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

Code Duplication Length = 12-12 lines in 2 locations

examples/distribution/Multiprocessing.py 1 location


    return scores.mean()


def model1(para, X, y):
    rfc = RandomForestClassifier(
        n_estimators=para["n_estimators"],
        criterion=para["criterion"],
        max_features=para["max_features"],
        min_samples_split=para["min_samples_split"],
        min_samples_leaf=para["min_samples_leaf"],
        bootstrap=para["bootstrap"],
    )
    scores = cross_val_score(rfc, X, y, cv=3)

    return scores.mean()


def model2(para, X, y):

examples/examples_v1.x.x/distribution/multiprocessing_example.py 1 location


    return scores.mean()


def model1(para, X, y):
    model = RandomForestClassifier(
        n_estimators=para["n_estimators"],
        criterion=para["criterion"],
        max_features=para["max_features"],
        min_samples_split=para["min_samples_split"],
        min_samples_leaf=para["min_samples_leaf"],
        bootstrap=para["bootstrap"],
    )
    scores = cross_val_score(model, X, y, cv=3)

    return scores.mean()


def model2(para, X, y):

		@@ 27-38 (lines=12) @@
24		return scores.mean()
25
26
27		def model1(para, X, y):
28		rfc = RandomForestClassifier(
29		n_estimators=para["n_estimators"],
30		criterion=para["criterion"],
31		max_features=para["max_features"],
32		min_samples_split=para["min_samples_split"],
33		min_samples_leaf=para["min_samples_leaf"],
34		bootstrap=para["bootstrap"],
35		)
36		scores = cross_val_score(rfc, X, y, cv=3)
37
38		return scores.mean()
39
40
41		def model2(para, X, y):

		@@ 27-38 (lines=12) @@
24		return scores.mean()
25
26
27		def model1(para, X, y):
28		model = RandomForestClassifier(
29		n_estimators=para["n_estimators"],
30		criterion=para["criterion"],
31		max_features=para["max_features"],
32		min_samples_split=para["min_samples_split"],
33		min_samples_leaf=para["min_samples_leaf"],
34		bootstrap=para["bootstrap"],
35		)
36		scores = cross_val_score(model, X, y, cv=3)
37
38		return scores.mean()
39
40
41		def model2(para, X, y):

SimonBlanke / Hyperactive

Code Duplication Length = 12-12 lines in 2 locations

examples/distribution/Multiprocessing.py 1 location

examples/examples_v1.x.x/distribution/multiprocessing_example.py 1 location