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)
def test_bondy1():
tri = cl.load_sample('tail_sample')['paid']
dev = cl.Development(average='simple').fit_transform(tri)
assert round(cl.TailBondy().fit(dev).cdf_.values[0, 0, 0, -2], 3) == 1.028
def test_bondy1():
tri = cl.load_sample("tail_sample")["paid"]
dev = cl.Development(average="simple").fit_transform(tri)
assert round(
float(cl.TailBondy(earliest_age=12).fit(dev).cdf_.values[0, 0, 0, -2]),
3) == 1.028
traditional Bondy method.
"""
import chainladder as cl
# Fit basic development to a triangle
tri = cl.load_sample('tail_sample')['paid']
dev = cl.Development(average='simple').fit_transform(tri)
# Return both the tail factor and the Bondy exponent in the scoring function
scoring = {
'tail_factor': lambda x: x.tail_.values[0, 0],
'bondy_exponent': lambda x: x.b_.values[0, 0]
}
# Vary the 'earliest_age' assumption in GridSearch
param_grid = dict(earliest_age=list(range(12, 120, 12)))
grid = cl.GridSearch(cl.TailBondy(), param_grid, scoring)
results = grid.fit(dev).results_
ax = results.plot(x='earliest_age',
y='bondy_exponent',
title='Bondy Assumption Sensitivity',
marker='o')
results.plot(x='earliest_age',
y='tail_factor',
grid=True,
secondary_y=True,
ax=ax,
marker='o')