# These lines are needed so that this file can be run from the terminal import os, sys sys.path.append(os.path.join(os.path.dirname(__file__), "..")) from mvp_individual_user_performance_datamv import collate_individual_user_results from pybossautils import prolific_academic_ids import numpy as np import statsmodels.api as sm if __name__ == "__main__": # Retrieve all individual user's statistical measure results all_project_results = collate_individual_user_results() list_of_f_measures = [] list_of_sensitivity_values = [] list_of_specificity_values = [] list_of_accuracy_values = [] list_of_precision_values = [] list_of_kappa_values = [] list_of_annotation_indicators = [] list_of_feedback_indicators = [] list_of_interaction_indicators = [] list_of_prolific_academic_indicators = [] # Make sure 4b and 4c are commented out in the project_results!! for project_results in all_project_results: for dictionary in project_results:
#### This code is used to test whether there are significant differences in the distribution of each of the statistical measures between two Trailblazer projects #### It is a non-parametric test #### You need to uncomment the projects of interest in the project_configuration list in the pybossautils.py file #### You also need to ensure the collate_individual_user_results function within the mvp_individual_user_performance_datamv.py file is set to run and not export to csv! # These lines are needed so that this file can be run from the terminal import os, sys sys.path.append(os.path.join(os.path.dirname(__file__), "..")) from mvp_individual_user_performance_datamv import collate_individual_user_results import numpy as np import scipy.stats as stats all_performance_data = collate_individual_user_results() statistics = ["accuracy", "sensitivity", "specificity", "precision", "f-measure", "kappa"] for statistic in statistics: overall_project_data = list() for trailblazer_iteration_data in all_performance_data: project_level_data = list() for user_performance_dictionary in trailblazer_iteration_data: statistical_measure_data = user_performance_dictionary[statistic] project_level_data.append(statistical_measure_data) overall_project_data.append(project_level_data)