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Long Term Support Release
- Bug fixes for general core bugs in 4.1.x will end 13 November 2023 (12 months).
- Bug fixes for security issues in 4.1.x will end 10 November 2025 (36 months).
- PHP version: minimum PHP 7.4.0 Note: minimum PHP version has increased since Moodle 4.0. PHP 8.0.x is supported too.
/analytics/classes/ -> model.php (source)
<?php
// This file is part of Moodle - http://moodle.org/
//
// Moodle is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// Moodle is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Moodle. If not, see <http://www.gnu.org/licenses/>.
/**
* Prediction model representation.
*
* @package core_analytics
* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
namespace core_analytics;
defined('MOODLE_INTERNAL') || die();
/**
* Prediction model representation.
*
* @package core_analytics
* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
class model {
/**
* All as expected.
*/
const OK = 0;
/**
* There was a problem.
*/
const GENERAL_ERROR = 1;
/**
* No dataset to analyse.
*/
const NO_DATASET = 2;
/**
* Model with low prediction accuracy.
*/
const LOW_SCORE = 4;
/**
* Not enough data to evaluate the model properly.
*/
const NOT_ENOUGH_DATA = 8;
/**
* Invalid analysable for the time splitting method.
*/
const ANALYSABLE_REJECTED_TIME_SPLITTING_METHOD = 4;
/**
* Invalid analysable for all time splitting methods.
*/
const ANALYSABLE_STATUS_INVALID_FOR_RANGEPROCESSORS = 8;
/**
* Invalid analysable for the target
*/
const ANALYSABLE_STATUS_INVALID_FOR_TARGET = 16;
/**
* Minimum score to consider a non-static prediction model as good.
*/
const MIN_SCORE = 0.7;
/**
* Minimum prediction confidence (from 0 to 1) to accept a prediction as reliable enough.
*/
const PREDICTION_MIN_SCORE = 0.6;
/**
* Maximum standard deviation between different evaluation repetitions to consider that evaluation results are stable.
*/
const ACCEPTED_DEVIATION = 0.05;
/**
* Number of evaluation repetitions.
*/
const EVALUATION_ITERATIONS = 10;
/**
* @var \stdClass
*/
protected $model = null;
/**
* @var \core_analytics\local\analyser\base
*/
protected $analyser = null;
/**
* @var \core_analytics\local\target\base
*/
protected $target = null;
/**
* @var \core_analytics\predictor
*/
protected $predictionsprocessor = null;
/**
* @var \core_analytics\local\indicator\base[]
*/
protected $indicators = null;
/**
* @var \context[]
*/
protected $contexts = null;
/**
* Unique Model id created from site info and last model modification.
*
* @var string
*/
protected $uniqueid = null;
/**
* Constructor.
*
* @param int|\stdClass $model
* @return void
*/
public function __construct($model) {
global $DB;
if (is_scalar($model)) {
$model = $DB->get_record('analytics_models', array('id' => $model), '*', MUST_EXIST);
if (!$model) {
throw new \moodle_exception('errorunexistingmodel', 'analytics', '', $model);
}
}
$this->model = $model;
}
/**
* Quick safety check to discard site models which required components are not available anymore.
*
* @return bool
*/
public function is_available() {
$target = $this->get_target();
if (!$target) {
return false;
}
$classname = $target->get_analyser_class();
if (!class_exists($classname)) {
return false;
}
return true;
}
/**
* Returns the model id.
*
* @return int
*/
public function get_id() {
return $this->model->id;
}
/**
* Returns a plain \stdClass with the model data.
*
* @return \stdClass
*/
public function get_model_obj() {
return $this->model;
}
/**
* Returns the model target.
*
* @return \core_analytics\local\target\base
*/
public function get_target() {
if ($this->target !== null) {
return $this->target;
}
$instance = \core_analytics\manager::get_target($this->model->target);
$this->target = $instance;
return $this->target;
}
/**
* Returns the model indicators.
*
* @return \core_analytics\local\indicator\base[]
*/
public function get_indicators() {
if ($this->indicators !== null) {
return $this->indicators;
}
$fullclassnames = json_decode($this->model->indicators);
if (!is_array($fullclassnames)) {
throw new \coding_exception('Model ' . $this->model->id . ' indicators can not be read');
}
$this->indicators = array();
foreach ($fullclassnames as $fullclassname) {
$instance = \core_analytics\manager::get_indicator($fullclassname);
if ($instance) {
$this->indicators[$fullclassname] = $instance;
} else {
debugging('Can\'t load ' . $fullclassname . ' indicator', DEBUG_DEVELOPER);
}
}
return $this->indicators;
}
/**
* 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[]
*/
public function get_potential_indicators() {
$indicators = \core_analytics\manager::get_all_indicators();
if (empty($this->analyser)) {
$this->init_analyser(array('notimesplitting' => true));
}
foreach ($indicators as $classname => $indicator) {
if ($this->analyser->check_indicator_requirements($indicator) !== true) {
unset($indicators[$classname]);
}
}
return $indicators;
}
/**
* Returns the model analyser (defined by the model target).
*
* @param array $options Default initialisation with no options.
* @return \core_analytics\local\analyser\base
*/
public function get_analyser($options = array()) {
if ($this->analyser !== null) {
return $this->analyser;
}
$this->init_analyser($options);
return $this->analyser;
}
/**
* Initialises the model analyser.
*
* @throws \coding_exception
* @param array $options
* @return void
*/
protected function init_analyser($options = array()) {
$target = $this->get_target();
$indicators = $this->get_indicators();
if (empty($target)) {
throw new \moodle_exception('errornotarget', 'analytics');
}
$potentialtimesplittings = $this->get_potential_timesplittings();
$timesplittings = array();
if (empty($options['notimesplitting'])) {
if (!empty($options['evaluation'])) {
// The evaluation process will run using all available time splitting methods unless one is specified.
if (!empty($options['timesplitting'])) {
$timesplitting = \core_analytics\manager::get_time_splitting($options['timesplitting']);
if (empty($potentialtimesplittings[$timesplitting->get_id()])) {
throw new \moodle_exception('errorcannotusetimesplitting', 'analytics');
}
$timesplittings = array($timesplitting->get_id() => $timesplitting);
} else {
$timesplittingsforevaluation = \core_analytics\manager::get_time_splitting_methods_for_evaluation();
// They both have the same objects, using $potentialtimesplittings as its items are sorted.
$timesplittings = array_intersect_key($potentialtimesplittings, $timesplittingsforevaluation);
}
} else {
if (empty($this->model->timesplitting)) {
throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
}
// Returned as an array as all actions (evaluation, training and prediction) go through the same process.
