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.
- 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
- Method names renamed to avoid interface changes once
we support regression and unsupervised learning
- Adding regressor interface even if not implemente
- predictor interface comments expanded
- Differentiate model's required accuracy from predictions quality
- Add missing get_callback_boundary call
- Updated datasets' metadata to allow 3rd parties to code
regressors themselves
- Add missing option to exception message
- Include target data into the dataset regardless of being a prediction
dataset or a training dataset
- Explicit in_array and array_search non-strict calls
- Overwrite discrete should_be_displayed implementation with the binary one
- Overwrite no_teacher get_display_value as it would otherwise look
wrong
- Other minor fixes
This was supposed to be split into multiple commits to make it easier to understand
but I failed to do it properly. So this is the list of changes:
- New analytics_indicator_calc db table to store indicators calculations
- Reuse previous calculations during prediction/training; other models
previous calculations should also be reused as long as they belong to
the same sample (sampleid depends on sampleorigin), time range and indicator
- Allow bulk inserting of these calculations as this can hurt database performance
- Block the same analysable to be analysed for training and for prediction
- Use a new instance of the target and use it for is_valid_* functions
as using ::is_valid_sample can lead to problems if people
uses it to cache stuff
Now we only predict using the most recent range available, this means
that if someone upgrades to moodle 3.4 at three quarters of a course
we will only calculate the latest range, previous ranges were not
displayed anyway once more recent predictions were available.
This commit deletes all previous predictions :) this shouldn't be a
problem in master as we don't provide any guarantee, the alternative
(retrive sampleids from mdl_files) would have been slow and a waste of
time as well as require horrible code in an upgrade step (text fields
do not accept defaults nor we can use NOTNULL).
- Split model::predict in parts
- JS promises updated according to eslint-plugin-promise
- New API methods replacing direct DB queries
- Reduce insights nav link display cost
- Increase time limit as well as memory for big processes
- Move prediction action event to core
- Dataset write locking and others
- Refine last time range end time
- Removed dodgy splitting method id to int
- Replace admin_setting_predictor output_html overwrite for write_setting overwrite
- New APIs for access control
- Discard invalid samples also during prediction
David Mudrák