tests.test_methods.model() - Code Metrics - Inspection of "bug fix" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( 75ea99...9efe12 )

by Simon

created 2019-10-22 15:08 UTC

tests.test_methods.model() A

↳ Parent: tests.test_methods

Complexity

Conditions

Size

Total Lines	10
Code Lines	8

Duplication

Lines	10
Ratio	100 %

Importance

Changes

Metric	Value
eloc	8
dl	10
loc	10
rs	10
c	0
b	0
f	0
cc	1
nop	3

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from hyperactive import Hyperactive

data = load_iris()
X = data.data
y = data.target

n_iter = 1


def model(para, X_train, y_train):

    model = DecisionTreeClassifier(
        criterion=para["criterion"],
        max_depth=para["max_depth"],
        min_samples_split=para["min_samples_split"],
        min_samples_leaf=para["min_samples_leaf"],
    )
    scores = cross_val_score(model, X_train, y_train, cv=2)

    return scores.mean()


search_config = {
    model: {
        "criterion": ["gini", "entropy"],
        "max_depth": range(1, 11),
        "min_samples_split": range(2, 11),
        "min_samples_leaf": range(1, 11),
    }
}


def test_get_results():
    opt = Hyperactive(search_config, n_iter=n_iter, optimizer="HillClimbing")
    opt.search(X, y)
    opt.get_results()

    opt = Hyperactive(search_config, n_iter=n_iter, n_jobs=2, optimizer="HillClimbing")
    opt.search(X, y)
    opt.get_results()


1		# Author: Simon Blanke
2		# Email: [email protected]
3		# License: MIT License
4
5		from sklearn.datasets import load_iris
6		from sklearn.model_selection import cross_val_score
7		from sklearn.tree import DecisionTreeClassifier
8		from hyperactive import Hyperactive
9
10		data = load_iris()
11		X = data.data
12		y = data.target
13
14		n_iter = 1
15
16
17	View Code Duplication	def model(para, X_train, y_train):
		0 ignored issues – show Duplication introduced 2019-09-14 15:27 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
18		model = DecisionTreeClassifier(
19		criterion=para["criterion"],
20		max_depth=para["max_depth"],
21		min_samples_split=para["min_samples_split"],
22		min_samples_leaf=para["min_samples_leaf"],
23		)
24		scores = cross_val_score(model, X_train, y_train, cv=2)
25
26		return scores.mean()
27
28
29		search_config = {
30		model: {
31		"criterion": ["gini", "entropy"],
32		"max_depth": range(1, 11),
33		"min_samples_split": range(2, 11),
34		"min_samples_leaf": range(1, 11),
35		}
36		}
37
38
39		def test_get_results():
40		opt = Hyperactive(search_config, n_iter=n_iter, optimizer="HillClimbing")
41		opt.search(X, y)
42		opt.get_results()
43
44		opt = Hyperactive(search_config, n_iter=n_iter, n_jobs=2, optimizer="HillClimbing")
45		opt.search(X, y)
46		opt.get_results()
47

SimonBlanke / Hyperactive

Push — master ( 75ea99...9efe12 )

tests.test_methods.model() A

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