Optimization tutorial#
from ice.remaining_useful_life_estimation.datasets import RulCmapss
from ice.remaining_useful_life_estimation.models import MLP
C:\Users\user\conda\envs\ice_testing\Lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Create the MLP model and dataset class.
dataset_class = RulCmapss()
data, target = dataset_class.df[0], dataset_class.target[0]
Reading data/C-MAPSS/fd1_train.csv: 100%|██████████| 20631/20631 [00:00<00:00, 517496.66it/s]
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model = MLP(device="cuda")
Optimization without changing the complexity of the training process. Tune the lr of the training procedure using validation loss as optimization target
# model_class.optimize(data, target, optimize_parameter, optimize_range, direction, n_trials, epochs, optimize_metric)
model.optimize(data, target, optimize_parameter="lr", optimize_range=(5e-5, 1e-3), direction="minimize", n_trials=3, epochs=5) # if optimize_metric is None, than validation loss is using as optimization target
[I 2024-08-13 09:53:33,784] A new study created in memory with name: /parameter_lr study
trial step with lr = 0.00018951382914416393
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Epochs ...: 20%|██ | 1/5 [00:00<00:01, 2.31it/s]
Epoch 1, Loss: 39.3309
Epoch 1, Validation Loss: 39.3238, Metrics: {'rmse': 50.063830627114406, 'cmapss_score': 1009001.2641988464}
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Epoch 2, Loss: 36.8534
Epoch 2, Validation Loss: 32.2449, Metrics: {'rmse': 40.68462185124231, 'cmapss_score': 326161.11634528585}
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Epoch 3, Loss: 22.9493
Epoch 3, Validation Loss: 29.7487, Metrics: {'rmse': 38.11920046818511, 'cmapss_score': 251687.82706875}
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Epochs ...: 80%|████████ | 4/5 [00:01<00:00, 2.74it/s]
Epoch 4, Loss: 27.0963
Epoch 4, Validation Loss: 28.3393, Metrics: {'rmse': 36.542765927986274, 'cmapss_score': 207206.28175345066}
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[I 2024-08-13 09:53:36,351] Trial 0 finished with value: 26.89373407131288 and parameters: {'lr': 0.00018951382914416393}. Best is trial 0 with value: 26.89373407131288.
Epoch 5, Loss: 30.0107
Epoch 5, Validation Loss: 26.8937, Metrics: {'rmse': 34.96109612817986, 'cmapss_score': 171362.21049284632}
trial step with lr = 0.0007874022874638446
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Epoch 1, Loss: 32.6104
Epoch 1, Validation Loss: 28.4569, Metrics: {'rmse': 36.641393207296055, 'cmapss_score': 206445.81209835963}
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Epochs ...: 40%|████ | 2/5 [00:00<00:01, 2.69it/s]
Epoch 2, Loss: 29.5834
Epoch 2, Validation Loss: 22.3682, Metrics: {'rmse': 29.68363304759782, 'cmapss_score': 98649.45005642212}
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Epochs ...: 60%|██████ | 3/5 [00:01<00:00, 2.10it/s]
Epoch 3, Loss: 17.5193
Epoch 3, Validation Loss: 17.9444, Metrics: {'rmse': 24.369740561776197, 'cmapss_score': 82673.16229614464}
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Epoch 4, Loss: 16.3577
Epoch 4, Validation Loss: 16.1891, Metrics: {'rmse': 22.407311130072234, 'cmapss_score': 90375.15431472883}
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[I 2024-08-13 09:53:39,216] Trial 1 finished with value: 16.189056094099836 and parameters: {'lr': 0.0007874022874638446}. Best is trial 1 with value: 16.189056094099836.
Epoch 5, Loss: 18.7968
Epoch 5, Validation Loss: 16.4563, Metrics: {'rmse': 22.47353109963666, 'cmapss_score': 73324.81707642713}
trial step with lr = 0.00012086132027556038
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Epoch 1, Loss: 42.6350
Epoch 1, Validation Loss: 41.5396, Metrics: {'rmse': 53.309621676839306, 'cmapss_score': 1652572.5547515503}
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Epoch 2, Loss: 36.4397
Epoch 2, Validation Loss: 37.1115, Metrics: {'rmse': 46.95681782704423, 'cmapss_score': 653010.5664245718}
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Epoch 3, Loss: 25.7715
Epoch 3, Validation Loss: 32.9065, Metrics: {'rmse': 41.430416515203575, 'cmapss_score': 349818.50039921864}
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Epoch 4, Loss: 29.5199
Epoch 4, Validation Loss: 30.6848, Metrics: {'rmse': 39.035223216989905, 'cmapss_score': 274871.78833234054}
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[I 2024-08-13 09:53:42,980] Trial 2 finished with value: 29.60851450664241 and parameters: {'lr': 0.00012086132027556038}. Best is trial 1 with value: 16.189056094099836.
