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<?php
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// This file is part of Moodle - http://moodle.org/
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//
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// Moodle is free software: you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation, either version 3 of the License, or
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// (at your option) any later version.
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//
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// Moodle is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with Moodle. If not, see <http://www.gnu.org/licenses/>.
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/**
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* Prediction model representation.
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*
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* @package core_analytics
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* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
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* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
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*/
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namespace core_analytics;
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defined('MOODLE_INTERNAL') || die();
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/**
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* Prediction model representation.
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*
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* @package core_analytics
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* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
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* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
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*/
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class model {
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/**
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* All as expected.
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*/
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const OK = 0;
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/**
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* There was a problem.
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*/
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const GENERAL_ERROR = 1;
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/**
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* No dataset to analyse.
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*/
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const NO_DATASET = 2;
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/**
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* Model with low prediction accuracy.
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*/
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const LOW_SCORE = 4;
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/**
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* Not enough data to evaluate the model properly.
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*/
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const NOT_ENOUGH_DATA = 8;
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/**
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* Invalid analysable for the time splitting method.
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*/
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const ANALYSABLE_REJECTED_TIME_SPLITTING_METHOD = 4;
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/**
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* Invalid analysable for all time splitting methods.
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*/
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const ANALYSABLE_STATUS_INVALID_FOR_RANGEPROCESSORS = 8;
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/**
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* Invalid analysable for the target
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*/
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const ANALYSABLE_STATUS_INVALID_FOR_TARGET = 16;
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/**
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* Minimum score to consider a non-static prediction model as good.
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*/
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const MIN_SCORE = 0.7;
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/**
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* Minimum prediction confidence (from 0 to 1) to accept a prediction as reliable enough.
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*/
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const PREDICTION_MIN_SCORE = 0.6;
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/**
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* Maximum standard deviation between different evaluation repetitions to consider that evaluation results are stable.
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*/
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const ACCEPTED_DEVIATION = 0.05;
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/**
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* Number of evaluation repetitions.
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*/
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const EVALUATION_ITERATIONS = 10;
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/**
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* @var \stdClass
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*/
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protected $model = null;
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/**
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* @var \core_analytics\local\analyser\base
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*/
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protected $analyser = null;
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/**
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* @var \core_analytics\local\target\base
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*/
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protected $target = null;
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/**
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* @var \core_analytics\predictor
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*/
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protected $predictionsprocessor = null;
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/**
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* @var \core_analytics\local\indicator\base[]
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*/
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protected $indicators = null;
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/**
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* @var \context[]
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*/
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protected $contexts = null;
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/**
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* Unique Model id created from site info and last model modification.
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*
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* @var string
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*/
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protected $uniqueid = null;
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/**
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* Constructor.
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*
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* @param int|\stdClass $model
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* @return void
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*/
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public function __construct($model) {
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global $DB;
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if (is_scalar($model)) {
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$model = $DB->get_record('analytics_models', array('id' => $model), '*', MUST_EXIST);
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if (!$model) {
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throw new \moodle_exception('errorunexistingmodel', 'analytics', '', $model);
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}
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}
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$this->model = $model;
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}
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/**
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* Quick safety check to discard site models which required components are not available anymore.
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*
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* @return bool
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*/
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public function is_available() {
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$target = $this->get_target();
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if (!$target) {
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return false;
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}
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$classname = $target->get_analyser_class();
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if (!class_exists($classname)) {
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return false;
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}
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return true;
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}
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/**
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* Returns the model id.
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*
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* @return int
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*/
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public function get_id() {
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return $this->model->id;
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}
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/**
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* Returns a plain \stdClass with the model data.
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*
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* @return \stdClass
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*/
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public function get_model_obj() {
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return $this->model;
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}
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/**
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* Returns the model target.
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*
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* @return \core_analytics\local\target\base
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*/
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public function get_target() {
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if ($this->target !== null) {
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return $this->target;
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}
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$instance = \core_analytics\manager::get_target($this->model->target);
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$this->target = $instance;
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return $this->target;
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}
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/**
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* Returns the model indicators.
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*
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* @return \core_analytics\local\indicator\base[]
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*/
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public function get_indicators() {
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if ($this->indicators !== null) {
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return $this->indicators;
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}
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$fullclassnames = json_decode($this->model->indicators);
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if (!is_array($fullclassnames)) {
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throw new \coding_exception('Model ' . $this->model->id . ' indicators can not be read');
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}
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$this->indicators = array();
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foreach ($fullclassnames as $fullclassname) {
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$instance = \core_analytics\manager::get_indicator($fullclassname);
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if ($instance) {
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$this->indicators[$fullclassname] = $instance;
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} else {
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debugging('Can\'t load ' . $fullclassname . ' indicator', DEBUG_DEVELOPER);
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}
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}
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return $this->indicators;
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}
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/**
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* Returns the list of indicators that could potentially be used by the model target.
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*
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* It includes the indicators that are part of the model.
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*
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* @return \core_analytics\local\indicator\base[]
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*/
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public function get_potential_indicators() {
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$indicators = \core_analytics\manager::get_all_indicators();
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if (empty($this->analyser)) {
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$this->init_analyser(array('notimesplitting' => true));
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}
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foreach ($indicators as $classname => $indicator) {
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if ($this->analyser->check_indicator_requirements($indicator) !== true) {
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unset($indicators[$classname]);
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}
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}
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return $indicators;
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}
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/**
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* Returns the model analyser (defined by the model target).
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*
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* @param array $options Default initialisation with no options.
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* @return \core_analytics\local\analyser\base
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*/
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public function get_analyser($options = array()) {
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if ($this->analyser !== null) {
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return $this->analyser;
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}
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$this->init_analyser($options);
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return $this->analyser;
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}
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/**
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* Initialises the model analyser.
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*
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* @throws \coding_exception
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* @param array $options
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* @return void
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*/
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protected function init_analyser($options = array()) {
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$target = $this->get_target();
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$indicators = $this->get_indicators();
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if (empty($target)) {
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throw new \moodle_exception('errornotarget', 'analytics');
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}
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$potentialtimesplittings = $this->get_potential_timesplittings();
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$timesplittings = array();
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if (empty($options['notimesplitting'])) {
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if (!empty($options['evaluation'])) {
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// The evaluation process will run using all available time splitting methods unless one is specified.
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if (!empty($options['timesplitting'])) {
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$timesplitting = \core_analytics\manager::get_time_splitting($options['timesplitting']);
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if (empty($potentialtimesplittings[$timesplitting->get_id()])) {
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throw new \moodle_exception('errorcannotusetimesplitting', 'analytics');
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}
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$timesplittings = array($timesplitting->get_id() => $timesplitting);
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} else {
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$timesplittingsforevaluation = \core_analytics\manager::get_time_splitting_methods_for_evaluation();
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// They both have the same objects, using $potentialtimesplittings as its items are sorted.
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$timesplittings = array_intersect_key($potentialtimesplittings, $timesplittingsforevaluation);
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}
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} else {
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if (empty($this->model->timesplitting)) {
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throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
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}
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// Returned as an array as all actions (evaluation, training and prediction) go through the same process.
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$timesplittings = array($this->model->timesplitting => $this->get_time_splitting());
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}
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if (empty($timesplittings)) {
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throw new \moodle_exception('errornotimesplittings', 'analytics');
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}
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}
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$classname = $target->get_analyser_class();
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if (!class_exists($classname)) {
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throw new \coding_exception($classname . ' class does not exists');
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}
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// Returns a \core_analytics\local\analyser\base class.
