:1.4617, mspe:31.3834, corr:0.8653 denormed mse:6.2872, mae:1.8984, rmse:2.5074, mape:0.1508, mspe:0.0608, corr:0.8653 --------start to test----------- normed mse:0.0819, mae:0.2269, rmse:0.2861, mape:0.1668, mspe:0.0438, corr:0.7987 denormed mse:6.8943, mae:2.0818, rmse:2.6257, mape:inf, mspe:inf, corr:0.7987 Epoch: 9, Steps: 1062 | Train Loss: 0.2619727 valid Loss: 0.2068796 Test Loss: 0.2268594 Validation loss decreased (0.208904 --> 0.206880). Saving model ... Updating learning rate to 0.0018907482291738273 iters: 100, epoch: 10 | loss: 0.2934171 speed: 0.2292s/iter; left time: 22127.8739s iters: 200, epoch: 10 | loss: 0.3041674 speed: 0.0652s/iter; left time: 6284.6789s iters: 300, epoch: 10 | loss: 0.2558330 speed: 0.0646s/iter; left time: 6225.6793s iters: 400, epoch: 10 | loss: 0.3225133 speed: 0.0643s/iter; left time: 6186.9587s iters: 500, epoch: 10 | loss: 0.2021957 speed: 0.0646s/iter; left time: 6214.6386s iters: 600, epoch: 10 | loss: 0.2634687 speed: 0.0644s/iter; left time: 6184.0262s iters: 700, epoch: 10 | loss: 0.3601710 speed: 0.0649s/iter; left time: 6231.3502s iters: 800, epoch: 10 | loss: 0.2715219 speed: 0.0643s/iter; left time: 6158.7077s iters: 900, epoch: 10 | loss: 0.3285161 speed: 0.0662s/iter; left time: 6337.7003s iters: 1000, epoch: 10 | loss: 0.2874655 speed: 0.0650s/iter; left time: 6218.2058s Epoch: 10 cost time: 69.2235357761383 --------start to validate----------- normed mse:0.0741, mae:0.2067, rmse:0.2721, mape:1.4288, mspe:32.2580, corr:0.8650 denormed mse:6.2362, mae:1.8968, rmse:2.4972, mape:0.1524, mspe:0.0658, corr:0.8650 --------start to test----------- normed mse:0.1252, mae:0.2862, rmse:0.3538, mape:0.2056, mspe:0.0598, corr:0.7859 denormed mse:10.5420, mae:2.6266, rmse:3.2468, mape:inf, mspe:inf, corr:0.7859 Epoch: 10, Steps: 1062 | Train Loss: 0.2617135 valid Loss: 0.2067047 Test Loss: 0.2862312 Validation loss decreased (0.206880 --> 0.206705). Saving model ... Updating learning rate to 0.001796210817715136 iters: 100, epoch: 11 | loss: 0.2604572 speed: 0.2211s/iter; left time: 21110.7798s iters: 200, epoch: 11 | loss: 0.1902495 speed: 0.0639s/iter; left time: 6093.1127s iters: 300, epoch: 11 | loss: 0.2706100 speed: 0.0645s/iter; left time: 6144.9446s iters: 400, epoch: 11 | loss: 0.2700502 speed: 0.0641s/iter; left time: 6099.0807s iters: 500, epoch: 11 | loss: 0.2039715 speed: 0.0644s/iter; left time: 6119.2977s iters: 600, epoch: 11 | loss: 0.2211753 speed: 0.0644s/iter; left time: 6116.2928s iters: 700, epoch: 11 | loss: 0.3060542 speed: 0.0641s/iter; left time: 6078.2290s iters: 800, epoch: 11 | loss: 0.2073108 speed: 0.0636s/iter; left time: 6031.3409s iters: 900, epoch: 11 | loss: 0.3166656 speed: 0.0642s/iter; left time: 6077.3573s iters: 1000, epoch: 11 | loss: 0.2857897 speed: 0.0636s/iter; left time: 6020.0385s Epoch: 11 cost time: 68.08029365539551 --------start to validate----------- normed mse:0.0780, mae:0.2101, rmse:0.2792, mape:1.4069, mspe:27.8713, corr:0.8645 denormed mse:6.5642, mae:1.9282, rmse:2.5621, mape:0.1562, mspe:0.0672, corr:0.8645 --------start to test----------- normed mse:0.0486, mae:0.1696, rmse:0.2206, mape:0.1346, mspe:0.0347, corr:0.8196 denormed mse:4.0965, mae:1.5563, rmse:2.0240, mape:inf, mspe:inf, corr:0.8196 Epoch: 11, Steps: 1062 | Train