2021-11-01 08:02:44.622 ERROR Traceback (most recent call last): File "/home/ma-user/work/automl-1.8_EI/vega/core/pipeline/pipeline.py", line 79, in run pipestep.do() File "/home/ma-user/work/automl-1.8_EI/vega/core/pipeline/search_pipe_step.py", line 55, in do self._dispatch_trainer(res) File "/home/ma-user/work/automl-1.8_EI/vega/core/pipeline/search_pipe_step.py", line 73, in _dispatch_trainer self.master.run(trainer, evaluator) File "/home/ma-user/work/automl-1.8_EI/vega/core/scheduler/local_master.py", line 63, in run self._update(step_name, worker_id) File "/home/ma-user/work/automl-1.8_EI/vega/core/scheduler/local_master.py", line 71, in _update self.update_func(step_name, worker_id) File "/home/ma-user/work/automl-1.8_EI/vega/core/pipeline/generator.py", line 131, in update self.search_alg.update(record.serialize()) File "/home/ma-user/work/automl-1.8_EI/vega/algorithms/hpo/hpo_base.py", line 84, in update self.hpo.add_score(config_id, int(rung_id), rewards) File "/home/ma-user/work/automl-1.8_EI/vega/algorithms/hpo/sha_base/boss.py", line 230, in add_score self._set_next_ssa() File "/home/ma-user/work/automl-1.8_EI/vega/algorithms/hpo/sha_base/boss.py", line 159, in _set_next_ssa configs = self.tuner.propose(self.iter_list[iter]) File "/home/ma-user/work/automl-1.8_EI/vega/algorithms/hpo/sha_base/hebo_adaptor.py", line 70, in propose suggestions = self.hebo.suggest(n_suggestions=num) File "/home/ma-user/work/model_zoo/HEBO-master/HEBO/hebo/optimizers/hebo.py", line 126, in suggest rec = opt.optimize(initial_suggest = best_x, fix_input = fix_input).drop_duplicates() File "/home/ma-user/work/model_zoo/HEBO-master/HEBO/hebo/acq_optimizers/evolution_optimizer.py", line 122, in optimize print("optimize: ", prob, algo,self.iter) File "/home/ma-user/miniconda3/envs/MindSpore-python3.7-aarch64/lib/python3.7/site-packages/pymoo/model/problem.py", line 448, in str s += "# f(xl): %s\n" % self.evaluate(self.xl)[0] File "/home/ma-user/miniconda3/envs/MindSpore-python3.7-aarch64/lib/python3.7/site-packages/pymoo/model/problem.py", line 267, in evaluate out = self._evaluate_batch(X, calc_gradient, out, *args, **kwargs) File "/home/ma-user/miniconda3/envs/MindSpore-python3.7-aarch64/lib/python3.7/site-packages/pymoo/model/problem.py", line 335, in _evaluate_batch self._evaluate(X, out, *args, **kwargs) File "/home/ma-user/work/model_zoo/HEBO-master/HEBO/hebo/acq_optimizers/evolution_optimizer.py", line 50, in _evaluate acq_eval = self.acq(xcont, xenum).numpy().reshape(num_x, self.acq.num_obj + self.acq.num_constr) File "/home/ma-user/work/model_zoo/HEBO-master/HEBO/hebo/acquisitions/acq.py", line 39, in call return self.eval(x, xe) File "/home/ma-user/work/model_zoo/HEBO-master/HEBO/hebo/acquisitions/acq.py", line 157, in eval log_phi = dist.log_prob(normed) File "/home/ma-user/miniconda3/envs/MindSpore-python3.7-aarch64/lib/python3.7/site-packages/torch/distributions/normal.py", line 73, in log_prob self._validate_sample(value) File "/home/ma-user/miniconda3/envs/MindSpore-python3.7-aarch64/lib/python3.7/site-packages/torch/distributions/distribution.py", line 277, in _validate_sample raise ValueError('The value argument must be within the support') ValueError: The value argument must be within the support

Hi, thanks for this repository! So far it works quite well, but now I suddenly encountered a weird error after 11 optimization steps of non-batched HEBO:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_2773121/4102601230.py in <module>
     35 
     36 for i in range(opt_steps):
---> 37     rec = opt.suggest()
     38     if "bs" in rec:
     39         rec["bs"] = 2 ** rec["bs"]

