#882 full exp

[henrydingliu]

Summary of Changes

allows a predicted triangle (e.g. cl.Chainladder().fit_predict(tri) to call the full_expectation_ property

Related GitHub Issue(s)

closes #882

Additional Context for Reviewers

mirrors the logic from full_triangle_

• I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

---

Note

Low Risk
Small property logic aligned with existing full_triangle_ behavior plus test tightening; no auth, security, or external API surface beyond actuarial helpers.

Overview
full_expectation_ now picks the cumulative vs incremental flag the same way as full_triangle_: use X_.is_cumulative when a fitted X_ exists, otherwise self.is_cumulative. That lets fit_predict outputs (e.g. cl.Chainladder().fit_predict(tri)) expose full_expectation_ without relying on a missing or wrong X_ attribute.

Tests add fit_predict vs fit full_expectation_ checks with np.allclose, and replace hard-coded 0.00001 tolerances with the shared atol fixture.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.05%. Comparing base (449b5c1) to head (91c528d).

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #884   +/-   ##
=======================================
  Coverage   87.04%   87.05%           
=======================================
  Files          86       86           
  Lines        4986     4989    +3     
  Branches      646      647    +1     
=======================================
+ Hits         4340     4343    +3     
  Misses        456      456           
  Partials      190      190           

Flag	Coverage Δ
unittests	87.05% <100.00%> (+<0.01%)	⬆️

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