awslabs / sagemaker-graph-fraud-detection
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
  • 7% duplication:
    • 2,080 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
    • 148 duplicated lines
  • 14 duplicates
system7% (148 lines)
Duplication per Extension
py7% (102 lines)
yaml7% (46 lines)
Duplication per Component (primary)
source/sagemaker/sagemaker_graph_fraud_detection/dgl_fraud_detection10% (81 lines)
deployment7% (46 lines)
deployment/solution-assistant/src20% (14 lines)
source/sagemaker/data-preprocessing7% (7 lines)
source/lambda/graph-modelling0% (0 lines)
source/lambda/data-preprocessing0% (0 lines)
source/sagemaker/sagemaker_graph_fraud_detection0% (0 lines)
source/sagemaker/sagemaker_graph_fraud_detection/container_build0% (0 lines)
source/sagemaker/baselines0% (0 lines)
deployment/solution-assistant0% (0 lines)
Longest Duplicates
The list of 14 longest duplicates.
See data for all 14 duplicates...
Size#FoldersFilesLinesCode
15 x 2 source/sagemaker/sagemak...l_fraud_detection/model
source/sagemaker/sagemak...l_fraud_detection/model
mxnet.py
pytorch.py
160:174 (10%)
152:166 (11%)
view
12 x 2 source/sagemaker/sagemak...l_fraud_detection/model
source/sagemaker/sagemak...l_fraud_detection/model
mxnet.py
pytorch.py
129:141 (8%)
122:134 (9%)
view
10 x 2 source/sagemaker/sagemak...l_fraud_detection/model
source/sagemaker/sagemak...l_fraud_detection/model
mxnet.py
pytorch.py
98:107 (7%)
91:100 (7%)
view
7 x 2 source/sagemaker/data-preprocessing
source/sagemaker/sagemak...ion/dgl_fraud_detection
graph_data_preprocessor.py
estimator_fns.py
21:29 (8%)
46:54 (15%)
view
7 x 2 deployment
deployment
sagemaker-notebook-instance-stack.yaml
sagemaker-permissions-stack.yaml
40:47 (7%)
19:26 (4%)
view
7 x 2 deployment/solution-assistant/src
deployment/solution-assistant/src
lambda_function.py
lambda_function.py
25:31 (10%)
69:75 (10%)
view
7 x 2 source/sagemaker/sagemak...l_fraud_detection/model
source/sagemaker/sagemak...l_fraud_detection/model
pytorch.py
pytorch.py
91:97 (5%)
122:128 (5%)
view
7 x 2 source/sagemaker/sagemak...l_fraud_detection/model
source/sagemaker/sagemak...l_fraud_detection/model
mxnet.py
mxnet.py
98:104 (5%)
129:135 (5%)
view
7 x 2 deployment
deployment
sagemaker-graph-fraud-detection.yaml
sagemaker-graph-fraud-detection.yaml
306:312 (2%)
340:346 (2%)
view
7 x 2 source/sagemaker/sagemak...l_fraud_detection/model
source/sagemaker/sagemak...l_fraud_detection/model
mxnet.py
pytorch.py
98:104 (5%)
122:128 (5%)
view
7 x 2 source/sagemaker/sagemak...l_fraud_detection/model
source/sagemaker/sagemak...l_fraud_detection/model
mxnet.py
pytorch.py
129:135 (5%)
91:97 (5%)
view
6 x 2 deployment
deployment
sagemaker-graph-fraud-detection.yaml
sagemaker-permissions-stack.yaml
141:146 (1%)
21:26 (3%)
view
6 x 2 deployment
deployment
sagemaker-graph-fraud-detection.yaml
sagemaker-notebook-instance-stack.yaml
141:146 (1%)
42:47 (6%)
view
6 x 2 deployment
deployment
sagemaker-graph-fraud-detection.yaml
sagemaker-graph-fraud-detection.yaml
299:304 (1%)
333:338 (1%)
view