Loss: 0.2599722 valid Loss: 0.2101241 Test Loss: 0.1695920 EarlyStopping counter: 1 out of 5 Updating learning rate to 0.0017064002768293791 iters: 100, epoch: 12 | loss: 0.3560360 speed: 0.2200s/iter; left time: 20776.4963s iters: 200, epoch: 12 | loss: 0.2906877 speed: 0.0638s/iter; left time: 6017.2672s iters: 300, epoch: 12 | loss: 0.3136698 speed: 0.0640s/iter; left time: 6029.7119s iters: 400, epoch: 12 | loss: 0.4521513 speed: 0.0637s/iter; left time: 5992.6421s iters: 500, epoch: 12 | loss: 0.2683279 speed: 0.0635s/iter; left time: 5973.5166s iters: 600, epoch: 12 | loss: 0.2095424 speed: 0.0632s/iter; left time: 5932.0850s iters: 700, epoch: 12 | loss: 0.3217563 speed: 0.0646s/iter; left time: 6062.9666s iters: 800, epoch: 12 | loss: 0.2670196 speed: 0.0635s/iter; left time: 5954.2641s iters: 900, epoch: 12 | loss: 0.2306930 speed: 0.0639s/iter; left time: 5977.7809s iters: 1000, epoch: 12 | loss: 0.2080201 speed: 0.0633s/iter; left time: 5915.4393s Epoch: 12 cost time: 67.63420724868774 --------start to validate----------- normed mse:0.0752, mae:0.2047, rmse:0.2742, mape:1.3057, mspe:24.8466, corr:0.8630 denormed mse:6.3301, mae:1.8782, rmse:2.5160, mape:0.1537, mspe:0.0697, corr:0.8630 --------start to test----------- normed mse:0.1127, mae:0.2651, rmse:0.3357, mape:0.1911, mspe:0.0544, corr:0.7504 denormed mse:9.4922, mae:2.4327, rmse:3.0809, mape:inf, mspe:inf, corr:0.7504 Epoch: 12, Steps: 1062 | Train Loss: 0.2593013 valid Loss: 0.2046781 Test Loss: 0.2651025 Validation loss decreased (0.206705 --> 0.204678). Saving model ... Updating learning rate to 0.00162108026298791 iters: 100, epoch: 13 | loss: 0.2679453 speed: 0.2223s/iter; left time: 20751.6236s iters: 200, epoch: 13 | loss: 0.2244501 speed: 0.0640s/iter; left time: 5970.0265s iters: 300, epoch: 13 | loss: 0.2729070 speed: 0.0638s/iter; left time: 5944.9959s iters: 400, epoch: 13 | loss: 0.2141117 speed: 0.0642s/iter; left time: 5973.2411s iters: 500, epoch: 13 | loss: 0.2737395 speed: 0.0649s/iter; left time: 6035.3685s iters: 600, epoch: 13 | loss: 0.3773285 speed: 0.0655s/iter; left time: 6084.1809s iters: 700, epoch: 13 | loss: 0.3060603 speed: 0.0651s/iter; left time: 6037.3794s iters: 800, epoch: 13 | loss: 0.3271270 speed: 0.0639s/iter; left time: 5919.0781s iters: 900, epoch: 13 | loss: 0.2570842 speed: 0.0644s/iter; left time: 5959.6727s iters: 1000, epoch: 13 | loss: 0.1967695 speed: 0.0650s/iter; left time: 6008.8446s Epoch: 13 cost time: 68.51006627082825 --------start to validate----------- normed mse:0.0754, mae:0.2070, rmse:0.2745, mape:1.3459, mspe:27.9367, corr:0.8626 denormed mse:6.3463, mae:1.8996, rmse:2.5192, mape:0.1547, mspe:0.0686, corr:0.8626 --------start to test----------- normed mse:0.1270, mae:0.2863, rmse:0.3563, mape:0.2047, mspe:0.0591, corr:0.7302 denormed mse:10.6924, mae:2.6270, rmse:3.2699, mape:inf, mspe:inf, corr:0.7302 Epoch: 13, Steps: 1062 | Train Loss: 0.2578851 valid Loss: 0.2070073 Test Loss: 0.2862706 EarlyStopping counter: 1 out of 5 Updating learning rate to 0.0015400262498385146 iters: 100, epoch: 14 | loss: 0.3828691 speed: 0.2242s/iter; left time: 20692.6053s iters: 200, epoch: 14 | loss: 0.2255980 speed: 0.0655s/iter; left time: 6034.3079s iters: 300, epoch: 14 | loss: 0.2057881 speed: 0.0651s/iter; left