Search moodle.org's
Developer Documentation
Developer Documentation
- Bug fixes for general core bugs in 3.10.x will end 8 November 2021 (12 months).
- Bug fixes for security issues in 3.10.x will end 9 May 2022 (18 months).
- PHP version: minimum PHP 7.2.0 Note: minimum PHP version has increased since Moodle 3.8. PHP 7.3.x and 7.4.x are supported too.
/analytics/classes/ -> model.php (source)
Differences Between: [Versions 310 and 401] [Versions 310 and 402] [Versions 310 and 403]
Prediction model representation.
| Copyright: | 2016 David Monllao {@link http://www.davidmonllao.com} |
| License: | http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later |
| File Size: | 2034 lines (73 kb) |
| Included or required: | 0 times |
| Referenced: | 0 times |
| Includes or requires: | 0 files |
Defines 2 classes
model:: (9 methods):
__construct()
is_available()
get_id()
get_model_obj()
get_target()
get_indicators()
get_potential_indicators()
get_analyser()
init_analyser()
does:: (54 methods):
get_time_splitting()
get_potential_timesplittings()
create()
exists()
update()
delete()
evaluate()
train()
predict()
get_predictions_processor()
format_predictor_predictions()
execute_prediction_callbacks()
trigger_insights()
get_static_predictions()
prepare_prediction_record()
save_predictions()
enable()
is_static()
is_enabled()
is_trained()
mark_as_trained()
get_predictions_contexts()
any_prediction_obtained()
uses_insights()
predictions_exist()
get_predictions()
get_prediction_actions()
prediction_sample_data()
predictions_sample_data()
append_calculations_info()
prediction_sample_description()
default_output_dir()
get_output_dir()
get_unique_id()
export()
export_model()
import_model()
can_export_configuration()
get_logs()
get_training_data()
trained_locally()
flag_file_as_used()
log_result()
indicator_classes()
clear()
get_name()
rename()
inplace_editable_name()
invalid_timesplitting_selected()
add_prediction_ids()
get_samples()
get_contexts()
purge_insights_cache()
heavy_duty_mode()
Prediction model representation.
| __construct($model) X-Ref |
| Constructor. param: int|\stdClass $model return: void |
| is_available() X-Ref |
| Quick safety check to discard site models which required components are not available anymore. return: bool |
| get_id() X-Ref |
| Returns the model id. return: int |
| get_model_obj() X-Ref |
| Returns a plain \stdClass with the model data. return: \stdClass |
| get_target() X-Ref |
| Returns the model target. return: \core_analytics\local\target\base |
| get_indicators() X-Ref |
| Returns the model indicators. return: \core_analytics\local\indicator\base[] |
| get_potential_indicators() X-Ref |
| Returns the list of indicators that could potentially be used by the model target. It includes the indicators that are part of the model. return: \core_analytics\local\indicator\base[] |
| get_analyser($options = array() X-Ref |
| Returns the model analyser (defined by the model target). param: array $options Default initialisation with no options. return: \core_analytics\local\analyser\base |
| init_analyser($options = array() X-Ref |
| Initialises the model analyser. param: array $options return: void |
| get_time_splitting() X-Ref |
| Returns the model time splitting method. return: \core_analytics\local\time_splitting\base|false Returns false if no time splitting. |
| get_potential_timesplittings() X-Ref |
| Returns the time-splitting methods that can be used by this model. return: \core_analytics\local\time_splitting\base[] |
| create(\core_analytics\local\target\base $target, array $indicators,$timesplittingid = false, $processor = null) X-Ref |
| Creates a new model. Enables it if $timesplittingid is specified. param: \core_analytics\local\target\base $target param: \core_analytics\local\indicator\base[] $indicators param: string|false $timesplittingid The time splitting method id (its fully qualified class name) param: string|null $processor The machine learning backend this model will use. return: \core_analytics\model |
| exists(\core_analytics\local\target\base $target, $indicators = false) X-Ref |
| Does this model exist? If no indicators are provided it considers any model with the provided target a match. param: \core_analytics\local\target\base $target param: \core_analytics\local\indicator\base[]|false $indicators return: bool |
| update($enabled, $indicators = false, $timesplittingid = '', $predictionsprocessor = false,$contextids = false) X-Ref |
| Updates the model. param: int|bool $enabled param: \core_analytics\local\indicator\base[]|false $indicators False to respect current indicators param: string|false $timesplittingid False to respect current time splitting method param: string|false $predictionsprocessor False to respect current predictors processor value param: int[]|false $contextids List of context ids for this model. False to respect the current list of contexts. return: void |
