To run:
If running from DSMLP cluster:
ssh user@dsmlp-login.ucsd.edu
launch-scipy-ml.sh -i freebreadstix/q1-replication
Else be sure to run in container: https://hub.docker.com/repository/docker/freebreadstix/q1-replication
Then:
git clone https://github.com/freebreadstix/capstone_B02.git
cd capstone_B02
If not merged to main, make sure to switch to branch with run.py
git checkout lucas-runpy
Configure config yaml with appropriate parameters. You can make your own .yml using config.yml as reference, just pass it as the argument on CLI
Run run.py w/ config yaml corresponding to configuration you are running. For testing this is test_config.yml
python3 run.py test_config.yml
Link to Presentation Website
https://micmiccitymax.github.io/dsc180b02-site/
Explainations of Config.yml output options
num_words: how many words are in the "important words" for the models
save_predictions: saves output of predictions to a file
print_results: prints results of evaluations to terminal
print words: Prints important words of each model in terminal
intersections: computes the important words similarity of all combinations of model and topics, USE ONLY WHEN YOU HAVE ALL MODELS MADE
decision_tree_model: outputs a plotting of decision tree to a figure
wordcloud: outputs an important word wordcloud to a figure in the figures folder
Note: if you are using intersections, decision_tree_model, or wordcloud options, make sure data is saved as 'data/processed/general.csv' and has columns 'Original Article Text' as the document text, 'Verdict' as 'TRUE' or 'FALSE', 'Category' as category, or change code within old_utils.py
├── 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
│
├── 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
│
├── 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
│ │
│ ├── 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
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience