Machine Learning methods for log file processing
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│ └── embeddings <- Log message embedding models, e.g., fastText.
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ ├── figures <- Generated graphics and figures to be used in reporting.
│ └── results <- All kinds of results in machine-friendly formats (human-readable reports can be generated out of these).
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`.
│
├── scripts <- Scripts running the code to reproduce published results The scripts are meant for the SLURM batching system.
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module.
│ │
│ ├── data <- Scripts to download or generate data.
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling.
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions.
│ │ ├── predict_model.py **FIX**
│ │ └── train_model.py **FIX**
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py **FIX**
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience