scatter_init_example.model() - Code Metrics - Inspection of "Merge branch 'master' into dev" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( b24ce1...799a98 )

by Simon

created 2019-09-23 16:13 UTC

scatter_init_example.model() A

↳ Parent: scatter_init_example

Complexity

Conditions

Size

Total Lines	9
Code Lines	7

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	7
dl	0
loc	9
rs	10
c	0
b	0
f	0
cc	1
nop	3

from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_breast_cancer
from hyperactive import Hyperactive

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


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

    return scores.mean()


search_config = {
    model: {
        "n_estimators": range(10, 200, 10),
        "max_depth": range(2, 12),
        "min_samples_split": range(2, 12),
    }
}

# Without scatter initialization
opt = Hyperactive(
    search_config,
    optimizer="HillClimbing",
    n_iter=10,
    random_state=0,
    scatter_init=False,
)
opt.fit(X, y)


# With scatter initialization
opt = Hyperactive(
    search_config, optimizer="HillClimbing", n_iter=10, random_state=0, scatter_init=10
)
opt.fit(X, y)


1			from sklearn.model_selection import cross_val_score
2			from sklearn.ensemble import GradientBoostingClassifier
3			from sklearn.datasets import load_breast_cancer
4			from hyperactive import Hyperactive
5
6			data = load_breast_cancer()
7			X, y = data.data, data.target
8
9
10			def model(para, X, y):
11			model = GradientBoostingClassifier(
12			n_estimators=para["n_estimators"],
13			max_depth=para["max_depth"],
14			min_samples_split=para["min_samples_split"],
15			)
16			scores = cross_val_score(model, X, y, cv=3)
17
18			return scores.mean()
19
20
21			search_config = {
22			model: {
23			"n_estimators": range(10, 200, 10),
24			"max_depth": range(2, 12),
25			"min_samples_split": range(2, 12),
26			}
27			}
28
29			# Without scatter initialization
30			opt = Hyperactive(
31			search_config,
32			optimizer="HillClimbing",
33			n_iter=10,
34			random_state=0,
35			scatter_init=False,
36			)
37			opt.fit(X, y)
38
39
40			# With scatter initialization
41			opt = Hyperactive(
42			search_config, optimizer="HillClimbing", n_iter=10, random_state=0, scatter_init=10
43			)
44			opt.fit(X, y)
45

SimonBlanke / Hyperactive

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scatter_init_example.model() A

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