fnc-1-baseline

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A baseline implementation for FNC-1

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# Baseline FNC implementation

Information about the fake news challenge can be found on [FakeChallenge.org](http://fakenewschallenge.org).

This repository contains code that reads the dataset, extracts some simple features, trains a cross-validated model and
performs an evaluation on a hold-out set of data.

Credit:
* Byron Galbraith (Github: @bgalbraith, Slack: @byron)
* Humza Iqbal (GitHub: @humzaiqbal, Slack: @humza)
* HJ van Veen (GitHub/Slack: @mlwave)
* Delip Rao (GitHub: @delip, Slack: @dr)
* James Thorne (GitHub/Slack: @j6mes)
* Yuxi Pan (GitHub: @yuxip, Slack: @yuxipan)

## Questions / Issues
Please raise questions in the slack group [fakenewschallenge.slack.com](https://fakenewschallenge.slack.com)

## Getting Started
The FNC dataset is inlcuded as a submodule and can be FNC Dataset is included as a submodule. You should download the fnc-1 dataset by running the following commands. This places the fnc-1 dataset into the folder fnc-1/

git submodule init
    git submodule update

## Useful functions
### dataset class
The dataset class reads the FNC-1 dataset and loads the stances and article bodies into two separate containers.

dataset = DataSet()

You can access these through the ``.stances`` and ``.articles`` variables

print("Total stances: " + str(len(dataset.stances)))
    print("Total article bodies: " + str(len(dataset.articles)))

* ``.articles`` is a dictionary of articles, indexed by the body id. For example, the text from the 144th article can be printed with the following command:
   ``print(dataset.articles[144])``

### Hold-out set split
Data is split using the ``generate_hold_out_split()`` function. This function ensures that the article bodies between the training set are not present in the hold-out set. This accepts the following arguments. The body IDs are written to disk.

* ``dataset`` - a dataset class that contains the articles and bodies
* ``training=0.8`` - the percentage of data used for the training set (``1-training`` is used for the hold-out set)
* ``base_dir="splits/"``- the directory in which the ids are to be written to disk

### k-fold split
The training set is split into ``k`` folds using the ``kfold_split`` function. This reads the holdout/training split from the disk and generates it if the split is not present.

* ``dataset`` - dataset reader
* ``training = 0.8`` - passed to the hold-out split generation function
* ``n_folds = 10`` - number of folds
* ``base_dir="splits"`` - directory to read dataset splits from or write to

This returns 2 items: a array of arrays that contain the ids for stances for each fold, an array that contains the holdout stance IDs.

### Getting headline/stance from IDs
The ``get_stances_for_folds`` function returns the stances from the original dataset. See ``fnc_kfold.py`` for example usage.

## Scoring Your Classifier

The ``report_score`` function in ``utils/score.py`` is based off the original scorer provided in the FNC-1 dataset repository written by @bgalbraith.

``report_score`` expects 2 parameters. A list of actual stances (i.e. from the dev dataset), and a list of predicted stances (i.e. what you classifier predicts on the dev dataset). In addition to computing the score, it will also print the score as a percentage of the max score given any set of gold-standard data (such as from a  fold or from the hold-out set).

predicted = ['unrelated','discuss',...]
    actual = [stance['Stance'] for stance in holdout_stances]

report_score(actual, predicted)

This will print a confusion matrix and a final score your classifier. We provide the scores for a classifier with a simple set of features which you should be able to match and eventually beat!

## Results

### Competition dataset (leaderboard score )
|               | agree         | disagree      | discuss       | unrelated     |
|-----------    |-------        |----------     |---------      |-----------    |
|   agree       |    173        |     10        |   1435        |   28          |
| disagree      |    39         |     7         |   413         |   238         |
|  discuss      |    221        |     7         |   3556        |   680         |
| unrelated     |    10         |     3         |   358         |   17978       |
Score: 8761.75 out of 11651.25     (75.20%)

### Hold-out split (for development)

|               | agree         | disagree      | discuss       | unrelated     |
|-----------    |-------        |----------     |---------      |-----------    |
|   agree       |    118        |     3         |    556        |    85         |
| disagree      |    14         |     3         |    130        |    15         |
|  discuss      |    58         |     5         |   1527        |    210        |
| unrelated     |     5         |     1         |    98         |   6794        |
Score: 3538.0 out of 4448.5	(79.53%)

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