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Intro

For duplication, we look at places in code where there are 6 or more lines of code that are exactly the same.
Before duplication is calculated, the code is cleaned to remove empty lines, comments, and frequently duplicated constructs such as imports.
You should aim at having as little as possible (<5%) of duplicated code as high-level of duplication can lead to maintenance difficulties, poor factoring, and logical contradictions.

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Duplication Overall

21% duplication:

3,792 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
831 duplicated lines

59 duplicates

Duplication per Extension

Duplication per Component (primary)

Duplication Between Components (50+ lines)

Download: SVG DOT (open online Graphviz editor)

From Component --> To Component	Duplicated Lines	File Pairs	Details
online_attacks/classifiers/cifar (5%) --> online_attacks/classifiers/mnist (19%)	78	3 file pairs	details...

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Longest Duplicates

The list of 20 longest duplicates.

See data for all 59 duplicates...

Size	#	Folders	Files	Lines	Code
30	x 2	online_attacks/experiments online_attacks/experiments	hyper_search_stochastic_toy.py stochastic_toy.py	46:83 (44%) 27:66 (28%)	view
29	x 2	online_attacks/experiments online_attacks/experiments	stochastic_toy.py toy.py	34:75 (27%) 34:71 (29%)	view
25	x 2	online_attacks/experiments online_attacks/experiments	stochastic_toy.py toy.py	87:113 (23%) 77:104 (25%)	view
23	x 2	online_attacks/experiments online_attacks/experiments	hyper_search_stochastic_toy.py toy.py	53:83 (34%) 34:62 (23%)	view
22	x 2	online_attacks/scripts online_attacks/scripts	eval_classifiers.py hist_classifiers.py	28:51 (38%) 96:119 (15%)	view
21	x 2	online_attacks/scripts online_attacks/scripts	compute_hist_all.py eval_all.py	109:131 (16%) 153:175 (13%)	view
20	x 2	online_attacks/scripts online_attacks/scripts	compute_hist_all.py eval_all.py	49:68 (15%) 43:62 (12%)	view
17	x 2	online_attacks/scripts online_attacks/scripts	batch_online_attacks.py compute_hist_all.py	35:51 (36%) 111:127 (13%)	view
17	x 2	online_attacks/scripts online_attacks/scripts	batch_online_attacks.py eval_all.py	35:51 (36%) 155:171 (10%)	view
16	x 2	online_attacks/online_algorithms online_attacks/online_algorithms	stochastic_modified_virtual.py stochastic_virtual.py	34:51 (36%) 34:51 (34%)	view
16	x 2	online_attacks/scripts online_attacks/scripts	eval_all.py online_attacks_sweep.py	51:67 (10%) 90:107 (11%)	view
15	x 2	online_attacks/online_algorithms online_attacks/online_algorithms	stochastic_optimistic.py stochastic_single_ref.py	34:50 (37%) 109:125 (14%)	view
14	x 2	online_attacks/classifiers/cifar/models online_attacks/classifiers/cifar/models	resnet.py resnet.py	25:39 (13%) 62:76 (13%)	view
12	x 2	online_attacks/online_algorithms online_attacks/online_algorithms	stochastic_modified_virtual.py stochastic_optimistic.py	34:47 (27%) 34:47 (30%)	view
12	x 2	online_attacks/classifiers/cifar online_attacks/classifiers/mnist	train.py train.py	36:49 (30%) 33:46 (34%)	view
12	x 2	online_attacks/online_algorithms online_attacks/online_algorithms	stochastic_optimistic.py stochastic_virtual.py	34:47 (30%) 34:47 (26%)	view
12	x 2	online_attacks/datastream online_attacks/datastream	toy_data.py toy_data.py	17:28 (22%) 49:60 (22%)	view
12	x 2	online_attacks/online_algorithms online_attacks/online_algorithms	stochastic_virtual.py stochastic_virtual_ref.py	34:47 (26%) 45:59 (22%)	view
12	x 2	online_attacks/online_algorithms online_attacks/online_algorithms	stochastic_single_ref.py stochastic_virtual_ref.py	109:122 (11%) 45:59 (22%)	view
12	x 2	online_attacks/scripts online_attacks/scripts	batch_online_attacks.py random_eval.py	18:33 (25%) 87:99 (15%)	view