#if deceased population is higher then 1.000.000 people, classify as 1 classes[result[:, -1] > 1000000] = 1 return classes #load data results = load_results(r'../analysis/1000 flu cases.cPickle') experiments, results = results #extract results for 1 policy logicalIndex = experiments['policy'] == 'no policy' newExperiments = experiments[ logicalIndex ] newResults = {} for key, value in results.items(): newResults[key] = value[logicalIndex] results = (newExperiments, newResults) #perform prim on modified results tuple boxes = prim.perform_prim(results, classify, threshold=0.8, threshold_type=1, pasting=True) #print prim to std_out prim.write_prim_to_stdout(boxes) #visualize prim.show_boxes_individually(boxes, results) prim.show_boxes_together(boxes, results) plt.show()
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()