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$this->analyser = new $classname($this->model->id, $target, $indicators, $timesplittings, $options);
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}
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/**
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* Returns the model time splitting method.
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*
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* @return \core_analytics\local\time_splitting\base|false Returns false if no time splitting.
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*/
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public function get_time_splitting() {
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if (empty($this->model->timesplitting)) {
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return false;
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}
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return \core_analytics\manager::get_time_splitting($this->model->timesplitting);
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}
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/**
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* Returns the time-splitting methods that can be used by this model.
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*
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* @return \core_analytics\local\time_splitting\base[]
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*/
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public function get_potential_timesplittings() {
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$timesplittings = \core_analytics\manager::get_all_time_splittings();
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uasort($timesplittings, function($a, $b) {
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return strcasecmp($a->get_name(), $b->get_name());
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});
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foreach ($timesplittings as $key => $timesplitting) {
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if (!$this->get_target()->can_use_timesplitting($timesplitting)) {
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unset($timesplittings[$key]);
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continue;
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}
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}
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return $timesplittings;
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}
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/**
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* Creates a new model. Enables it if $timesplittingid is specified.
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*
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* @param \core_analytics\local\target\base $target
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* @param \core_analytics\local\indicator\base[] $indicators
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* @param string|false $timesplittingid The time splitting method id (its fully qualified class name)
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* @param string|null $processor The machine learning backend this model will use.
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* @return \core_analytics\model
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*/
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public static function create(\core_analytics\local\target\base $target, array $indicators,
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$timesplittingid = false, $processor = null) {
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global $USER, $DB;
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$indicatorclasses = self::indicator_classes($indicators);
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$now = time();
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$modelobj = new \stdClass();
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$modelobj->target = $target->get_id();
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|
384 |
$modelobj->indicators = json_encode($indicatorclasses);
|
|
|
385 |
$modelobj->version = $now;
|
|
|
386 |
$modelobj->timecreated = $now;
|
|
|
387 |
$modelobj->timemodified = $now;
|
|
|
388 |
$modelobj->usermodified = $USER->id;
|
|
|
389 |
|
|
|
390 |
if ($target->based_on_assumptions()) {
|
|
|
391 |
$modelobj->trained = 1;
|
|
|
392 |
}
|
|
|
393 |
|
|
|
394 |
if ($timesplittingid) {
|
|
|
395 |
if (!\core_analytics\manager::is_valid($timesplittingid, '\core_analytics\local\time_splitting\base')) {
|
|
|
396 |
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
|
|
|
397 |
}
|
|
|
398 |
if (substr($timesplittingid, 0, 1) !== '\\') {
|
|
|
399 |
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
|
|
|
400 |
}
|
|
|
401 |
$modelobj->timesplitting = $timesplittingid;
|
|
|
402 |
}
|
|
|
403 |
|
|
|
404 |
if ($processor &&
|
|
|
405 |
!manager::is_valid($processor, '\core_analytics\classifier') &&
|
|
|
406 |
!manager::is_valid($processor, '\core_analytics\regressor')) {
|
|
|
407 |
throw new \coding_exception('The provided predictions processor \\' . $processor . '\processor is not valid');
|
|
|
408 |
} else {
|
|
|
409 |
$modelobj->predictionsprocessor = $processor;
|
|
|
410 |
}
|
|
|
411 |
|
|
|
412 |
$id = $DB->insert_record('analytics_models', $modelobj);
|
|
|
413 |
|
|
|
414 |
// Get db defaults.
|
|
|
415 |
$modelobj = $DB->get_record('analytics_models', array('id' => $id), '*', MUST_EXIST);
|
|
|
416 |
|
|
|
417 |
$model = new static($modelobj);
|
|
|
418 |
|
|
|
419 |
return $model;
|
|
|
420 |
}
|
|
|
421 |
|
|
|
422 |
/**
|
|
|
423 |
* Does this model exist?
|
|
|
424 |
*
|
|
|
425 |
* If no indicators are provided it considers any model with the provided
|
|
|
426 |
* target a match.
|
|
|
427 |
*
|
|
|
428 |
* @param \core_analytics\local\target\base $target
|
|
|
429 |
* @param \core_analytics\local\indicator\base[]|false $indicators
|
|
|
430 |
* @return bool
|
|
|
431 |
*/
|
|
|
432 |
public static function exists(\core_analytics\local\target\base $target, $indicators = false) {
|
|
|
433 |
global $DB;
|
|
|
434 |
|
|
|
435 |
$existingmodels = $DB->get_records('analytics_models', array('target' => $target->get_id()));
|
|
|
436 |
|
|
|
437 |
if (!$existingmodels) {
|
|
|
438 |
return false;
|
|
|
439 |
}
|
|
|
440 |
|
|
|
441 |
if (!$indicators && $existingmodels) {
|
|
|
442 |
return true;
|
|
|
443 |
}
|
|
|
444 |
|
|
|
445 |
$indicatorids = array_keys($indicators);
|
|
|
446 |
sort($indicatorids);
|
|
|
447 |
|
|
|
448 |
foreach ($existingmodels as $modelobj) {
|
|
|
449 |
$model = new \core_analytics\model($modelobj);
|
|
|
450 |
$modelindicatorids = array_keys($model->get_indicators());
|
|
|
451 |
sort($modelindicatorids);
|
|
|
452 |
|
|
|
453 |
if ($indicatorids === $modelindicatorids) {
|
|
|
454 |
return true;
|
|
|
455 |
}
|
|
|
456 |
}
|
|
|
457 |
return false;
|
|
|
458 |
}
|
|
|
459 |
|
|
|
460 |
/**
|
|
|
461 |
* Updates the model.
|
|
|
462 |
*
|
|
|
463 |
* @param int|bool $enabled
|
|
|
464 |
* @param \core_analytics\local\indicator\base[]|false $indicators False to respect current indicators
|
|
|
465 |
* @param string|false $timesplittingid False to respect current time splitting method
|
|
|
466 |
* @param string|false $predictionsprocessor False to respect current predictors processor value
|
|
|
467 |
* @param int[]|false $contextids List of context ids for this model. False to respect the current list of contexts.
|
|
|
468 |
* @return void
|
|
|
469 |
*/
|
|
|
470 |
public function update($enabled, $indicators = false, $timesplittingid = '', $predictionsprocessor = false,
|
|
|
471 |
$contextids = false) {
|
|
|
472 |
global $USER, $DB;
|
|
|
473 |
|
|
|
474 |
\core_analytics\manager::check_can_manage_models();
|
|
|
475 |
|
|
|
476 |
$now = time();
|
|
|
477 |
|
|
|
478 |
if ($indicators !== false) {
|
|
|
479 |
$indicatorclasses = self::indicator_classes($indicators);
|
|
|
480 |
$indicatorsstr = json_encode($indicatorclasses);
|
|
|
481 |
} else {
|
|
|
482 |
// Respect current value.
|
|
|
483 |
$indicatorsstr = $this->model->indicators;
|
|
|
484 |
}
|
|
|
485 |
|
|
|
486 |
if ($timesplittingid === false) {
|
|
|
487 |
// Respect current value.
|
|
|
488 |
$timesplittingid = $this->model->timesplitting;
|
|
|
489 |
}
|
|
|
490 |
|
|
|
491 |
if ($predictionsprocessor === false) {
|
|
|
492 |
// Respect current value.