$timesplittings = array($this->model->timesplitting => $this->get_time_splitting());
}
if (empty($timesplittings)) {
throw new \moodle_exception('errornotimesplittings', 'analytics');
}
}
$classname = $target->get_analyser_class();
if (!class_exists($classname)) {
throw new \coding_exception($classname . ' class does not exists');
}
// Returns a \core_analytics\local\analyser\base class.
$this->analyser = new $classname($this->model->id, $target, $indicators, $timesplittings, $options);
}
/**
* Returns the model time splitting method.
*
* @return \core_analytics\local\time_splitting\base|false Returns false if no time splitting.
*/
public function get_time_splitting() {
if (empty($this->model->timesplitting)) {
return false;
}
return \core_analytics\manager::get_time_splitting($this->model->timesplitting);
}
/**
* Returns the time-splitting methods that can be used by this model.
*
* @return \core_analytics\local\time_splitting\base[]
*/
public function get_potential_timesplittings() {
$timesplittings = \core_analytics\manager::get_all_time_splittings();
uasort($timesplittings, function($a, $b) {
return strcasecmp($a->get_name(), $b->get_name());
});
foreach ($timesplittings as $key => $timesplitting) {
if (!$this->get_target()->can_use_timesplitting($timesplitting)) {
unset($timesplittings[$key]);
continue;
}
}
return $timesplittings;
}
/**
* 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
*/
public static function create(\core_analytics\local\target\base $target, array $indicators,
$timesplittingid = false, $processor = null) {
global $USER, $DB;
$indicatorclasses = self::indicator_classes($indicators);
$now = time();
$modelobj = new \stdClass();
$modelobj->target = $target->get_id();
$modelobj->indicators = json_encode($indicatorclasses);
$modelobj->version = $now;
$modelobj->timecreated = $now;
$modelobj->timemodified = $now;
$modelobj->usermodified = $USER->id;
if ($target->based_on_assumptions()) {
$modelobj->trained = 1;
}
if ($timesplittingid) {
if (!\core_analytics\manager::is_valid($timesplittingid, '\core_analytics\local\time_splitting\base')) {
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
}
if (substr($timesplittingid, 0, 1) !== '\\') {
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
}
$modelobj->timesplitting = $timesplittingid;
}
if ($processor &&
!manager::is_valid($processor, '\core_analytics\classifier') &&
!manager::is_valid($processor, '\core_analytics\regressor')) {
throw new \coding_exception('The provided predictions processor \\' . $processor . '\processor is not valid');
} else {
$modelobj->predictionsprocessor = $processor;
}
$id = $DB->insert_record('analytics_models', $modelobj);
// Get db defaults.
$modelobj = $DB->get_record('analytics_models', array('id' => $id), '*', MUST_EXIST);
$model = new static($modelobj);
return $model;
}
/**
* 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
*/
public static function exists(\core_analytics\local\target\base $target, $indicators = false) {
global $DB;
$existingmodels = $DB->get_records('analytics_models', array('target' => $target->get_id()));
if (!$existingmodels) {
return false;
}
if (!$indicators && $existingmodels) {
return true;
}
$indicatorids = array_keys($indicators);
sort($indicatorids);
foreach ($existingmodels as $modelobj) {
$model = new \core_analytics\model($modelobj);
$modelindicatorids = array_keys($model->get_indicators());
sort($modelindicatorids);
if ($indicatorids === $modelindicatorids) {
return true;
}
}
return false;
}
/**
* 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 $contextids List of context ids for this model. False to respect the current list of contexts.
* @return void
*/
public function update($enabled, $indicators = false, $timesplittingid = '', $predictionsprocessor = false,
$contextids = false) {
global $USER, $DB;
\core_analytics\manager::check_can_manage_models();
$now = time();
if ($indicators !== false) {
$indicatorclasses = self::indicator_classes($indicators);
$indicatorsstr = json_encode($indicatorclasses);
} else {
// Respect current value.
$indicatorsstr = $this->model->indicators;
}
if ($timesplittingid === false) {
// Respect current value.
$timesplittingid = $this->model->timesplitting;
}
if ($predictionsprocessor === false) {
// Respect current value.
$predictionsprocessor = $this->model->predictionsprocessor;
}
if ($contextids === false) {
$contextsstr = $this->model->contextids;
} else if (!$contextids) {
$contextsstr = null;
} else {
$contextsstr = json_encode($contextids);
// Reset the internal cache.
$this->contexts = null;
}
if ($this->model->timesplitting !== $timesplittingid ||
$this->model->indicators !== $indicatorsstr ||
$this->model->predictionsprocessor !== $predictionsprocessor) {
// Delete generated predictions before changing the model version.
$this->clear();
// It needs to be reset as the version changes.
$this->uniqueid = null;
$this->indicators = null;
// We update the version of the model so different time splittings are not mixed up.
$this->model->version = $now;
// Reset trained flag.
if (!$this->is_static()) {
$this->model->trained = 0;
}
} else if ($this->model->enabled != $enabled) {
// We purge the cached contexts with insights as some will not be visible anymore.
$this->purge_insights_cache();
}
$this->model->enabled = intval($enabled);
$this->model->indicators = $indicatorsstr;
$this->model->timesplitting = $timesplittingid;
$this->model->predictionsprocessor = $predictionsprocessor;
$this->model->contextids = $contextsstr;
$this->model->timemodified = $now;
$this->model->usermodified = $USER->id;
$DB->update_record('analytics_models', $this->model);
}
/**
* Removes the model.
*
* @return void
*/
public function delete() {
global $DB;
\core_analytics\manager::check_can_manage_models();
$this->clear();
// Method self::clear is already clearing the current model version.
$predictor = $this->get_predictions_processor(false);
if ($predictor->is_ready() !== true) {
$predictorname = \core_analytics\manager::get_predictions_processor_name($predictor);
debugging('Prediction processor ' . $predictorname . ' is not ready to be used. Model ' .
$this->model->id . ' could not be deleted.');
} else {
$predictor->delete_output_dir($this->get_output_dir(array(), true), $this->get_unique_id());
}
$DB->delete_records('analytics_models', array('id' => $this->model->id));
$DB->delete_records('analytics_models_log', array('modelid' => $this->model->id));
}
/**
* 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 $options
* @return \stdClass[]
*/
public function evaluate($options = array()) {
\core_analytics\manager::check_can_manage_models();
if ($this->is_static()) {
$this->get_analyser()->add_log(get_string('noevaluationbasedassumptions', 'analytics'));
$result = new \stdClass();
$result->status = self::NO_DATASET;
return array($result);
}
$options['evaluation'] = true;
if (empty($options['mode'])) {
$options['mode'] = 'configuration';
}
switch ($options['mode']) {
case 'trainedmodel':
// We are only interested on the time splitting method used by the trained model.
$options['timesplitting'] = $this->model->timesplitting;
// Provide the trained model directory to the ML backend if that is what we want to evaluate.
