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Home Page: https://autooed.org
AutoOED: Automated Optimal Experimental Design Platform
Home Page: https://autooed.org
Hi there! Great to see a useful tool for MOBO, thanks for sharing the code!
I am quite interested in mixed variables (on my way learning how to implement this), I saw in your original code you did the transformation to change the variables to one-hot encoding. Wonder if there is any changes regarding the acquisition function to deal with mixed variables, could you share some insights on this?
Any advice would be much appreciated!!
Your project is very valuable. When I experience the relevant function, run_team_scientist.py, the basic method described in the documentation is that the whole program stops responding after the manual optimization is started and before the optimization is finished. I hope you can check it out.
Hello,
It appears that you are utilizing Python version 3.7.7 as specified in [environment.yml], which seems to be no longer compatible with the Matlab Engine of newer versions. As a result, optimization using Matlab scripts (e.g. the simulation example) is not possible.
Hi there! Stumbled on AutoOED recently and it looks to be a great fit for what I need. I'm attempting to run a manual experiment and use AutoOED to help guide tuning of parameters. Unfortunately I haven't been able to progress past the initial randomly generated samples. Optimization seems to hang no matter what I do.
min(f1)
, max(f2)
)."Enter Performance"
on the database tabI'm using the DGEMO algorithm with default values as recommended in the docs.
If you run it via the terminal (master
branch) instead of the downloadable executable, I can see that one of the workers dies with ValueError: Buffer has wrong number of dimensions (expected 2, got 1)
:
(autooed) C:\Users\polyf\Downloads\AutoOED-master>python run_gui.py
Process Process-2:
Traceback (most recent call last):
File "C:\Users\polyf\miniconda3\envs\autooed\lib\multiprocessing\process.py", line 297, in _bootstrap
self.run()
File "C:\Users\polyf\miniconda3\envs\autooed\lib\multiprocessing\process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\system\agent.py", line 712, in optimize
X_next, (Y_pred_mean, Y_pred_std) = optimize_predict(config, X, Y, X_busy, batch_size=batch_size)
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\core.py", line 160, in optimize_predict
X_next = optimizer.optimize(X, Y, X_busy, batch_size)
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\mobo\mobo.py", line 94, in optimize
return self._optimize(X, Y, batch_size)
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\mobo\mobo.py", line 109, in _optimize
X_candidate, Y_candidate = self.solver.solve(X, Y, batch_size, self.acquisition)
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\mobo\solver\base.py", line 47, in solve
X_candidate, Y_candidate = self._solve(X, Y, batch_size)
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\mobo\solver\pareto_discovery\pareto_discovery.py", line 632, in _solve
res = minimize_ea(self.problem, self.algo, ('n_gen', self.n_gen))
File "C:\Users\polyf\miniconda3\envs\autooed\lib\site-packages\pymoo\optimize.py", line 76, in minimize
res = algorithm.solve()
File "C:\Users\polyf\miniconda3\envs\autooed\lib\site-packages\pymoo\model\algorithm.py", line 208, in solve
self._solve(self.problem)
File "C:\Users\polyf\miniconda3\envs\autooed\lib\site-packages\pymoo\model\algorithm.py", line 295, in _solve
self.finalize()
File "C:\Users\polyf\miniconda3\envs\autooed\lib\site-packages\pymoo\model\algorithm.py", line 269, in finalize
return self._finalize()
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\mobo\solver\pareto_discovery\pareto_discovery.py", line 613, in _finalize
self.fam_lbls, self.approx_set, self.approx_front = self.buffer.sparse_approximation()
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\mobo\solver\pareto_discovery\buffer.py", line 249, in sparse_approximation
labels_opt = cut_from_graph(edges, unary_cost, pairwise_cost, label_cost)
File "gco_python.pyx", line 139, in pygco.cut_from_graph
ValueError: Buffer has wrong number of dimensions (expected 2, got 1)
Exception in Tkinter callback
Traceback (most recent call last):
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\system\scheduler.py", line 359, in _refresh_optimize
rowids = self.opt_queue.get(block=False)
File "C:\Users\polyf\miniconda3\envs\autooed\lib\multiprocessing\queues.py", line 107, in get
raise Empty
_queue.Empty
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\polyf\miniconda3\envs\autooed\lib\tkinter\__init__.py", line 1705, in __call__
return self.func(*args)
File "C:\Users\polyf\miniconda3\envs\autooed\lib\tkinter\__init__.py", line 749, in callit
func(*args)
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\system\gui\gui.py", line 302, in refresh
self.scheduler.refresh()
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\system\scheduler.py", line 454, in refresh
opt_rowids = self._refresh_optimize()
File "C:\Users\polyf\Downloads\AutoOED-master\autooed\system\scheduler.py", line 361, in _refresh_optimize
raise Exception('optimization worker finished without returning rowids of the design to evaluate')
Exception: optimization worker finished without returning rowids of the design to evaluate
I thought maybe an evaluation function was mandatory (although the docs say it isn't), so I added an eval function that just returns the experimental results:
def evaluate_objective(x):
print(x)
x1, x2, x3 = x
if x1 == 20143:
return 233628.0, 47.66
if x1 == 24897:
return 316294.0, 4.416
if x1 == 15235:
return 366988.0,23.02
if x1 == 11547:
return 224685.0,16.32
if x1 == 29999:
return 289703.0,25.38
if x1 == 17973:
return 191488.0,32.72
return 10000000,0
But that didn't seem to help, and I didn't see any print statements in the terminal so I don't think it was being invoked.
Let me know what extra debugging information might be needed! Looking forward to getting this working, would really help my experiment :)
Hi !
I am having troubles trying to apply multiple constraints to my design variables. To reproduce my case :
I think the problem comes from the evaluate_feasible() in problem.py, more specifically this call to pymoo when G contains multiple constraint evaluations :
Problem.calc_constraint_violation(np.column_stack(np.atleast_2d(G))).
If you need more informations please tell me.
Currently, the problem configuration is saved in the "problem/custom/yaml" folder, while the experiment configuration can only be accessed through SQL. I would like to suggest adding a folder in the "system/experiment/custom/yaml" directory.
This addition would allow for easy viewing of previous experiment configurations and enables corrections within the YAML file itself.
Many thanks for this promising work.
Currently, I am applying AutoOED to explore the performance space in my project (3 variables to 2 objectives, and we don't know the objective function). However, the model prediction error increases as the evaluation add, please refer to the figure for more details. In this case, the ranges of 2 objectives are supposed to be limited in [-30, 30], can I constrain these ranges in the code?
Moreover, I wonder how to select the tuning strategy for Gaussian Process, I have tried to change the hyperparameter nu but it didn't work. Could you please share some tunning experiences?
I will appreciate your help.
Best
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