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Merged
henrydingliu merged 17 commits into from
Jul 10, 2026
Merged

Various Doctest Fixes #1101

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ecf50e7
FIX: Fix doctest.
genedan Jul 8, 2026
c3e839e
FIX: Fix doctest.
genedan Jul 8, 2026
c0c9c57
FIX: Fix doctest.
genedan Jul 8, 2026
7f5ae19
Update common.py
henrydingliu Jul 9, 2026
a8494e1
Update bornferg.py
henrydingliu Jul 9, 2026
b185c51
Update clark.py
henrydingliu Jul 9, 2026
66347fa
Update capecod.py
henrydingliu Jul 9, 2026
7edf249
Update benktander.py
henrydingliu Jul 9, 2026
8078da9
Update chainladder.py
henrydingliu Jul 9, 2026
bfb6b36
Update chainladder.py
henrydingliu Jul 9, 2026
7ca0a66
Update bornferg.py
henrydingliu Jul 9, 2026
327cbd1
Update bornferg.py
henrydingliu Jul 10, 2026
bf9a4f4
Update benktander.py
henrydingliu Jul 10, 2026
0275df8
Update benktander.py
henrydingliu Jul 10, 2026
5f8207d
Update clark.py
henrydingliu Jul 10, 2026
7c6072c
Update bornferg.py
henrydingliu Jul 10, 2026
a568e33
Update pytest.yml
henrydingliu Jul 10, 2026
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11 changes: 11 additions & 0 deletions chainladder/core/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,6 +174,17 @@ def pipe(self, func, *args, **kwargs):
>>> import chainladder as cl
>>> raa = cl.load_sample('raa')
>>> raa.pipe(lambda tri: tri.loc[..., 48:])
48 60 72 84 96 108 120
1981 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0
1982 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN
1983 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN
1984 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN
1985 22169.0 25955.0 26180.0 NaN NaN NaN NaN
1986 12935.0 15852.0 NaN NaN NaN NaN NaN
1987 12314.0 NaN NaN NaN NaN NaN NaN
1988 NaN NaN NaN NaN NaN NaN NaN
1989 NaN NaN NaN NaN NaN NaN NaN
1990 NaN NaN NaN NaN NaN NaN NaN
"""
return func(self, *args, **kwargs)

Expand Down
32 changes: 16 additions & 16 deletions chainladder/development/clark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,29 +134,29 @@ class ClarkLDF(DevelopmentBase):
clrd = cl.load_sample("clrd")[["CumPaidLoss"]]
print(len(clrd.index))
m = cl.ClarkLDF(groupby="LOB").fit(clrd)
print(m.omega_.round(3))
print(m.theta_.round(3))
print(m.omega_.round(2))
print(m.theta_.round(2))

.. testoutput::
:options: +NORMALIZE_WHITESPACE

775
CumPaidLoss
LOB
comauto 1.082
medmal 1.889
othliab 1.468
ppauto 1.149
prodliab 1.441
wkcomp 1.107
LOB
comauto 1.08
medmal 1.89
othliab 1.47
ppauto 1.15
prodliab 1.44
wkcomp 1.11
CumPaidLoss
LOB
comauto 20.481
medmal 35.128
othliab 37.745
ppauto 10.023
prodliab 64.352
wkcomp 20.111
LOB
comauto 20.48
medmal 35.13
othliab 37.75
ppauto 10.02
prodliab 64.35
wkcomp 20.11

"""

Expand Down
9 changes: 3 additions & 6 deletions chainladder/methods/benktander.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,6 @@ class Benktander(MethodBase):
.. testcode::

xyz = cl.load_sample("xyz")

ibnr = cl.Benktander().fit(X=xyz["Paid"], sample_weight=xyz["Premium"].latest_diagonal).ibnr_
print(ibnr)

