Our work is a Framework for Statistically-Sound Group Testing, that combines coverage and statistical testing to verify common hypotheses and return interpretable data groups.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have python3 installed
• You have installed requirements pip install -r requirements.txt
• You have jupyter notebook installed

Running

• To run the data, go to source_code/notebooks and run Run_Experiments.py.
  • dataset : string - Name of the Dataset
  • alphas : float - Threshold of p-value
  • support : integer - Minimum number of points in a group
  • dimension : string - variable used by aggregate function
  • grouping_type : list of lists - items or users features for clustering. Each sub-list contains features of a period.
  • grouping_value : list of lists - values of clustering features. Each sub-list contains the values of the corresponding the features in grouping_type.
  • agg_type : string - Aggregation function
  • test_args : list - [Type of test (one sample, two samples ...), Value for One Sample]
  • periods_start : list of datetime - Datetime of starting of each period
  • period_arg : list of integer - [Number of months, Number of days]
  • top_ns : list of integer- List of number of results (n)
  • period_type : string - Time_Based ('time')
  • num_hyps : list of float - Percentages of samples
  • r : string - Type of request (Ri)
  • methods : list of integer - List of method {0:TRAD, 1:COVER_G ,4:COVER_⍺, 5:β-Farsighted, 6:γ-Fixed, 7:ẟ Hopeful, 8:Ɛ-Hybrid, 9:Ψ-Support}

The execution will create results files in experiments\dataset\r\.

• Use graphs.ipynb to generate the different graphs presented in the paper using the results files in experiments\dataset\r\.

Contributing

To contribute, follow these steps:

1. Fork this repository.
2. Create a branch: git checkout -b <branch_name>.
3. Make your changes and commit them: git commit -m '<commit_message>'
4. Push to the original branch: git push origin <project_name>/<location>
5. Create the pull request.

Alternatively see the GitHub documentation on creating a pull request.