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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

24% duplication:

1,934 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
470 duplicated lines

39 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
attacks (6%) --> datasets (19%)	104	1 file pair	details...

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Longest Duplicates

The list of 20 longest duplicates.

See data for all 39 duplicates...

Size	#	Folders	Files	Lines	Code
31	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	621:656 (3%) 669:704 (3%)	view
30	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	621:655 (3%) 1050:1084 (3%)	view
30	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	669:703 (3%) 1050:1084 (3%)	view
26	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	334:367 (3%) 388:421 (3%)	view
24	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	440:464 (2%) 1050:1074 (2%)	view
24	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	440:464 (2%) 669:693 (2%)	view
24	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	440:464 (2%) 621:645 (2%)	view
22	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	716:739 (2%) 847:870 (2%)	view
21	x 2	attacks datasets	privacy_attacks.py __init__.py	217:242 (2%) 268:294 (8%)	view
10	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	263:272 (1%) 292:301 (1%)	view
9	x 2	attacks datasets	privacy_attacks.py __init__.py	168:178 (1%) 201:212 (3%)	view
9	x 2	attacks datasets	privacy_attacks.py __init__.py	193:205 (1%) 234:246 (3%)	view
9	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	572:584 (1%) 599:611 (1%)	view
9	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	942:954 (1%) 992:1004 (1%)	view
8	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	440:447 (<1%) 910:917 (<1%)	view
8	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	621:628 (<1%) 847:854 (<1%)	view
8	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	847:854 (<1%) 1050:1057 (<1%)	view
8	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	621:628 (<1%) 716:723 (<1%)	view
8	x 2	utils utils	trainer.py trainer.py	240:250 (4%) 269:278 (4%)	view
8	x 2	attacks attacks	privacy_attacks.py privacy_attacks.py	847:854 (<1%) 910:917 (<1%)	view