Expand All @@ -76,7 +75,6 @@ class Benktander(MethodBase):
.. testcode::

xyz = cl.load_sample("xyz")

bk_ibnr = (
cl.Benktander(n_iters=1)
.fit(X=xyz["Paid"], sample_weight=xyz["Premium"].latest_diagonal)
Expand Down Expand Up @@ -109,7 +107,6 @@ class Benktander(MethodBase):
.. testcode::

xyz = cl.load_sample("xyz")

bk_ibnr = cl.Benktander(n_iters=1000).fit(X=xyz["Paid"], sample_weight=xyz["Premium"].latest_diagonal).ibnr_
cl_ibnr = cl.Chainladder().fit(xyz["Paid"]).ibnr_
print(bk_ibnr - cl_ibnr)
Expand Down Expand Up @@ -165,7 +162,6 @@ def fit(self, X, y=None, sample_weight=None):
.. testcode::

xyz = cl.load_sample("xyz")

ultimate = (
cl.Benktander(apriori=1, n_iters=2)
.fit(X=xyz["Paid"], sample_weight=xyz["Premium"].latest_diagonal)
Expand Down Expand Up @@ -218,6 +214,7 @@ def predict(self, X, sample_weight=None):
current Triangle and a refreshed apriori.

.. testsetup::

import chainladder as cl

.. testcode::
Expand All @@ -229,8 +226,8 @@ def predict(self, X, sample_weight=None):
model = cl.Benktander(apriori=1.0, n_iters=2).fit(
tr_prior, sample_weight=apriori_prior
)

print(model.predict(tr, sample_weight=apriori).ultimate_)
ultimate = model.predict(tr, sample_weight=apriori).ultimate_
print(ultimate)

.. testoutput::

Expand Down
13 changes: 7 additions & 6 deletions chainladder/methods/bornferg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,6 @@ class BornhuetterFerguson(Benktander):

raa = cl.load_sample("raa")
premium = raa.latest_diagonal * 0 + 40_000 # zero out and add 40,000 to each origin

ibnr = cl.BornhuetterFerguson(apriori=0.7).fit(X=raa, sample_weight=premium).ibnr_
print(ibnr)

Expand All @@ -77,7 +76,6 @@ class BornhuetterFerguson(Benktander):

raa = cl.load_sample("raa")
premium = raa.latest_diagonal * 0 + 40_000 * 0.7 # premium is modified by 70%

ibnr = cl.BornhuetterFerguson().fit(X=raa, sample_weight=premium).ibnr_
print(ibnr)

Expand Down Expand Up @@ -123,13 +121,15 @@ def fit(self, X, y=None, sample_weight=None):
Fit returns the estimator itself, with ``ultimate_`` populated.

.. testsetup::

import chainladder as cl

.. testcode::

tr = cl.load_sample('ukmotor')
apriori = cl.Chainladder().fit(tr).ultimate_ * 0 + 14000
print(cl.BornhuetterFerguson(apriori=1.0).fit(tr, sample_weight=apriori))
model = cl.BornhuetterFerguson(apriori=1.0).fit(tr, sample_weight=apriori)
print(model)

.. testoutput::

Expand Down Expand Up @@ -160,6 +160,7 @@ def predict(self, X, sample_weight=None):
current Triangle and a refreshed apriori.

.. testsetup::

import chainladder as cl

.. testcode::
Expand All @@ -171,10 +172,10 @@ def predict(self, X, sample_weight=None):
model = cl.BornhuetterFerguson(apriori=1.0).fit(
tr_prior, sample_weight=apriori_prior
)
ultimate = model.predict(tr, sample_weight=apriori).ultimate_
print(ultimate)

print(model.predict(tr, sample_weight=apriori).ultimate_)

.. testoutput
.. testoutput::

2261
2007 12690.000000
Expand Down
2 changes: 1 addition & 1 deletion chainladder/methods/capecod.py
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Original file line number Diff line number Diff line change
Expand Up @@ -218,7 +218,7 @@ def fit(self, X, y=None, sample_weight=None):
exposure = cl.Chainladder().fit(tr).ultimate_ * 0 + 20000
print(cl.CapeCod(trend=0.05).fit(tr, sample_weight=exposure))

.. testoutput:
.. testoutput::

CapeCod(trend=0.05)
"""
Expand Down
3 changes: 2 additions & 1 deletion chainladder/methods/chainladder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -124,12 +124,13 @@ def fit(self, X, y=None, sample_weight=None):
attribute access.

.. testsetup::

import chainladder as cl

.. testcode::

tr = cl.load_sample('ukmotor')
cl.Chainladder().fit(tr)
print(cl.Chainladder().fit(tr))

.. testoutput::

Expand Down
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