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()
