def test_constant_balances():
raa = cl.load_dataset('quarterly')
assert np.prod(
cl.TailConstant(1.05,
decay=0.8).fit(raa).ldf_.iloc[0,
1].values[0, 0, 0,
-5:]) == 1.05
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_struhuss():
X = cl.load_sample("cc_sample")["loss"]
X = cl.TailConstant(tail=1 / 0.85).fit_transform(
cl.Development().fit_transform(X))
sample_weight = cl.load_sample("cc_sample")["exposure"].latest_diagonal
ibnr = int(
cl.CapeCod(trend=0.07,
decay=0.75).fit(X, sample_weight=sample_weight).ibnr_.sum())
assert ibnr == 17052
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)
def test_constant_balances():
raa = cl.load_sample('quarterly')
xp = raa.get_array_module()
assert round(
float(
xp.prod(
cl.TailConstant(1.05,
decay=0.8).fit(raa).ldf_.iloc[0,
1].values[0, 0,
0,
-5:])),
3) == 1.050
def test_constant_balances(qtr):
xp = qtr.get_array_module()
assert (round(
float(
xp.prod(
cl.TailConstant(1.05,
decay=0.8).fit(qtr).ldf_.iloc[0,
1].values[0, 0,
0,
-5:])),
3,
) == 1.050)
def test_mack_to_triangle():
assert (
cl.MackChainladder()
.fit(
cl.TailConstant().fit_transform(
cl.Development().fit_transform(cl.load_sample("ABC"))
)
)
.summary_
== cl.MackChainladder()
.fit(cl.Development().fit_transform(cl.load_sample("ABC")))
.summary_
)
def test_mack_to_triangle():
assert cl.MackChainladder().fit(cl.TailConstant().fit_transform(cl.Development().fit_transform(cl.load_dataset('ABC')))).summary_ == \
cl.MackChainladder().fit(cl.Development().fit_transform(cl.load_dataset('ABC'))).summary_