|
|
|
493 |
$predictionsprocessor = $this->model->predictionsprocessor;
|
|
|
494 |
}
|
|
|
495 |
|
|
|
496 |
if ($contextids === false) {
|
|
|
497 |
$contextsstr = $this->model->contextids;
|
|
|
498 |
} else if (!$contextids) {
|
|
|
499 |
$contextsstr = null;
|
|
|
500 |
} else {
|
|
|
501 |
$contextsstr = json_encode($contextids);
|
|
|
502 |
|
|
|
503 |
// Reset the internal cache.
|
|
|
504 |
$this->contexts = null;
|
|
|
505 |
}
|
|
|
506 |
|
|
|
507 |
if ($this->model->timesplitting !== $timesplittingid ||
|
|
|
508 |
$this->model->indicators !== $indicatorsstr ||
|
|
|
509 |
$this->model->predictionsprocessor !== $predictionsprocessor) {
|
|
|
510 |
|
|
|
511 |
// Delete generated predictions before changing the model version.
|
|
|
512 |
$this->clear();
|
|
|
513 |
|
|
|
514 |
// It needs to be reset as the version changes.
|
|
|
515 |
$this->uniqueid = null;
|
|
|
516 |
$this->indicators = null;
|
|
|
517 |
|
|
|
518 |
// We update the version of the model so different time splittings are not mixed up.
|
|
|
519 |
$this->model->version = $now;
|
|
|
520 |
|
|
|
521 |
// Reset trained flag.
|
|
|
522 |
if (!$this->is_static()) {
|
|
|
523 |
$this->model->trained = 0;
|
|
|
524 |
}
|
|
|
525 |
|
|
|
526 |
} else if ($this->model->enabled != $enabled) {
|
|
|
527 |
// We purge the cached contexts with insights as some will not be visible anymore.
|
|
|
528 |
$this->purge_insights_cache();
|
|
|
529 |
}
|
|
|
530 |
|
|
|
531 |
$this->model->enabled = intval($enabled);
|
|
|
532 |
$this->model->indicators = $indicatorsstr;
|
|
|
533 |
$this->model->timesplitting = $timesplittingid;
|
|
|
534 |
$this->model->predictionsprocessor = $predictionsprocessor;
|
|
|
535 |
$this->model->contextids = $contextsstr;
|
|
|
536 |
$this->model->timemodified = $now;
|
|
|
537 |
$this->model->usermodified = $USER->id;
|
|
|
538 |
|
|
|
539 |
$DB->update_record('analytics_models', $this->model);
|
|
|
540 |
}
|
|
|
541 |
|
|
|
542 |
/**
|
|
|
543 |
* Removes the model.
|
|
|
544 |
*
|
|
|
545 |
* @return void
|
|
|
546 |
*/
|
|
|
547 |
public function delete() {
|
|
|
548 |
global $DB;
|
|
|
549 |
|
|
|
550 |
\core_analytics\manager::check_can_manage_models();
|
|
|
551 |
|
|
|
552 |
$this->clear();
|
|
|
553 |
|
|
|
554 |
// Method self::clear is already clearing the current model version.
|
|
|
555 |
$predictor = $this->get_predictions_processor(false);
|
|
|
556 |
if ($predictor->is_ready() !== true) {
|
|
|
557 |
$predictorname = \core_analytics\manager::get_predictions_processor_name($predictor);
|
|
|
558 |
debugging('Prediction processor ' . $predictorname . ' is not ready to be used. Model ' .
|
|
|
559 |
$this->model->id . ' could not be deleted.');
|
|
|
560 |
} else {
|
|
|
561 |
$predictor->delete_output_dir($this->get_output_dir(array(), true), $this->get_unique_id());
|
|
|
562 |
}
|
|
|
563 |
|
|
|
564 |
$DB->delete_records('analytics_models', array('id' => $this->model->id));
|
|
|
565 |
$DB->delete_records('analytics_models_log', array('modelid' => $this->model->id));
|
|
|
566 |
}
|
|
|
567 |
|
|
|
568 |
/**
|
|
|
569 |
* Evaluates the model.
|
|
|
570 |
*
|
|
|
571 |
* This method gets the site contents (through the analyser) creates a .csv dataset
|
|
|
572 |
* with them and evaluates the model prediction accuracy multiple times using the
|
|
|
573 |
* machine learning backend. It returns an object where the model score is the average
|
|
|
574 |
* prediction accuracy of all executed evaluations.
|
|
|
575 |
*
|
|
|
576 |
* @param array $options
|
|
|
577 |
* @return \stdClass[]
|
|
|
578 |
*/
|
|
|
579 |
public function evaluate($options = array()) {
|
|
|
580 |
|
|
|
581 |
\core_analytics\manager::check_can_manage_models();
|
|
|
582 |
|
|
|
583 |
if ($this->is_static()) {
|
|
|
584 |
$this->get_analyser()->add_log(get_string('noevaluationbasedassumptions', 'analytics'));
|
|
|
585 |
$result = new \stdClass();
|
|
|
586 |
$result->status = self::NO_DATASET;
|
|
|
587 |
return array($result);
|
|
|
588 |
}
|
|
|
589 |
|
|
|
590 |
$options['evaluation'] = true;
|
|
|
591 |
|
|
|
592 |
if (empty($options['mode'])) {
|
|
|
593 |
$options['mode'] = 'configuration';
|
|
|
594 |
}
|
|
|
595 |
|
|
|
596 |
switch ($options['mode']) {
|
|
|
597 |
case 'trainedmodel':
|
|
|
598 |
|
|
|
599 |
// We are only interested on the time splitting method used by the trained model.
|
|
|
600 |
$options['timesplitting'] = $this->model->timesplitting;
|
|
|
601 |
|
|
|
602 |
// Provide the trained model directory to the ML backend if that is what we want to evaluate.
|
|
|
603 |
$trainedmodeldir = $this->get_output_dir(['execution']);
|
|
|
604 |
break;
|
|
|
605 |
case 'configuration':
|
|
|
606 |
|
|
|
607 |
$trainedmodeldir = false;
|
|
|
608 |
break;
|
|
|
609 |
|
|
|
610 |
default:
|
|
|
611 |
throw new \moodle_exception('errorunknownaction', 'analytics');
|
|
|
612 |
}
|
|
|
613 |
|
|
|
614 |
$this->init_analyser($options);
|
|
|
615 |
|
|
|
616 |
if (empty($this->get_indicators())) {
|
|
|
617 |
throw new \moodle_exception('errornoindicators', 'analytics');
|
|
|
618 |
}
|
|
|
619 |
|
|
|
620 |
$this->heavy_duty_mode();
|
|
|
621 |
|
|
|
622 |
// Before get_labelled_data call so we get an early exception if it is not ready.
|
|
|
623 |
$predictor = $this->get_predictions_processor();
|
|
|
624 |
|
|
|
625 |
$datasets = $this->get_analyser()->get_labelled_data($this->get_contexts());
|
|
|
626 |
|
|
|
627 |
// No datasets generated.
