def test_clarkldf_weibull(genins):
model = cl.ClarkLDF(growth="weibull").fit(genins)
df = r('ClarkLDF(GenIns, G="weibull")').rx("THETAG")
r_omega = df[0][0]
r_theta = df[0][1]
assert abs(model.omega_.iloc[0, 0] - r_omega) < 1e-2
assert abs(model.theta_.iloc[0, 0] / 12 - r_theta) < 1e-2
def test_clarkldf(genins):
model = cl.ClarkLDF().fit(genins)
df = r("ClarkLDF(GenIns)").rx("THETAG")
r_omega = df[0][0]
r_theta = df[0][1]
assert abs(model.omega_.iloc[0, 0] - r_omega) < 1e-2
assert abs(model.theta_.iloc[0, 0] / 12 - r_theta) < 1e-2
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_clarkcapcod_weibull(genins):
df = r('ClarkCapeCod(GenIns, Premium=10000000+400000*0:9, G="weibull")')
r_omega = df.rx("THETAG")[0][0]
r_theta = df.rx("THETAG")[0][1]
r_elr = df.rx("ELR")[0][0]
premium = genins.latest_diagonal * 0 + 1
premium.values = (np.arange(10) * 400000 + 10000000)[None, None, :, None]
model = cl.ClarkLDF(growth="weibull").fit(genins, sample_weight=premium)
assert abs(model.omega_.iloc[0, 0] - r_omega) < 1e-2
assert abs(model.theta_.iloc[0, 0] / 12 - r_theta) < 1e-2
assert abs(model.elr_.iloc[0, 0] - r_elr) < 1e-2
"""
====================
Clark Growth Curves
====================
This example demonstrates one of the attributes of the :class:`ClarkLDF`. We can
use the growth curve ``G_`` to estimate the percent of ultimate at any given
age.
"""
import chainladder as cl
import numpy as np
# Grab Industry triangles
clrd = cl.load_sample('clrd').groupby('LOB').sum()
# Fit Clark Cape Cod method
model = cl.ClarkLDF(growth='loglogistic').fit(
clrd['CumPaidLoss'], sample_weight=clrd['EarnedPremDIR'].latest_diagonal)
# sample ages
ages = np.linspace(1, 300, 30)
# Plot results
model.G_(ages).T.plot(title='Loglogistic Growth Curves',
grid=True).set(xlabel='Age', ylabel='% of Ultimate')
"""
====================
Clark Residual Plots
====================
This example demonstrates how to recreate the normalized residual plots in
Clarks LDF Curve-Fitting paper (2003).
"""
import chainladder as cl
import matplotlib.pyplot as plt
# Fit the basic model
genins = cl.load_sample('genins')
genins = cl.ClarkLDF().fit(genins)
# Grab Normalized Residuals as a DataFrame
norm_resid = genins.norm_resid_.melt(
var_name='Development Age', value_name='Normalized Residual').dropna()
# Grab Fitted Incremental values as a DataFrame
incremental_fits = genins.incremental_fits_.melt(
var_name='Development Age',
value_name='Expected Incremental Loss').dropna()
# Plot the residuals vs Age and vs Expected Incrementals
fig, ((ax0, ax1)) = plt.subplots(ncols=2, figsize=(15, 5))
# Left plot
norm_resid.plot(x='Development Age',
y='Normalized Residual',
kind='scatter',
grid=True,