Code Duplication - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Hyperactive

Code Duplication Length = 10-10 lines in 2 locations

tests/test_arguments_api.py 2 locations




def test_func_return():
    def model1(para, X, y):
        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, y, cv=3)

        return scores.mean(), model

    search_config1 = {
        model1: {

y = data.target


def model(para, X, y):
    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, y, cv=3)

    return scores.mean()


search_config = {

		@@ 47-56 (lines=10) @@
44
45
46		def test_func_return():
47		def model1(para, X, y):
48		model = DecisionTreeClassifier(
49		criterion=para["criterion"],
50		max_depth=para["max_depth"],
51		min_samples_split=para["min_samples_split"],
52		min_samples_leaf=para["min_samples_leaf"],
53		)
54		scores = cross_val_score(model, X, y, cv=3)
55
56		return scores.mean(), model
57
58		search_config1 = {
59		model1: {
		@@ 15-24 (lines=10) @@
12		y = data.target
13
14
15		def model(para, X, y):
16		model = DecisionTreeClassifier(
17		criterion=para["criterion"],
18		max_depth=para["max_depth"],
19		min_samples_split=para["min_samples_split"],
20		min_samples_leaf=para["min_samples_leaf"],
21		)
22		scores = cross_val_score(model, X, y, cv=3)
23
24		return scores.mean()
25
26
27		search_config = {

SimonBlanke / Hyperactive

Code Duplication Length = 10-10 lines in 2 locations

tests/test_arguments_api.py 2 locations