Error when running bayesian optimizer for real data

[Abdu-Hekal]

Error trace:

File "/Users/b6062805/Documents/Koopman/AutoKoopman/benchmarks/realDataAll.py", line 175, in perc_error, mse = compute_error(model, test_data) File "/Users/b6062805/Documents/Koopman/AutoKoopman/benchmarks/realDataAll.py", line 103, in compute_error trajectory = model.solve_ivp( File "/Users/b6062805/Documents/Koopman/AutoKoopman/autokoopman/core/system.py", line 264, in solve_ivp states[idx + 1] = self.step( File "/Users/b6062805/Documents/Koopman/AutoKoopman/autokoopman/core/system.py", line 344, in step return self._step_func(time, state, sinput) File "/Users/b6062805/Documents/Koopman/AutoKoopman/autokoopman/estimator/koopman.py", line 78, in step_func obs = (self.obs(x).flatten())[np.newaxis, :] File "/Users/b6062805/Documents/Koopman/AutoKoopman/autokoopman/core/observables.py", line 32, in call return self.obs_fcn(X) File "/Users/b6062805/Documents/Koopman/AutoKoopman/autokoopman/core/observables.py", line 165, in obs_fcn return self.poly.transform(np.atleast_2d(X)) File "/usr/local/lib/python3.9/site-packages/sklearn/preprocessing/_polynomial.py", line 369, in transform X = self._validate_data( File "/usr/local/lib/python3.9/site-packages/sklearn/base.py", line 566, in _validate_data X = check_array(X, **check_params) File "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py", line 800, in check_array _assert_all_finite(array, allow_nan=force_all_finite == "allow-nan") File "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py", line 114, in _assert_all_finite raise ValueError( ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). Tuning BayesianOptTuner: 5%|▌ | 10/200 [00:03<00:57, 3.28it/s] Tuning BayesianOptTuner: 2%|▎ | 5/200 [00:03<02:09, 1.50it/s]

[Abdu-Hekal]

Same issue arises when running bayesian optimizer for symbolic data and poly observables are used

[EthanJamesLew]

@Abdu-Hekal this is not from auto_koopman, but from solve_ivp called in the script directly. See commit 8cfb531 for an error handling line . The model learned can be unstable in some regions, so failing solve_ivp can be expected from a trained model (which may have done well in the test region).

[Abdu-Hekal]

Thanks, I have added it. Interesting that this happens often with the bayesian optimizer and not with grid. may result in better options with grid. I will aim to test both