ANN Model

Requires Python 3.x.x+

This model was created as part of a Bachelor's thesis at Chalmers University of Technology.

Usage

To run a certain config, do:

> python main.py <config ids>

Examples are:

# Run config number 4
> python main.py 3
# Run config number 2, 3 and 5
> python main.py 1 2 4

Application Endpoint

There is a simple RESTful endpoint that can be used to make predictions on a specified title. To use the endpoint the model has to be trained alreay. Once the network is trained and ready to make some predictions, run

python main.py <config id> --application

Where <config id> is the ID of the config for the trained model.

The endpoint is then available at localhost:5000/predict. An example request is:

HTTP GET localhost:5000/predict?text=Hello%20World!&subreddit=funny

Where users for the title Hello World! and subreddit funny are predicted. It's important that the title string is URL encoded (i.e. replacing " " with "%20"). The response looks like the following:

{
    "predictions": [
        "UNK",
        "izzycat",
        "binarylogik",
        "bopolissimus",
        "flezgodrit"
    ],
    "predicted": "Hello World!",
    "subreddit": "funny"
}

A simple client app that uses this endpoint can be found at: kandidat-hightlights/app

Datasets

Make sure to put the data in a folder called resources/datasets. The data should have the following names prefixes:

• Training data: training_data
• Validation data: validation_data
• Test data: test_data

For more details, take a look at the dataset repository.

Configuration

To edit configs, take a look at the config.yaml file. Please prefer making new configs instead of editing old (for academic purposes). If implementing a new model, make sure to add support for it in the main.py file so its configs can be automatically parsed.

Build/Run with Docker

Build with

docker build -t YOURTAG

Run with

nvidia-docker run [-v YOURLOGDIR:/app/logs] -t -rm YOURTAG python -u ./YOURENTRYPOINT.py