$trainedmodeldir = $this->get_output_dir(['execution']);
break;
case 'configuration':
$trainedmodeldir = false;
break;
default:
throw new \moodle_exception('errorunknownaction', 'analytics');
}
$this->init_analyser($options);
if (empty($this->get_indicators())) {
throw new \moodle_exception('errornoindicators', 'analytics');
}
$this->heavy_duty_mode();
// Before get_labelled_data call so we get an early exception if it is not ready.
$predictor = $this->get_predictions_processor();
$datasets = $this->get_analyser()->get_labelled_data($this->get_contexts());
// No datasets generated.
if (empty($datasets)) {
$result = new \stdClass();
$result->status = self::NO_DATASET;
$result->info = $this->get_analyser()->get_logs();
return array($result);
}
if (!PHPUNIT_TEST && CLI_SCRIPT) {
echo PHP_EOL . get_string('processingsitecontents', 'analytics') . PHP_EOL;
}
$results = array();
foreach ($datasets as $timesplittingid => $dataset) {
$timesplitting = \core_analytics\manager::get_time_splitting($timesplittingid);
$result = new \stdClass();
$dashestimesplittingid = str_replace('\\', '', $timesplittingid);
$outputdir = $this->get_output_dir(array('evaluation', $dashestimesplittingid));
// Evaluate the dataset, the deviation we accept in the results depends on the amount of iterations.
if ($this->get_target()->is_linear()) {
$predictorresult = $predictor->evaluate_regression($this->get_unique_id(), self::ACCEPTED_DEVIATION,
self::EVALUATION_ITERATIONS, $dataset, $outputdir, $trainedmodeldir);
} else {
$predictorresult = $predictor->evaluate_classification($this->get_unique_id(), self::ACCEPTED_DEVIATION,
self::EVALUATION_ITERATIONS, $dataset, $outputdir, $trainedmodeldir);
}
$result->status = $predictorresult->status;
$result->info = $predictorresult->info;
if (isset($predictorresult->score)) {
$result->score = $predictorresult->score;
} else {
// Prediction processors may return an error, default to 0 score in that case.
$result->score = 0;
}
$dir = false;
if (!empty($predictorresult->dir)) {
$dir = $predictorresult->dir;
}
$result->logid = $this->log_result($timesplitting->get_id(), $result->score, $dir, $result->info, $options['mode']);
$results[$timesplitting->get_id()] = $result;
}
return $results;
}
/**
* Trains the model using the site contents.
*
* This method prepares a dataset from the site contents (through the analyser)
* and passes it to the machine learning backends. Static models are skipped as
* they do not require training.
*
* @return \stdClass
*/
public function train() {
\core_analytics\manager::check_can_manage_models();
if ($this->is_static()) {
$this->get_analyser()->add_log(get_string('notrainingbasedassumptions', 'analytics'));
$result = new \stdClass();
$result->status = self::OK;
return $result;
}
if (!$this->is_enabled() || empty($this->model->timesplitting)) {
throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
}
if (empty($this->get_indicators())) {
throw new \moodle_exception('errornoindicators', 'analytics');
}
$this->heavy_duty_mode();
// Before get_labelled_data call so we get an early exception if it is not writable.
$outputdir = $this->get_output_dir(array('execution'));
// Before get_labelled_data call so we get an early exception if it is not ready.
$predictor = $this->get_predictions_processor();
$datasets = $this->get_analyser()->get_labelled_data($this->get_contexts());
// No training if no files have been provided.
if (empty($datasets) || empty($datasets[$this->model->timesplitting])) {
$result = new \stdClass();
$result->status = self::NO_DATASET;
$result->info = $this->get_analyser()->get_logs();
return $result;
}
$samplesfile = $datasets[$this->model->timesplitting];
// Train using the dataset.
if ($this->get_target()->is_linear()) {
$predictorresult = $predictor->train_regression($this->get_unique_id(), $samplesfile, $outputdir);
} else {
$predictorresult = $predictor->train_classification($this->get_unique_id(), $samplesfile, $outputdir);
}
$result = new \stdClass();
$result->status = $predictorresult->status;
$result->info = $predictorresult->info;
if ($result->status !== self::OK) {
return $result;
}
$this->flag_file_as_used($samplesfile, 'trained');
// Mark the model as trained if it wasn't.
if ($this->model->trained == false) {
$this->mark_as_trained();
}
return $result;
}
/**
* 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
*/
public function predict() {
global $DB;
\core_analytics\manager::check_can_manage_models();
if (!$this->is_enabled() || empty($this->model->timesplitting)) {
throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
}
if (empty($this->get_indicators())) {
throw new \moodle_exception('errornoindicators', 'analytics');
}
$this->heavy_duty_mode();
// Before get_unlabelled_data call so we get an early exception if it is not writable.
$outputdir = $this->get_output_dir(array('execution'));
if (!$this->is_static()) {
// Predictions using a machine learning backend.
// Before get_unlabelled_data call so we get an early exception if it is not ready.
$predictor = $this->get_predictions_processor();
$samplesdata = $this->get_analyser()->get_unlabelled_data($this->get_contexts());
// Get the prediction samples file.
if (empty($samplesdata) || empty($samplesdata[$this->model->timesplitting])) {
$result = new \stdClass();
$result->status = self::NO_DATASET;
$result->info = $this->get_analyser()->get_logs();
return $result;
}
$samplesfile = $samplesdata[$this->model->timesplitting];
// We need to throw an exception if we are trying to predict stuff that was already predicted.
$params = array('modelid' => $this->model->id, 'action' => 'predicted', 'fileid' => $samplesfile->get_id());
if ($predicted = $DB->get_record('analytics_used_files', $params)) {
throw new \moodle_exception('erroralreadypredict', 'analytics', '', $samplesfile->get_id());
}
$indicatorcalculations = \core_analytics\dataset_manager::get_structured_data($samplesfile);
// Estimation and classification processes run on the machine learning backend side.
if ($this->get_target()->is_linear()) {
$predictorresult = $predictor->estimate($this->get_unique_id(), $samplesfile, $outputdir);
} else {
$predictorresult = $predictor->classify($this->get_unique_id(), $samplesfile, $outputdir);
}
// Prepare the results object.
$result = new \stdClass();
$result->status = $predictorresult->status;
$result->info = $predictorresult->info;
$result->predictions = $this->format_predictor_predictions($predictorresult);
} else {
// Predictions based on assumptions.
$indicatorcalculations = $this->get_analyser()->get_static_data($this->get_contexts());
// Get the prediction samples file.
if (empty($indicatorcalculations) || empty($indicatorcalculations[$this->model->timesplitting])) {
$result = new \stdClass();
$result->status = self::NO_DATASET;
$result->info = $this->get_analyser()->get_logs();
return $result;
}
// Same as reset($indicatorcalculations) as models based on assumptions only analyse 1 single
// time-splitting method.
