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

Code Duplication Length = 10-10 lines in 4 locations

tests/test_data.py 1 location


y_pd = pd.DataFrame(y_np, columns=["y1"])


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=3)

    return scores.mean(), model


search_config = {

tests/test_packages.py 1 location


def test_sklearn():
    from sklearn.tree import DecisionTreeClassifier

    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=3)

        return scores.mean(), model

    search_config = {
        model: {

tests/test_arguments_api.py 1 location


y = data.target


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=3)

    return scores.mean(), model


search_config = {

tests/test_optimizers.py 1 location


y = data.target


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=3)

    return scores.mean(), model


search_config = {

		@@ 19-28 (lines=10) @@
16		y_pd = pd.DataFrame(y_np, columns=["y1"])
17
18
19		def model(para, X_train, y_train):
20		model = DecisionTreeClassifier(
21		criterion=para["criterion"],
22		max_depth=para["max_depth"],
23		min_samples_split=para["min_samples_split"],
24		min_samples_leaf=para["min_samples_leaf"],
25		)
26		scores = cross_val_score(model, X_train, y_train, cv=3)
27
28		return scores.mean(), model
29
30
31		search_config = {

		@@ 17-26 (lines=10) @@
14		def test_sklearn():
15		from sklearn.tree import DecisionTreeClassifier
16
17		def model(para, X_train, y_train):
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=3)
25
26		return scores.mean(), model
27
28		search_config = {
29		model: {

		@@ 15-24 (lines=10) @@
12		y = data.target
13
14
15		def model(para, X_train, y_train):
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_train, y_train, cv=3)
23
24		return scores.mean(), model
25
26
27		search_config = {

		@@ 15-24 (lines=10) @@
12		y = data.target
13
14
15		def model(para, X_train, y_train):
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_train, y_train, cv=3)
23
24		return scores.mean(), model
25
26
27		search_config = {

SimonBlanke / Hyperactive

Code Duplication Length = 10-10 lines in 4 locations

tests/test_data.py 1 location

tests/test_packages.py 1 location

tests/test_arguments_api.py 1 location

tests/test_optimizers.py 1 location