|
|
|
628 |
if (empty($datasets)) {
|
|
|
629 |
$result = new \stdClass();
|
|
|
630 |
$result->status = self::NO_DATASET;
|
|
|
631 |
$result->info = $this->get_analyser()->get_logs();
|
|
|
632 |
return array($result);
|
|
|
633 |
}
|
|
|
634 |
|
|
|
635 |
if (!PHPUNIT_TEST && CLI_SCRIPT) {
|
|
|
636 |
echo PHP_EOL . get_string('processingsitecontents', 'analytics') . PHP_EOL;
|
|
|
637 |
}
|
|
|
638 |
|
|
|
639 |
$results = array();
|
|
|
640 |
foreach ($datasets as $timesplittingid => $dataset) {
|
|
|
641 |
|
|
|
642 |
$timesplitting = \core_analytics\manager::get_time_splitting($timesplittingid);
|
|
|
643 |
|
|
|
644 |
$result = new \stdClass();
|
|
|
645 |
|
|
|
646 |
$dashestimesplittingid = str_replace('\\', '', $timesplittingid);
|
|
|
647 |
$outputdir = $this->get_output_dir(array('evaluation', $dashestimesplittingid));
|
|
|
648 |
|
|
|
649 |
// Evaluate the dataset, the deviation we accept in the results depends on the amount of iterations.
|
|
|
650 |
if ($this->get_target()->is_linear()) {
|
|
|
651 |
$predictorresult = $predictor->evaluate_regression($this->get_unique_id(), self::ACCEPTED_DEVIATION,
|
|
|
652 |
self::EVALUATION_ITERATIONS, $dataset, $outputdir, $trainedmodeldir);
|
|
|
653 |
} else {
|
|
|
654 |
$predictorresult = $predictor->evaluate_classification($this->get_unique_id(), self::ACCEPTED_DEVIATION,
|
|
|
655 |
self::EVALUATION_ITERATIONS, $dataset, $outputdir, $trainedmodeldir);
|
|
|
656 |
}
|
|
|
657 |
|
|
|
658 |
$result->status = $predictorresult->status;
|
|
|
659 |
$result->info = $predictorresult->info;
|
|
|
660 |
|
|
|
661 |
if (isset($predictorresult->score)) {
|
|
|
662 |
$result->score = $predictorresult->score;
|
|
|
663 |
} else {
|
|
|
664 |
// Prediction processors may return an error, default to 0 score in that case.
|
|
|
665 |
$result->score = 0;
|
|
|
666 |
}
|
|
|
667 |
|
|
|
668 |
$dir = false;
|
|
|
669 |
if (!empty($predictorresult->dir)) {
|
|
|
670 |
$dir = $predictorresult->dir;
|
|
|
671 |
}
|
|
|
672 |
|
|
|
673 |
$result->logid = $this->log_result($timesplitting->get_id(), $result->score, $dir, $result->info, $options['mode']);
|
|
|
674 |
|
|
|
675 |
$results[$timesplitting->get_id()] = $result;
|
|
|
676 |
}
|
|
|
677 |
|
|
|
678 |
return $results;
|
|
|
679 |
}
|
|
|
680 |
|
|
|
681 |
/**
|
|
|
682 |
* Trains the model using the site contents.
|
|
|
683 |
*
|
|
|
684 |
* This method prepares a dataset from the site contents (through the analyser)
|
|
|
685 |
* and passes it to the machine learning backends. Static models are skipped as
|
|
|
686 |
* they do not require training.
|
|
|
687 |
*
|
|
|
688 |
* @return \stdClass
|
|
|
689 |
*/
|
|
|
690 |
public function train() {
|
|
|
691 |
|
|
|
692 |
\core_analytics\manager::check_can_manage_models();
|
|
|
693 |
|
|
|
694 |
if ($this->is_static()) {
|
|
|
695 |
$this->get_analyser()->add_log(get_string('notrainingbasedassumptions', 'analytics'));
|
|
|
696 |
$result = new \stdClass();
|
|
|
697 |
$result->status = self::OK;
|
|
|
698 |
return $result;
|
|
|
699 |
}
|
|
|
700 |
|
|
|
701 |
if (!$this->is_enabled() || empty($this->model->timesplitting)) {
|
|
|
702 |
throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
|
|
|
703 |
}
|
|
|
704 |
|
|
|
705 |
if (empty($this->get_indicators())) {
|
|
|
706 |
throw new \moodle_exception('errornoindicators', 'analytics');
|
|
|
707 |
}
|
|
|
708 |
|
|
|
709 |
$this->heavy_duty_mode();
|
|
|
710 |
|
|
|
711 |
// Before get_labelled_data call so we get an early exception if it is not writable.
|
|
|
712 |
$outputdir = $this->get_output_dir(array('execution'));
|
|
|
713 |
|
|
|
714 |
// Before get_labelled_data call so we get an early exception if it is not ready.
|
|
|
715 |
$predictor = $this->get_predictions_processor();
|
|
|
716 |
|
|
|
717 |
$datasets = $this->get_analyser()->get_labelled_data($this->get_contexts());
|
|
|
718 |
|
|
|
719 |
// No training if no files have been provided.
|
|
|
720 |
if (empty($datasets) || empty($datasets[$this->model->timesplitting])) {
|
|
|
721 |
|
|
|
722 |
$result = new \stdClass();
|
|
|
723 |
$result->status = self::NO_DATASET;
|
|
|
724 |
$result->info = $this->get_analyser()->get_logs();
|
|
|
725 |
return $result;
|
|
|
726 |
}
|
|
|
727 |
$samplesfile = $datasets[$this->model->timesplitting];
|
|
|
728 |
|
|
|
729 |
// Train using the dataset.
|
|
|
730 |
if ($this->get_target()->is_linear()) {
|
|
|
731 |
$predictorresult = $predictor->train_regression($this->get_unique_id(), $samplesfile, $outputdir);
|
|
|
732 |
} else {
|
|
|
733 |
$predictorresult = $predictor->train_classification($this->get_unique_id(), $samplesfile, $outputdir);
|
|
|
734 |
}
|
|
|
735 |
|
|
|
736 |
$result = new \stdClass();
|
|
|
737 |
$result->status = $predictorresult->status;
|
|
|
738 |
$result->info = $predictorresult->info;
|
|
|
739 |
|
|
|
740 |
if ($result->status !== self::OK) {
|
|
|
741 |
return $result;
|
|
|
742 |
}
|
|
|
743 |
|
|
|
744 |
$this->flag_file_as_used($samplesfile, 'trained');
|
|
|
745 |
|
|
|
746 |
// Mark the model as trained if it wasn't.
|
|
|
747 |
if ($this->model->trained == false) {
|
|
|
748 |
$this->mark_as_trained();
|
|
|
749 |
}
|
|
|
750 |
|
|
|
751 |
return $result;
|
|
|
752 |
}
|
|
|
753 |
|
|
|
754 |
/**
|
|
|
755 |
* Get predictions from the site contents.
|
|
|
756 |
*
|
|
|
757 |
* It analyses the site contents (through analyser classes) looking for samples
|
|
|
758 |
* ready to receive predictions. It generates a dataset with all samples ready to
|
|
|
759 |
* get predictions and it passes it to the machine learning backends or to the
|
|
|
760 |
* targets based on assumptions to get the predictions.
|
|
|
761 |
*
|
|
|
762 |
* @return \stdClass
|
|
|
763 |
*/
|
|
|
764 |
public function predict() {
|
|
|
765 |
global $DB;
|
|
|
766 |
|
|
|
767 |
\core_analytics\manager::check_can_manage_models();
|
|
|
768 |
|
|
|
769 |
if (!$this->is_enabled() || empty($this->model->timesplitting)) {
|
|
|
770 |
throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
|
|
|
771 |
}
|
|
|
772 |
|
|
|
773 |
if (empty($this->get_indicators())) {
|
|
|
774 |
throw new \moodle_exception('errornoindicators', 'analytics');
|
|
|
775 |
}
|
|
|
776 |
|
|
|
777 |
$this->heavy_duty_mode();
|
|
|
778 |
|
|
|
779 |
// Before get_unlabelled_data call so we get an early exception if it is not writable.