time: 5999.9542s iters: 400, epoch: 14 | loss: 0.2044961 speed: 0.0654s/iter; left time: 6012.5291s iters: 500, epoch: 14 | loss: 0.1950546 speed: 0.0659s/iter; left time: 6053.8845s iters: 600, epoch: 14 | loss: 0.2513721 speed: 0.0663s/iter; left time: 6081.8917s iters: 700, epoch: 14 | loss: 0.3617742 speed: 0.0676s/iter; left time: 6199.0184s iters: 800, epoch: 14 | loss: 0.1818448 speed: 0.0660s/iter; left time: 6047.2192s iters: 900, epoch: 14 | loss: 0.2921709 speed: 0.0637s/iter; left time: 5831.9500s iters: 1000, epoch: 14 | loss: 0.4443547 speed: 0.0636s/iter; left time: 5812.0902s Epoch: 14 cost time: 69.46138763427734 --------start to validate----------- normed mse:0.0749, mae:0.2054, rmse:0.2737, mape:1.3964, mspe:29.3059, corr:0.8646 denormed mse:6.3101, mae:1.8853, rmse:2.5120, mape:0.1517, mspe:0.0638, corr:0.8646 --------start to test----------- normed mse:0.0582, mae:0.1803, rmse:0.2413, mape:0.1373, mspe:0.0348, corr:0.7685 denormed mse:4.9051, mae:1.6544, rmse:2.2147, mape:inf, mspe:inf, corr:0.7685 Epoch: 14, Steps: 1062 | Train Loss: 0.2567903 valid Loss: 0.2054437 Test Loss: 0.1802866 EarlyStopping counter: 2 out of 5 Updating learning rate to 0.0014630249373465886 iters: 100, epoch: 15 | loss: 0.2464596 speed: 0.2214s/iter; left time: 20196.9599s iters: 200, epoch: 15 | loss: 0.3060012 speed: 0.0648s/iter; left time: 5902.4050s iters: 300, epoch: 15 | loss: 0.3132369 speed: 0.0647s/iter; left time: 5892.1457s iters: 400, epoch: 15 | loss: 0.3352527 speed: 0.0645s/iter; left time: 5867.1592s iters: 500, epoch: 15 | loss: 0.2343948 speed: 0.0640s/iter; left time: 5814.1662s iters: 600, epoch: 15 | loss: 0.1983065 speed: 0.0636s/iter; left time: 5772.0216s iters: 700, epoch: 15 | loss: 0.1803256 speed: 0.0638s/iter; left time: 5782.6625s iters: 800, epoch: 15 | loss: 0.2608042 speed: 0.0640s/iter; left time: 5795.1071s iters: 900, epoch: 15 | loss: 0.3982482 speed: 0.0656s/iter; left time: 5930.5014s iters: 1000, epoch: 15 | loss: 0.2794555 speed: 0.0649s/iter; left time: 5859.7955s Epoch: 15 cost time: 68.22825241088867 --------start to validate----------- normed mse:0.0751, mae:0.2062, rmse:0.2740, mape:1.4129, mspe:29.4700, corr:0.8638 denormed mse:6.3221, mae:1.8922, rmse:2.5144, mape:0.1511, mspe:0.0607, corr:0.8638 --------start to test----------- normed mse:0.0743, mae:0.2067, rmse:0.2726, mape:0.1542, mspe:0.0414, corr:0.7309 denormed mse:6.2598, mae:1.8971, rmse:2.5020, mape:inf, mspe:inf, corr:0.7309 Epoch: 15, Steps: 1062 | Train Loss: 0.2556333 valid Loss: 0.2062003 Test Loss: 0.2067328 EarlyStopping counter: 3 out of 5 Updating learning rate to 0.001389873690479259 iters: 100, epoch: 16 | loss: 0.4233045 speed: 0.2250s/iter; left time: 20289.9657s iters: 200, epoch: 16 | loss: 0.2079662 speed: 0.0654s/iter; left time: 5887.5618s iters: 300, epoch: 16 | loss: 0.2657562 speed: 0.0672s/iter; left time: 6045.0847s iters: 400, epoch: 16 | loss: 0.1947590 speed: 0.0716s/iter; left time: 6432.6096s iters: 500, epoch: 16 | loss: 0.2555844 speed: 0.0686s/iter; left time: 6157.6966s iters: 600, epoch: 16 | loss: 0.2228586 speed: 0.0686s/iter; left time: 6153.4446s iters: 700, epoch: 16 | loss: 0.2561190 speed: 0.0693s/iter; left time: 6211.2939s iters: 800, epoch: 16 | loss: 0.2335158 speed: 0.0682s/iter; left