Replication code for AAAI 2016 paper "Robust Text Classification in the Presence of Confounding Bias"
Jupyter Notebook Python
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README.md
requirements.txt

README.md

aaai-2016-robust

This github repository contains all the required code to replicate the experiments from the AAAI 2016 paper entitled "Robust Text Classification in the Presence of Confounding Bias".

Dependencies

If you want to re-run the programs to collect the data from the Canadian Parliament, you will need Python 2 and the Scrapy module.

The rest of the repo uses Python 3 and the following Python modules:

  • numpy
  • scipy
  • matplotlib
  • scikit-learn
  • bs4 (only for the Canadian Parliament experiments)

See requirements.txt for more details.

Organization

Following is a description of the files that are available through this repo. The most important files are the iPython notebooks (*.ipynb files), they use the other files to run the experiments and plot the results.

.
├── notebook # analysis folder where the iPython notebooks and the Python helper scripts are stored.
│   ├── CanadianParliament.ipynb # Canadian Parliament experiments.
│   ├── DemographicTweets.ipynb # Twitter experiments.
│   ├── IMDb.ipynb # IMDb experiments.
│   ├── __init__.py
│   ├── ba_c_study.py # script to evaluate the best C value for the back-door adjustment method.
│   ├── confound_plot.py # all functions used to plot the results of confounding experiments.
│   ├── imdb_confounding_experiments.py # IMDb experiments.
│   ├── injecting_bias.py # functions to inject confounding bias into a dataset.
│   ├── models.py # classification models (LR, SO, BA, etc).
│   ├── most_changing_coef.py # functions to compute and plot the change in coefficients.
│   ├── simpson_paradox.py # functions to compute and plot features that display Simpson's paradox.
│   └── top_terms_table.py # function to display the terms most correlated with the confounder.
├── data_collect # stores the script to collect the data
│   ├── hansard # scrapper to collect the data for the Canadian Parliament experiments.
│   │   ├── hansard
│   │   │   ├── __init__.py
│   │   │   ├── items.py
│   │   │   ├── pipelines.py
│   │   │   ├── settings.py
│   │   │   └── spiders
│   │   │       ├── __init__.py
│   │   │       └── HansardSpider.py
│   │   └── scrapy.cfg
│   └── tweets # scripts using the Twitter API to collect data for the Twitter experiments.
│       ├── geolocated_tweets.py
│       ├── get_users_gender.py
│       └── user_tweets.py
└── README.md # this help file.