project
|- README # the top level description of content (this doc)
|- CONTRIBUTING # instructions for how to contribute to your project
|- LICENSE # the license for this project
|
|- data/ # raw and primary data, are not changed once created
| |- process/ # .tsv and .csv files generated with main.py that runs the models
| |- baxter.0.03.subsample.shared # subsampled mothur generated file with OTUs from Marc Sze's analysis
| |- metadata.tsv # metadata with clinical information from Marc Sze's analysis
|- code/ # any programmatic code
| |- learning/ # generalization performance of model
| |- testing/ # building final model
|
|- results/ # all output from workflows and analyses
| |- tables/ # tables and .Rmd code of the tables to be rendered with kable in R
| |- figures/ # graphs, likely designated for manuscript figures
|
|- submission/
| |- manuscript.Rmd # executable Rmarkdown for this study, if applicable
| |- manuscript.md # Markdown (GitHub) version of the *.Rmd file
| |- manuscript.tex # TeX version of *.Rmd file
| |- manuscript.pdf # PDF version of *.Rmd file
| |- header.tex # LaTeX header file to format pdf version of manuscript
| |- references.bib # BibTeX formatted references
|
|- Makefile # Reproduce the manuscript, figures and tables
- Python 3.6.5 or Python 2.7, Matplotlib, Numpy, Scipy, Sympy, Pandas, Sklearn and XGBoost to run Shallow Learning code.
- If running Deep Learning code you need to have Python 3 and Latest PyTorch and Latest Keras with Theano backend.
- Run everything from project directory.
- The files mentioned above at process/ from Marc Sze's analysis: https://github.com/SchlossLab/Sze_CRCMetaAnalysis_mBio_2018
- R version 3.5.0
- Caret package is used as the wrapper for all models
- Run from project directory.
- The files mentioned above at process/ from Marc Sze's analysis: https://github.com/SchlossLab/Sze_CRCMetaAnalysis_mBio_2018
git clone https://github.com/BTopcuoglu/DeepLearning
To run L2 Logistic Regression, L1 and L2 Linear SVM, RBF SVM, Decision Tree, Random Forest and XGBoost in Python
- Generate tab-delimited files: Cross-validation and testing AUC scores of each model.
- Generate tab-delimited files: The AUC scores of each hyper-parameter tested for each model.
- Generate a comma-seperated file: The hyper-parameters tuned for each model in one file.
- Generate ROC curve figures: The cross-validation and testing ROC curves for each model.
python code/learning/main.py
To run L2 Logistic Regression, L1 and L2 Linear SVM, RBF SVM, Decision Tree, Random Forest and XGBoost in R
- Generate tab-delimited files: Cross-validation and testing AUC scores for each data-split and hyper-parameter AUC scores of each model.
- Generate ROC curve figures: The testing ROC curves for each model.
R CMD BATCH code/learning/main.R
make submission/manuscript.pdf