~/.local/lib/python3.8/site-packages/hebo/optimizers/hebo.py in suggest(self, n_suggestions, fix_input)
    151             sig = Sigma(model, linear_a = -1.)
    152             opt = EvolutionOpt(self.space, acq, pop = 100, iters = 100, verbose = False, es=self.es)
--> 153             rec = opt.optimize(initial_suggest = best_x, fix_input = fix_input).drop_duplicates()
    154             rec = rec[self.check_unique(rec)]
    155 

~/.local/lib/python3.8/site-packages/hebo/acq_optimizers/evolution_optimizer.py in optimize(self, initial_suggest, fix_input, return_pop)
    125         crossover = self.get_crossover()
    126         algo      = get_algorithm(self.es, pop_size = self.pop, sampling = init_pop, mutation = mutation, crossover = crossover, repair = self.repair)
--> 127         res       = minimize(prob, algo, ('n_gen', self.iter), verbose = self.verbose)
    128         if res.X is not None and not return_pop:
    129             opt_x = res.X.reshape(-1, len(lb)).astype(float)

~/.local/lib/python3.8/site-packages/pymoo/optimize.py in minimize(problem, algorithm, termination, copy_algorithm, copy_termination, **kwargs)
     81 
     82     # actually execute the algorithm
---> 83     res = algorithm.run()
     84 
     85     # store the deep copied algorithm in the result object

~/.local/lib/python3.8/site-packages/pymoo/core/algorithm.py in run(self)
    211         # while termination criterion not fulfilled
    212         while self.has_next():
--> 213             self.next()
    214 
    215         # create the result object to be returned

~/.local/lib/python3.8/site-packages/pymoo/core/algorithm.py in next(self)
    231         # call the advance with them after evaluation
    232         if infills is not None:
--> 233             self.evaluator.eval(self.problem, infills, algorithm=self)
    234             self.advance(infills=infills)
    235 

~/.local/lib/python3.8/site-packages/pymoo/core/evaluator.py in eval(self, problem, pop, skip_already_evaluated, evaluate_values_of, count_evals, **kwargs)
     93         # actually evaluate all solutions using the function that can be overwritten
     94         if len(I) > 0:
---> 95             self._eval(problem, pop[I], evaluate_values_of=evaluate_values_of, **kwargs)
     96 
     97             # set the feasibility attribute if cv exists

~/.local/lib/python3.8/site-packages/pymoo/core/evaluator.py in _eval(self, problem, pop, evaluate_values_of, **kwargs)
    110         evaluate_values_of = self.evaluate_values_of if evaluate_values_of is None else evaluate_values_of
    111 
--> 112         out = problem.evaluate(pop.get("X"),
    113                                return_values_of=evaluate_values_of,
    114                                return_as_dictionary=True,

~/.local/lib/python3.8/site-packages/pymoo/core/problem.py in evaluate(self, X, return_values_of, return_as_dictionary, *args, **kwargs)
    122 
    123         # do the actual evaluation for the given problem - calls in _evaluate method internally
--> 124         self.do(X, out, *args, **kwargs)
    125 
    126         # make sure the array is 2d before doing the shape check

~/.local/lib/python3.8/site-packages/pymoo/core/problem.py in do(self, X, out, *args, **kwargs)
    160 
    161     def do(self, X, out, *args, **kwargs):
--> 162         self._evaluate(X, out, *args, **kwargs)
    163         out_to_2d_ndarray(out)
    164 

~/.local/lib/python3.8/site-packages/hebo/acq_optimizers/evolution_optimizer.py in _evaluate(self, x, out, *args, **kwargs)
     46 
     47         with torch.no_grad():
---> 48             acq_eval = self.acq(xcont, xenum).numpy().reshape(num_x, self.acq.num_obj + self.acq.num_constr)
     49             out['F'] = acq_eval[:, :self.acq.num_obj]
     50 

~/.local/lib/python3.8/site-packages/hebo/acquisitions/acq.py in __call__(self, x, xe)
     37 
     38     def __call__(self, x : Tensor,  xe : Tensor):
---> 39         return self.eval(x, xe)
     40 
     41 class SingleObjectiveAcq(Acquisition):

~/.local/lib/python3.8/site-packages/hebo/acquisitions/acq.py in eval(self, x, xe)
    155             normed    = ((self.tau - self.eps - py - noise * torch.randn(py.shape)) / ps)
    156             dist      = Normal(0., 1.)
--> 157             log_phi   = dist.log_prob(normed)
    158             Phi       = dist.cdf(normed)
    159             PI        = Phi

