bandicoot: a Python Toolbox for Mobile Phone Metadata
Authors: Yves-Alexandre de Montjoye, Luc Rocher, Alex Sandy Pentland
JMLR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested bandicoot on a computer with an Intel i7 CPU (2.6GHz) and 8GB of memory for users with an average of 20 records per days over 3 months. It takes on average 250ms to compute all 1442 behavioral and mobility indicators using the standard Python interpreter, CPython, and 160ms using pypy, a fast just-in-time compiler. For all indicators, including network features, the total time is 1.11s using CPython (740ms with pypy). |
| Researcher Affiliation | Academia | Yves-Alexandre de Montjoye de EMAIL MIT Media Lab, 02139 Cambridge MA, USA Imperial College London, Dept. of Computing and Data Science Institute, London SW7 2AZ, UK Luc Rocher EMAIL Universit e catholique de Louvain, ICTEAM, 1348 Louvain-la-Neuve, Belgium Alex Sandy Pentland EMAIL MIT Media Lab, Cambridge, 02139 Cambridge MA, USA |
| Pseudocode | No | The paper describes the functionalities and performance of the bandicoot toolbox but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. ... bandicoot is hosted on Git Hub and has already been developed by 9 contributors over the last three years... |
| Open Datasets | No | The paper discusses mobile phone metadata in general and mentions testing bandicoot with users having 'an average of 20 records per days over 3 months', but it does not specify a publicly available dataset with concrete access information (link, DOI, citation) that was used for its experiments. |
| Dataset Splits | No | The paper describes testing bandicoot's performance using data for 'users with an average of 20 records per days over 3 months', but it does not specify any dataset splits (e.g., training, validation, test sets, or percentages). |
| Hardware Specification | Yes | We tested bandicoot on a computer with an Intel i7 CPU (2.6GHz) and 8GB of memory for users with an average of 20 records per days over 3 months. |
| Software Dependencies | No | bandicoot runs on Python 2 and 3... To make its use (incl. in Hadoop environments) and installation easier, we developed bandicoot to be free of any dependencies such as pandas or compilers. |
| Experiment Setup | Yes | We tested bandicoot on a computer with an Intel i7 CPU (2.6GHz) and 8GB of memory for users with an average of 20 records per days over 3 months. It takes on average 250ms to compute all 1442 behavioral and mobility indicators using the standard Python interpreter, CPython, and 160ms using pypy, a fast just-in-time compiler. For all indicators, including network features, the total time is 1.11s using CPython (740ms with pypy). |