$indicatorcalculations = $indicatorcalculations[$this->model->timesplitting];
// Prepare the results object.
$result = new \stdClass();
$result->status = self::OK;
$result->info = [];
$result->predictions = $this->get_static_predictions($indicatorcalculations);
}
if ($result->status !== self::OK) {
return $result;
}
if ($result->predictions) {
list($samplecontexts, $predictionrecords) = $this->execute_prediction_callbacks($result->predictions,
$indicatorcalculations);
}
if (!empty($samplecontexts) && $this->uses_insights()) {
$this->trigger_insights($samplecontexts, $predictionrecords);
}
if (!$this->is_static()) {
$this->flag_file_as_used($samplesfile, 'predicted');
}
return $result;
}
/**
* Returns the model predictions processor.
*
* @param bool $checkisready
* @return \core_analytics\predictor
*/
public function get_predictions_processor($checkisready = true) {
return manager::get_predictions_processor($this->model->predictionsprocessor, $checkisready);
}
/**
* Formats the predictor results.
*
* @param array $predictorresult
* @return array
*/
private function format_predictor_predictions($predictorresult) {
$predictions = array();
if (!empty($predictorresult->predictions)) {
foreach ($predictorresult->predictions as $sampleinfo) {
// We parse each prediction.
switch (count($sampleinfo)) {
case 1:
// For whatever reason the predictions processor could not process this sample, we
// skip it and do nothing with it.
debugging($this->model->id . ' model predictions processor could not process the sample with id ' .
$sampleinfo[0], DEBUG_DEVELOPER);
continue 2;
case 2:
// Prediction processors that do not return a prediction score will have the maximum prediction
// score.
list($uniquesampleid, $prediction) = $sampleinfo;
$predictionscore = 1;
break;
case 3:
list($uniquesampleid, $prediction, $predictionscore) = $sampleinfo;
break;
default:
break;
}
$predictiondata = (object)['prediction' => $prediction, 'predictionscore' => $predictionscore];
$predictions[$uniquesampleid] = $predictiondata;
}
}
return $predictions;
}
/**
* Execute the prediction callbacks defined by the target.
*
* @param \stdClass[] $predictions
* @param array $indicatorcalculations
* @return array
*/
protected function execute_prediction_callbacks(&$predictions, $indicatorcalculations) {
// Here we will store all predictions' contexts, this will be used to limit which users will see those predictions.
$samplecontexts = array();
$records = array();
foreach ($predictions as $uniquesampleid => $prediction) {
// The unique sample id contains both the sampleid and the rangeindex.
list($sampleid, $rangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
if ($this->get_target()->triggers_callback($prediction->prediction, $prediction->predictionscore)) {
// Prepare the record to store the predicted values.
list($record, $samplecontext) = $this->prepare_prediction_record($sampleid, $rangeindex, $prediction->prediction,
$prediction->predictionscore, json_encode($indicatorcalculations[$uniquesampleid]));
// We will later bulk-insert them all.
$records[$uniquesampleid] = $record;
// Also store all samples context to later generate insights or whatever action the target wants to perform.
$samplecontexts[$samplecontext->id] = $samplecontext;
$this->get_target()->prediction_callback($this->model->id, $sampleid, $rangeindex, $samplecontext,
$prediction->prediction, $prediction->predictionscore);
}
}
if (!empty($records)) {
$this->save_predictions($records);
}
return [$samplecontexts, $records];
}
/**
* Generates insights and updates the cache.
*
* @param \context[] $samplecontexts
* @param \stdClass[] $predictionrecords
* @return void
*/
protected function trigger_insights($samplecontexts, $predictionrecords) {
// Notify the target that all predictions have been processed.
if ($this->get_analyser()::one_sample_per_analysable()) {
// We need to do something unusual here. self::save_predictions uses the bulk-insert function (insert_records()) for
// performance reasons and that function does not return us the inserted ids. We need to retrieve them from
// the database, and we need to do it using one single database query (for performance reasons as well).
$predictionrecords = $this->add_prediction_ids($predictionrecords);
$samplesdata = $this->predictions_sample_data($predictionrecords);
$samplesdata = $this->append_calculations_info($predictionrecords, $samplesdata);
$predictions = array_map(function($predictionobj) use ($samplesdata) {
$prediction = new \core_analytics\prediction($predictionobj, $samplesdata[$predictionobj->sampleid]);
return $prediction;
}, $predictionrecords);
} else {
$predictions = [];
}
$this->get_target()->generate_insight_notifications($this->model->id, $samplecontexts, $predictions);
if ($this->get_target()->link_insights_report()) {
// Update cache.
foreach ($samplecontexts as $context) {
\core_analytics\manager::cached_models_with_insights($context, $this->get_id());
}
}
}
/**
* Get predictions from a static model.
*
* @param array $indicatorcalculations
* @return \stdClass[]
*/
protected function get_static_predictions(&$indicatorcalculations) {
$headers = array_shift($indicatorcalculations);
// Get rid of the sampleid header.
array_shift($headers);
// Group samples by analysable for \core_analytics\local\target::calculate.
$analysables = array();
// List all sampleids together.
$sampleids = array();
foreach ($indicatorcalculations as $uniquesampleid => $indicators) {
// Get rid of the sampleid column.
unset($indicators[0]);
$indicators = array_combine($headers, $indicators);
$indicatorcalculations[$uniquesampleid] = $indicators;
list($sampleid, $rangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
$analysable = $this->get_analyser()->get_sample_analysable($sampleid);
$analysableclass = get_class($analysable);
if (empty($analysables[$analysableclass])) {
$analysables[$analysableclass] = array();
}
if (empty($analysables[$analysableclass][$rangeindex])) {
$analysables[$analysableclass][$rangeindex] = (object)[
'analysable' => $analysable,
'indicatorsdata' => array(),
'sampleids' => array()
];
}
// Using the sampleid as a key so we can easily merge indicators data later.
$analysables[$analysableclass][$rangeindex]->indicatorsdata[$sampleid] = $indicators;
// We could use indicatorsdata keys but the amount of redundant data is not that big and leaves code below cleaner.
$analysables[$analysableclass][$rangeindex]->sampleids[$sampleid] = $sampleid;
// Accumulate sample ids to get all their associated data in 1 single db query (analyser::get_samples).
$sampleids[$sampleid] = $sampleid;
}
// Get all samples data.
list($sampleids, $samplesdata) = $this->get_samples($sampleids);
// Calculate the targets.
$predictions = array();
foreach ($analysables as $analysableclass => $rangedata) {
foreach ($rangedata as $rangeindex => $data) {
// Attach samples data and calculated indicators data.
$this->get_target()->clear_sample_data();
$this->get_target()->add_sample_data($samplesdata);
$this->get_target()->add_sample_data($data->indicatorsdata);
// Append new elements (we can not get duplicates because sample-analysable relation is N-1).
