BUG: Fixes gh-12218, TypeError converting int to float inside stats.ks_2samp #12280
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A multiplication of large integers fed as input to np.sqrt exceeded its ability to convert to float, generating a TypeError inside stats.ks_2samp() for large samples. If both sample sizes were larger than about 2^21 (~cube root of 2^64, or about 2million), this condition would be triggered.
WarrenWeckesser
requested changes
Jun 27, 2020
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Thank @pvanmulbregt, this looks good. I made one small suggestion inline.
scipy/stats/stats.py
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| @@ -6731,13 +6731,16 @@ def ks_2samp(data1, data2, alternative='two-sided', mode='auto'): | |||
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| if mode == 'asymp': | |||
| # The product n1*n2 is large. Use Smirnov's asymptoptic formula. | |||
| # Ensure float to avoid overflow in multiplication | |||
| # sorted because the one-sided formula is not symmetric in n1, n2 | |||
| m, n = sorted(np.array([n1, n2], float), reverse=True) | |||
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No need to create a numpy array here. This is quite a bit faster:
Suggested change
| m, n = sorted(np.array([n1, n2], float), reverse=True) | |
| m, n = sorted([float(n1), float(n2)], reverse=True) |
Replace the use of np.array([]) with [] as the np array doesn't provide any additional value.
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Thanks @pvanmulbregt, merged. |
tylerjereddy
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Jul 4, 2020
…ats.ks_2samp (scipy#12280) A multiplication of large integers fed as input to np.sqrt exceeded its ability to convert to float, generating a TypeError inside stats.ks_2samp() for large samples. If both sample sizes were larger than about 2^21 (~cube root of 2^64, or about 2million), this condition would be triggered.
tylerjereddy
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A multiplication of large integers fed as input to
np.sqrtexceeded its abilityto convert to float, generating a TypeError inside
stats.ks_2samp() for largesamples. If both sample sizes were larger than about 2^21 (~cube root of 2^64,
or about 2million), this condition would be triggered.
Reference issue
Closes gh-12218
What does this implement/fix?
Converts some large integers to floats before multiplying them. Python could handle the large result as an int, but
np.sqrt()couldn't.