NOTE 11/12/18: An updated version of this code has been created to help to ensure replicability. If you are interested in running the code or sections of it then please use the code there. The new repository also contains detailed instructions for package installation and running the code. The code contained in this repository includes output (viewable in the Jupyter notebooks) that corresponds to that reported in the published paper. If you plan to compare the results in the paper to the code then please use this repository. This repository does not contain the Fragile Families data. Please contact the Fragile Families and Child-Wellbeing Survey organization. An FAQ on data access can be found here
This repository contains code to reproduce the results of my final submissions for the Fragile Families Challenge as described in the paper forthcoming in Socius. A pre-print is available here. All of the code used was written in Python 3.6 using the latest versions of the packages available in July 2017.
The model directory contains a Jupyter notebook with the code used to run the neural networks, gpa.ipynb. This notebook contains a lot of output that will require scrolling through in the Github version. To view it I suggest downloading or cloning this repository and opening the file as a Jupyter notebook. This will require Python 3.5 and jupyter to be installed, as well as any other dependencies if you intend to run the notebook. The notebook regression_baseline.ipynb contains the code to implement the baseline OLS model.
The preprocess directory contains the Python script used to clean, impute, and preprocess the raw data, although note that the final pre-proccessing tasks are done in the modeling notebook.
The LIME directory contains the a notebook used to run the LIME algorithm (LIME_explanations.ipynb) and a notebook used to examine these explanations more closely (examining_explanations.ipynb). Note 12/11/18: Due to changes in the Fragile Families metadata API this code will no longer work. Please consult the updated repository linked above. The explanations themselves, the output of the first notebook, are stored in new_lime_explanations_dict.p, a pickled Python dictionary. In addition, there is also a copy of the variable metadata CSV created by Connor Gilroy (see this repository).
The results directory contains the CSVs necessary to reproduce the figures in the paper, as well as the predicted values for GPA obtained from the final 5 models and the baseline model, as discussed in the paper.
The figures directory contains the two figures used in the body of the papers along with the notebook used to create the activation functions visualization. The network diagram was created using Draw.io, a free online tool to draw diagrams.
The supplementary directory contains notebooks, data, and figures discussed in the Supplementary Materials section of the paper.
If you have any questions please don't hesitate to contact me via e-mail.