Conformal predictive distribution repository

Ivan Petej 7df23af7a6 Initial experiments 3 weeks ago
.dvc 1f11723866 Initial commit 3 weeks ago
notebooks 13f9798f58 Generated artificial data and explored via Jupyter NB 3 weeks ago
references 1f11723866 Initial commit 3 weeks ago
reports 8e5d386a23 Processed artificial data 3 weeks ago
src 8e5d386a23 Processed artificial data 3 weeks ago
.gitattributes 1f11723866 Initial commit 3 weeks ago
.gitignore 8e5d386a23 Processed artificial data 3 weeks ago
Makefile 1f11723866 Initial commit 3 weeks ago
PRSA_data_2010.1.1-2014.12.31.csv 7300ab7f30 Downloaded data 3 weeks ago
README.md 1f11723866 Initial commit 3 weeks ago
eval.dvc 7df23af7a6 Initial experiments 3 weeks ago
process_data.dvc 8e5d386a23 Processed artificial data 3 weeks ago
raw_data.dvc 13f9798f58 Generated artificial data and explored via Jupyter NB 3 weeks ago
requirements.txt 1f11723866 Initial commit 3 weeks ago
setup.py 1f11723866 Initial commit 3 weeks ago
tox.ini 1f11723866 Initial commit 3 weeks ago
train.dvc 1f11723866 Initial commit 3 weeks ago

Data Pipeline

Legend
DVC Managed File
Git Managed File
Metric
Stage File
External File

README.md

conf-pred

Conformal predictive distribution repository

Instructions

  1. Clone the repo.
  2. Run make dirs to create the missing parts of the directory structure described below.
  3. Optional: Run make virtualenv to create a python virtual environment. Skip if using conda or some other env manager.
    1. Run source env/bin/activate to activate the virtualenv.
  4. Run make requirements to install required python packages.
  5. Put the raw data in data/raw.
  6. To save the raw data to the DVC cache, run dvc commit raw_data.dvc
  7. Edit the code files to your heart's desire.
  8. Process your data, train and evaluate your model using dvc repro eval.dvc or make reproduce
  9. When you're happy with the result, commit files (including .dvc files) to git.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── eval.dvc           <- The end of the data pipeline - evaluates the trained model on the test dataset.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── 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`.
│
├── process_data.dvc   <- Process the raw data and prepare it for training.
├── raw_data.dvc       <- Keeps the raw data versioned.
│
├── 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
│   └── metrics.txt    <- Relevant metrics after evaluating the model.
│   └── training_metrics.txt    <- Relevant metrics from training the model.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── 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
│   │
│   ├── 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
│
├── tox.ini            <- tox file with settings for running tox; see tox.testrun.org
└── train.dvc          <- Traing a model on the processed data.

Project based on the cookiecutter data science project template. #cookiecutterdatascience