When the API had originally been designed, it was assumed that the
presence of the time splitting method implies no requirement for the
machine learning backend and that it is a sign of less performance
demanding models. So it seemed to be safe and useful to have such models
automatically enabled. That assumption did not prove to be valid. Most
of the time is spent calculating indicators that depend on log tables.
We realized it would be useful to be able to specify the default time
splitting method without the need to have such models auto-enabled.
Static predictions models (i.e. those using a target based on
assumptions, not facts) are always considered as trained. Clearing them
must not mark them as untrained. Doing so would make them being skipped
by the prediction scheduled task.
This commit reviews all continue uses in core happening within a
loop / switch / case hierarchy. This does not cover:
- Changes to libraries. Will be handled in another issue / commit.
- Uses out from loops, will be reviewed by other commit.
The policy followed has been:
- When possible, take rid of the continue.
- When clearly the intention was to jump to next element in loop
change to continue 2
- When it was not clear, keep old behavior switching to break, no
matter how weird the behavior may be.
The new recordset support for Postgres requires transactions and
will cause errors if recordsets are not closed correctly. This
commit fixes problems that were identified during unit tests, and
via some basic code analysis, across all core code. Most of these
are incorrect usage of recordset (forgetting to close them).
This method was used when the API was tied to students at risk model,
this method does not make sense any more as it is up to each target
to define what is a valid course.
- Basic unit test for minimum machine learning backends requirements
- Warning return messages now include not enough data
- Clear models when the predictions processor is changed
- Refined the name of a couple of constants / methods
