Skip to content

Disposal #1034

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
henrydingliu merged 3 commits into from
Jun 21, 2026
+73 −53
Merged

Disposal #1034

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
c6588fe
docstring and additional tests
henrydingliu Jun 20, 2026
3bb4e35
bugbot proof
henrydingliu Jun 20, 2026
4b8dea4
Update test_disposal.py
henrydingliu Jun 21, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
96 changes: 51 additions & 45 deletions chainladder/adjustments/disposal.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -77,64 +77,70 @@ class DisposalRate(DevelopmentBase):

Examples
--------
``trend`` tilts the case-adequacy adjustment before ``Incurred`` is rebuilt;
on the ``MedMal`` slice the inner diagonals of the adjusted ``Incurred``
triangle restate materially between ``0%`` and ``15%`` annual drift, while
the latest diagonal is preserved.

This adjustment method re-apportions future loss emergence based on a '% of ultimate' emergence pattern.
The ultimate can come from another triangle. A common use case is to forecast payment pattern based on incurred ultimate.

.. testsetup::

import chainladder as cl
import numpy as np

.. testcode::

tri = cl.load_sample("berqsherm").loc["MedMal"]
base = cl.BerquistSherman(
paid_amount="Paid",
incurred_amount="Incurred",
reported_count="Reported",
closed_count="Closed",
trend=0.0,
).fit(tri)
tilted = cl.BerquistSherman(
paid_amount="Paid",
incurred_amount="Incurred",
reported_count="Reported",
closed_count="Closed",
trend=0.15,
).fit(tri)
print(np.round(base.adjusted_triangle_["Incurred"], 0))
clrd = cl.load_sample('clrd').sum()
ult = cl.Chainladder().fit(clrd['IncurLoss']).ultimate_
dr = cl.DisposalRate().fit_transform(clrd['CumPaidLoss'],sample_weight = ult)

Once we apply this adjustment method via a `fit_transform`, we can examin the emergence pattern via `disposal_rate_tri`.

.. testcode::

dr.disposal_rate_tri

.. testoutput::
:options: +NORMALIZE_WHITESPACE

12 24 36 48 60 72 84 96
1969 9883293.0 27420103.0 35879085.0 43105257.0 33438702.0 30397324.0 25723694.0 23506000.0
1970 8641763.0 31305782.0 41543535.0 48550616.0 38203864.0 36222888.0 32216000.0 NaN
1971 11733960.0 43887171.0 61649896.0 64917222.0 51410209.0 48377000.0 NaN NaN
1972 13638651.0 50987209.0 66696278.0 72777529.0 61163000.0 NaN NaN NaN
1973 14387930.0 45470590.0 56577593.0 73733000.0 NaN NaN NaN NaN
1974 13630366.0 47189379.0 63477000.0 NaN NaN NaN NaN NaN
1975 15036351.0 48904000.0 NaN NaN NaN NaN NaN NaN
1976 15791000.0 NaN NaN NaN NaN NaN NaN NaN

12 24 36 48 60 72 84 96 108 120
1988 0.313923 0.619459 0.774429 0.865377 0.919077 0.948898 0.964643 0.973184 0.980224 0.983063
1989 0.321526 0.626023 0.781086 0.872345 0.924842 0.952533 0.967690 0.977373 0.981938 NaN
1990 0.329567 0.634056 0.790752 0.880273 0.927029 0.952951 0.968379 0.976049 NaN NaN
1991 0.330035 0.636233 0.791888 0.881010 0.929460 0.954694 0.968533 NaN NaN NaN
1992 0.342613 0.650521 0.801875 0.885976 0.932865 0.956495 NaN NaN NaN NaN
1993 0.353784 0.663303 0.810639 0.894414 0.939009 NaN NaN NaN NaN NaN
1994 0.367530 0.670460 0.814661 0.897244 NaN NaN NaN NaN NaN NaN
1995 0.379650 0.680979 0.821603 NaN NaN NaN NaN NaN NaN NaN
1996 0.395603 0.688621 NaN NaN NaN NaN NaN NaN NaN NaN
1997 0.393820 NaN NaN NaN NaN NaN NaN NaN NaN NaN

The estimated pattern is stored in `disposal_`.

.. testcode::

print(np.round(tilted.adjusted_triangle_["Incurred"], 0))
dr.disposal_

.. testoutput::

12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult 120-Ult 132-Ult
(All) 0.112105 0.336242 0.545897 0.693774 0.812877 0.905045 0.942998 0.974365 0.990868 1.0 1.0

`full_triangle_` now reflects the disposal-rate-based forecast.

