Comments (5)
stephanlachnit
commented on July 29, 2024
I think overall option 4 provides the best balance between precision, performance, ease of implementation and platform compatibility.
Unfortunately, this is not safe either:
=============================== warnings summary ===============================
.pybuild/cpython3_3.11_iminuit/build/tests/test_cost.py::test_multinominal_chi2
.pybuild/cpython3_3.11_iminuit/build/tests/test_without_numba.py::test_no_numba[_safe_log-args0]
.pybuild/cpython3_3.11_iminuit/build/tests/test_without_numba.py::test_no_numba[_safe_log-args0]
.pybuild/cpython3_3.11_iminuit/build/tests/test_without_numba.py::test_no_numba[_unbinned_nll-args2]
.pybuild/cpython3_3.11_iminuit/build/tests/test_without_numba.py::test_no_numba[multinominal_chi2-args3]
.pybuild/cpython3_3.11_iminuit/build/tests/test_without_numba.py::test_no_numba[poisson_chi2-args5]
/home/stephan/Projects/debian/iminuit/iminuit/.pybuild/cpython3_3.11_iminuit/build/iminuit/cost.py:119: RuntimeWarning: divide by zero encountered in log
return np.clip(np.log(x), -1e3, None)
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stephanlachnit
commented on July 29, 2024
Okay, I can up with another option:
np.log(np.maximum(np.finfo(np.float64).smallest_subnormal, x))It takes the smallest subnormal of the 64-bit float representation and has with no loss in precision. Drawback is that this returns at least float64 type, no matter if x is float32. I have to check whether this actually works though.
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HDembinski
commented on July 29, 2024
@stephanlachnit Thank you for this great and deep analysis, this all makes sense to me.
Why do you think that np.log(np.finfo(np.float64).smallest_subnormal + x) would not work?
Adding is a bit faster, but if your version is better for compatibility, then we should use that.
In [7]: x = np.linspace(0, 20, 1000)
In [8]: %timeit np.log(np.finfo(np.float64).smallest_subnormal + x)
6.51 µs ± 69.2 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
In [9]: %timeit np.log(np.maximum(np.finfo(np.float64).smallest_subnormal, x))
6.64 µs ± 54.8 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
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stephanlachnit
commented on July 29, 2024
Why do you think that
np.log(np.finfo(np.float64).smallest_subnormal + x)would not work?Adding is a bit faster, but if your version is better for compatibility, then we should use that.
If x is negative for some reason, the operation is not safe.
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HDembinski
commented on July 29, 2024
Good point.
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