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.

Learn more...

Duplication Overall

15% duplication:

20,736 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
3,127 duplicated lines

725 duplicates

Duplication per Extension

Duplication per Component (primary)

Longest Duplicates

The list of 50 longest duplicates.

See data for all 725 duplicates...

Size	#	Folders	Files	Lines	Code
28	x 2	botorch/models/kernels botorch/models/kernels	downsampling.py exponential_decay.py	73:106 (35%) 78:112 (39%)	view
25	x 2	tutorials tutorials	multi_fidelity_bo.ipynb one_shot_kg.ipynb	602:626 (3%) 289:313 (7%)	view
22	x 2	botorch/sampling botorch/sampling	samplers.py samplers.py	149:211 (14%) 227:290 (14%)	view
21	x 2	botorch/models/kernels botorch/models/kernels	downsampling.py exponential_decay.py	46:70 (26%) 51:75 (29%)	view
19	x 2	tutorials tutorials	compare_mc_analytic_acquisition.ipynb multi_fidelity_bo.ipynb	348:366 (5%) 602:620 (3%)	view
19	x 2	tutorials tutorials	compare_mc_analytic_acquisition.ipynb one_shot_kg.ipynb	348:366 (5%) 289:307 (6%)	view
17	x 2	botorch/models botorch/models	gp_regression_fidelity.py gp_regression_fidelity.py	97:114 (8%) 207:223 (8%)	view
17	x 2	botorch/models botorch/models	gp_regression_fidelity.py gp_regression_fidelity.py	116:140 (8%) 225:249 (8%)	view
15	x 2	tutorials tutorials	compare_mc_analytic_acquisition.ipynb max_value_entropy.ipynb	352:366 (4%) 322:336 (4%)	view
15	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	859:874 (2%) 890:904 (2%)	view
15	x 2	botorch/utils/multi_objective/box_decompositions botorch/utils/multi_objective/box_decompositions	utils.py utils.py	223:252 (8%) 291:319 (8%)	view
15	x 2	tutorials tutorials	max_value_entropy.ipynb multi_fidelity_bo.ipynb	322:336 (4%) 606:620 (2%)	view
15	x 2	botorch/acquisition botorch/acquisition	analytic.py analytic.py	73:113 (4%) 203:241 (4%)	view
15	x 2	tutorials tutorials	max_value_entropy.ipynb one_shot_kg.ipynb	322:336 (4%) 293:307 (4%)	view
14	x 2	botorch/models/transforms botorch/models/transforms	input.py input.py	331:365 (2%) 488:518 (2%)	view
14	x 2	botorch/models botorch/models	gp_regression.py higher_order_gp.py	106:119 (5%) 199:213 (3%)	view
13	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	829:843 (1%) 910:924 (1%)	view
12	x 2	botorch/models botorch/models	approximate_gp.py higher_order_gp.py	339:350 (3%) 201:212 (3%)	view
12	x 2	botorch/acquisition botorch/acquisition	max_value_entropy_search.py max_value_entropy_search.py	298:324 (3%) 650:678 (3%)	view
12	x 2	tutorials tutorials	optimize_stochastic.ipynb optimize_with_cmaes.ipynb	209:220 (5%) 136:147 (8%)	view
12	x 2	botorch/models botorch/models	approximate_gp.py gp_regression.py	339:350 (3%) 107:118 (5%)	view
11	x 2	botorch/posteriors botorch/posteriors	deterministic.py gpytorch.py	65:91 (26%) 65:87 (9%)	view
11	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	392:420 (1%) 444:479 (1%)	view
11	x 2	botorch/models/transforms botorch/models/transforms	outcome.py outcome.py	438:449 (3%) 567:578 (3%)	view
11	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	392:420 (1%) 501:535 (1%)	view
11	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	444:479 (1%) 556:585 (1%)	view
11	x 2	botorch/acquisition botorch/acquisition	knowledge_gradient.py knowledge_gradient.py	57:77 (3%) 320:339 (3%)	view
11	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	392:420 (1%) 556:585 (1%)	view
11	x 2	botorch/sampling botorch/sampling	pairwise_samplers.py pairwise_samplers.py	76:86 (16%) 97:107 (16%)	view
11	x 2	botorch/acquisition botorch/acquisition	knowledge_gradient.py monte_carlo.py	145:155 (3%) 77:87 (4%)	view
11	x 2	tutorials tutorials	optimize_stochastic.ipynb optimize_with_cmaes.ipynb	154:164 (5%) 75:85 (7%)	view
11	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	444:479 (1%) 501:535 (1%)	view
11	x 2	botorch/acquisition botorch/acquisition	input_constructors.py input_constructors.py	501:535 (1%) 556:585 (1%)	view
11	x 2	botorch/acquisition botorch/acquisition	knowledge_gradient.py multi_step_lookahead.py	134:144 (3%) 122:132 (3%)	view
11	x 2	botorch/models/transforms botorch/models/transforms	outcome.py outcome.py	472:483 (3%) 601:612 (3%)	view
11	x 2	tutorials tutorials	max_value_entropy.ipynb one_shot_kg.ipynb	156:166 (3%) 81:91 (3%)	view
10	x 2	tutorials tutorials	compare_mc_analytic_acquisition.ipynb one_shot_kg.ipynb	97:106 (2%) 147:156 (3%)	view
10	x 2	botorch/models botorch/models	pairwise_gp.py pairwise_gp.py	374:383 (1%) 417:426 (1%)	view
10	x 2	tutorials tutorials	multi_fidelity_bo.ipynb multi_fidelity_bo.ipynb	208:217 (1%) 458:467 (1%)	view
10	x 2	botorch/models/transforms botorch/models/transforms	outcome.py outcome.py	399:415 (3%) 528:544 (3%)	view
10	x 2	botorch/utils botorch/utils	sampling.py sampling.py	232:256 (2%) 271:295 (2%)	view
10	x 2	tutorials tutorials	compare_mc_analytic_acquisition.ipynb one_shot_kg.ipynb	242:251 (2%) 269:278 (3%)	view
10	x 2	botorch/acquisition botorch/acquisition	monte_carlo.py monte_carlo.py	133:158 (3%) 412:441 (3%)	view
10	x 2	tutorials tutorials	optimize_stochastic.ipynb optimize_with_cmaes.ipynb	87:96 (4%) 23:32 (6%)	view
10	x 2	botorch/optim botorch/optim	initializers.py initializers.py	297:308 (2%) 400:411 (2%)	view
10	x 2	tutorials tutorials	optimize_stochastic.ipynb optimize_with_cmaes.ipynb	116:125 (4%) 47:56 (6%)	view
10	x 2	tutorials tutorials	compare_mc_analytic_acquisition.ipynb max_value_entropy.ipynb	97:106 (2%) 251:260 (2%)	view
10	x 2	botorch/models/kernels botorch/models/kernels	downsampling.py linear_truncated_fidelity.py	82:93 (12%) 170:181 (7%)	view
10	x 2	botorch/models/kernels botorch/models/kernels	exponential_decay.py linear_truncated_fidelity.py	88:99 (14%) 170:181 (7%)	view
10	x 2	botorch/acquisition botorch/acquisition	monte_carlo.py monte_carlo.py	132:157 (3%) 214:252 (3%)	view