def test_constant_cdf():
dev = cl.Development().fit(cl.load_dataset('raa'))
link_ratios = {(num + 1) * 12: item
for num, item in enumerate(dev.ldf_.values[0, 0, 0, :])}
dev_c = cl.DevelopmentConstant(patterns=link_ratios,
style='ldf').fit(cl.load_dataset('raa'))
assert_allclose(dev.cdf_.values, dev_c.cdf_.values, atol=1e-5)
def test_constant_callable(clrd, atol):
agway = clrd.loc['Agway Ins Co', 'CumPaidLoss']
def paid_cdfs(x):
""" A function that returns different CDFs depending on a specified LOB """
cdfs = {
'comauto':
[3.832, 1.874, 1.386, 1.181, 1.085, 1.043, 1.022, 1.013, 1.007, 1],
'medmal': [
24.168, 4.127, 2.103, 1.528, 1.275, 1.161, 1.088, 1.047, 1.018,
1
],
'othliab':
[10.887, 3.416, 1.957, 1.433, 1.231, 1.119, 1.06, 1.031, 1.011, 1],
'ppauto':
[2.559, 1.417, 1.181, 1.084, 1.04, 1.019, 1.009, 1.004, 1.001, 1],
'prodliab':
[13.703, 5.613, 2.92, 1.765, 1.385, 1.177, 1.072, 1.034, 1.008, 1],
'wkcomp':
[4.106, 1.865, 1.418, 1.234, 1.141, 1.09, 1.056, 1.03, 1.01, 1]
}
patterns = pd.DataFrame(cdfs, index=range(12, 132, 12)).T
return patterns.loc[x.loc['LOB']].to_dict()
model = cl.DevelopmentConstant(patterns=paid_cdfs,
callable_axis=1,
style='cdf')
assert abs(
model.fit_transform(agway).cdf_.loc['comauto'].iloc[..., 0].sum() -
3.832) < atol
def test_constant_cdf(raa):
dev = cl.Development().fit(raa)
xp = dev.ldf_.get_array_module()
link_ratios = {(num + 1) * 12: item
for num, item in enumerate(dev.ldf_.values[0, 0, 0, :])}
dev_c = cl.DevelopmentConstant(patterns=link_ratios, style="ldf").fit(raa)
assert xp.allclose(dev.cdf_.values, dev_c.cdf_.values, atol=1e-5)
def test_constant_cdf():
dev = cl.Development().fit(cl.load_sample('raa'))
xp = cp.get_array_module(dev.ldf_.values)
link_ratios = {(num + 1) * 12: item
for num, item in enumerate(dev.ldf_.values[0, 0, 0, :])}
dev_c = cl.DevelopmentConstant(patterns=link_ratios,
style='ldf').fit(cl.load_sample('raa'))
xp.testing.assert_allclose(dev.cdf_.values, dev_c.cdf_.values, atol=1e-5)
def paid_cdfs(x):
""" A function that returns different CDFs depending on a specified LOB """
cdfs = {
'comauto':
[3.832, 1.874, 1.386, 1.181, 1.085, 1.043, 1.022, 1.013, 1.007, 1],
'medmal':
[24.168, 4.127, 2.103, 1.528, 1.275, 1.161, 1.088, 1.047, 1.018, 1],
'othliab':
[10.887, 3.416, 1.957, 1.433, 1.231, 1.119, 1.06, 1.031, 1.011, 1],
'ppauto':
[2.559, 1.417, 1.181, 1.084, 1.04, 1.019, 1.009, 1.004, 1.001, 1],
'prodliab':
[13.703, 5.613, 2.92, 1.765, 1.385, 1.177, 1.072, 1.034, 1.008, 1],
'wkcomp':
[4.106, 1.865, 1.418, 1.234, 1.141, 1.09, 1.056, 1.03, 1.01, 1]
}
patterns = pd.DataFrame(cdfs, index=range(12, 132, 12)).T
return patterns.loc[x.loc['LOB']].to_dict()
# If it works with pandas apply on the triangle index...
agway.index.apply(paid_cdfs, axis=1)
# ... then it will work in DevelopmentConstant
model = cl.DevelopmentConstant(patterns=paid_cdfs,
callable_axis=1,
style='cdf')
model.fit(agway)
model.ldf_.T.plot(kind='bar', grid=True, title='Agway Insurance LDFs')