$timesplitting = $this->get_time_splitting();
$timesplitting->set_modelid($this->get_id());
$timesplitting->set_analysable($data->analysable);
$range = $timesplitting->get_range_by_index($rangeindex);
$this->get_target()->filter_out_invalid_samples($data->sampleids, $data->analysable, false);
$calculations = $this->get_target()->calculate($data->sampleids, $data->analysable, $range['start'], $range['end']);
// Missing $indicatorcalculations values in $calculations are caused by is_valid_sample. We need to remove
// these $uniquesampleid from $indicatorcalculations because otherwise they will be stored as calculated
// by self::save_prediction.
$indicatorcalculations = array_filter($indicatorcalculations, function($indicators, $uniquesampleid)
use ($calculations, $rangeindex) {
list($sampleid, $indicatorsrangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
if ($rangeindex == $indicatorsrangeindex && !isset($calculations[$sampleid])) {
return false;
}
return true;
}, ARRAY_FILTER_USE_BOTH);
foreach ($calculations as $sampleid => $value) {
$uniquesampleid = $this->get_time_splitting()->append_rangeindex($sampleid, $rangeindex);
// Null means that the target couldn't calculate the sample, we also remove them from $indicatorcalculations.
if (is_null($calculations[$sampleid])) {
unset($indicatorcalculations[$uniquesampleid]);
continue;
}
// Even if static predictions are based on assumptions we flag them as 100% because they are 100%
// true according to what the developer defined.
$predictions[$uniquesampleid] = (object)['prediction' => $value, 'predictionscore' => 1];
}
}
}
return $predictions;
}
/**
* Stores the prediction in the database.
*
* @param int $sampleid
* @param int $rangeindex
* @param int $prediction
* @param float $predictionscore
* @param string $calculations
* @return \context
*/
protected function prepare_prediction_record($sampleid, $rangeindex, $prediction, $predictionscore, $calculations) {
$context = $this->get_analyser()->sample_access_context($sampleid);
$record = new \stdClass();
$record->modelid = $this->model->id;
$record->contextid = $context->id;
$record->sampleid = $sampleid;
$record->rangeindex = $rangeindex;
$record->prediction = $prediction;
$record->predictionscore = $predictionscore;
$record->calculations = $calculations;
$record->timecreated = time();
$analysable = $this->get_analyser()->get_sample_analysable($sampleid);
$timesplitting = $this->get_time_splitting();
$timesplitting->set_modelid($this->get_id());
$timesplitting->set_analysable($analysable);
$range = $timesplitting->get_range_by_index($rangeindex);
if ($range) {
$record->timestart = $range['start'];
$record->timeend = $range['end'];
}
return array($record, $context);
}
/**
* Save the prediction objects.
*
* @param \stdClass[] $records
*/
protected function save_predictions($records) {
global $DB;
$DB->insert_records('analytics_predictions', $records);
}
/**
* Enabled the model using the provided time splitting method.
*
* @param string|false $timesplittingid False to respect the current time splitting method.
* @return void
*/
public function enable($timesplittingid = false) {
global $DB, $USER;
$now = time();
if ($timesplittingid && $timesplittingid !== $this->model->timesplitting) {
if (!\core_analytics\manager::is_valid($timesplittingid, '\core_analytics\local\time_splitting\base')) {
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
}
if (substr($timesplittingid, 0, 1) !== '\\') {
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
}
// Delete generated predictions before changing the model version.
$this->clear();
// It needs to be reset as the version changes.
$this->uniqueid = null;
$this->model->timesplitting = $timesplittingid;
$this->model->version = $now;
// Reset trained flag.
if (!$this->is_static()) {
$this->model->trained = 0;
}
} else if (empty($this->model->timesplitting)) {
// A valid timesplitting method needs to be supplied before a model can be enabled.
throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
}
// Purge pages with insights as this may change things.
if ($this->model->enabled != 1) {
$this->purge_insights_cache();
}
$this->model->enabled = 1;
$this->model->timemodified = $now;
$this->model->usermodified = $USER->id;
// We don't always update timemodified intentionally as we reserve it for target, indicators or timesplitting updates.
$DB->update_record('analytics_models', $this->model);
}
/**
* Is this a static model (as defined by the target)?.
*
* Static models are based on assumptions instead of in machine learning
* backends results.
*
* @return bool
*/
public function is_static() {
return (bool)$this->get_target()->based_on_assumptions();
}
/**
* Is this model enabled?
*
* @return bool
*/
public function is_enabled() {
return (bool)$this->model->enabled;
}
/**
* Is this model already trained?
*
* @return bool
*/
public function is_trained() {
// Models which targets are based on assumptions do not need training.
return (bool)$this->model->trained || $this->is_static();
}
/**
* Marks the model as trained
*
* @return void
*/
public function mark_as_trained() {
global $DB;
\core_analytics\manager::check_can_manage_models();
$this->model->trained = 1;
$DB->update_record('analytics_models', $this->model);
}
/**
* Get the contexts with predictions.
*
* @param bool $skiphidden Skip hidden predictions
* @return \stdClass[]
*/
public function get_predictions_contexts($skiphidden = true) {
global $DB, $USER;
$sql = "SELECT DISTINCT ap.contextid FROM {analytics_predictions} ap
JOIN {context} ctx ON ctx.id = ap.contextid
WHERE ap.modelid = :modelid";
$params = array('modelid' => $this->model->id);
if ($skiphidden) {
$sql .= " AND NOT EXISTS (
SELECT 1
FROM {analytics_prediction_actions} apa
WHERE apa.predictionid = ap.id AND apa.userid = :userid AND
(apa.actionname = :fixed OR apa.actionname = :notuseful OR
apa.actionname = :useful OR apa.actionname = :notapplicable OR
apa.actionname = :incorrectlyflagged)
)";
$params['userid'] = $USER->id;
$params['fixed'] = \core_analytics\prediction::ACTION_FIXED;
$params['notuseful'] = \core_analytics\prediction::ACTION_NOT_USEFUL;
$params['useful'] = \core_analytics\prediction::ACTION_USEFUL;
$params['notapplicable'] = \core_analytics\prediction::ACTION_NOT_APPLICABLE;
$params['incorrectlyflagged'] = \core_analytics\prediction::ACTION_INCORRECTLY_FLAGGED;
}
return $DB->get_records_sql($sql, $params);
}
/**
* 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
*/
public function any_prediction_obtained() {
global $DB;
return $DB->record_exists('analytics_predict_samples',
array('modelid' => $this->model->id, 'timesplitting' => $this->model->timesplitting));
}
/**
* Whether this model generates insights or not (defined by the model's target).
*
* @return bool
*/
public function uses_insights() {
$target = $this->get_target();
return $target::uses_insights();
}
/**
* Whether predictions exist for this context.