.. testcode::

dr.full_triangle_

.. testoutput::
:options: +NORMALIZE_WHITESPACE

12 24 36 48 60 72 84 96
1969 3793504.0 12084942.0 18563821.0 25924316.0 23516364.0 24979245.0 24016864.0 23506000.0
1970 3760482.0 15830500.0 24615996.0 33169802.0 30722141.0 33362729.0 32216000.0 NaN
1971 5982185.0 25583831.0 41384825.0 50323342.0 46191356.0 48377000.0 NaN NaN
1972 7819355.0 33794110.0 51361061.0 64559286.0 61163000.0 NaN NaN NaN
1973 9533246.0 34585431.0 49667342.0 73733000.0 NaN NaN NaN NaN
1974 10348458.0 41241243.0 63477000.0 NaN NaN NaN NaN NaN
1975 13102479.0 48904000.0 NaN NaN NaN NaN NaN NaN
1976 15791000.0 NaN NaN NaN NaN NaN NaN NaN

12 24 36 48 60 72 84 96 108 120 9999
1988 3577780.0 7.059966e+06 8.826151e+06 9.862687e+06 1.047470e+07 1.081458e+07 1.099401e+07 1.109136e+07 1.117159e+07 1.120395e+07 1.139698e+07
1989 4090680.0 7.964702e+06 9.937520e+06 1.109859e+07 1.176649e+07 1.211879e+07 1.231163e+07 1.243483e+07 1.249290e+07 1.251646e+07 1.272270e+07
1990 4578442.0 8.808486e+06 1.098535e+07 1.222900e+07 1.287854e+07 1.323867e+07 1.345299e+07 1.355956e+07 1.363458e+07 1.366101e+07 1.389229e+07
1991 4648756.0 8.961755e+06 1.115424e+07 1.240959e+07 1.309204e+07 1.344748e+07 1.364241e+07 1.375400e+07 1.382878e+07 1.385512e+07 1.408564e+07
1992 5139142.0 9.757699e+06 1.202798e+07 1.328948e+07 1.399282e+07 1.434727e+07 1.454438e+07 1.465904e+07 1.473589e+07 1.476295e+07 1.499983e+07
1993 5653379.0 1.059942e+07 1.295381e+07 1.429252e+07 1.500514e+07 1.533589e+07 1.553037e+07 1.564351e+07 1.571933e+07 1.574603e+07 1.597976e+07
1994 6246447.0 1.139496e+07 1.384576e+07 1.524933e+07 1.593547e+07 1.629529e+07 1.650686e+07 1.662994e+07 1.671242e+07 1.674147e+07 1.699574e+07
1995 6473843.0 1.161215e+07 1.401010e+07 1.527961e+07 1.597603e+07 1.634123e+07 1.655597e+07 1.668088e+07 1.676460e+07 1.679409e+07 1.705215e+07
1996 6591599.0 1.147391e+07 1.365956e+07 1.491261e+07 1.559998e+07 1.596045e+07 1.617240e+07 1.629570e+07 1.637833e+07 1.640743e+07 1.666215e+07
1997 6451896.0 1.106345e+07 1.330436e+07 1.458909e+07 1.529384e+07 1.566342e+07 1.588073e+07 1.600714e+07 1.609186e+07 1.612170e+07 1.638286e+07

"""

Expand Down Expand Up @@ -216,7 +222,7 @@ def fit(
super().fit(ult.values,X.values,self.w_)
#keep attributes
self.disposal_ = self._param_property(self.disposal_rate_tri,self.params_.slope_[...,0][..., None, :])
self.disposal_ = concat((self.disposal_,(ult/ult).iloc[:,:,0,:].rename("development", [9999])),axis=3)
self.disposal_ = concat((self.disposal_,(X.latest_diagonal*0 + 1).iloc[:,:,0,:].rename("development", [9999])),axis=3)
self.disposal_.is_cumulative = True
self.disposal_.is_pattern = False
self.incr_disposal_ = self.disposal_.cum_to_incr()
Expand Down
30 changes: 22 additions & 8 deletions chainladder/adjustments/tests/test_disposal.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
import numpy as np
import pytest

def test_disposal():
def test_friedland_fidelity():
tri = cl.load_sample('friedland_gl_insurer')['Closed Claim Counts']
ult_tri = cl.Triangle(
data = {
Expand Down Expand Up @@ -30,13 +30,27 @@ def test_disposal():
])
assert np.all(abs(lhs[~np.isnan(lhs)] - rhs <= 1))

def test_disposal_no_weight(raa):
tri = raa.set_backend('sparse')
def test_no_weight_exception(raa):
with pytest.raises(ValueError):
dr = cl.DisposalRate().fit(tri)
ult = cl.Chainladder().fit(tri).ultimate_
dr = cl.DisposalRate().fit(tri,sample_weight=ult)
dr = cl.DisposalRate().fit(raa)
ult = cl.Chainladder().fit(raa).ultimate_
dr = cl.DisposalRate().fit(raa,sample_weight=ult)
with pytest.raises(ValueError):
est = dr.transform(tri)
est = dr.transform(raa)

def test_cl_parity(raa):
"""
A no-tail, full-triangle, volume-weighted Chainladder estimator coincides with the disposal rate adjustment.
"""
tri = raa.set_backend('sparse')
dev = cl.Development().fit_transform(tri)
est = cl.Chainladder().fit(dev)
dr = cl.DisposalRate().fit_transform(raa,sample_weight=est.ultimate_)
assert np.all(dr.full_triangle_.round(3).values[...,:-1] == est.full_triangle_.round(3).values[...,:-2])

def test_sparse_transform(raa):
raa_sparse = raa.set_backend('sparse')
from chainladder.utils.sparse import sp
dr = cl.DisposalRate().fit_transform(raa_sparse,sample_weight=cl.Chainladder().fit(raa_sparse).ultimate_)
assert isinstance(dr.full_triangle_.values,sp.COO)


Loading