ValueError: The value argument must be within the support

[hujiaxin0]

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

[hengzhe-zhang]

This is a rather frequent issue if I only have one option for a specific hyperparameter. Sometimes, I just want to fix the optimizer of a neural network to be "Adam". It will raise this exception.

[Alaya-in-Matrix]

@hujiaxin0 @zhenlingcn Can you provide me a minimal code to reproduce this issue?

[ywran]

This is a rather frequent issue if I only have one option for a specific hyperparameter. Sometimes, I just want to fix the optimizer of a neural network to be "Adam". It will raise this exception.

Why not utilize the "fix_input"? It is working.

[hengzhe-zhang]

@ywran Thanks! This is a solution to solve my problem. However, it seems that this is not the root cause of this problem. From the exception stack, it seems that this issue has a relationship with PyTorch.

[Alaya-in-Matrix]

Hi @zhenlingcn I write the below code trying to reproduce your issue, but it seems that the code works well without crashing?

import numpy as np
import pandas as pd
from hebo.design_space import DesignSpace
from hebo.optimizers.hebo import HEBO

def dummy_obj(para : pd.DataFrame) -> np.ndarray:
    return np.ones((para.shape[0], 1))

if __name__ == '__main__':
    space = DesignSpace().parse([{'name' : 'optimizer', 'type' : 'cat', 'categories' : ['adam']}])
    opt   = HEBO(space)
    for i in range(10):
        rec = opt.suggest()
        obs = dummy_obj(rec)
        opt.observe(rec, obs)
        print(obs)

[hengzhe-zhang]

@Alaya-in-Matrix Thanks for your response. I find this problem occurs when using HEBO v0.1, but this problem disappears if I install the latest version of HEBO.

[Alaya-in-Matrix]

@zhenlingcn Nice!

[hujiaxin0]

Changing Hebo(space) to Hebo(space, model_name='gp') can solve this problem.