*
* @param \context $context
* @return bool
*/
public function predictions_exist(\context $context) {
global $DB;
// Filters out previous predictions keeping only the last time range one.
$select = "modelid = :modelid AND contextid = :contextid";
$params = array('modelid' => $this->model->id, 'contextid' => $context->id);
return $DB->record_exists_select('analytics_predictions', $select, $params);
}
/**
* 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[])
*/
public function get_predictions(\context $context, $skiphidden = true, $page = false, $perpage = 100) {
global $DB, $USER;
\core_analytics\manager::check_can_list_insights($context);
// Filters out previous predictions keeping only the last time range one.
$sql = "SELECT ap.*
FROM {analytics_predictions} ap
JOIN (
SELECT sampleid, max(rangeindex) AS rangeindex
FROM {analytics_predictions}
WHERE modelid = :modelidsubap and contextid = :contextidsubap
GROUP BY sampleid
) apsub
ON ap.sampleid = apsub.sampleid AND ap.rangeindex = apsub.rangeindex
WHERE ap.modelid = :modelid and ap.contextid = :contextid";
$params = array('modelid' => $this->model->id, 'contextid' => $context->id,
'modelidsubap' => $this->model->id, 'contextidsubap' => $context->id);
if ($skiphidden) {
$sql .= " AND NOT EXISTS (
SELECT 1
FROM {analytics_prediction_actions} apa
WHERE apa.predictionid = ap.id AND apa.userid = :userid AND
(apa.actionname = :fixed OR apa.actionname = :notuseful OR
apa.actionname = :useful OR apa.actionname = :notapplicable OR
apa.actionname = :incorrectlyflagged)
)";
$params['userid'] = $USER->id;
$params['fixed'] = \core_analytics\prediction::ACTION_FIXED;
$params['notuseful'] = \core_analytics\prediction::ACTION_NOT_USEFUL;
$params['useful'] = \core_analytics\prediction::ACTION_USEFUL;
$params['notapplicable'] = \core_analytics\prediction::ACTION_NOT_APPLICABLE;
$params['incorrectlyflagged'] = \core_analytics\prediction::ACTION_INCORRECTLY_FLAGGED;
}
$sql .= " ORDER BY ap.timecreated DESC";
if (!$predictions = $DB->get_records_sql($sql, $params)) {
return array();
}
// Get predicted samples' ids.
$sampleids = array_map(function($prediction) {
return $prediction->sampleid;
}, $predictions);
list($unused, $samplesdata) = $this->get_samples($sampleids);
$current = 0;
if ($page !== false) {
$offset = $page * $perpage;
$limit = $offset + $perpage;
}
foreach ($predictions as $predictionid => $predictiondata) {
$sampleid = $predictiondata->sampleid;
// Filter out predictions which samples are not available anymore.
if (empty($samplesdata[$sampleid])) {
unset($predictions[$predictionid]);
continue;
}
// Return paginated dataset - we cannot paginate in the DB because we post filter the list.
if ($page === false || ($current >= $offset && $current < $limit)) {
// Replace \stdClass object by \core_analytics\prediction objects.
$prediction = new \core_analytics\prediction($predictiondata, $samplesdata[$sampleid]);
$predictions[$predictionid] = $prediction;
} else {
unset($predictions[$predictionid]);
}
$current++;
}
if (empty($predictions)) {
return array();
}
return [$current, $predictions];
}
/**
* Returns the actions executed by users on the predictions.
*
* @param \context|null $context
* @return \moodle_recordset
*/
public function get_prediction_actions(?\context $context): \moodle_recordset {
global $DB;
$sql = "SELECT apa.id, apa.predictionid, apa.userid, apa.actionname, apa.timecreated,
ap.contextid, ap.sampleid, ap.rangeindex, ap.prediction, ap.predictionscore
FROM {analytics_prediction_actions} apa
JOIN {analytics_predictions} ap ON ap.id = apa.predictionid
WHERE ap.modelid = :modelid";
$params = ['modelid' => $this->model->id];
if ($context) {
$sql .= " AND ap.contextid = :contextid";
$params['contextid'] = $context->id;
}
return $DB->get_recordset_sql($sql, $params);
}
/**
* Returns the sample data of a prediction.
*
* @param \stdClass $predictionobj
* @return array
*/
public function prediction_sample_data($predictionobj) {
list($unused, $samplesdata) = $this->get_samples(array($predictionobj->sampleid));
if (empty($samplesdata[$predictionobj->sampleid])) {
throw new \moodle_exception('errorsamplenotavailable', 'analytics');
}
return $samplesdata[$predictionobj->sampleid];
}
/**
* Returns the samples data of the provided predictions.
*
* @param \stdClass[] $predictionrecords
* @return array
*/
public function predictions_sample_data(array $predictionrecords): array {
$sampleids = [];
foreach ($predictionrecords as $predictionobj) {
$sampleids[] = $predictionobj->sampleid;
}
list($sampleids, $samplesdata) = $this->get_analyser()->get_samples($sampleids);
return $samplesdata;
}
/**
* Appends the calculation info to the samples data.
*
* @param \stdClass[] $predictionrecords
* @param array $samplesdata
* @return array
*/
public function append_calculations_info(array $predictionrecords, array $samplesdata): array {
if ($extrainfo = calculation_info::pull_info($predictionrecords)) {
foreach ($samplesdata as $sampleid => $data) {
// The extra info come prefixed by extra: so we will not have overwrites here.
$samplesdata[$sampleid] = $samplesdata[$sampleid] + $extrainfo[$sampleid];
}
}
return $samplesdata;
}
/**
* Returns the description of a sample
*
* @param \core_analytics\prediction $prediction
* @return array 2 elements: list(string, \renderable)
*/
public function prediction_sample_description(\core_analytics\prediction $prediction) {
return $this->get_analyser()->sample_description($prediction->get_prediction_data()->sampleid,
$prediction->get_prediction_data()->contextid, $prediction->get_sample_data());
}
/**
* Returns the default output directory for prediction processors
*
* @return string
*/
public static function default_output_dir(): string {
global $CFG;
return $CFG->dataroot . DIRECTORY_SEPARATOR . 'models';
}
/**
* Returns the output directory for prediction processors.
*
* Directory structure as follows:
* - Evaluation runs:
* models/$model->id/$model->version/evaluation/$model->timesplitting
* - Training & prediction runs:
* models/$model->id/$model->version/execution
*
* @param array $subdirs
* @param bool $onlymodelid Preference over $subdirs
* @return string
*/
public function get_output_dir($subdirs = array(), $onlymodelid = false) {
$subdirstr = '';
foreach ($subdirs as $subdir) {
$subdirstr .= DIRECTORY_SEPARATOR . $subdir;
}
$outputdir = get_config('analytics', 'modeloutputdir');
if (empty($outputdir)) {
// Apply default value.