| delete() X-Ref |
| Removes the model. return: void |
| evaluate($options = array() X-Ref |
| Evaluates the model. This method gets the site contents (through the analyser) creates a .csv dataset with them and evaluates the model prediction accuracy multiple times using the machine learning backend. It returns an object where the model score is the average prediction accuracy of all executed evaluations. param: array $options return: \stdClass[] |
| train() X-Ref |
| Trains the model using the site contents. This method prepares a dataset from the site contents (through the analyser) and passes it to the machine learning backends. Static models are skipped as they do not require training. return: \stdClass |
| predict() X-Ref |
| Get predictions from the site contents. It analyses the site contents (through analyser classes) looking for samples ready to receive predictions. It generates a dataset with all samples ready to get predictions and it passes it to the machine learning backends or to the targets based on assumptions to get the predictions. return: \stdClass |
| get_predictions_processor($checkisready = true) X-Ref |
| Returns the model predictions processor. param: bool $checkisready return: \core_analytics\predictor |
| format_predictor_predictions($predictorresult) X-Ref |
| Formats the predictor results. param: array $predictorresult return: array |
| execute_prediction_callbacks(&$predictions, $indicatorcalculations) X-Ref |
| Execute the prediction callbacks defined by the target. param: \stdClass[] $predictions param: array $indicatorcalculations return: array |
| trigger_insights($samplecontexts, $predictionrecords) X-Ref |
| Generates insights and updates the cache. param: \context[] $samplecontexts param: \stdClass[] $predictionrecords return: void |
| get_static_predictions(&$indicatorcalculations) X-Ref |
| Get predictions from a static model. param: array $indicatorcalculations return: \stdClass[] |
| prepare_prediction_record($sampleid, $rangeindex, $prediction, $predictionscore, $calculations) X-Ref |
| Stores the prediction in the database. param: int $sampleid param: int $rangeindex param: int $prediction param: float $predictionscore param: string $calculations return: \context |
| save_predictions($records) X-Ref |
| Save the prediction objects. param: \stdClass[] $records |
| enable($timesplittingid = false) X-Ref |
| Enabled the model using the provided time splitting method. param: string|false $timesplittingid False to respect the current time splitting method. return: void |
| is_static() X-Ref |
| Is this a static model (as defined by the target)?. Static models are based on assumptions instead of in machine learning backends results. return: bool |
| is_enabled() X-Ref |
| Is this model enabled? return: bool |
| is_trained() X-Ref |
| Is this model already trained? return: bool |
| mark_as_trained() X-Ref |
| Marks the model as trained return: void |
| get_predictions_contexts($skiphidden = true) X-Ref |
| Get the contexts with predictions. param: bool $skiphidden Skip hidden predictions return: \stdClass[] |
| any_prediction_obtained() X-Ref |
| Has this model generated predictions? We don't check analytics_predictions table because targets have the ability to ignore some predicted values, if that is the case predictions are not even stored in db. return: bool |
| uses_insights() X-Ref |
| Whether this model generates insights or not (defined by the model's target). return: bool |
| predictions_exist(\context $context) X-Ref |
| Whether predictions exist for this context. param: \context $context return: bool |
| get_predictions(\context $context, $skiphidden = true, $page = false, $perpage = 100) X-Ref |
| Gets the predictions for this context. param: \context $context param: bool $skiphidden Skip hidden predictions param: int $page The page of results to fetch. False for all results. param: int $perpage The max number of results to fetch. Ignored if $page is false. return: array($total, \core_analytics\prediction[]) |
| get_prediction_actions(?\context $context) X-Ref |
| Returns the actions executed by users on the predictions. param: \context|null $context return: \moodle_recordset |
| prediction_sample_data($predictionobj) X-Ref |
| Returns the sample data of a prediction. param: \stdClass $predictionobj return: array |
| predictions_sample_data(array $predictionrecords) X-Ref |
| Returns the samples data of the provided predictions. param: \stdClass[] $predictionrecords return: array |
