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import numpy as np
import pandas as pd
from hyperactive import Hyperactive
def function_():
pass
class class_:
def __init__(self):
# Hyperactive can handle python objects in the search space
search_space = {
"int": list(range(1, 10)),
"float": [0.1, 0.01, 0.001],
"string": ["string1", "string2"],
"function": [function_],
"class": [class_],
"list": [[1, 1, 1], [1, 1, 2], [1, 2, 1]],
"numpy": [np.array([1, 2, 3])],
"pandas": [pd.DataFrame([[1, 2], [3, 4]], columns=["y1", "y2"])],
}
def objective_function(para):
# score must be a single number
score = 1
return score
hyper = Hyperactive()
hyper.add_search(objective_function, search_space, n_iter=20)
hyper.run()
search_data = hyper.results(objective_function)
for col_name in search_data.columns:
print("\nColumn name:", col_name, "\n", search_data[col_name][0])
SimonBlanke /
Hyperactive