|
|
|
780 |
$outputdir = $this->get_output_dir(array('execution'));
|
|
|
781 |
|
|
|
782 |
if (!$this->is_static()) {
|
|
|
783 |
// Predictions using a machine learning backend.
|
|
|
784 |
|
|
|
785 |
// Before get_unlabelled_data call so we get an early exception if it is not ready.
|
|
|
786 |
$predictor = $this->get_predictions_processor();
|
|
|
787 |
|
|
|
788 |
$samplesdata = $this->get_analyser()->get_unlabelled_data($this->get_contexts());
|
|
|
789 |
|
|
|
790 |
// Get the prediction samples file.
|
|
|
791 |
if (empty($samplesdata) || empty($samplesdata[$this->model->timesplitting])) {
|
|
|
792 |
|
|
|
793 |
$result = new \stdClass();
|
|
|
794 |
$result->status = self::NO_DATASET;
|
|
|
795 |
$result->info = $this->get_analyser()->get_logs();
|
|
|
796 |
return $result;
|
|
|
797 |
}
|
|
|
798 |
$samplesfile = $samplesdata[$this->model->timesplitting];
|
|
|
799 |
|
|
|
800 |
// We need to throw an exception if we are trying to predict stuff that was already predicted.
|
|
|
801 |
$params = array('modelid' => $this->model->id, 'action' => 'predicted', 'fileid' => $samplesfile->get_id());
|
|
|
802 |
if ($predicted = $DB->get_record('analytics_used_files', $params)) {
|
|
|
803 |
throw new \moodle_exception('erroralreadypredict', 'analytics', '', $samplesfile->get_id());
|
|
|
804 |
}
|
|
|
805 |
|
|
|
806 |
$indicatorcalculations = \core_analytics\dataset_manager::get_structured_data($samplesfile);
|
|
|
807 |
|
|
|
808 |
// Estimation and classification processes run on the machine learning backend side.
|
|
|
809 |
if ($this->get_target()->is_linear()) {
|
|
|
810 |
$predictorresult = $predictor->estimate($this->get_unique_id(), $samplesfile, $outputdir);
|
|
|
811 |
} else {
|
|
|
812 |
$predictorresult = $predictor->classify($this->get_unique_id(), $samplesfile, $outputdir);
|
|
|
813 |
}
|
|
|
814 |
|
|
|
815 |
// Prepare the results object.
|
|
|
816 |
$result = new \stdClass();
|
|
|
817 |
$result->status = $predictorresult->status;
|
|
|
818 |
$result->info = $predictorresult->info;
|
|
|
819 |
$result->predictions = $this->format_predictor_predictions($predictorresult);
|
|
|
820 |
|
|
|
821 |
} else {
|
|
|
822 |
// Predictions based on assumptions.
|
|
|
823 |
|
|
|
824 |
$indicatorcalculations = $this->get_analyser()->get_static_data($this->get_contexts());
|
|
|
825 |
// Get the prediction samples file.
|
|
|
826 |
if (empty($indicatorcalculations) || empty($indicatorcalculations[$this->model->timesplitting])) {
|
|
|
827 |
|
|
|
828 |
$result = new \stdClass();
|
|
|
829 |
$result->status = self::NO_DATASET;
|
|
|
830 |
$result->info = $this->get_analyser()->get_logs();
|
|
|
831 |
return $result;
|
|
|
832 |
}
|
|
|
833 |
|
|
|
834 |
// Same as reset($indicatorcalculations) as models based on assumptions only analyse 1 single
|
|
|
835 |
// time-splitting method.
|
|
|
836 |
$indicatorcalculations = $indicatorcalculations[$this->model->timesplitting];
|
|
|
837 |
|
|
|
838 |
// Prepare the results object.
|
|
|
839 |
$result = new \stdClass();
|
|
|
840 |
$result->status = self::OK;
|
|
|
841 |
$result->info = [];
|
|
|
842 |
$result->predictions = $this->get_static_predictions($indicatorcalculations);
|
|
|
843 |
}
|
|
|
844 |
|
|
|
845 |
if ($result->status !== self::OK) {
|
|
|
846 |
return $result;
|
|
|
847 |
}
|
|
|
848 |
|
|
|
849 |
if ($result->predictions) {
|
|
|
850 |
list($samplecontexts, $predictionrecords) = $this->execute_prediction_callbacks($result->predictions,
|
|
|
851 |
$indicatorcalculations);
|
|
|
852 |
}
|
|
|
853 |
|
|
|
854 |
if (!empty($samplecontexts) && $this->uses_insights()) {
|
|
|
855 |
$this->trigger_insights($samplecontexts, $predictionrecords);
|
|
|
856 |
}
|
|
|
857 |
|
|
|
858 |
if (!$this->is_static()) {
|
|
|
859 |
$this->flag_file_as_used($samplesfile, 'predicted');
|
|
|
860 |
}
|
|
|
861 |
|
|
|
862 |
return $result;
|
|
|
863 |
}
|
|
|
864 |
|
|
|
865 |
/**
|
|
|
866 |
* Returns the model predictions processor.
|
|
|
867 |
*
|
|
|
868 |
* @param bool $checkisready
|
|
|
869 |
* @return \core_analytics\predictor
|
|
|
870 |
*/
|
|
|
871 |
public function get_predictions_processor($checkisready = true) {
|
|
|
872 |
return manager::get_predictions_processor($this->model->predictionsprocessor, $checkisready);
|
|
|
873 |
}
|
|
|
874 |
|
|
|
875 |
/**
|
|
|
876 |
* Formats the predictor results.
|
|
|
877 |
*
|
|
|
878 |
* @param array $predictorresult
|
|
|
879 |
* @return array
|
|
|
880 |
*/
|
|
|
881 |
private function format_predictor_predictions($predictorresult) {
|
|
|
882 |
|
|
|
883 |
$predictions = array();
|
|
|
884 |
if (!empty($predictorresult->predictions)) {
|
|
|
885 |
foreach ($predictorresult->predictions as $sampleinfo) {
|
|
|
886 |
|
|
|
887 |
// We parse each prediction.
|
|
|
888 |
switch (count($sampleinfo)) {
|
|
|
889 |
case 1:
|
|
|
890 |
// For whatever reason the predictions processor could not process this sample, we
|
|
|
891 |
// skip it and do nothing with it.
|
|
|
892 |
debugging($this->model->id . ' model predictions processor could not process the sample with id ' .
|
|
|
893 |
$sampleinfo[0], DEBUG_DEVELOPER);
|
|
|
894 |
continue 2;
|
|
|
895 |
case 2:
|
|
|
896 |
// Prediction processors that do not return a prediction score will have the maximum prediction
|
|
|
897 |
// score.