$outputdir = self::default_output_dir();
}
// Append model id.
$outputdir .= DIRECTORY_SEPARATOR . $this->model->id;
if (!$onlymodelid) {
// Append version + subdirs.
$outputdir .= DIRECTORY_SEPARATOR . $this->model->version . $subdirstr;
}
make_writable_directory($outputdir);
return $outputdir;
}
/**
* Returns a unique id for this model.
*
* This id should be unique for this site.
*
* @return string
*/
public function get_unique_id() {
global $CFG;
if (!is_null($this->uniqueid)) {
return $this->uniqueid;
}
// Generate a unique id for this site, this model and this time splitting method, considering the last time
// that the model target and indicators were updated.
$ids = array($CFG->wwwroot, $CFG->prefix, $this->model->id, $this->model->version);
$this->uniqueid = sha1(implode('$$', $ids));
return $this->uniqueid;
}
/**
* Exports the model data for displaying it in a template.
*
* @param \renderer_base $output The renderer to use for exporting
* @return \stdClass
*/
public function export(\renderer_base $output) {
\core_analytics\manager::check_can_manage_models();
$data = clone $this->model;
$data->modelname = format_string($this->get_name());
$data->name = $this->inplace_editable_name()->export_for_template($output);
$data->target = $this->get_target()->get_name();
$data->targetclass = $this->get_target()->get_id();
if ($timesplitting = $this->get_time_splitting()) {
$data->timesplitting = $timesplitting->get_name();
}
$data->indicators = array();
foreach ($this->get_indicators() as $indicator) {
$data->indicators[] = $indicator->get_name();
}
return $data;
}
/**
* 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
*/
public function export_model(string $zipfilename, bool $includeweights = true) : string {
\core_analytics\manager::check_can_manage_models();
$modelconfig = new model_config($this);
return $modelconfig->export($zipfilename, $includeweights);
}
/**
* Imports the provided model.
*
* Note that this method assumes that model_config::check_dependencies has already been called.
*
* @throws \moodle_exception
* @param string $zipfilepath Zip file path
* @return \core_analytics\model
*/
public static function import_model(string $zipfilepath) : \core_analytics\model {
\core_analytics\manager::check_can_manage_models();
$modelconfig = new \core_analytics\model_config();
return $modelconfig->import($zipfilepath);
}
/**
* Can this model be exported?
*
* @return bool
*/
public function can_export_configuration() : bool {
if (empty($this->model->timesplitting)) {
return false;
}
if (!$this->get_indicators()) {
return false;
}
if ($this->is_static()) {
return false;
}
return true;
}
/**
* Returns the model logs data.
*
* @param int $limitfrom
* @param int $limitnum
* @return \stdClass[]
*/
public function get_logs($limitfrom = 0, $limitnum = 0) {
global $DB;
\core_analytics\manager::check_can_manage_models();
return $DB->get_records('analytics_models_log', array('modelid' => $this->get_id()), 'timecreated DESC', '*',
$limitfrom, $limitnum);
}
/**
* Merges all training data files into one and returns it.
*
* @return \stored_file|false
*/
public function get_training_data() {
\core_analytics\manager::check_can_manage_models();
$timesplittingid = $this->get_time_splitting()->get_id();
return \core_analytics\dataset_manager::export_training_data($this->get_id(), $timesplittingid);
}
/**
* 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
*/
public function trained_locally() : bool {
global $DB;
if (!$this->is_trained() || $this->is_static()) {
// Early exit.
return false;
}
if ($DB->record_exists('analytics_train_samples', ['modelid' => $this->model->id])) {
return true;
}
return false;
}
/**
* Flag the provided file as used for training or prediction.
*
* @param \stored_file $file
* @param string $action
* @return void
*/
protected function flag_file_as_used(\stored_file $file, $action) {
global $DB;
$usedfile = new \stdClass();
$usedfile->modelid = $this->model->id;
$usedfile->fileid = $file->get_id();
$usedfile->action = $action;
$usedfile->time = time();
$DB->insert_record('analytics_used_files', $usedfile);
}
/**
* Log the evaluation results in the database.
*
* @param string $timesplittingid
* @param float $score
* @param string $dir
* @param array $info
* @param string $evaluationmode
* @return int The inserted log id
*/
protected function log_result($timesplittingid, $score, $dir = false, $info = false, $evaluationmode = 'configuration') {
global $DB, $USER;
$log = new \stdClass();
$log->modelid = $this->get_id();
$log->version = $this->model->version;
$log->evaluationmode = $evaluationmode;
$log->target = $this->model->target;
$log->indicators = $this->model->indicators;
$log->timesplitting = $timesplittingid;
$log->dir = $dir;
if ($info) {
// Ensure it is not an associative array.
$log->info = json_encode(array_values($info));
}
$log->score = $score;
$log->timecreated = time();
$log->usermodified = $USER->id;
return $DB->insert_record('analytics_models_log', $log);
}
/**
* Utility method to return indicator class names from a list of indicator objects
*
* @param \core_analytics\local\indicator\base[] $indicators
* @return string[]
*/
private static function indicator_classes($indicators) {
// What we want to check and store are the indicator classes not the keys.
$indicatorclasses = array();
foreach ($indicators as $indicator) {
if (!\core_analytics\manager::is_valid($indicator, '\core_analytics\local\indicator\base')) {
if (!is_object($indicator) && !is_scalar($indicator)) {
$indicator = strval($indicator);
} else if (is_object($indicator)) {
$indicator = '\\' . get_class($indicator);
}
throw new \moodle_exception('errorinvalidindicator', 'analytics', '', $indicator);
}
$indicatorclasses[] = $indicator->get_id();
}
return $indicatorclasses;
}
/**
* Clears the model training and prediction data.
*
* Executed after updating model critical elements like the time splitting method
* or the indicators.
*
* @return void
*/
public function clear() {
global $DB, $USER;
\core_analytics\manager::check_can_manage_models();
// Delete current model version stored stuff.
$predictor = $this->get_predictions_processor(false);
if ($predictor->is_ready() !== true) {
$predictorname = \core_analytics\manager::get_predictions_processor_name($predictor);
debugging('Prediction processor ' . $predictorname . ' is not ready to be used. Model ' .
$this->model->id . ' could not be cleared.');
} else {
$predictor->clear_model($this->get_unique_id(), $this->get_output_dir());
}
$DB->delete_records_select('analytics_prediction_actions', "predictionid IN
(SELECT id FROM {analytics_predictions} WHERE modelid = :modelid)", ['modelid' => $this->get_id()]);
$DB->delete_records('analytics_predictions', array('modelid' => $this->model->id));
$DB->delete_records('analytics_predict_samples', array('modelid' => $this->model->id));
$DB->delete_records('analytics_train_samples', array('modelid' => $this->model->id));
$DB->delete_records('analytics_used_files', array('modelid' => $this->model->id));
$DB->delete_records('analytics_used_analysables', array('modelid' => $this->model->id));
// Purge all generated files.
