def load_model(spec, all):
name = spec["name"]
params = spec["parameters"]
if "simulations" in spec:
simul = int(spec["simulations"])
else:
simul = 10000
if type(params) == dict:
model = pyfair.FairModel(name=name, n_simulations=simul)
model.bulk_import_data(params)
all[name] = model
return model
if type(params) == list:
models = [load_model(m, all) for m in params]
metamodel = pyfair.FairMetaModel(name=name, models=models)
all[name] = metamodel
return metamodel
raise RuntimeError("Bad model parameters")
import pyfair
# Create using LEF (PERT), PL, (PERT), and SL (constant)
model1 = pyfair.FairModel(name="Regular Model 1", n_simulations=10_000)
model1.input_data('Loss Event Frequency', low=20, mode=100, high=900)
model1.input_data('Primary Loss', low=3_000_000, mode=3_500_000, high=5_000_000)
model1.input_data('Secondary Loss', constant=3_500_000)
model1.calculate_all()
# Create another model using LEF (Normal) and LM (PERT)
model2 = pyfair.FairModel(name="Regular Model 2", n_simulations=10_000)
model2.input_data('Loss Event Frequency', mean=.3, stdev=.1)
model2.input_data('Loss Magnitude', low=2_000_000_000, mode=3_000_000_000, high=5_000_000_000)
model2.calculate_all()
# Create metamodel by combining 1 and 2
mm = pyfair.FairMetaModel(name='My Meta Model!', models=[model1, model2])
mm.calculate_all()
# Create report comparing 2 vs metamodel.
fsr = pyfair.FairSimpleReport([model1, mm])
fsr.to_html('output.html')
def result():
if 'Email' in session:
g.username = session['Email']
if request.method == "POST":
if request.form['action'] == 'generate':
try:
global members2
members2 = []
# storing size of form values like number of participants
rows = int((len(request.form) - 1) / 8)
for i in range(1, rows + 1):
row_array = {}
fields = Field()
fields.field_name = request.form["field{}".format(i)]
fields.gamma = request.form["gamma{}".format(i)]
fields.low = request.form["low{}".format(i)]
fields.mode = request.form["mode{}".format(i)]
fields.high = request.form["high{}".format(i)]
fields.constant = request.form["constant{}".format(i)]
fields.mean = request.form["mean{}".format(i)]
fields.std = request.form["std{}".format(i)]
row_array['name'] = fields.field_name
if (fields.gamma != ""):
row_array['gamma'] = int(fields.gamma)
if (fields.low != ""):
row_array['low'] = int(fields.low)
if (fields.mode != ""):
row_array['mode'] = int(fields.mode)
if (fields.high != ""):
row_array['high'] = int(fields.high)
if (fields.constant != ""):
row_array['constant'] = int(fields.constant)
if (fields.mean != ""):
row_array['mean'] = float(fields.mean)
if (fields.std != ""):
row_array['std'] = float(fields.std)
members2.append(row_array)
# for storing data into array
model1 = pyfair.FairModel(name="Regular Model 1",
n_simulations=simulation)
global members1
for i in range(len(members1)):
if 'low' in members1[i].keys():
model1.input_data(members1[i]['name'],
low=members1[i]['low'],
mode=members1[i]['mode'],
high=members1[i]['high'])
elif 'constant' in members1[i].keys():
model1.input_data(members1[i]['name'],
constant=members1[i]['constant'])
elif 'mean' in members1[i].keys():
model1.input_data(members1[i]['name'],
mean=members1[i]['mean'],
stdev=members1[i]['std'])
model1.calculate_all()
model2 = pyfair.FairModel(name="Regular Model 2",
n_simulations=simulation)
for i in range(len(members2)):
if 'low' in members2[i].keys():
model2.input_data(members2[i]['name'],
low=members2[i]['low'],
mode=members2[i]['mode'],
high=members2[i]['high'])
elif 'constant' in members2[i].keys():
model2.input_data(members2[i]['name'],
constant=members2[i]['constant'])
elif 'mean' in members2[i].keys():
model2.input_data(members2[i]['name'],
mean=members2[i]['mean'],
stdev=members2[i]['std'])
mm = pyfair.FairMetaModel(name='My Meta Model!',
models=[model1, model2])
mm.calculate_all()
# #modelMMResult = mm.export_results().to_dict()
fsr = pyfair.FairSimpleReport([model1, mm])
fsr.to_html('./templates/result.html')
# print (modelMMResult)
return render_template("result.html")
except:
flash("Please Fill the information Carefully", "danger")
return redirect(url_for("dashboard"))
else:
redirect(url_for("dashboard"))
else:
return redirect(url_for("dashboard"))
else:
return redirect(url_for("login"))