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

Code Duplication Length = 12-12 lines in 3 locations

examples/tested_and_supported_packages/multiprocessing_example.py 1 location


    return scores.mean()


def model_rfc(opt):
    rfc = RandomForestClassifier(
        n_estimators=opt["n_estimators"],
        criterion=opt["criterion"],
        max_features=opt["max_features"],
        min_samples_split=opt["min_samples_split"],
        min_samples_leaf=opt["min_samples_leaf"],
        bootstrap=opt["bootstrap"],
    )
    scores = cross_val_score(rfc, X, y, cv=3)

    return scores.mean()


def model_gbc(opt):

examples/tested_and_supported_packages/joblib_example.py 1 location


    return scores.mean()


def model_rfc(opt):
    rfc = RandomForestClassifier(
        n_estimators=opt["n_estimators"],
        criterion=opt["criterion"],
        max_features=opt["max_features"],
        min_samples_split=opt["min_samples_split"],
        min_samples_leaf=opt["min_samples_leaf"],
        bootstrap=opt["bootstrap"],
    )
    scores = cross_val_score(rfc, X, y, cv=3)

    return scores.mean()


def model_gbc(opt):

examples/optimization_applications/multiple_different_optimizers.py 1 location


X, y = data.data, data.target


def model_rfc(opt):
    rfc = RandomForestClassifier(
        n_estimators=opt["n_estimators"],
        criterion=opt["criterion"],
        max_features=opt["max_features"],
        min_samples_split=opt["min_samples_split"],
        min_samples_leaf=opt["min_samples_leaf"],
        bootstrap=opt["bootstrap"],
    )
    scores = cross_val_score(rfc, X, y, cv=3)

    return scores.mean()


def model_gbc(opt):

		@@ 42-53 (lines=12) @@
39		return scores.mean()
40
41
42		def model_rfc(opt):
43		rfc = RandomForestClassifier(
44		n_estimators=opt["n_estimators"],
45		criterion=opt["criterion"],
46		max_features=opt["max_features"],
47		min_samples_split=opt["min_samples_split"],
48		min_samples_leaf=opt["min_samples_leaf"],
49		bootstrap=opt["bootstrap"],
50		)
51		scores = cross_val_score(rfc, X, y, cv=3)
52
53		return scores.mean()
54
55
56		def model_gbc(opt):

		@@ 28-39 (lines=12) @@
25		return scores.mean()
26
27
28		def model_rfc(opt):
29		rfc = RandomForestClassifier(
30		n_estimators=opt["n_estimators"],
31		criterion=opt["criterion"],
32		max_features=opt["max_features"],
33		min_samples_split=opt["min_samples_split"],
34		min_samples_leaf=opt["min_samples_leaf"],
35		bootstrap=opt["bootstrap"],
36		)
37		scores = cross_val_score(rfc, X, y, cv=3)
38
39		return scores.mean()
40
41
42		def model_gbc(opt):

		@@ 18-29 (lines=12) @@
15		X, y = data.data, data.target
16
17
18		def model_rfc(opt):
19		rfc = RandomForestClassifier(
20		n_estimators=opt["n_estimators"],
21		criterion=opt["criterion"],
22		max_features=opt["max_features"],
23		min_samples_split=opt["min_samples_split"],
24		min_samples_leaf=opt["min_samples_leaf"],
25		bootstrap=opt["bootstrap"],
26		)
27		scores = cross_val_score(rfc, X, y, cv=3)
28
29		return scores.mean()
30
31
32		def model_gbc(opt):

SimonBlanke / Hyperactive

Code Duplication Length = 12-12 lines in 3 locations

examples/tested_and_supported_packages/multiprocessing_example.py 1 location

examples/tested_and_supported_packages/joblib_example.py 1 location

examples/optimization_applications/multiple_different_optimizers.py 1 location