|
|
|
898 |
list($uniquesampleid, $prediction) = $sampleinfo;
|
|
|
899 |
$predictionscore = 1;
|
|
|
900 |
break;
|
|
|
901 |
case 3:
|
|
|
902 |
list($uniquesampleid, $prediction, $predictionscore) = $sampleinfo;
|
|
|
903 |
break;
|
|
|
904 |
default:
|
|
|
905 |
break;
|
|
|
906 |
}
|
|
|
907 |
$predictiondata = (object)['prediction' => $prediction, 'predictionscore' => $predictionscore];
|
|
|
908 |
$predictions[$uniquesampleid] = $predictiondata;
|
|
|
909 |
}
|
|
|
910 |
}
|
|
|
911 |
return $predictions;
|
|
|
912 |
}
|
|
|
913 |
|
|
|
914 |
/**
|
|
|
915 |
* Execute the prediction callbacks defined by the target.
|
|
|
916 |
*
|
|
|
917 |
* @param \stdClass[] $predictions
|
|
|
918 |
* @param array $indicatorcalculations
|
|
|
919 |
* @return array
|
|
|
920 |
*/
|
|
|
921 |
protected function execute_prediction_callbacks(&$predictions, $indicatorcalculations) {
|
|
|
922 |
|
|
|
923 |
// Here we will store all predictions' contexts, this will be used to limit which users will see those predictions.
|
|
|
924 |
$samplecontexts = array();
|
|
|
925 |
$records = array();
|
|
|
926 |
|
|
|
927 |
foreach ($predictions as $uniquesampleid => $prediction) {
|
|
|
928 |
|
|
|
929 |
// The unique sample id contains both the sampleid and the rangeindex.
|
|
|
930 |
list($sampleid, $rangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
|
|
|
931 |
if ($this->get_target()->triggers_callback($prediction->prediction, $prediction->predictionscore)) {
|
|
|
932 |
|
|
|
933 |
// Prepare the record to store the predicted values.
|
|
|
934 |
list($record, $samplecontext) = $this->prepare_prediction_record($sampleid, $rangeindex, $prediction->prediction,
|
|
|
935 |
$prediction->predictionscore, json_encode($indicatorcalculations[$uniquesampleid]));
|
|
|
936 |
|
|
|
937 |
// We will later bulk-insert them all.
|
|
|
938 |
$records[$uniquesampleid] = $record;
|
|
|
939 |
|
|
|
940 |
// Also store all samples context to later generate insights or whatever action the target wants to perform.
|
|
|
941 |
$samplecontexts[$samplecontext->id] = $samplecontext;
|
|
|
942 |
|
|
|
943 |
$this->get_target()->prediction_callback($this->model->id, $sampleid, $rangeindex, $samplecontext,
|
|
|
944 |
$prediction->prediction, $prediction->predictionscore);
|
|
|
945 |
}
|
|
|
946 |
}
|
|
|
947 |
|
|
|
948 |
if (!empty($records)) {
|
|
|
949 |
$this->save_predictions($records);
|
|
|
950 |
}
|
|
|
951 |
|
|
|
952 |
return [$samplecontexts, $records];
|
|
|
953 |
}
|
|
|
954 |
|
|
|
955 |
/**
|
|
|
956 |
* Generates insights and updates the cache.
|
|
|
957 |
*
|
|
|
958 |
* @param \context[] $samplecontexts
|
|
|
959 |
* @param \stdClass[] $predictionrecords
|
|
|
960 |
* @return void
|
|
|
961 |
*/
|
|
|
962 |
protected function trigger_insights($samplecontexts, $predictionrecords) {
|
|
|
963 |
|
|
|
964 |
// Notify the target that all predictions have been processed.
|
|
|
965 |
if ($this->get_analyser()::one_sample_per_analysable()) {
|
|
|
966 |
|
|
|
967 |
// We need to do something unusual here. self::save_predictions uses the bulk-insert function (insert_records()) for
|
|
|
968 |
// performance reasons and that function does not return us the inserted ids. We need to retrieve them from
|
|
|
969 |
// the database, and we need to do it using one single database query (for performance reasons as well).
|
|
|
970 |
$predictionrecords = $this->add_prediction_ids($predictionrecords);
|
|
|
971 |
|
|
|
972 |
$samplesdata = $this->predictions_sample_data($predictionrecords);
|
|
|
973 |
$samplesdata = $this->append_calculations_info($predictionrecords, $samplesdata);
|
|
|
974 |
|
|
|
975 |
$predictions = array_map(function($predictionobj) use ($samplesdata) {
|
|
|
976 |
$prediction = new \core_analytics\prediction($predictionobj, $samplesdata[$predictionobj->sampleid]);
|
|
|
977 |
return $prediction;
|
|
|
978 |
}, $predictionrecords);
|
|
|
979 |
} else {
|
|
|
980 |
$predictions = [];
|
|
|
981 |
}
|
|
|
982 |
|
|
|
983 |
$this->get_target()->generate_insight_notifications($this->model->id, $samplecontexts, $predictions);
|
|
|
984 |
|
|
|
985 |
if ($this->get_target()->link_insights_report()) {
|
|
|
986 |
|
|
|
987 |
// Update cache.
|
|
|
988 |
foreach ($samplecontexts as $context) {
|
|
|
989 |
\core_analytics\manager::cached_models_with_insights($context, $this->get_id());
|
|
|
990 |
}
|
|
|
991 |
}
|
|
|
992 |
}
|
|
|
993 |
|
|
|
994 |
/**
|
|
|
995 |
* Get predictions from a static model.
|
|
|
996 |
*
|
|
|
997 |
* @param array $indicatorcalculations
|
|
|
998 |
* @return \stdClass[]
|
|
|
999 |
*/
|
|
|
1000 |
protected function get_static_predictions(&$indicatorcalculations) {
|
|
|
1001 |
|
|
|
1002 |
$headers = array_shift($indicatorcalculations);
|
|
|
1003 |
|
|
|
1004 |
// Get rid of the sampleid header.
|
|
|
1005 |
array_shift($headers);
|
|
|
1006 |
|
|
|
1007 |
// Group samples by analysable for \core_analytics\local\target::calculate.
|
|
|
1008 |
$analysables = array();
|
|
|
1009 |
// List all sampleids together.
|
|
|
1010 |
$sampleids = array();
|
|
|
1011 |
|
|
|
1012 |
foreach ($indicatorcalculations as $uniquesampleid => $indicators) {
|
|
|
1013 |
|
|
|
1014 |
// Get rid of the sampleid column.
|
|
|
1015 |
unset($indicators[0]);
|
|
|
1016 |
$indicators = array_combine($headers, $indicators);
|
|
|
1017 |
$indicatorcalculations[$uniquesampleid] = $indicators;
|
|
|
1018 |
|
|
|
1019 |
list($sampleid, $rangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
|
|
|
1020 |
|
|
|
1021 |
$analysable = $this->get_analyser()->get_sample_analysable($sampleid);
|
|
|
1022 |
$analysableclass = get_class($analysable);
|
|
|
1023 |
if (empty($analysables[$analysableclass])) {
|
|
|
1024 |
$analysables[$analysableclass] = array();
|
|
|
1025 |
}
|
|
|
1026 |
if (empty($analysables[$analysableclass][$rangeindex])) {
|
|
|
1027 |
$analysables[$analysableclass][$rangeindex] = (object)[
|
|
|
1028 |
'analysable' => $analysable,
|
|
|
1029 |
'indicatorsdata' => array(),
|
|
|
1030 |
'sampleids' => array()
|
|
|
1031 |
];
|
|
|
1032 |
}
|
|
|
1033 |
|
|
|
1034 |
// Using the sampleid as a key so we can easily merge indicators data later.