~/.local/lib/python3.8/site-packages/torch/distributions/normal.py in log_prob(self, value)
     71     def log_prob(self, value):
     72         if self._validate_args:
---> 73             self._validate_sample(value)
     74         # compute the variance
     75         var = (self.scale ** 2)

~/.local/lib/python3.8/site-packages/torch/distributions/distribution.py in _validate_sample(self, value)
    286         valid = support.check(value)
    287         if not valid.all():
--> 288             raise ValueError(
    289                 "Expected value argument "
    290                 f"({type(value).__name__} of shape {tuple(value.shape)}) "

ValueError: Expected value argument (Tensor of shape (100, 1)) to be within the support (Real()) of the distribution Normal(loc: 0.0, scale: 1.0), but found invalid values:
tensor([[ -1.1836],
        [ -1.2862],
        [-11.6360],
        [-11.3412],
        [  0.3811],
        [ -2.0235],
        [ -1.7288],
        [ -8.3472],
        [-10.1714],
        [ -2.6084],
        [ -0.8098],
        [ -0.9687],
        [ -9.0626],
        [ -2.2273],
        [ -9.0942],
        [ -1.6956],
        [ -6.6197],
        [ -9.3882],
        [ -6.1594],
        [ -9.2895],
        [ -1.7074],
        [  0.8382],
        [-14.6693],
        [ -0.8303],
        [-10.2741],
        [  0.2808],
        [ -9.3681],
        [ -0.6729],
        [ -2.0288],
        [ -1.4389],
        [ -7.1975],
        [-11.5732],
        [-10.2751],
        [ -1.3800],
        [ -1.9773],
        [ -1.4668],
        [ -9.7166],
        [ -8.3093],
        [-15.5914],
        [ -0.0808],
        [  0.3732],
        [-16.2714],
        [ -2.3120],
        [ -8.7503],
        [ -1.6276],
        [     nan],
        [-15.3692],
        [ -9.1615],
        [ -9.8093],
        [ -2.0716],
        [ -1.9259],
        [  0.9543],
        [ -8.1521],
        [ -2.5709],
        [ -1.6153],
        [-10.7236],
        [ -0.0763],
        [  0.0543],
        [ -7.2755],
        [-10.6411],
        [ -7.9253],
        [-19.4996],
        [ -2.0001],
        [-11.7616],
        [-11.0187],
        [-12.0727],
        [ -1.3243],
        [-11.2528],
        [ -1.5527],
        [ -0.9219],
        [ -1.0130],
        [-10.1825],
        [-18.3420],
        [-11.1005],
        [ -8.5818],
        [-11.1588],
        [ -8.8115],
        [ -1.0410],
        [-15.2722],
        [ -1.8399],
        [ -1.0827],
        [ -1.0277],
        [ -6.4768],
        [ -8.3902],
        [ -0.9513],
        [ -1.3429],
        [ -1.0889],
        [ -7.2952],
        [ -7.8548],
        [ -0.0231],
        [ -7.1898],
        [-20.4194],
        [ -1.2503],
        [-19.6157],
        [ -0.3398],
        [-15.7221],
        [-10.3210],
        [ -9.5764],
        [ -0.2335],
        [ -0.3788]])

Seems like there is a NaN in some distribution of HEBO. But my input parameters (opt.X) and losses (opt.y) are never NaN. This is the design space I'm using:

space = DesignSpace().parse([{'name': 'lr', 'type' : 'num', 'lb' : 0.00005, 'ub' : 0.1},
                                 {'name': 'n_estimators', 'type' : 'int', 'lb' : 1, 'ub' : 20},  # multiplied by 10
                                 {'name': 'max_depth', 'type' : 'int', 'lb' : 1, 'ub' : 10},
                                 {'name': 'subsample', 'type' : 'num', 'lb' : 0.5, 'ub' : 0.99},
                                 {'name': 'colsample_bytree', 'type' : 'num', 'lb' : 0.5, 'ub' : 0.99},
                                 {'name': 'gamma', 'type' : 'num', 'lb' : 0.01, 'ub' : 10.0},
                                 {'name': 'min_child_weight', 'type' : 'int', 'lb' : 1, 'ub' : 10},
                                 
                                 {'name': 'fill_type', 'type' : 'cat', 'categories' : ['median', 'pat_median','pat_ema']},
                                 {'name': 'flat_block_size', 'type' : 'int', 'lb' : 1, 'ub' : 1}
                                ])
    
opt = HEBO(space)

I already commented out flat_block_size as I thought that maybe it is a problem if lb == ub, but it still crashes.

