plot_extrap_period.py

"""
================================
Extrapolation Period Sensitivity
================================

This example demonstrates the ``extrap_periods`` functionality of the `TailCurve`
estimator.  The estimator defaults to extrapolating out 100 periods.  However,
we can see that the "Inverse Power" curve fit doesn't converge to its asymptotic
value.

"""
import chainladder as cl

tri = cl.load_sample('clrd').groupby('LOB').sum().loc['medmal', 'CumPaidLoss']

# Create a fuction to grab the scalar tail value.
def scoring(model):
    """ Scoring functions must return a scalar """
    return model.tail_.iloc[0, 0]

# Create a grid of scenarios
param_grid = dict(
    extrap_periods=list(range(1, 100, 6)),
    curve=['inverse_power', 'exponential'])

# Fit Grid
model = cl.GridSearch(cl.TailCurve(), param_grid=param_grid, scoring=scoring).fit(tri)

# Plot results
model.results_.pivot(columns='curve', index='extrap_periods', values='score').plot(
    grid=True, ylim=(1,None), title='Curve Fit Sensitivity to Extrapolation Period').set(
    ylabel='Tail Factor');