Repository: https://github.com/vcedeno/PLOS_ONE_Pipelines_Supporting_Information

PLOS_ONE_Pipelines_Supporting_Information

Data analysis and modeling pipelines for controlled networked social science experiments

User Manual

This code is included as the Supplemental Material for the paper "Data analysis and modeling pipelines for controlled networked social science experiments" in the PLOS ONE Journal. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. This documentation includes instructions for installing and getting started with the pipeline software.

Prerequisite Software

Python

https://www.python.org/downloads/

Download

https://github.com/vcedeno/PLOS_ONE_Pipelines_Supporting_Information

Installation

The folder S1 _ Software _ Table _ 5 contains code for the third of the five pipelines described in the paper. The property-inference-pipeline format follows the pipeline design and implementation described in Section 7, "Pipeline design and implementation". These pipelines are composable. We say that the pipelines in this repo contain the “pipeline framework” code for extensibility to other pipelines.

• S1 _ Software _ Table _ 5
  • property-inference-pipeline
    • json-schema: This folder contains the pipeline configuration files and datasets.
      • h1: Dataset for the function h1, that generates the properties for a Markovian transition matrix.
      • h2: Dataset for the function h2, that outputs a file with the properties for an adapted conditional random fields (CRF) model.
      • inputTransformation:
        • datasets: Transformation data in the file h1.json.
        • schemas: Transformation schema in the file h1.json. The JSON schemas control the accessing of the input files in the datasets folder.
    • src:
      • h1: Function h1 generates the properties for a Markovian transition matrix.
      • h2: Function h1outputs a file with the properties for an adapted conditional random fields (CRF) model.
      • property-inference-pipeline.py: code that runs the pipeline.
      • validateJson.py : code that check files against their JSON schema and terminate gracefully if a verification fails.
  • jsonInputPipeline
    • input: property-inference-pipeline.json contains the list of functions to execute. The example shows how to execute function h1.
    • schema: JSON schema for input verification.

Invocation

The following is an example of how to invoke the function h1. From the folder S1 _ Software _ Table _ 5 execute:

python property-inference-pipeline/src/property-inference-pipeline.py jsonInputPipeline/input/property-inference-pipeline.json