|
|
|
1035 |
$analysables[$analysableclass][$rangeindex]->indicatorsdata[$sampleid] = $indicators;
|
|
|
1036 |
// We could use indicatorsdata keys but the amount of redundant data is not that big and leaves code below cleaner.
|
|
|
1037 |
$analysables[$analysableclass][$rangeindex]->sampleids[$sampleid] = $sampleid;
|
|
|
1038 |
|
|
|
1039 |
// Accumulate sample ids to get all their associated data in 1 single db query (analyser::get_samples).
|
|
|
1040 |
$sampleids[$sampleid] = $sampleid;
|
|
|
1041 |
}
|
|
|
1042 |
|
|
|
1043 |
// Get all samples data.
|
|
|
1044 |
list($sampleids, $samplesdata) = $this->get_samples($sampleids);
|
|
|
1045 |
|
|
|
1046 |
// Calculate the targets.
|
|
|
1047 |
$predictions = array();
|
|
|
1048 |
foreach ($analysables as $analysableclass => $rangedata) {
|
|
|
1049 |
foreach ($rangedata as $rangeindex => $data) {
|
|
|
1050 |
|
|
|
1051 |
// Attach samples data and calculated indicators data.
|
|
|
1052 |
$this->get_target()->clear_sample_data();
|
|
|
1053 |
$this->get_target()->add_sample_data($samplesdata);
|
|
|
1054 |
$this->get_target()->add_sample_data($data->indicatorsdata);
|
|
|
1055 |
|
|
|
1056 |
// Append new elements (we can not get duplicates because sample-analysable relation is N-1).
|
|
|
1057 |
$timesplitting = $this->get_time_splitting();
|
|
|
1058 |
$timesplitting->set_modelid($this->get_id());
|
|
|
1059 |
$timesplitting->set_analysable($data->analysable);
|
|
|
1060 |
$range = $timesplitting->get_range_by_index($rangeindex);
|
|
|
1061 |
|
|
|
1062 |
$this->get_target()->filter_out_invalid_samples($data->sampleids, $data->analysable, false);
|
|
|
1063 |
$calculations = $this->get_target()->calculate($data->sampleids, $data->analysable, $range['start'], $range['end']);
|
|
|
1064 |
|
|
|
1065 |
// Missing $indicatorcalculations values in $calculations are caused by is_valid_sample. We need to remove
|
|
|
1066 |
// these $uniquesampleid from $indicatorcalculations because otherwise they will be stored as calculated
|
|
|
1067 |
// by self::save_prediction.
|
|
|
1068 |
$indicatorcalculations = array_filter($indicatorcalculations, function($indicators, $uniquesampleid)
|
|
|
1069 |
use ($calculations, $rangeindex) {
|
|
|
1070 |
list($sampleid, $indicatorsrangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
|
|
|
1071 |
if ($rangeindex == $indicatorsrangeindex && !isset($calculations[$sampleid])) {
|
|
|
1072 |
return false;
|
|
|
1073 |
}
|
|
|
1074 |
return true;
|
|
|
1075 |
}, ARRAY_FILTER_USE_BOTH);
|
|
|
1076 |
|
|
|
1077 |
foreach ($calculations as $sampleid => $value) {
|
|
|
1078 |
|
|
|
1079 |
$uniquesampleid = $this->get_time_splitting()->append_rangeindex($sampleid, $rangeindex);
|
|
|
1080 |
|
|
|
1081 |
// Null means that the target couldn't calculate the sample, we also remove them from $indicatorcalculations.
|
|
|
1082 |
if (is_null($calculations[$sampleid])) {
|
|
|
1083 |
unset($indicatorcalculations[$uniquesampleid]);
|
|
|
1084 |
continue;
|
|
|
1085 |
}
|
|
|
1086 |
|
|
|
1087 |
// Even if static predictions are based on assumptions we flag them as 100% because they are 100%
|
|
|
1088 |
// true according to what the developer defined.
|
|
|
1089 |
$predictions[$uniquesampleid] = (object)['prediction' => $value, 'predictionscore' => 1];
|
|
|
1090 |
}
|
|
|
1091 |
}
|
|
|
1092 |
}
|
|
|
1093 |
return $predictions;
|
|
|
1094 |
}
|
|
|
1095 |
|
|
|
1096 |
/**
|
|
|
1097 |
* Stores the prediction in the database.
|
|
|
1098 |
*
|
|
|
1099 |
* @param int $sampleid
|
|
|
1100 |
* @param int $rangeindex
|
|
|
1101 |
* @param int $prediction
|
|
|
1102 |
* @param float $predictionscore
|
|
|
1103 |
* @param string $calculations
|
|
|
1104 |
* @return \context
|
|
|
1105 |
*/
|
|
|
1106 |
protected function prepare_prediction_record($sampleid, $rangeindex, $prediction, $predictionscore, $calculations) {
|
|
|
1107 |
$context = $this->get_analyser()->sample_access_context($sampleid);
|
|
|
1108 |
|
|
|
1109 |
$record = new \stdClass();
|
|
|
1110 |
$record->modelid = $this->model->id;
|
|
|
1111 |
$record->contextid = $context->id;
|
|
|
1112 |
$record->sampleid = $sampleid;
|
|
|
1113 |
$record->rangeindex = $rangeindex;
|
|
|
1114 |
$record->prediction = $prediction;
|
|
|
1115 |
$record->predictionscore = $predictionscore;
|
|
|
1116 |
$record->calculations = $calculations;
|
|
|
1117 |
$record->timecreated = time();
|
|
|
1118 |
|
|
|
1119 |
$analysable = $this->get_analyser()->get_sample_analysable($sampleid);
|
|
|
1120 |
$timesplitting = $this->get_time_splitting();
|
|
|
1121 |
$timesplitting->set_modelid($this->get_id());
|
|
|
1122 |
$timesplitting->set_analysable($analysable);
|
|
|
1123 |
$range = $timesplitting->get_range_by_index($rangeindex);
|
|
|
1124 |
if ($range) {
|
|
|
1125 |
$record->timestart = $range['start'];
|
|
|
1126 |
$record->timeend = $range['end'];
|
|
|
1127 |
}
|
|
|
1128 |
|
|
|
1129 |
return array($record, $context);
|
|
|
1130 |
}
|
|
|
1131 |
|
|
|
1132 |
/**
|
|
|
1133 |
* Save the prediction objects.
|
|
|
1134 |
*
|
|
|
1135 |
* @param \stdClass[] $records
|
|
|
1136 |
*/
|
|
|
1137 |
protected function save_predictions($records) {
|
|
|
1138 |
global $DB;
|
|
|
1139 |
$DB->insert_records('analytics_predictions', $records);
|
|
|
1140 |
}
|
|
|
1141 |
|
|
|
1142 |
/**
|
|
|
1143 |
* Enabled the model using the provided time splitting method.
|
|
|
1144 |
*
|
|
|
1145 |
* @param string|false $timesplittingid False to respect the current time splitting method.
|
|
|
1146 |
* @return void
|
|
|
1147 |
*/
|
|
|
1148 |
public function enable($timesplittingid = false) {
|
|
|
1149 |
global $DB, $USER;
|
|
|
1150 |
|
|
|
1151 |
$now = time();
|
|
|
1152 |
|
|
|
1153 |
if ($timesplittingid && $timesplittingid !== $this->model->timesplitting) {
|
|
|
1154 |
|
|
|
1155 |
if (!\core_analytics\manager::is_valid($timesplittingid, '\core_analytics\local\time_splitting\base')) {
|
|
|
1156 |
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
|
|
|
1157 |
}
|
|
|
1158 |
|
|
|
1159 |
if (substr($timesplittingid, 0, 1) !== '\\') {
|
|
|
1160 |
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
|
|
|
1161 |
}
|
|
|
1162 |
|
|
|
1163 |
// Delete generated predictions before changing the model version.
