Comments (1)
Hello @mlittmanabbvie,
The api-ref section of the readme and the faq was updated with the new information about pathos. I modified your example to use pathos:
import numpy as np
import pandas as pd
from hyperactive import Hyperactive
from hyperactive import RepulsingHillClimbingOptimizer
def f():
def optimization_func(opt):
return 5
h = Hyperactive(distribution="pathos")
search_space = {
"exp": list(range(0, 5)),
"slope": list(np.arange(0.001, 10, step=0.05)),
"clust": [5],
}
h.add_search(
optimization_func,
search_space=search_space,
n_iter=10,
optimizer=RepulsingHillClimbingOptimizer(
epsilon=0.05,
distribution="normal",
n_neighbours=3,
rand_rest_p=0.03,
repulsion_factor=3,
),
n_jobs=1,
max_score=None,
initialize={"warm_start": [{"exp": 2, "slope": 5, "clust": 5}]},
early_stopping={"tol_rel": 0.001, "n_iter_no_change": 3},
random_state=0,
memory=True,
memory_warm_start=None,
)
h.add_search(optimization_func, search_space=search_space, n_iter=10)
h.run()
f()
This works fine on my system. Let me know if it also works on yours.
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