tensorflow / neural-structured-learning
Duplication

Places in code with 6 or more lines that are exactly the same.

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:
    • 17,545 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
    • 2,682 duplicated lines
  • 151 duplicates
system15% (2,682 lines)
Duplication per Extension
py19% (2,290 lines)
cc8% (302 lines)
bzl10% (72 lines)
h1% (18 lines)
Duplication per Component (primary)
research/gam38% (1,855 lines)
research/carls6% (434 lines)
research/a2n13% (255 lines)
research/kg_hyp_emb7% (66 lines)
neural_structured_learning/lib5% (34 lines)
research/gnn-survey4% (24 lines)
research/multi_representation_adversary2% (14 lines)
ROOT0% (0 lines)
g3doc0% (0 lines)
neural_structured_learning/estimator0% (0 lines)
neural_structured_learning/experimental0% (0 lines)
neural_structured_learning/configs0% (0 lines)
neural_structured_learning/tools0% (0 lines)
neural_structured_learning0% (0 lines)
neural_structured_learning/keras0% (0 lines)
research/neural_clustering0% (0 lines)
Longest Duplicates
The list of 20 longest duplicates.
See data for all 151 duplicates...
Size#FoldersFilesLinesCode
83 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 578:702 (13%)
606:730 (14%)
 view
72 x 2 research/gam/gam/experiments
research/gam/gam/experiments
run_train_gam.py
run_train_gam_graph.py
 350:433 (19%)
321:404 (20%)
 view
46 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 289:352 (7%)
322:385 (8%)
 view
45 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 818:873 (7%)
817:872 (7%)
 view
39 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 358:400 (6%)
392:434 (6%)
 view
38 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 154:193 (6%)
154:193 (6%)
 view
38 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 115:152 (6%)
115:152 (6%)
 view
37 x 2 research/gam/gam/experiments
research/gam/gam/experiments
run_train_gam.py
run_train_gam_graph.py
 204:240 (9%)
182:218 (10%)
 view
31 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 484:528 (5%)
512:556 (5%)
 view
30 x 2 research/gam/gam/models
research/gam/gam/models
gcn.py
mlp.py
 204:278 (17%)
175:249 (30%)
 view
27 x 2 research/kg_hyp_emb/models
research/kg_hyp_emb/models
euclidean.py
hyperbolic.py
 142:177 (21%)
121:156 (25%)
 view
25 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_agreement.py
trainer_classification.py
 220:247 (3%)
302:329 (4%)
 view
25 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 786:811 (4%)
785:810 (4%)
 view
25 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_agreement.py
trainer_classification_gcn.py
 220:247 (3%)
335:362 (4%)
 view
22 x 2 research/gam/gam/experiments
research/gam/gam/experiments
run_train_gam.py
run_train_gam_graph.py
 251:272 (5%)
229:250 (6%)
 view
21 x 2 research/gam/gam/experiments
research/gam/gam/experiments
run_train_gam.py
run_train_gam_graph.py
 179:199 (5%)
157:177 (5%)
 view
21 x 2 research/gam/gam/data
research/gam/gam/data
dataset.py
dataset.py
 139:160 (3%)
305:325 (3%)
 view
20 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 733:770 (3%)
745:782 (3%)
 view
20 x 2 research/gam/gam/experiments
research/gam/gam/experiments
run_train_gam.py
run_train_gam_graph.py
 84:103 (5%)
69:88 (5%)
 view
20 x 2 research/gam/gam/experiments
research/gam/gam/experiments
run_train_gam.py
run_train_gam_graph.py
 328:347 (5%)
295:314 (5%)
 view
Duplicated Units
The list of top 11 duplicated units.
See data for all 11 unit duplicates...
Size#FoldersFilesLinesCode
50 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 0:0 
0:0 
 view
20 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 0:0 
0:0 
 view
33 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 0:0 
0:0 
 view
42 x 2 research/gam/gam/models
research/gam/gam/models
gcn.py
mlp.py
 0:0 
0:0 
 view
12 x 2 research/carls/base
research/carls/base
input_context_helper.cc
input_context_helper.cc
 227:239 
259:271 
 view
8 x 2 research/gam/gam/trainer
research/gam/gam/trainer
trainer_classification.py
trainer_classification_gcn.py
 0:0 
0:0 
 view
6 x 2 research/carls/base
research/carls/base
input_context_helper.cc
input_context_helper.cc
 66:72 
76:82 
 view
6 x 2 research/carls/base
research/carls/base
input_context_helper.cc
input_context_helper.cc
 394:400 
403:409 
 view
6 x 2 research/carls/base
research/carls/base
input_context_helper.cc
input_context_helper.cc
 413:419 
423:429 
 view
7 x 2 research/gam/gam/data
research/gam/gam/data
dataset.py
dataset.py
 0:0 
0:0 
 view
13 x 3 research/gam/gam/models
research/gam/gam/models
research/gam/gam/models
cnn.py
gcn.py
mlp.py
 0:0 
0:0 
0:0 
 view