tree 689b27523c2d4ab4a56454e1a6c800925db7160d
parent 38e399f2fa749e8b8d544a1de6e56666a410490c
author Domino Valdno <dvaldano@vmware.com> 1598561393 -0400
committer Domino Valdano <dvaldano@vmware.com> 1599697877 -0700

DL: Check for nan in loss or metrics

JIRA: MADLIB-1443

Handles NaN in madlib_keras_fit() or madlivb_keras_fit_multiple()
    by casting each float in an array to a derived class sqlfloat,
    which overrides the default representation of itself to show
    up as the corresponding SQL string.

  - Keeps metric history for each model up until the NaN failure
    instead of just discarding.

  - Translates all None's into NULL's and python nan's
    into SQL NaN's in any array of floats in one step,
    so that this can be used in all of the places we
    need it.

pgsanity:

  - Some of the unit tests included make use of the
    python module pgsanity to verify the representation of
    an array of floats translates to a valid string that
    the SQL parser will understand as a numeric array.
    If it isn't installed, or isn't functioning properly, this
    part of the test will be skipped.

  - pgsanity calls ecpg under the hood, which is the same library
    used by postgres & greenplum to parse the SQL commands.  I
    hope we can use it for more unit tests in the future.