Either running locally from the web server or using a server if
TEST_MLBACKEND_* vars are set. The tests will still be skipped if
the python backend is not available in the web server.
- Removed redundant query to analytics_predict_samples
- Analysers API now uses recordsets to iterate through the analysable
elements. They take the last analysed time into account.
- New method for targets so there is no need to always update the last
analysis time. Useful for lightweight targets.
Similarly to how the scheduled tasks work, we now automatically check
and make sure that all the models specified in the component's
db/analytics.php file exist during the installation or upgrade of the
component.
The original implementation did not work well for checking that the
given model does NOT exist. If no record was found in the
analytics_models table, the code execution continued and it reached the
moment when indicators were checked. If no indicators were provided, the
call ended up with error 'array_keys() expects parameter 1 to be array,
boolean given' (because indicators were set to false).
The functionality of the \core_analytics\manager::add_builtin_models()
method is to be replaced with automatic update of models provided by the
core moodle component. There is no need to call this method explicitly
any more. Instead, adding new models will be done by updating the
lib/db/analytics.php file and bumping the core version.
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.
David Monllaó