Any ideas on how I can debug this?

The main process of HEBO take up 1600% cpus, which makes other parts of the program run very slowly. Is there any way to reduce the cpu usage of hebo? Thanks!

I get the following error TypeError: 'float' object cannot be interpreted as an integer when attempting to search with a XGB model.

space_cfg = [ {'name' : 'max_depth', 'type' : 'int', 'lb' : 1, 'ub' : 10}, {'name' : 'min_child_weight', 'type' : 'int', 'lb' : 1, 'ub' : 100}, {'name': 'n_estimators', 'type' : 'int', 'lb': 1, 'ub': 10000}, {'name': 'alpha', 'type': 'num', 'lb': 0, 'ub': 100}, {'name': 'lambda', 'type': 'num', 'lb': 0, 'ub': 100}, {'name': 'gamma', 'type': 'num', 'lb': 0, 'ub': 100}, {'name': 'eta', 'type': 'pow', 'lb': 1e-5, 'ub': 1}, {'name': 'colsample_bytree', 'type': 'num', 'lb': 1/3, 'ub': 1}, {'name': 'colsample_bylevel', 'type': 'num', 'lb': 1/3, 'ub': 1}, {'name': 'colsample_bynode', 'type': 'num', 'lb': 1/3, 'ub': 1}, {'name': 'subsample', 'type': 'num', 'lb': 1/27, 'ub': 100}, ]

Hi,

I installed the HEBO library, and tried to run the demo codes here:

1. https://github.com/huawei-noah/HEBO/tree/master/HEBO#demo
2. https://github.com/huawei-noah/HEBO/tree/master/HEBO#auto-tuning-via-sklearn-estimator

Both begin executing but eventually fail with the error: TypeError: __init__() got an unexpected keyword argument 'prob_per_variable'

The complete stacktrace for the second demo code (auto-tuning sklearn estimator) is:

Iter 0, best metric: 0.398791  
Iter 1, best metric: 0.492467
Iter 2, best metric: 0.658477
Iter 3, best metric: 0.658477
Iter 4, best metric: 0.658477
Iter 5, best metric: 0.658477
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/redacted_path_1/HEBO-master/HEBO/hebo/sklearn_tuner.py", line 74, in sklearn_tuner
    rec     = opt.suggest()
  File "/redacted_path_1/HEBO-master/HEBO/hebo/optimizers/hebo.py", line 153, in suggest
    rec = opt.optimize(initial_suggest = best_x, fix_input = fix_input).drop_duplicates()
  File "/redacted_path_1/HEBO-master/HEBO/hebo/acq_optimizers/evolution_optimizer.py", line 126, in optimize
    algo      = get_algorithm(self.es, pop_size = self.pop, sampling = init_pop, mutation = mutation, crossover = crossover, repair = self.repair)
  File "/redacted_path_2/anaconda3/lib/python3.7/site-packages/pymoo-0.6.0.dev0-py3.7-linux-x86_64.egg/pymoo/factory.py", line 85, in get_algorithm
    return get_from_list(get_algorithm_options(), name, args, {**d, **kwargs})
  File "/redacted_path_2/anaconda3/lib/python3.7/site-packages/pymoo-0.6.0.dev0-py3.7-linux-x86_64.egg/pymoo/factory.py", line 49, in get_algorithm_options
    from pymoo.algorithms.moo.ctaea import CTAEA
  File "/redacted_path_2/anaconda3/lib/python3.7/site-packages/pymoo-0.6.0.dev0-py3.7-linux-x86_64.egg/pymoo/algorithms/moo/ctaea.py", line 223, in <module>
    class CTAEA(GeneticAlgorithm):
  File "/redacted_path_2/anaconda3/lib/python3.7/site-packages/pymoo-0.6.0.dev0-py3.7-linux-x86_64.egg/pymoo/algorithms/moo/ctaea.py", line 230, in CTAEA
    mutation=PM(eta=20, prob_per_variable=None),
  File "/redacted_path_2/anaconda3/lib/python3.7/site-packages/pymoo-0.6.0.dev0-py3.7-linux-x86_64.egg/pymoo/operators/mutation/pm.py", line 77, in __init__
    super().__init__(prob=prob, **kwargs)
  File "/redacted_path_2/anaconda3/lib/python3.7/site-packages/pymoo-0.6.0.dev0-py3.7-linux-x86_64.egg/pymoo/core/mutation.py", line 29, in __init__
    super().__init__(**kwargs)
  File "/redacted_path_2/anaconda3/lib/python3.7/site-packages/pymoo-0.6.0.dev0-py3.7-linux-x86_64.egg/pymoo/core/mutation.py", line 10, in __init__
    super().__init__(**kwargs)
TypeError: __init__() got an unexpected keyword argument 'prob_per_variable'

