Ejemplo n.º 1
0
def test_save_and_load():
    import matplotlib.pyplot as plt

    from expWorkbench.EMAlogging import log_to_stderr, INFO
    from expWorkbench.model import SimpleModelEnsemble
    from examples.FLUvensimExample import FluModel
    from analysis.graphs import lines
    
    log_to_stderr(level= INFO)
        
    model = FluModel(r'..\..\models\flu', "fluCase")
    ensemble = SimpleModelEnsemble()
#    ensemble.parallel = True
    ensemble.set_model_structure(model)
    
    policies = [{'name': 'no policy',
                 'file': r'\FLUvensimV1basecase.vpm'},
                {'name': 'static policy',
                 'file': r'\FLUvensimV1static.vpm'},
                {'name': 'adaptive policy',
                 'file': r'\FLUvensimV1dynamic.vpm'}
                ]
    ensemble.add_policies(policies)
    
    results = ensemble.perform_experiments(10)
    file = r'C:\eclipse\workspace\EMA workbench\models\results.cPickle'
    save_results(results, file)
    
    results = load_results(file)
   
    lines(results)
    plt.show()
Ejemplo n.º 2
0
def test_feature_selection():
    from expWorkbench.model import SimpleModelEnsemble
    from examples.FLUvensimExample import FluModel
    
    log_to_stderr(level= INFO)
        
    model = FluModel(r'..\..\models\flu', "fluCase")
    ensemble = SimpleModelEnsemble()
    ensemble.parallel = True
    ensemble.set_model_structure(model)
    
    policies = [{'name': 'no policy',
                 'file': r'\FLUvensimV1basecase.vpm'},
                {'name': 'static policy',
                 'file': r'\FLUvensimV1static.vpm'},
                {'name': 'adaptive policy',
                 'file': r'\FLUvensimV1dynamic.vpm'}
                ]
    ensemble.add_policies(policies)
    
    results = ensemble.perform_experiments(5000)
   
    results = feature_selection(results, classify)
    for entry in results:
        print entry[0] +"\t" + str(entry[1])
Ejemplo n.º 3
0
            value = temp_box.dtype.fields.get(name)[0]
            if value == 'object':
                c_b = box[name][0]
                values = np.asarray([cats[name].index(c) for c in c_b])
                if a[i] != 1:
                    a_i = 1/(maxima[i]-1)
                    values = a_i*values
                else:
                    values = [1/2]
                temp_box[name][0] = values
                temp_box[name][1] =  values
            else:
                temp_box[name][0] = a[i]*box[name][0] + b[i]
                temp_box[name][1] = a[i]*box[name][1] + b[i]
        temp_boxes.append(temp_box)
    boxes= temp_boxes
    return boxes

if __name__ == '__main__':
    log_to_stderr(level= INFO)
    
    model = FluModel(r'..\..\models\flu', "fluCase")
    results = expWorkbench.util.load_results(r'1000 flu cases.cPickle')
    boxes = perform_prim(results, 
                         classify=model.outcomes[1].name, 
                         threshold_type=1,
                         threshold=0.8)
    write_prim_to_stdout(boxes)
    show_boxes_individually(boxes, results)
    plt.show()