Zenodo spam classifiers

Spam classification machine learning models for Zenodo records and communities.

Usage

First of all, create a virtualenv, install the depencencies, and run the Jupyter notebook server:

# Create a virtual environment
   mkvirtualenv --python python3.9 zenodo-classifier
   (zenodo-classifier) pip install -e .
   
# This will also open Jupyter notebook in your browser
   (zenodo-classifier) jupyter notebook

To re-train the model:

1. Go to Zenodo Open Metadata record at https://doi.org/10.5281/zenodo.787062 to acces all dataset versions.
2. Download the latest dump locally under data
3. Open the model_spam_detection_record.ipynb notebook
4. Update the data_file and model_path variables to point to the new dump location
5. Run all the cells up to 4. Dump model.

To compare with older models:

TODO

Structure

├── 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.
│
├── experiments        <- Experimental model notebooks, previously trained
│
├── legacy             <- Legacy model notebooks
│
├── 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`.
│
├── 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