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%matplotlib inline

Analyzing Cape Cod Apriori Sensitivity

This example demonstrates the usage of the deterministic CapeCod method and shows the sensitivity of the apriori expectation to various choices of trend and decay.



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import chainladder as cl
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')

# Grab data
ppauto_loss = cl.load_dataset('clrd').groupby('LOB').sum().loc['ppauto']['CumPaidLoss']
ppauto_prem = cl.load_dataset('clrd').groupby('LOB').sum() \
                .loc['ppauto']['EarnedPremDIR'].latest_diagonal.rename('development',['Premium'])

def get_apriori(decay, trend):
    """ Function to grab apriori array from cape cod method """
    cc = cl.CapeCod(decay=decay, trend=trend)
    cc.fit(ppauto_loss, sample_weight=ppauto_prem)
    return cc.detrended_apriori_.to_frame()

def get_plot_data(trend):
    """ Function to grab plot data """
    # Initial apriori DataFrame
    detrended_aprioris = get_apriori(0,trend)
    detrended_aprioris.columns=['decay: 0%']

    # Add columns to apriori DataFrame
    for item in [25, 50, 75, 100]:
        detrended_aprioris[f'decay: {item}%'] = get_apriori(item/100, trend)
    return detrended_aprioris


# Plot Data
fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, sharex=True, figsize=(10,10))
get_plot_data(-0.05).plot(ax=ax00)
get_plot_data(-.025).plot(ax=ax01)
get_plot_data(0).plot(ax=ax10)
get_plot_data(0.025).plot(ax=ax11)
ax00.set_title('Trend: -5%')
ax01.set_title('Trend: -2.5%')
ax10.set_title('Trend: 0%')
ax11.set_title('Trend: 2.5%')
g = fig.suptitle("Private Passenger Auto Cape Cod Detrended Aprioris")