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

Code Duplication Length = 33-33 lines in 2 locations

examples/examples_v1.x.x/use_cases/meta_opt_example.py 1 location


X, y = data.data, data.target


def meta_opt(para, X, y):
    def model(para, X, y):
        model = DecisionTreeClassifier(
            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 = {
        model: {
            "max_depth": range(2, 50),
            "min_samples_split": range(2, 50),
            "min_samples_leaf": range(1, 50),
        }
    }

    opt = Hyperactive(
        search_config,
        optimizer={
            "ParticleSwarm": {
                "inertia": para["inertia"],
                "cognitive_weight": para["cognitive_weight"],
                "social_weight": para["social_weight"],
            }
        },
        verbosity=None,
    )
    opt.search(X, y)

    return opt.score_best


search_config = {

examples/use_cases/MetaOptimization.py 1 location


X, y = data.data, data.target


def meta_opt(para, X, y):
    def model(para, X, y):
        model = DecisionTreeClassifier(
            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 = {
        model: {
            "max_depth": range(2, 50),
            "min_samples_split": range(2, 50),
            "min_samples_leaf": range(1, 50),
        }
    }

    opt = Hyperactive(
        search_config,
        optimizer={
            "ParticleSwarm": {
                "inertia": para["inertia"],
                "cognitive_weight": para["cognitive_weight"],
                "social_weight": para["social_weight"],
            }
        },
        verbosity=None,
    )
    opt.search(X, y)

    return opt.score_best


search_config = {

		@@ 11-43 (lines=33) @@
8		X, y = data.data, data.target
9
10
11		def meta_opt(para, X, y):
12		def model(para, X, y):
13		model = DecisionTreeClassifier(
14		max_depth=para["max_depth"],
15		min_samples_split=para["min_samples_split"],
16		min_samples_leaf=para["min_samples_leaf"],
17		)
18		scores = cross_val_score(model, X, y, cv=3)
19
20		return scores.mean()
21
22		search_config = {
23		model: {
24		"max_depth": range(2, 50),
25		"min_samples_split": range(2, 50),
26		"min_samples_leaf": range(1, 50),
27		}
28		}
29
30		opt = Hyperactive(
31		search_config,
32		optimizer={
33		"ParticleSwarm": {
34		"inertia": para["inertia"],
35		"cognitive_weight": para["cognitive_weight"],
36		"social_weight": para["social_weight"],
37		}
38		},
39		verbosity=None,
40		)
41		opt.search(X, y)
42
43		return opt.score_best
44
45
46		search_config = {

		@@ 11-43 (lines=33) @@
8		X, y = data.data, data.target
9
10
11		def meta_opt(para, X, y):
12		def model(para, X, y):
13		model = DecisionTreeClassifier(
14		max_depth=para["max_depth"],
15		min_samples_split=para["min_samples_split"],
16		min_samples_leaf=para["min_samples_leaf"],
17		)
18		scores = cross_val_score(model, X, y, cv=3)
19
20		return scores.mean()
21
22		search_config = {
23		model: {
24		"max_depth": range(2, 50),
25		"min_samples_split": range(2, 50),
26		"min_samples_leaf": range(1, 50),
27		}
28		}
29
30		opt = Hyperactive(
31		search_config,
32		optimizer={
33		"ParticleSwarm": {
34		"inertia": para["inertia"],
35		"cognitive_weight": para["cognitive_weight"],
36		"social_weight": para["social_weight"],
37		}
38		},
39		verbosity=None,
40		)
41		opt.search(X, y)
42
43		return opt.score_best
44
45
46		search_config = {

SimonBlanke / Hyperactive

Code Duplication Length = 33-33 lines in 2 locations

examples/examples_v1.x.x/use_cases/meta_opt_example.py 1 location

examples/use_cases/MetaOptimization.py 1 location