from expWorkbench import load_results from analysis.prim import perform_prim, write_prim_to_stdout from analysis.prim import show_boxes_individually def classify(data): result = data["total fraction new technologies"] classes = np.zeros(result.shape[0]) classes[result[:, -1] > 0.8] = 1 return classes if __name__ == "__main__": results = load_results(r"CESUN_optimized_1000_new.cPickle") experiments, results = results logicalIndex = experiments["policy"] == "Optimized Adaptive Policy" newExperiments = experiments[logicalIndex] newResults = {} for key, value in results.items(): newResults[key] = value[logicalIndex] results = (newExperiments, newResults) boxes = perform_prim(results, "total fraction new technologies", threshold=0.6, threshold_type=-1) write_prim_to_stdout(boxes) show_boxes_individually(boxes, results) plt.show()
def classify(data): result = data['total fraction new technologies'] classes = np.zeros(result.shape[0]) classes[result[:, -1] > 0.8] = 1 return classes if __name__ == '__main__': results = load_results(r'CESUN_optimized_1000_new.cPickle') experiments, results = results logicalIndex = experiments['policy'] == 'Optimized Adaptive Policy' newExperiments = experiments[logicalIndex] newResults = {} for key, value in results.items(): newResults[key] = value[logicalIndex] results = (newExperiments, newResults) boxes = perform_prim(results, 'total fraction new technologies', threshold=0.6, threshold_type=-1) write_prim_to_stdout(boxes) show_boxes_individually(boxes, results) plt.show()
result = data['deceased population region 1'] #make an empty array of length equal to number of cases classes = np.zeros(result.shape[0]) #if deceased population is higher then 1.000.000 people, classify as 1 classes[result[:, -1] > 1500000] = 1 return classes results = load_results(r".\data\1000 flu cases no policy.cPickle") #perform prim on modified results tuple res = pca_prim.perform_pca_prim(results, classify, mass_min=0.075, threshold=0.8, threshold_type=1) rotation_matrix, row_names, column_names, rotated_experiments, boxes = res #visualize results prim.write_prim_to_stdout(boxes) # we need to use the rotated experiments now results = (rotated_experiments, results[1]) prim.show_boxes_together(boxes, results) plt.show()
raise CaseError("run not completed", case) def classify(data): result = data['total fraction new technologies'] classes = np.zeros(result.shape[0]) classes[result[:, -1] < 0.6] = 1 return classes if __name__ == "__main__": ema_logging.log_to_stderr(ema_logging.INFO) model = EnergyTrans(r"..\..\models\EnergyTrans", "ESDMAElecTrans") ensemble = ModelEnsemble() ensemble.set_model_structure(model) ensemble.parallel = True results = ensemble.perform_experiments(1000) results = load_results(r'prim data 100 cases.cPickle') boxes = prim.perform_prim(results, classify=classify, mass_min=0.05, threshold=0.8) prim.write_prim_to_stdout(boxes, filter=True) prim.show_boxes_together(boxes, results) plt.show()