Can you provide pointers for fixing this? Thanks!

At one point, I may have understood this, but I find myself often wondering and asking again: what does "compositional" in "compositional optimizer" refer to? Part of my confusion probably stems from my materials informatics background, where composition often refers to the chemical make-up (i.e. chemical formula) of a particular compound. In other cases, it just implies that the contribution of individual components sums to one.

https://github.com/huawei-noah/HEBO/tree/master/CompBO

Hello. VCBO optimizer call of pymoo's ElementwiseProblem is fixed. New version of pymoo requires that problem should be of type "ElementwiseProblem".

When I using hebo.sklearn_tuner to optimize the hyperparameters of xgboost, there is an error that "TypeError: 'float' object cannot be interpreted as an integer", so I made a breakpoint to enter the function to find out the reason. It's because the dataframe class method iloc will transform the data type from int to float, which will cause the error.

When running the example code for sklearn tuner, I get the following error message

ModuleNotFoundError: No module named 'pymoo.algorithms.so_genetic_algorithm'

The line of code in question is from pymoo.algorithms.so_genetic_algorithm import GA within evolution_optimizer.py

I can reproduce it standalone as well (i.e. installing pymoo and trying to run that import), and I don't see this algorithm in pymoo's API reference.

Looks like this comes from a breaking change in pymoo 0.5.0, which they detailed "The package structure has been modified to distinguish between single- and multi-objective optimization more clearly."

Based on their updated API for version 0.5.0, I believe but am not sure that the genetic algorithm import needs to be changed to:

from pymoo.algorithms.soo.nonconvex.ga import GA

Using the latest version of pymoo==0.5.0 the sklearn_tuner.py example fails with the error:

ModuleNotFoundError: No module named 'pymoo.algorithms.so_genetic_algorithm'

It would be great if we could either make the example compatible with pymoo==0.5.0 or update the requirements and install instructions to specify pymoo==0.4.2 as the required version.

The example runs with pymoo==0.4.2 albeit breaks with a different error after 5 iterations:

/Users/~/opt/miniconda3/envs/hebo/bin/python /Users/ryan_rhys/ml_physics/HEBO/HEBO/hebo/sklearn_tuner.py
Iter 0, best metric: 0.389011
Iter 1, best metric: 0.496764
Iter 2, best metric: 0.649803
Iter 3, best metric: 0.649803
Iter 4, best metric: 0.649803
Iter 5, best metric: 0.649803
Traceback (most recent call last):
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/hebo/acq_optimizers/evolution_optimizer.py", line 119, in optimize
    res   = minimize(prob, algo, ('n_gen', self.iter), verbose = self.verbose)
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/optimize.py", line 85, in minimize
    res = algorithm.solve()
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/model/algorithm.py", line 226, in solve
    self._solve(self.problem)
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/model/algorithm.py", line 321, in _solve
    self.next()
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/model/algorithm.py", line 246, in next
    self._next()
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/algorithms/genetic_algorithm.py", line 93, in _next
    self.off = self.mating.do(self.problem, self.pop, self.n_offsprings, algorithm=self)
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/model/infill.py", line 40, in do
    _off = self.eliminate_duplicates.do(_off, pop, off)
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/model/duplicate.py", line 26, in do
    pop = pop[~self._do(pop, None, np.full(len(pop), False))]
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/model/duplicate.py", line 75, in _do
    D = self.calc_dist(pop, other)
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/model/duplicate.py", line 66, in calc_dist
    D = cdist(X, X)
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/pymoo/util/misc.py", line 90, in cdist
    return scipy.spatial.distance.cdist(A, B, **kwargs)
  File "/Users/~/opt/miniconda3/envs/hebo/lib/python3.8/site-packages/scipy/spatial/distance.py", line 2954, in cdist
    return cdist_fn(XA, XB, out=out, **kwargs)
ValueError: Unsupported dtype object

