How to structure python code some some generic ML project. The idea is to have a generic template with an automatic workflow.
├── LICENSE
│
├── Makefile <- Makefile with commands like `make data` or `make train`
│
├── README.md <- The top-level README for developers using this project
│
├── environment.yml <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
├── config-private.yml <- config file in YAML, can be exported as env vars if need
│
├── data
│ ├── external <- Data from third party sources.
│ ├── intermediate <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── results
│ ├── outputs
│ └── models <- Trained and serialized models, model predictions, or model summaries
│
├── documents
│ ├── docs
│ ├── images
│ └── 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
│
├── 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`.d
│
├── 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
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
conda env create -f environment.yml -n env_ds
conda env update -f environment.yml -n env_ds
python src/data/download.py "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" data/raw/iris.csv
https://github.com/artofai/overcome-the-chaos
https://github.com/ThomasRobertFr/ml-project-structure
There are seven “core” automatic variables:
$@: The filename representing the target.
$%: The filename element of an archive member specification.
$<: The filename of the first prerequisite.
$?: The names of all prerequisites that are newer than the target, separated by spaces.
$^: The filenames of all the prerequisites, separated by spaces. This list has duplicate filenames removed since for most uses, such as compiling, copying, etc., duplicates are not wanted.
$+: Similar to $^, this is the names of all the prerequisites separated by spaces, except that $+ includes duplicates. This variable was created for specific situations such as arguments to linkers where duplicate values have meaning.
$*: The stem of the target filename. A stem is typically a filename without its suffix. Its use outside of pattern rules is discouraged.
https://stackoverflow.com/questions/3220277/what-do-the-makefile-symbols-and-mean