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fix(correlation): keep empty diagonals when building z_critical (#320) #1262

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Merged
henrydingliu merged 1 commit into from
Sep 19, 2026
+18 −4
Merged

fix(correlation): keep empty diagonals when building z_critical (#320) #1262

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12 changes: 8 additions & 4 deletions chainladder/core/correlation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -324,10 +324,14 @@ def p_z_lower(z: int, n: int, p: float = 0.5) -> float:
T = np.array(T)
z_idx, n_idx = z.astype(int), n.astype(int)
self.probs = T[z_idx, n_idx]
z_critical = triangle[triangle.valuation > triangle.valuation.min()]
# z_critical = z_critical[z_critical.development > z_critical.development.min()].dev_to_val().sum(
# "origin") * 0
z_critical = z_critical.dev_to_val().dropna().sum("origin") * 0
# One column per link-ratio diagonal, labeled by ending valuation.
# Slicing by valuation (rather than dropna) keeps diagonals that are
# entirely NaN, which a triangle missing its earliest diagonals has:
# dropna() removed them while self.probs still carried one entry per
# diagonal, so the two disagreed and the DataFrame below raised
# "Shape of passed values is (1, 10), indices imply (1, 9)" (#320).
z_critical = triangle.dev_to_val().sum("origin")
z_critical = z_critical[z_critical.valuation > triangle.valuation.min()] * 0
z_critical.values = np.array(self.probs) < p_critical
z_critical.odims = triangle.odims[0:1]
self.z_critical = z_critical
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10 changes: 10 additions & 0 deletions chainladder/core/tests/test_correlation.py
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Expand Up @@ -23,3 +23,13 @@ def test_dev_corr(raa: Triangle) -> None:
def test_validate_critical(raa: Triangle) -> None:
with pytest.raises(ValueError):
raa.valuation_correlation(p_critical=1.5, total=True)


def test_val_corr_incomplete_triangle(xyz: Triangle) -> None:
# GH #320: a triangle missing its earliest diagonals raised
# "Shape of passed values is (1, 10), indices imply (1, 9)" on repr,
# because z_critical dropped all-NaN diagonals while its values kept
# one entry per link-ratio diagonal.
z_critical = xyz["Paid"].valuation_correlation(p_critical=0.1, total=False).z_critical
assert z_critical.values.shape[-1] == len(z_critical.ddims)
assert repr(z_critical)
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