def mack_p(data, average, est_sigma, tail):
if tail:
return cl.MackChainladder().fit(
cl.TailCurve(curve='exponential').fit_transform(
cl.Development(average=average,
sigma_interpolation=est_sigma).fit_transform(
cl.load_sample(data))))
else:
return cl.MackChainladder().fit(
cl.Development(average=average,
sigma_interpolation=est_sigma).fit_transform(
cl.load_sample(data)))
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_asymmetric():
r("Paid <- matrix(NA, 45, 45)")
r("Paid[seq(1,45,4),] <- qpaid")
out = r("M <- MackChainLadder(Paid)")
tri = cl.load_sample("quarterly")["paid"]
xp = tri.get_array_module()
assert round(float(xp.array(out.rx("Mack.S.E")[0])[-1, -1]), 2) == round(
float(cl.MackChainladder().fit(tri).summary_.to_frame().iloc[-1, -1]), 2
)
def test_mack_predict():
mack = cl.MackChainladder().fit(raa_1989)
mack.predict(raa_1989)
def test_mack_malformed():
a = cl.load_sample('raa')
b = a.iloc[:, :, :-1]
x = cl.MackChainladder().fit(a)
y = cl.MackChainladder().fit(b)
assert x.process_risk_.iloc[:, :, :-1] == y.process_risk_
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_
"""
========================
Mack Chainladder Example
========================
This example demonstrates how you can can use the Mack Chainladder method.
"""
import pandas as pd
import chainladder as cl
import seaborn as sns
sns.set_style('whitegrid')
# Load the data
data = cl.load_dataset('raa')
# Compute Mack Chainladder ultimates and Std Err using 'simple' average
mack = cl.MackChainladder()
dev = cl.Development(average='volume')
mack.fit(dev.fit_transform(data))
# Plotting
plot_data = mack.summary_.to_frame()
g = plot_data[['Latest', 'IBNR']] \
.plot(kind='bar', stacked=True,
yerr=pd.DataFrame({'latest': plot_data['Mack Std Err']*0,
'IBNR': plot_data['Mack Std Err']}),
ylim=(0, None), title='Mack Chainladder Ultimate')
g.set_xlabel('Accident Year')
g.set_ylabel('Loss')
