Selecting BornhuetterFerguson Apriori

import chainladder as cl
import pandas as pd

This example demonstrates how you can can use the output of one method as the apriori selection for the Bornhuetter-Ferguson Method.

We use basic arithmetic to build up an Apriori rather than initializing it explicitely with cl.Triangle

# Create Aprioris as the mean AY chainladder ultimate
raa = cl.load_sample("RAA")

cl_ult = cl.Chainladder().fit(raa).ultimate_  # Chainladder Ultimate
apriori = cl_ult * 0 + (cl_ult.sum() / 10)  # Mean Chainladder Ultimate
bf_ult = cl.BornhuetterFerguson(apriori=1).fit(raa, sample_weight=apriori).ultimate_

output = pd.concat(
    (
        cl_ult.to_frame().rename({"2261": "Chainladder"}, axis=1),
        bf_ult.to_frame().rename({"2261": "BornhuetterFerguson"}, axis=1),
    ),
    axis=1,
)

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import matplotlib.pyplot as plt

plt.style.use("ggplot")
%config InlineBackend.figure_format = 'retina'

# Plot of Ultimates

ax = output.plot(grid=True, marker="o", xlabel="Accident Year", ylabel="Ultimate");