tests._test_meta_learn.model() - Code Metrics - Inspection of "rename test" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 6c8f7d...da8f19 )

by Simon

created 2019-09-15 05:10 UTC

tests._test_meta_learn.model() A

↳ Parent: tests._test_meta_learn

Complexity

Conditions

Size

Total Lines	10
Code Lines	8

Duplication

Lines	10
Ratio	100 %

Importance

Changes

Metric	Value
eloc	8
dl	10
loc	10
rs	10
c	0
b	0
f	0
cc	1
nop	3

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from hyperactive import Hyperactive
from hyperactive import MetaLearn

data = load_iris()
X = data.data
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 = {
    model: {
        "criterion": ["gini", "entropy"],
        "max_depth": range(1, 21),
        "min_samples_split": range(2, 21),
        "min_samples_leaf": range(1, 21),
    }
}


def test_metalearn():
    ml = MetaLearn(search_config)
    ml.collect(X, y)
    # ml.train()
    # ml.search(X, y)


def test_metalearn1():
    opt = Hyperactive(search_config, meta_learn=True)
    opt.fit(X, y)


test_metalearn()


1		# Author: Simon Blanke
2		# Email: [email protected]
3		# License: MIT License
4
5		from sklearn.datasets import load_iris
6		from sklearn.model_selection import cross_val_score
7		from sklearn.tree import DecisionTreeClassifier
8		from hyperactive import Hyperactive
9		from hyperactive import MetaLearn
10
11		data = load_iris()
12		X = data.data
13		y = data.target
14
15
16	View Code Duplication	def model(para, X_train, y_train):
		0 ignored issues – show Duplication introduced 2019-09-15 05:13 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
17		model = DecisionTreeClassifier(
18		criterion=para["criterion"],
19		max_depth=para["max_depth"],
20		min_samples_split=para["min_samples_split"],
21		min_samples_leaf=para["min_samples_leaf"],
22		)
23		scores = cross_val_score(model, X_train, y_train, cv=3)
24
25		return scores.mean(), model
26
27
28		search_config = {
29		model: {
30		"criterion": ["gini", "entropy"],
31		"max_depth": range(1, 21),
32		"min_samples_split": range(2, 21),
33		"min_samples_leaf": range(1, 21),
34		}
35		}
36
37
38		def test_metalearn():
39		ml = MetaLearn(search_config)
40		ml.collect(X, y)
41		# ml.train()
42		# ml.search(X, y)
43
44
45		def test_metalearn1():
46		opt = Hyperactive(search_config, meta_learn=True)
47		opt.fit(X, y)
48
49
50		test_metalearn()
51

SimonBlanke / Hyperactive

Push — master ( 6c8f7d...da8f19 )

tests._test_meta_learn.model() A

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