[Numpy-discussion] `allclose` vs `assert_allclose`

[josef.pktd at gmail.com]

On Fri, Jul 18, 2014 at 2:44 PM, Nathaniel Smith <njs at pobox.com> wrote:

> On 18 Jul 2014 19:31, <josef.pktd at gmail.com> wrote:
> >>
> >>
> >> > Making the behavior of assert_allclose depending on whether desired is
> >> > exactly zero or 1e-20 looks too difficult to remember, and which
> desired I
> >> > use would depend on what I get out of R or Stata.
> >>
> >> I thought your whole point here was that 1e-20 and zero are
> >> qualitatively different values that you would not want to accidentally
> >> confuse? Surely R and Stata aren't returning exact zeros for small
> >> non-zero values like probability tails?
> >>
> >
> > I was thinking of the case when we only see "pvalue < 1e-16" or
> something like this, and we replace this by assert close to zero.
> > which would translate to `assert_allclose(pvalue, 0, atol=1e-16)`
> > with maybe an additional rtol=1e-11 if we have an array of pvalues where
> some are "large" (>0.5).
>
> This example is also handled correctly by my proposal :-)
>
depends on the details of your proposal

alternative: desired is exactly zero means assert_equal

(Pdb) self.res_reg.params[m:]
array([ 0.,  0.,  0.])
(Pdb) assert_allclose(0, self.res_reg.params[m:])
(Pdb) assert_allclose(0, self.res_reg.params[m:], rtol=0, atol=0)
(Pdb)

This test uses currently assert_almost_equal with decimal=4   :(

regularized estimation with hard thresholding: the first m values are
estimate not equal zero, the m to the end elements are "exactly zero".

This is discrete models fit_regularized which predates numpy
assert_allclose.  I haven't checked what the unit test of Kerby's current
additions for fit_regularized looks like.

unit testing is serious business:
I'd rather have good unit test in SciPy related packages than convincing a
few more newbies that they can use the defaults for everything.

Josef



> -n
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