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adding support for multi-triangle in mackchainladder #873

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2 changes: 1 addition & 1 deletion chainladder/methods/mack.py
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Original file line number Diff line number Diff line change
Expand Up @@ -242,7 +242,7 @@ def _get_full_std_err_(self, X=None):
val = xp.broadcast_to(xp.array(avg + [avg[-1]]), X.shape)
weight = xp.sqrt(full.values[..., : len(X.ddims)] ** (2 - val))
obj.values = X.sigma_.values / num_to_nan(weight)
w = lxp.concatenate((X.w_, lxp.ones((1, 1, val.shape[2], 1))), 3)
w = lxp.concatenate((X.w_, lxp.ones((val.shape[0], val.shape[1], val.shape[2], 1))), 3)
w[xp.isnan(w)] = 1
obj.values = xp.nan_to_num(obj.values) * xp.array(w)
obj.valuation_date = full.valuation_date
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11 changes: 9 additions & 2 deletions chainladder/methods/tests/test_mack.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import chainladder as cl
import numpy as np

def test_mack_to_triangle():
assert (
Expand All @@ -14,10 +15,16 @@ def test_mack_to_triangle():
.summary_
)


def test_mack_malformed():
a = cl.load_sample('raa')
b = a.iloc[:, :, :-1]
x = cl.MackChainladder().fit(a)
y = cl.MackChainladder().fit(b)
assert x.process_risk_.iloc[:,:,:-1] == y.process_risk_
assert x.process_risk_.iloc[:,:,:-1] == y.process_risk_

def test_multi_triangle_mack(clrd,atol):
tri = clrd.loc['Agway Ins Co']['IncurLoss','CumPaidLoss']
mack = cl.MackChainladder().fit(tri)
for i in range(len(tri.index)):
for j in range(len(tri.columns)):
assert np.all(abs(mack.full_std_err_.iloc[i,j].values-cl.MackChainladder().fit(tri.iloc[i,j]).full_std_err_.values) < atol)
157 changes: 0 additions & 157 deletions docs/friedland/chapter_7.rst
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@kennethshsu kennethshsu May 28, 2026

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What happened here? Accident?

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@henrydingliu henrydingliu May 29, 2026

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i was trying to show gene that regular html doesn't automatically look as nice as ipynb. basically, trying to make .rst visually pleasing would be quite a feat.

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@kennethshsu kennethshsu May 29, 2026

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Ahh ok, I think I'd like a show & tell on this lol... I still don't know what's best.

Anyways I'll shut up here to not hijack the thread and let @genedan do the review.

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@henrydingliu henrydingliu May 29, 2026

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we'll have a basis to do a comparison once you are done with your notebook recreation of chapter 8.

Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,6 @@ PART 1 - Data Triangle
We have already imported the necessary packages loading the ``Triangle`` at the top of p106. Let's take a look at the ``Triangle`` we just loaded.

.. doctest::
:hide:

>>> tri = cl.load_sample('friedland_us_industry_auto')
>>> tri['Reported Claims']
Expand All @@ -43,162 +42,6 @@ We have already imported the necessary packages loading the ``Triangle`` at the
2006 46582684.0 54641339.0 NaN NaN NaN NaN NaN NaN NaN NaN
2007 48853563.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN

.. code-block::

>>> tri = cl.load_sample('friedland_us_industry_auto')

.. raw:: html

<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>12</th>
<th>24</th>
<th>36</th>
<th>48</th>
<th>60</th>
<th>72</th>
<th>84</th>
<th>96</th>
<th>108</th>
<th>120</th>
</tr>
</thead>
<tbody>
<tr>
<th>1998</th>
<td>37,017,487</td>
<td>43,169,009</td>
<td>45,568,919</td>
<td>46,784,558</td>
<td>47,337,318</td>
<td>47,533,264</td>
<td>47,634,419</td>
<td>47,689,655</td>
<td>47,724,678</td>
<td>47,742,304</td>
</tr>
<tr>
<th>1999</th>
<td>38,954,484</td>
<td>46,045,718</td>
<td>48,882,924</td>
<td>50,219,672</td>
<td>50,729,292</td>
<td>50,926,779</td>
<td>51,069,285</td>
<td>51,163,540</td>
<td>51,185,767</td>
<td></td>
</tr>
<tr>
<th>2000</th>
<td>41,155,776</td>
<td>49,371,478</td>
<td>52,358,476</td>
<td>53,780,322</td>
<td>54,303,086</td>
<td>54,582,950</td>
<td>54,742,188</td>
<td>54,837,929</td>
<td></td>
<td></td>
</tr>
<tr>
<th>2001</th>
<td>42,394,069</td>
<td>50,584,112</td>
<td>53,704,296</td>
<td>55,150,118</td>
<td>55,895,583</td>
<td>56,156,727</td>
<td>56,299,562</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th>2002</th>
<td>44,755,243</td>
<td>52,971,643</td>
<td>56,102,312</td>
<td>57,703,851</td>
<td>58,363,564</td>
<td>58,592,712</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th>2003</th>
<td>45,163,102</td>
<td>52,497,731</td>
<td>55,468,551</td>
<td>57,015,411</td>
<td>57,565,344</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th>2004</th>
<td>45,417,309</td>
<td>52,640,322</td>
<td>55,553,673</td>
<td>56,976,657</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th>2005</th>
<td>46,360,869</td>
<td>53,790,061</td>
<td>56,786,410</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th>2006</th>
<td>46,582,684</td>
<td>54,641,339</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th>2007</th>
<td>48,853,563</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

PART 2 - Age-to-Age Factors
----------------------------

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