fix: keep shape validation in _normalize_output for non-float output - #1148
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ikrommyd
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Only left a comment regarding the testing which is a take it or leave it. I normally would test this from a higher level or not test it at all as the change looks trivial to me.
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| def test_normalize_output_wrong_shape_int(): | ||
| # an integer array with the wrong shape must still raise the descriptive | ||
| # shape error instead of silently slipping through the float early-return | ||
| from iminuit.cost import _normalize_output | ||
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| x = np.array([1, 2], dtype=int) | ||
| with pytest.warns(PerformanceWarning): | ||
| with pytest.raises( | ||
| ValueError, | ||
| match=r"output of model has shape \(2,\), but \(3,\) is required", | ||
| ): | ||
| _normalize_output(list(x), "model", 3) | ||
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I normally would not add a test for an internal _normalize_output function and I would have created a test with a real model that actually reproduces the problem. Take it or leave it.
A model that returned a non-float sequence took an early return before the shape check, so a wrong shape passed silently. Convert to float and continue to the shape check. Assisted-by: ClaudeCode:claude-opus-5
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Split out of #1137.
A model that returned a non-float sequence took an early return before the shape check, so a wrong shape passed silently. The value is now converted to float and falls through to the shape check.