IEEE Fraud Detection

A full Python project for IEEE-CIS Fraud Detection

Prerequisites

• Anaconda >=5.x

Set up conda environment

conda create -n ieee-fraud-detection python=3.6
conda activate ieee-fraud-detection
pip install -r requirements.txt

To move beyond notebook prototyping, all reusable code should go into the src/ folder package. To use that package inside your project, install the project's module in editable mode, so you can edit files in the src/ folder and use the modules inside your notebooks :

pip install --editable .

Then launch jupyter notebook with jupyter notebook or lab with jupyter lab.

Formatting src code

The following commands will sort imports and apply black formatting to code ine the src/ folder.

You may need to run in admin mode.

seed-isort-config --application-directories src/
isort -rc src/ && black src/

Precommit hooks

The previous commands are installed in pre-commit hooks.

pre-commit install to install pre-commit into your git hooks.

pre-commit run if you want to manually run pre-commit hooks on staged files.

pre-commit run --all-files if you want to manually run all pre-commit hooks on a repository.

pre-commit uninstall will remove hooks if needed.

git commit -n ... will skip hooks verification.

Run experiments

Build dataset versions

In a ipython console, rebuild dataset in interim :

from src.dataset.data import Dataset
ds = Dataset()
ds.load_raw()
ds.save_dataset()

ds.load_raw(nrows=30000)
ds.save_dataset(version="30000")

You should now be able to launch run_experiment --version=30000 and run_experiment.

Run full pipeline

Choose a key in conf.json to run, example :

XGB run with dataset of 30000 rows: run_experiment --version=30000 ---key=xgb

LGB test run with full dataset : run_experiment --key=lgb_test

(ieee-fraud-detection) λ run_experiment --help
Usage: run_experiment [OPTIONS]

Options:
  --version TEXT  Dataset version to load
  --key TEXT      Experiment to run, see keys in conf.json file
  --help          Show this message and exit.

Kaggle API credentials

To use the Kaggle client library, sign up for a Kaggle account at https://www.kaggle.com. Then go to the 'Account' tab of your user profile (https://www.kaggle.com/<username>/account) and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location ~/.kaggle/kaggle.json (on Windows in the location C:\Users\<Windows-username>\.kaggle\kaggle.json).

For your security, ensure that other users of your computer do not have read access to your credentials. On Unix-based systems you can do this with the following command:

chmod 600 ~/.kaggle/kaggle.json

Submit to Kaggle

kaggle competitions submit -c ieee-fraud-detection -f data/submissions/sample_submission.csv -m "My submission message"

Project organization

├── 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.
│   ├── submissions    <- All predictions to submit
│   └── raw            <- The original, immutable data dump.
│
├── 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
│
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    |
    ├── app.py         <- Main entry point
    │
    ├── dataset        <- 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

Project based on the cookiecutter Kaggle template project. #cookiecutterdatascience