\core_analytics\dataset_manager::clear_model_files($this->model->id);
// We don't expect people to clear models regularly and the cost of filling the cache is
// 1 db read per context.
$this->purge_insights_cache();
if (!$this->is_static()) {
$this->model->trained = 0;
}
$this->model->timemodified = time();
$this->model->usermodified = $USER->id;
$DB->update_record('analytics_models', $this->model);
}
/**
* Returns the name of the model.
*
* By default, models use their target's name as their own name. They can have their explicit name, too. In which
* case, the explicit name is used instead of the default one.
*
* @return string|lang_string
*/
public function get_name() {
< if (trim($this->model->name) === '') {
> if (trim($this->model->name ?? '') === '') {
return $this->get_target()->get_name();
} else {
return $this->model->name;
}
}
/**
* 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.
*/
public function rename(string $name) {
global $DB, $USER;
$this->model->name = $name;
$this->model->timemodified = time();
$this->model->usermodified = $USER->id;
$DB->update_record('analytics_models', $this->model);
}
/**
* Returns an inplace editable element with the model's name.
*
* @return \core\output\inplace_editable
*/
public function inplace_editable_name() {
$displayname = format_string($this->get_name());
return new \core\output\inplace_editable('core_analytics', 'modelname', $this->model->id,
has_capability('moodle/analytics:managemodels', \context_system::instance()), $displayname, $this->model->name);
}
/**
* Returns true if the time-splitting method used by this model is invalid for this model.
* @return bool
*/
public function invalid_timesplitting_selected(): bool {
$currenttimesplitting = $this->model->timesplitting;
if (empty($currenttimesplitting)) {
// Not set is different from invalid. This function is used to identify invalid
// time-splittings.
return false;
}
$potentialtimesplittings = $this->get_potential_timesplittings();
if ($currenttimesplitting && empty($potentialtimesplittings[$currenttimesplitting])) {
return true;
}
return false;
}
/**
* Adds the id from {analytics_predictions} db table to the prediction \stdClass objects.
*
* @param \stdClass[] $predictionrecords
* @return \stdClass[] The prediction records including their ids in {analytics_predictions} db table.
*/
private function add_prediction_ids($predictionrecords) {
global $DB;
$firstprediction = reset($predictionrecords);
$contextids = array_map(function($predictionobj) {
return $predictionobj->contextid;
}, $predictionrecords);
// Limited to 30000 records as a middle point between the ~65000 params limit in pgsql and the size limit for mysql which
// can be increased if required up to a reasonable point.
$chunks = array_chunk($contextids, 30000);
foreach ($chunks as $contextidschunk) {
list($contextsql, $contextparams) = $DB->get_in_or_equal($contextidschunk, SQL_PARAMS_NAMED);
// We select the fields that will allow us to map ids to $predictionrecords. Given that we already filter by modelid
// we have enough with sampleid and rangeindex. The reason is that the sampleid relation to a site is N - 1.
$fields = 'id, sampleid, rangeindex';
// We include the contextid and the timecreated filter to reduce the number of records in $dbpredictions. We can not
// add as many OR conditions as records in $predictionrecords.
$sql = "SELECT $fields
FROM {analytics_predictions}
WHERE modelid = :modelid
AND contextid $contextsql
AND timecreated >= :firsttimecreated";
$params = $contextparams + ['modelid' => $this->model->id, 'firsttimecreated' => $firstprediction->timecreated];
$dbpredictions = $DB->get_recordset_sql($sql, $params);
foreach ($dbpredictions as $id => $dbprediction) {
// The append_rangeindex implementation is the same regardless of the time splitting method in use.
$uniqueid = $this->get_time_splitting()->append_rangeindex($dbprediction->sampleid, $dbprediction->rangeindex);
$predictionrecords[$uniqueid]->id = $dbprediction->id;
}
}
return $predictionrecords;
}
/**
* Wrapper around analyser's get_samples to skip DB's max-number-of-params exception.
*
* @param array $sampleids
* @return array
*/
public function get_samples(array $sampleids): array {
if (empty($sampleids)) {
throw new \coding_exception('No sample ids provided');
}
$chunksize = count($sampleids);
// We start with just 1 chunk, if it is too large for the db we split the list of sampleids in 2 and we
// try again. We repeat this process until the chunk is small enough for the db engine to process. The
// >= has been added in case there are other \dml_read_exceptions unrelated to the max number of params.
while (empty($done) && $chunksize >= 1) {
$chunks = array_chunk($sampleids, $chunksize);
$allsampleids = [];
$allsamplesdata = [];
foreach ($chunks as $index => $chunk) {
try {
list($chunksampleids, $chunksamplesdata) = $this->get_analyser()->get_samples($chunk);
} catch (\dml_read_exception $e) {
// Reduce the chunksize, we use floor() so the $chunksize is always less than the previous $chunksize value.
$chunksize = floor($chunksize / 2);
break;
}
// We can sum as these two arrays are indexed by sampleid and there are no collisions.
$allsampleids = $allsampleids + $chunksampleids;
$allsamplesdata = $allsamplesdata + $chunksamplesdata;
if ($index === count($chunks) - 1) {
// We successfully processed all the samples in all chunks, we are done.
$done = true;
}
}
}
if (empty($done)) {
if (!empty($e)) {
// Throw the last exception we caught, the \dml_read_exception we have been catching is unrelated to the max number
// of param's exception.
throw new \dml_read_exception($e);
} else {
throw new \coding_exception('We should never reach this point, there is a bug in ' .
'core_analytics\\model::get_samples\'s code');
}
}
return [$allsampleids, $allsamplesdata];
}
/**
* Contexts where this model should be active.
*
* @return \context[] Empty array if there are no context restrictions.
*/
public function get_contexts() {
if ($this->contexts !== null) {
return $this->contexts;
}
if (!$this->model->contextids) {
$this->contexts = [];
return $this->contexts;
}
$contextids = json_decode($this->model->contextids);
// We don't expect this list to be massive as contexts need to be selected manually using the edit model form.
$this->contexts = array_map(function($contextid) {
return \context::instance_by_id($contextid, IGNORE_MISSING);
}, $contextids);
return $this->contexts;
}
/**
* Purges the insights cache.
*/
private function purge_insights_cache() {
$cache = \cache::make('core', 'contextwithinsights');
$cache->purge();
}
/**
* Increases system memory and time limits.
*
* @return void
*/
private function heavy_duty_mode() {
if (ini_get('memory_limit') != -1) {
raise_memory_limit(MEMORY_HUGE);
}
\core_php_time_limit::raise();
}
}
