machine_learning.model() - Code Metrics - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

machine_learning.model() A
last analyzed 2025-06-28 09:03 UTC

↳ Parent: machine_learning

Complexity

Conditions

Size

Total Lines	10
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	8
nop	1
dl	0
loc	10
rs	10
c	0
b	0
f	0

import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_wine

from gradient_free_optimizers import HillClimbingOptimizer


data = load_wine()
X, y = data.data, data.target


def model(para):
    gbc = GradientBoostingClassifier(
        n_estimators=para["n_estimators"],
        max_depth=para["max_depth"],
        min_samples_split=para["min_samples_split"],
        min_samples_leaf=para["min_samples_leaf"],
    )
    scores = cross_val_score(gbc, X, y, cv=3)

    return scores.mean()


search_space = {
    "n_estimators": np.arange(20, 120, 1),
    "max_depth": np.arange(2, 12, 1),
    "min_samples_split": np.arange(2, 12, 1),
    "min_samples_leaf": np.arange(1, 12, 1),
}

opt = HillClimbingOptimizer(search_space)
opt.search(model, n_iter=50)


1			import numpy as np
2			from sklearn.model_selection import cross_val_score
3			from sklearn.ensemble import GradientBoostingClassifier
4			from sklearn.datasets import load_wine
5
6			from gradient_free_optimizers import HillClimbingOptimizer
7
8
9			data = load_wine()
10			X, y = data.data, data.target
11
12
13			def model(para):
14			gbc = GradientBoostingClassifier(
15			n_estimators=para["n_estimators"],
16			max_depth=para["max_depth"],
17			min_samples_split=para["min_samples_split"],
18			min_samples_leaf=para["min_samples_leaf"],
19			)
20			scores = cross_val_score(gbc, X, y, cv=3)
21
22			return scores.mean()
23
24
25			search_space = {
26			"n_estimators": np.arange(20, 120, 1),
27			"max_depth": np.arange(2, 12, 1),
28			"min_samples_split": np.arange(2, 12, 1),
29			"min_samples_leaf": np.arange(1, 12, 1),
30			}
31
32			opt = HillClimbingOptimizer(search_space)
33			opt.search(model, n_iter=50)
34

SimonBlanke / Gradient-Free-Optimizers

machine_learning.model() A last analyzed 2025-06-28 09:03 UTC

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machine_learning.model() A
last analyzed 2025-06-28 09:03 UTC