time: 6099.8479s iters: 900, epoch: 16 | loss: 0.1972947 speed: 0.0681s/iter; left time: 6082.7818s iters: 1000, epoch: 16 | loss: 0.2210546 speed: 0.0684s/iter; left time: 6108.1396s Epoch: 16 cost time: 72.44369554519653 --------start to validate----------- normed mse:0.0767, mae:0.2066, rmse:0.2769, mape:1.3287, mspe:26.0432, corr:0.8614 denormed mse:6.4587, mae:1.8956, rmse:2.5414, mape:0.1549, mspe:0.0696, corr:0.8614 --------start to test----------- normed mse:0.0722, mae:0.2028, rmse:0.2686, mape:0.1513, mspe:0.0397, corr:0.7538 denormed mse:6.0773, mae:1.8609, rmse:2.4652, mape:inf, mspe:inf, corr:0.7538 Epoch: 16, Steps: 1062 | Train Loss: 0.2538822 valid Loss: 0.2065719 Test Loss: 0.2027889 EarlyStopping counter: 4 out of 5 Updating learning rate to 0.001320380005955296 iters: 100, epoch: 17 | loss: 0.2435877 speed: 0.2235s/iter; left time: 19918.0769s iters: 200, epoch: 17 | loss: 0.2681281 speed: 0.0643s/iter; left time: 5724.9103s iters: 300, epoch: 17 | loss: 0.2386408 speed: 0.0645s/iter; left time: 5732.2681s iters: 400, epoch: 17 | loss: 0.1940629 speed: 0.0645s/iter; left time: 5730.9041s iters: 500, epoch: 17 | loss: 0.1982391 speed: 0.0647s/iter; left time: 5735.3843s iters: 600, epoch: 17 | loss: 0.2039342 speed: 0.0645s/iter; left time: 5713.2374s iters: 700, epoch: 17 | loss: 0.2077632 speed: 0.0648s/iter; left time: 5738.5635s iters: 800, epoch: 17 | loss: 0.3183163 speed: 0.0640s/iter; left time: 5656.7522s iters: 900, epoch: 17 | loss: 0.2791365 speed: 0.0648s/iter; left time: 5723.3435s iters: 1000, epoch: 17 | loss: 0.2257991 speed: 0.0642s/iter; left time: 5660.2778s Epoch: 17 cost time: 68.39220643043518 --------start to validate----------- normed mse:0.0785, mae:0.2103, rmse:0.2803, mape:1.3704, mspe:27.2711, corr:0.8559 denormed mse:6.6141, mae:1.9294, rmse:2.5718, mape:0.1568, mspe:0.0703, corr:0.8559 --------start to test----------- normed mse:0.1018, mae:0.2485, rmse:0.3191, mape:0.1800, mspe:0.0498, corr:0.7221 denormed mse:8.5757, mae:2.2802, rmse:2.9284, mape:inf, mspe:inf, corr:0.7221 Epoch: 17, Steps: 1062 | Train Loss: 0.2532137 valid Loss: 0.2102546 Test Loss: 0.2484858 EarlyStopping counter: 5 out of 5 Early stopping save model in exp/ETT_checkpoints/SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0/ETTh148.bin testing : SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< test 2833 normed mse:0.1127, mae:0.2651, rmse:0.3357, mape:0.1911, mspe:0.0544, corr:0.7504 TTTT denormed mse:9.4922, mae:2.4327, rmse:3.0809, mape:inf, mspe:inf, corr:0.7504 Final mean normed mse:0.1127,mae:0.2651,denormed mse:9.4922,mae:2.4327`

After this a new folder in exp is formed exp/ETT_checkpoints/SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0

and there are 2 folders withing it 1.) ETTh148.bin 2.) checkpoint.pth Upon extracting it we get 1.) archive/data.pkl

When i try to convert this data.pkl file into a data frame by running this code: import numpy as np import pandas as pd import pickle

df = pd.read_pickle('out.pkl') print(df) I get the following error: unpicklingerror: a load persistent id instruction was encountered, but no persistent_load function was specified.

How do i Plot the results of ETTh1 ? Thank you

cure-lab / SCINet

How to Plot the Results of ETTh1 dataset #27