Original Codes: https://github.com/huawei-noah/HEBO/blob/f9b7f578bd703ba2f47840a581bb4294faff1e12/HEBO/hebo/design_space/int_exponent_param.py#L33-L34

As I understand here the function inverse_transform() should worked as taking a arbiraty input(s) as exponent, however we cannot guaranteed the input(s) as Int dtype. In such case, if we have codes as below:

https://github.com/huawei-noah/HEBO/blob/f9b7f578bd703ba2f47840a581bb4294faff1e12/HEBO/hebo/optimizers/evolution.py#L71-L83

Here x could be arbitary number, assuming we have x=np.array([5.4]), write some toy codes below to demostrate the problem:

from hebo.design_space.int_exponent_param import IntExponentPara
p = IntExponentPara({'name': 'p', 'base': 2, 'lb': 32, 'ub': 256})  # expected parameter values [32, 64, 128, 256]
x = np.array([5.4])
print(p.inverse_transform(x))
# >>> [42]
# 2**5.4=42.22, after astype(int) is 42, but not in valid values [32, 64, 128, 256]

That is to say, what optimzer.evolution.suggest() produces suggested parameter(s) could violate its definition. To resovled this issue, I open a pull request. #33

…possible to use with python > 3.8; this fix moved those files (from pymoo 0.5.0) back to folder pymoo_fix; use torch.linalg.eig since Tensor.eig was removed after pytorch 1.9

Can one specify a conditional/hierarchical search space to HEBO? I.e., something similar to SMAC3?

E.g., only sample the number of convoutional filters in the second layer of a neural net only if we decide (by another parameter )to have a network with at least two layers.

I'm thinking that this can be somewhat circumvented by returning a high cost value for infeasible combinations, but I imagine this might be a suboptimal approach as it might affect optimization performance since, e.g., some optima might lie on the edge of feasible regions, and the cost estimator, if it has some sort of smoothness prior (which they usually do), has a chance of assigning unfaithfully high cost values near the infeasible configurations - at least initially (i.e., the a priori probability of a kink in the error surface is generally lower).

Some optimizers are able to address this by training a different model - a feasability predictor (which has the advantage of being able to work with unknown feasibility constraints).

So how should one deal with this in HEBO?

Is it possible to have HEBO search the optimum over a design space where a parameter is defined in a regular fashion (e.g., as a real or integer), but also temporarily (i.e., during a single optimization session) to constrain the search space to a single value of that parameter, and to search only the subspace defined by the remaining unconstrained parameters?

I.e., something similar to Optuna's PartialFixedSampler

Why this is useful

Context

Generally, it is useful to be able to run several optimization sessions, and use results of trials from previous sessions to improve convergence in the current session. As far as I can understand from the docs, using HEBO, one can run a bunch of trials in one or more sessions and then use observe api calls to feed the previous results to the bayesian model before further exploring using suggest in the current session.

Scenario no. 1 - Accelerate optimization by reducing the search space

Now, as explained in the Optuna docs linked above and in the related issue, after running a bunch of trials, one might decide upon a particular value of a parameter, and want to prevent the optimizer from investing more time on exploring values of that parameter are clear not to yield better results, but taking into account the results obtained with other values of that parameter (along the dimensions of other unbound params, the trained cost predictor might still provide valuable insights).

Scenario no. 2 - Transfer Learning

If this is implemented, one could do transfer learning, in a fashion similar to the one proposed, e.g., by SMAC3 (they call this feature 'Optimization across Instances') or OpenBox, i.e., to reuse knowledge about high yield sub-regions from one instance (dataset/task) to another.

Potential Solution

I'm thinking that one could simply change the bounds of a particular parameter every time a new optimization session is started. However, I'm not quite sure that the observe call will accept values that are out of bounds, and, even if it doesn't crash, that the underlying bayesian model is trained correctly.

Hi！When I run this example, I got the following error

I noticed that when I set clip true ,the example run successfully，but When clip is the default value of False, the return value MACE_Embedding is not a kind of class MACE ,which lead to above error.

HI can you add licence info in the setup file ?

Something like

setup( .... , 
     classifiers=[
        'License :: OSI Approved :: MIT License',
     ]
)

This will make licenses flow through PyPi correctly