| append_calculations_info(array $predictionrecords, array $samplesdata) X-Ref |
| Appends the calculation info to the samples data. param: \stdClass[] $predictionrecords param: array $samplesdata return: array |
| prediction_sample_description(\core_analytics\prediction $prediction) X-Ref |
| Returns the description of a sample param: \core_analytics\prediction $prediction return: array 2 elements: list(string, \renderable) |
| default_output_dir() X-Ref |
| Returns the default output directory for prediction processors return: string |
| get_output_dir($subdirs = array() X-Ref |
| Returns the output directory for prediction processors. Directory structure as follows: - Evaluation runs: models/$model->id/$model->version/evaluation/$model->timesplitting - Training & prediction runs: models/$model->id/$model->version/execution param: array $subdirs param: bool $onlymodelid Preference over $subdirs return: string |
| get_unique_id() X-Ref |
| Returns a unique id for this model. This id should be unique for this site. return: string |
| export(\renderer_base $output) X-Ref |
| Exports the model data for displaying it in a template. param: \renderer_base $output The renderer to use for exporting return: \stdClass |
| export_model(string $zipfilename, bool $includeweights = true) X-Ref |
| Exports the model data to a zip file. param: string $zipfilename param: bool $includeweights Include the model weights if available return: string Zip file path |
| import_model(string $zipfilepath) X-Ref |
| Imports the provided model. Note that this method assumes that model_config::check_dependencies has already been called. param: string $zipfilepath Zip file path return: \core_analytics\model |
| can_export_configuration() X-Ref |
| Can this model be exported? return: bool |
| get_logs($limitfrom = 0, $limitnum = 0) X-Ref |
| Returns the model logs data. param: int $limitfrom param: int $limitnum return: \stdClass[] |
| get_training_data() X-Ref |
| Merges all training data files into one and returns it. return: \stored_file|false |
| trained_locally() X-Ref |
| Has the model been trained using data from this site? This method is useful to determine if a trained model can be evaluated as we can not use the same data for training and for evaluation. return: bool |
| flag_file_as_used(\stored_file $file, $action) X-Ref |
| Flag the provided file as used for training or prediction. param: \stored_file $file param: string $action return: void |
| log_result($timesplittingid, $score, $dir = false, $info = false, $evaluationmode = 'configuration') X-Ref |
| Log the evaluation results in the database. param: string $timesplittingid param: float $score param: string $dir param: array $info param: string $evaluationmode return: int The inserted log id |
| indicator_classes($indicators) X-Ref |
| Utility method to return indicator class names from a list of indicator objects param: \core_analytics\local\indicator\base[] $indicators return: string[] |
| clear() X-Ref |
| Clears the model training and prediction data. Executed after updating model critical elements like the time splitting method or the indicators. return: void |
| get_name() X-Ref |
| Returns the name of the model. By default, models use their target's name as their own name. They can have their explicit name, too. In which case, the explicit name is used instead of the default one. return: string|lang_string |
| rename(string $name) X-Ref |
| Renames the model to the given name. When given an empty string, the model falls back to using the associated target's name as its name. param: string $name The new name for the model, empty string for using the default name. |
| inplace_editable_name() X-Ref |
| Returns an inplace editable element with the model's name. return: \core\output\inplace_editable |
| invalid_timesplitting_selected() X-Ref |
| Returns true if the time-splitting method used by this model is invalid for this model. return: bool |
| add_prediction_ids($predictionrecords) X-Ref |
| Adds the id from {analytics_predictions} db table to the prediction \stdClass objects. param: \stdClass[] $predictionrecords return: \stdClass[] The prediction records including their ids in {analytics_predictions} db table. |
| get_samples(array $sampleids) X-Ref |
| Wrapper around analyser's get_samples to skip DB's max-number-of-params exception. param: array $sampleids return: array |
| get_contexts() X-Ref |
| Contexts where this model should be active. return: \context[] Empty array if there are no context restrictions. |
| purge_insights_cache() X-Ref |
| Purges the insights cache. |
| heavy_duty_mode() X-Ref |
| Increases system memory and time limits. return: void |
