Passed
Push — master ( 65e4f4...bd0b66 )
by Simon
01:38
created

_optimizer_example   A

Complexity

Total Complexity 0

Size/Duplication

Total Lines 38
Duplicated Lines 0 %

Importance

Changes 0
Metric Value
wmc 0
eloc 24
dl 0
loc 38
rs 10
c 0
b 0
f 0
1
import numpy as np
2
from sklearn.datasets import load_diabetes
3
from sklearn.tree import DecisionTreeRegressor
4
5
6
from hyperactive.search_config import SearchConfig
7
from hyperactive.optimization.gradient_free_optimizers import (
8
    HillClimbingOptimizer,
9
    RandomRestartHillClimbingOptimizer,
10
    RandomSearchOptimizer,
11
)
12
from hyperactive.optimization.talos import TalosOptimizer
13
14
from experiments.sklearn import SklearnExperiment
15
from experiments.test_function import AckleyFunction
16
17
18
data = load_diabetes()
19
X, y = data.data, data.target
20
21
22
search_config1 = SearchConfig(
23
    max_depth=list(np.arange(2, 15, 1)),
24
    min_samples_split=list(np.arange(2, 25, 2)),
25
)
26
27
28
TalosOptimizer()
29
30
experiment1 = SklearnExperiment()
31
experiment1.setup(DecisionTreeRegressor, X, y, cv=4)
32
33
34
optimizer = HillClimbingOptimizer()
35
optimizer.add_search(experiment1, search_config1, n_iter=100)
36
hyper = optimizer
37
hyper.run(max_time=5)
38