We recommend you wrap your project (or jupyter notebook) in a parent folder and run the following command on that folder. The output txt file, by default, will be generated at the folder where you run your command on.
Install dslinter from the Python Package Index:
pip install dslinter
A __init__.py file (can be empty) is expected at the <path_to_the_project> folder.
Copy the following command in your terminal, type in the path to your project, and press enter to run:
[For Linux/Mac OS Users]:
pylint \
--load-plugins=dslinter \
--disable=all \
--enable=import,unnecessary-iteration-pandas,unnecessary-iteration-tensorflow,\
nan-numpy,chain-indexing-pandas,datatype-pandas,\
column-selection-pandas,merge-parameter-pandas,inplace-pandas,\
dataframe-conversion-pandas,scaler-missing-scikitlearn,hyperparameters-scikitlearn,\
hyperparameters-tensorflow,hyperparameters-pytorch,memory-release-tensorflow,\
deterministic-pytorch,randomness-control-numpy,randomness-control-scikitlearn,\
randomness-control-tensorflow,randomness-control-pytorch,randomness-control-dataloader-pytorch,\
missing-mask-tensorflow,missing-mask-pytorch,tensor-array-tensorflow,\
forward-pytorch,gradient-clear-pytorch,pipeline-not-used-scikitlearn,\
dependent-threshold-scikitlearn,dependent-threshold-tensorflow,dependent-threshold-pytorch \
--output-format=text:report.txt,colorized \
--reports=y \
<path_to_the_project>
[For Windows Users]:
pylint --load-plugins=dslinter --disable=all --enable=import,unnecessary-iteration-pandas,unnecessary-iteration-tensorflow,nan-numpy,chain-indexing-pandas,datatype-pandas,column-selection-pandas,merge-parameter-pandas,inplace-pandas,dataframe-conversion-pandas,scaler-missing-scikitlearn,hyperparameters-scikitlearn,hyperparameters-tensorflow,hyperparameters-pytorch,memory-release-tensorflow,deterministic-pytorch,randomness-control-numpy,randomness-control-scikitlearn,randomness-control-tensorflow,randomness-control-pytorch,randomness-control-dataloader-pytorch,missing-mask-tensorflow,missing-mask-pytorch,tensor-array-tensorflow,forward-pytorch,gradient-clear-pytorch,pipeline-not-used-scikitlearn,dependent-threshold-scikitlearn,dependent-threshold-tensorflow,dependent-threshold-pytorch --output-format=text:report.txt,colorized --reports=y <path_to_sources>
For notebook, we need to convert it to Python file first and run dslinter on the Python file.
To convert the notebook to Python file, run:
jupyter nbconvert --to script <path_to_the_notebook>
Install dslinter from the Python Package Index:
pip install dslinter
Copy the following command in your terminal, type in the path to your project, and press enter to run:
[For Linux/Mac OS Users]:
pylint \
--load-plugins=dslinter \
--disable=all \
--enable=import,unnecessary-iteration-pandas,unnecessary-iteration-tensorflow,\
nan-numpy,chain-indexing-pandas,datatype-pandas,\
column-selection-pandas,merge-parameter-pandas,inplace-pandas,\
dataframe-conversion-pandas,scaler-missing-scikitlearn,hyperparameters-scikitlearn,\
hyperparameters-tensorflow,hyperparameters-pytorch,memory-release-tensorflow,\
deterministic-pytorch,randomness-control-numpy,randomness-control-scikitlearn,\
randomness-control-tensorflow,randomness-control-pytorch,randomness-control-dataloader-pytorch,\
missing-mask-tensorflow,missing-mask-pytorch,tensor-array-tensorflow,\
forward-pytorch,gradient-clear-pytorch,pipeline-not-used-scikitlearn,\
dependent-threshold-scikitlearn,dependent-threshold-tensorflow,dependent-threshold-pytorch \
--output-format=text:report.txt,colorized \
--reports=y \
<path_to_the_python_file>
[For Windows Users]:
pylint --load-plugins=dslinter --disable=all --enable=import,unnecessary-iteration-pandas,unnecessary-iteration-tensorflow,nan-numpy,chain-indexing-pandas,datatype-pandas,column-selection-pandas,merge-parameter-pandas,inplace-pandas,dataframe-conversion-pandas,scaler-missing-scikitlearn,hyperparameters-scikitlearn,hyperparameters-tensorflow,hyperparameters-pytorch,memory-release-tensorflow,deterministic-pytorch,randomness-control-numpy,randomness-control-scikitlearn,randomness-control-tensorflow,randomness-control-pytorch,randomness-control-dataloader-pytorch,missing-mask-tensorflow,missing-mask-pytorch,tensor-array-tensorflow,forward-pytorch,gradient-clear-pytorch,pipeline-not-used-scikitlearn,dependent-threshold-scikitlearn,dependent-threshold-tensorflow,dependent-threshold-pytorch --output-format=text:report.txt,colorized --reports=y <path_to_the_python_file>