Epoch 5, Loss: 33.5629
Epoch 5, Validation Loss: 29.6085, Metrics: {'rmse': 37.93360429205696, 'cmapss_score': 242353.786303807}
Best hyperparameters: {'lr': 0.0007874022874638446}
Best trial: FrozenTrial(number=1, state=1, values=[16.189056094099836], datetime_start=datetime.datetime(2024, 8, 13, 9, 53, 36, 351082), datetime_complete=datetime.datetime(2024, 8, 13, 9, 53, 39, 216093), params={'lr': 0.0007874022874638446}, user_attrs={}, system_attrs={}, intermediate_values={1: 28.456903271558808, 2: 22.368176297443668, 3: 17.944359942180355, 4: 16.189056094099836, 5: 16.456260332247105}, distributions={'lr': FloatDistribution(high=0.001, log=False, low=5e-05, step=None)}, trial_id=1, value=None)
Optimization with changing the complexity of the training process. Tune the MLP hidden dimension size using MSE metric as optimization target
# model_class.optimize(data, target, optimize_parameter, optimize_range, direction, n_trials, epochs, optimize_metric)
model.optimize(data, target, optimize_parameter="hidden_dim", optimize_range=(256, 1024), direction="minimize", optimize_metric="rmse", n_trials=3, epochs=5)
[I 2024-08-13 09:53:42,992] A new study created in memory with name: /parameter_hidden_dim study
trial step with hidden_dim = 702
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Epochs ...: 20%|██ | 1/5 [00:00<00:03, 1.28it/s]
Epoch 1, Loss: 41.7009
Epoch 1, Validation Loss: 41.0446, Metrics: {'rmse': 52.46324288978529, 'cmapss_score': 1459642.2102222845}
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Epoch 2, Loss: 34.5571
Epoch 2, Validation Loss: 35.5294, Metrics: {'rmse': 44.69688917194367, 'cmapss_score': 487095.93532787054}
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Epoch 3, Loss: 24.0595
Epoch 3, Validation Loss: 31.7725, Metrics: {'rmse': 40.124605880566875, 'cmapss_score': 305081.3116466362}
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Epoch 4, Loss: 27.7565
Epoch 4, Validation Loss: 29.9569, Metrics: {'rmse': 38.28838337636238, 'cmapss_score': 252440.2810893617}
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[I 2024-08-13 09:53:46,884] Trial 0 finished with value: 37.12774637917532 and parameters: {'hidden_dim': 702}. Best is trial 0 with value: 37.12774637917532.
Epoch 5, Loss: 31.4343
Epoch 5, Validation Loss: 28.8321, Metrics: {'rmse': 37.12774637917532, 'cmapss_score': 219477.3266793877}
trial step with hidden_dim = 662
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Epoch 1, Loss: 41.5648
Epoch 1, Validation Loss: 41.1247, Metrics: {'rmse': 52.64622625632633, 'cmapss_score': 1497278.352392366}
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Epoch 2, Loss: 37.5466
Epoch 2, Validation Loss: 35.6801, Metrics: {'rmse': 45.00773698916855, 'cmapss_score': 511059.8643841665}
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Epoch 3, Loss: 26.5062
Epoch 3, Validation Loss: 31.7830, Metrics: {'rmse': 40.07700663323968, 'cmapss_score': 305080.57834935177}
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Epoch 4, Loss: 28.9049
Epoch 4, Validation Loss: 29.9922, Metrics: {'rmse': 38.29525364434387, 'cmapss_score': 252989.93131717102}
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[I 2024-08-13 09:53:50,841] Trial 1 finished with value: 37.207252080735245 and parameters: {'hidden_dim': 662}. Best is trial 0 with value: 37.12774637917532.
Epoch 5, Loss: 31.7666
Epoch 5, Validation Loss: 28.9132, Metrics: {'rmse': 37.207252080735245, 'cmapss_score': 221679.17640028123}
trial step with hidden_dim = 879
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Epoch 1, Loss: 40.2073
Epoch 1, Validation Loss: 40.6210, Metrics: {'rmse': 51.9431875404865, 'cmapss_score': 1331357.9658768093}
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Epoch 2, Loss: 37.4625
Epoch 2, Validation Loss: 34.5596, Metrics: {'rmse': 43.41147259207038, 'cmapss_score': 421283.66798380984}
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Epochs ...: 60%|██████ | 3/5 [00:02<00:01, 1.33it/s]
Epoch 3, Loss: 23.8398
Epoch 3, Validation Loss: 30.9023, Metrics: {'rmse': 39.31126980464057, 'cmapss_score': 286766.446915035}
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Epoch 4, Loss: 28.2913
Epoch 4, Validation Loss: 29.3355, Metrics: {'rmse': 37.68203820452062, 'cmapss_score': 236872.37425031906}
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[I 2024-08-13 09:53:54,617] Trial 2 finished with value: 36.46475764089671 and parameters: {'hidden_dim': 879}. Best is trial 2 with value: 36.46475764089671.
Epoch 5, Loss: 31.4287
Epoch 5, Validation Loss: 28.2309, Metrics: {'rmse': 36.46475764089671, 'cmapss_score': 203393.37691007284}
Best hyperparameters: {'hidden_dim': 879}
Best trial: FrozenTrial(number=2, state=1, values=[36.46475764089671], datetime_start=datetime.datetime(2024, 8, 13, 9, 53, 50, 842965), datetime_complete=datetime.datetime(2024, 8, 13, 9, 53, 54, 617905), params={'hidden_dim': 879}, user_attrs={}, system_attrs={}, intermediate_values={1: 40.62102061946218, 2: 34.55964553646925, 3: 30.902342726544635, 4: 29.335512719503264, 5: 28.230895298283276}, distributions={'hidden_dim': IntDistribution(high=1024, log=False, low=256, step=1)}, trial_id=2, value=None)
The best results are printed at the end of optimization processand saved in the outputs/task_name/traininig/param_name_optimization folder