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%matplotlib inline

Basic Assumption Tuning with Pipeline and Gridsearch

This example demonstrates testing multiple number of periods in the development transformer to see its influence on the overall ultimate estimate.



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import seaborn as sns
sns.set_style('whitegrid')

import chainladder as cl

tri = cl.load_dataset('abc')

# Set up Pipeline
steps = [('dev',cl.Development()),
         ('chainladder',cl.Chainladder())]
params = dict(dev__n_periods=[item for item in range(2,11)])
pipe = cl.Pipeline(steps=steps)

# Develop scoring function that returns an Ultimate/Incurred Ratio
scoring = lambda x: x.named_steps.chainladder.ultimate_.sum() / tri.latest_diagonal.sum()

# Run GridSearch
grid = cl.GridSearch(pipe, params, scoring).fit(tri)

# Plot Results
grid.results_.plot(x='dev__n_periods',y='score', marker='o').set(ylabel='Ultimate / Incurred');