def test_pipeline():
tri = cl.load_sample('clrd').groupby('LOB').sum()[[
'CumPaidLoss', 'IncurLoss', 'EarnedPremDIR'
]]
tri['CaseIncurredLoss'] = tri['IncurLoss'] - tri['CumPaidLoss']
X = tri[['CumPaidLoss', 'CaseIncurredLoss']]
sample_weight = tri['EarnedPremDIR'].latest_diagonal
dev = [
cl.Development(),
cl.ClarkLDF(),
cl.Trend(),
cl.IncrementalAdditive(),
cl.MunichAdjustment(paid_to_incurred=('CumPaidLoss',
'CaseIncurredLoss')),
cl.CaseOutstanding(paid_to_incurred=('CumPaidLoss',
'CaseIncurredLoss'))
]
tail = [cl.TailCurve(), cl.TailConstant(), cl.TailBondy(), cl.TailClark()]
ibnr = [
cl.Chainladder(),
cl.BornhuetterFerguson(),
cl.Benktander(n_iters=2),
cl.CapeCod()
]
for model in list(itertools.product(dev, tail, ibnr)):
print(model)
cl.Pipeline(
steps=[('dev',
model[0]), ('tail',
model[1]), ('ibnr', model[2])]).fit_predict(
X, sample_weight=sample_weight).ibnr_.sum(
'origin').sum('columns').sum()
def test_basic_transform(raa):
cl.Development().fit_transform(raa)
cl.ClarkLDF().fit_transform(raa)
cl.TailClark().fit_transform(raa)
cl.TailBondy().fit_transform(raa)
cl.TailConstant().fit_transform(raa)
cl.TailCurve().fit_transform(raa)
cl.BootstrapODPSample().fit_transform(raa)
cl.IncrementalAdditive().fit_transform(raa,
sample_weight=raa.latest_diagonal)
def test_basic_transform():
tri = cl.load_sample("raa")
cl.Development().fit_transform(tri)
cl.ClarkLDF().fit_transform(tri)
cl.TailClark().fit_transform(tri)
cl.TailBondy().fit_transform(tri)
cl.TailConstant().fit_transform(tri)
cl.TailCurve().fit_transform(tri)
cl.BootstrapODPSample().fit_transform(tri)
cl.IncrementalAdditive().fit_transform(tri,
sample_weight=tri.latest_diagonal)