tests.test_memory_helpers - Code Metrics - Inspection of "add opt para tests" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 77ef8d...d45822 )

by Simon

created 2020-02-20 15:32 UTC

tests.test_memory_helpers A

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Complexity

Total Complexity

Size/Duplication

Total Lines	116
Duplicated Lines	31.03 %

Importance

Changes

Metric	Value
eloc	72
dl	36
loc	116
rs	10
c	0
b	0
f	0
wmc	9

9 Functions

Rating	Name	Duplication	Size	Complexity
A	test_delete_model_dataset()	0	7	1
A	test_delete_model()	0	7	1
A	test_get_best_model()	0	4	1
A	test_split_model_IDs()	19	19	1
A	model2()	0	5	1
A	test_connect_model_IDs()	17	17	1
A	model()	0	5	1
A	model1()	0	5	1
A	test_reset_memory()	0	2	1

How to fix Duplicated Code

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

import time

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.memory import (
    delete_model,
    delete_model_dataset,
    connect_model_IDs,
    split_model_IDs,
    get_best_model,
    reset_memory,
)

data = load_iris()
X, y = data.data, data.target


def model(para, X_train, y_train):
    model = DecisionTreeClassifier(criterion=para["criterion"])
    scores = cross_val_score(model, X_train, y_train, cv=2)

    return scores.mean()


def model1(para, X_train, y_train):
    model = DecisionTreeClassifier(max_depth=para["max_depth"])
    scores = cross_val_score(model, X_train, y_train, cv=2)

    return scores.mean()


def model2(para, X_train, y_train):
    model = DecisionTreeClassifier(max_depth=para["max_depth"])
    scores = cross_val_score(model, X_train, y_train, cv=2)

    return scores.mean()


search_config = {model: {"criterion": ["gini"]}}
search_config1 = {model1: {"max_depth": range(2, 500)}}
search_config2 = {model2: {"max_depth": range(2, 500)}}


def test_reset_memory():
    reset_memory(force_true=True)


def test_delete_model():
    delete_model(model)

    opt = Hyperactive(X, y)
    opt.search(search_config)

    delete_model(model)


def test_delete_model_dataset():
    delete_model_dataset(model, X, y)

    opt = Hyperactive(X, y)
    opt.search(search_config)

    delete_model_dataset(model, X, y)


def test_connect_model_IDs():

    delete_model(model1)
    delete_model(model2)

    connect_model_IDs(model1, model2)

    c_time = time.time()
    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config1, n_iter=1000)
    diff_time_0 = time.time() - c_time

    c_time = time.time()
    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config2, n_iter=1000)
    diff_time_1 = time.time() - c_time

    assert diff_time_0 / 2 > diff_time_1


def test_split_model_IDs():

    delete_model(model1)
    delete_model(model2)

    connect_model_IDs(model1, model2)

    split_model_IDs(model1, model2)

    c_time = time.time()
    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config1, n_iter=1000)
    diff_time_0 = time.time() - c_time

    c_time = time.time()
    opt = Hyperactive(X, y, memory="long")
    opt.search(search_config2, n_iter=1000)
    diff_time_1 = time.time() - c_time

    assert diff_time_0 / 2 < diff_time_1


def test_get_best_model():
    score, search_config, init_config = get_best_model(X, y)

    reset_memory(force_true=True)


1		# Author: Simon Blanke
2		# Email: [email protected]
3		# License: MIT License
4
5		import time
6
7		from sklearn.datasets import load_iris
8		from sklearn.model_selection import cross_val_score
9		from sklearn.tree import DecisionTreeClassifier
10		from hyperactive import Hyperactive
11		from hyperactive.memory import (
12		delete_model,
13		delete_model_dataset,
14		connect_model_IDs,
15		split_model_IDs,
16		get_best_model,
17		reset_memory,
18		)
19
20		data = load_iris()
21		X, y = data.data, data.target
22
23
24		def model(para, X_train, y_train):
25		model = DecisionTreeClassifier(criterion=para["criterion"])
26		scores = cross_val_score(model, X_train, y_train, cv=2)
27
28		return scores.mean()
29
30
31		def model1(para, X_train, y_train):
32		model = DecisionTreeClassifier(max_depth=para["max_depth"])
33		scores = cross_val_score(model, X_train, y_train, cv=2)
34
35		return scores.mean()
36
37
38		def model2(para, X_train, y_train):
39		model = DecisionTreeClassifier(max_depth=para["max_depth"])
40		scores = cross_val_score(model, X_train, y_train, cv=2)
41
42		return scores.mean()
43
44
45		search_config = {model: {"criterion": ["gini"]}}
46		search_config1 = {model1: {"max_depth": range(2, 500)}}
47		search_config2 = {model2: {"max_depth": range(2, 500)}}
48
49
50		def test_reset_memory():
51		reset_memory(force_true=True)
52
53
54		def test_delete_model():
55		delete_model(model)
56
57		opt = Hyperactive(X, y)
58		opt.search(search_config)
59
60		delete_model(model)
61
62
63		def test_delete_model_dataset():
64		delete_model_dataset(model, X, y)
65
66		opt = Hyperactive(X, y)
67		opt.search(search_config)
68
69		delete_model_dataset(model, X, y)
70
71
72	View Code Duplication	def test_connect_model_IDs():
		0 ignored issues – show Duplication introduced 2020-02-15 13:45 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
73		delete_model(model1)
74		delete_model(model2)
75
76		connect_model_IDs(model1, model2)
77
78		c_time = time.time()
79		opt = Hyperactive(X, y, memory="long")
80		opt.search(search_config1, n_iter=1000)
81		diff_time_0 = time.time() - c_time
82
83		c_time = time.time()
84		opt = Hyperactive(X, y, memory="long")
85		opt.search(search_config2, n_iter=1000)
86		diff_time_1 = time.time() - c_time
87
88		assert diff_time_0 / 2 > diff_time_1
89
90
91	View Code Duplication	def test_split_model_IDs():
		0 ignored issues – show Duplication introduced 2020-02-15 13:45 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
92		delete_model(model1)
93		delete_model(model2)
94
95		connect_model_IDs(model1, model2)
96
97		split_model_IDs(model1, model2)
98
99		c_time = time.time()
100		opt = Hyperactive(X, y, memory="long")
101		opt.search(search_config1, n_iter=1000)
102		diff_time_0 = time.time() - c_time
103
104		c_time = time.time()
105		opt = Hyperactive(X, y, memory="long")
106		opt.search(search_config2, n_iter=1000)
107		diff_time_1 = time.time() - c_time
108
109		assert diff_time_0 / 2 < diff_time_1
110
111
112		def test_get_best_model():
113		score, search_config, init_config = get_best_model(X, y)
114
115		reset_memory(force_true=True)
116

SimonBlanke / Hyperactive

Push — master ( 77ef8d...d45822 )

tests.test_memory_helpers A

Complexity

Size/Duplication

Importance

9 Functions

How to fix Duplicated Code

Duplicated Code

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