|
|
|
1164 |
$this->clear();
|
|
|
1165 |
|
|
|
1166 |
// It needs to be reset as the version changes.
|
|
|
1167 |
$this->uniqueid = null;
|
|
|
1168 |
|
|
|
1169 |
$this->model->timesplitting = $timesplittingid;
|
|
|
1170 |
$this->model->version = $now;
|
|
|
1171 |
|
|
|
1172 |
// Reset trained flag.
|
|
|
1173 |
if (!$this->is_static()) {
|
|
|
1174 |
$this->model->trained = 0;
|
|
|
1175 |
}
|
|
|
1176 |
} else if (empty($this->model->timesplitting)) {
|
|
|
1177 |
// A valid timesplitting method needs to be supplied before a model can be enabled.
|
|
|
1178 |
throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
|
|
|
1179 |
|
|
|
1180 |
}
|
|
|
1181 |
|
|
|
1182 |
// Purge pages with insights as this may change things.
|
|
|
1183 |
if ($this->model->enabled != 1) {
|
|
|
1184 |
$this->purge_insights_cache();
|
|
|
1185 |
}
|
|
|
1186 |
|
|
|
1187 |
$this->model->enabled = 1;
|
|
|
1188 |
$this->model->timemodified = $now;
|
|
|
1189 |
$this->model->usermodified = $USER->id;
|
|
|
1190 |
|
|
|
1191 |
// We don't always update timemodified intentionally as we reserve it for target, indicators or timesplitting updates.
|
|
|
1192 |
$DB->update_record('analytics_models', $this->model);
|
|
|
1193 |
}
|
|
|
1194 |
|
|
|
1195 |
/**
|
|
|
1196 |
* Is this a static model (as defined by the target)?.
|
|
|
1197 |
*
|
|
|
1198 |
* Static models are based on assumptions instead of in machine learning
|
|
|
1199 |
* backends results.
|
|
|
1200 |
*
|
|
|
1201 |
* @return bool
|
|
|
1202 |
*/
|
|
|
1203 |
public function is_static() {
|
|
|
1204 |
return (bool)$this->get_target()->based_on_assumptions();
|
|
|
1205 |
}
|
|
|
1206 |
|
|
|
1207 |
/**
|
|
|
1208 |
* Is this model enabled?
|
|
|
1209 |
*
|
|
|
1210 |
* @return bool
|
|
|
1211 |
*/
|
|
|
1212 |
public function is_enabled() {
|
|
|
1213 |
return (bool)$this->model->enabled;
|
|
|
1214 |
}
|
|
|
1215 |
|
|
|
1216 |
/**
|
|
|
1217 |
* Is this model already trained?
|
|
|
1218 |
*
|
|
|
1219 |
* @return bool
|
|
|
1220 |
*/
|
|
|
1221 |
public function is_trained() {
|
|
|
1222 |
// Models which targets are based on assumptions do not need training.
|
|
|
1223 |
return (bool)$this->model->trained || $this->is_static();
|
|
|
1224 |
}
|
|
|
1225 |
|
|
|
1226 |
/**
|
|
|
1227 |
* Marks the model as trained
|
|
|
1228 |
*
|
|
|
1229 |
* @return void
|
|
|
1230 |
*/
|
|
|
1231 |
public function mark_as_trained() {
|
|
|
1232 |
global $DB;
|
|
|
1233 |
|
|
|
1234 |
\core_analytics\manager::check_can_manage_models();
|
|
|
1235 |
|
|
|
1236 |
$this->model->trained = 1;
|
|
|
1237 |
$DB->update_record('analytics_models', $this->model);
|
|
|
1238 |
}
|
|
|
1239 |
|
|
|
1240 |
/**
|
|
|
1241 |
* Get the contexts with predictions.
|
|
|
1242 |
*
|
|
|
1243 |
* @param bool $skiphidden Skip hidden predictions
|
|
|
1244 |
* @return \stdClass[]
|
|
|
1245 |
*/
|
|
|
1246 |
public function get_predictions_contexts($skiphidden = true) {
|
|
|
1247 |
global $DB, $USER;
|
|
|
1248 |
|
|
|
1249 |
$sql = "SELECT DISTINCT ap.contextid FROM {analytics_predictions} ap
|
|
|
1250 |
JOIN {context} ctx ON ctx.id = ap.contextid
|
|
|
1251 |
WHERE ap.modelid = :modelid";
|
|
|
1252 |
$params = array('modelid' => $this->model->id);
|
|
|
1253 |
|
|
|
1254 |
if ($skiphidden) {
|
|
|
1255 |
$sql .= " AND NOT EXISTS (
|
|
|
1256 |
SELECT 1
|
|
|
1257 |
FROM {analytics_prediction_actions} apa
|
|
|
1258 |
WHERE apa.predictionid = ap.id AND apa.userid = :userid AND
|
|
|
1259 |
(apa.actionname = :fixed OR apa.actionname = :notuseful OR
|
|
|
1260 |
apa.actionname = :useful OR apa.actionname = :notapplicable OR
|
|
|
1261 |
apa.actionname = :incorrectlyflagged)
|
|
|
1262 |
)";
|
|
|
1263 |
$params['userid'] = $USER->id;
|
|
|
1264 |
$params['fixed'] = \core_analytics\prediction::ACTION_FIXED;
|
|
|
1265 |
$params['notuseful'] = \core_analytics\prediction::ACTION_NOT_USEFUL;
|
|
|
1266 |
$params['useful'] = \core_analytics\prediction::ACTION_USEFUL;
|
|
|
1267 |
$params['notapplicable'] = \core_analytics\prediction::ACTION_NOT_APPLICABLE;
|
|
|
1268 |
$params['incorrectlyflagged'] = \core_analytics\prediction::ACTION_INCORRECTLY_FLAGGED;
|
|
|
1269 |
}
|
|
|
1270 |
|
|
|
1271 |
return $DB->get_records_sql($sql, $params);
|
|
|
1272 |
}
|
|
|
1273 |
|
|
|
1274 |
/**
|
|
|
1275 |
* Has this model generated predictions?
|
|
|
1276 |
*
|
|
|
1277 |
* We don't check analytics_predictions table because targets have the ability to
|
|
|
1278 |
* ignore some predicted values, if that is the case predictions are not even stored
|
|
|
1279 |
* in db.
|
|
|
1280 |
*
|
|
|
1281 |
* @return bool
|
|
|
1282 |
*/
|
|
|
1283 |
public function any_prediction_obtained() {
|
|
|
1284 |
global $DB;
|
|
|
1285 |
return $DB->record_exists('analytics_predict_samples',
|
|
|
1286 |
array('modelid' => $this->model->id, 'timesplitting' => $this->model->timesplitting));
|
|
|
1287 |
}
|
|
|
1288 |
|
|
|
1289 |
/**
|
|
|
1290 |
* Whether this model generates insights or not (defined by the model's target).
|
|
|
1291 |
*
|
|
|
1292 |
* @return bool
|
|
|
1293 |
*/
|
|
|
1294 |
public function uses_insights() {
|
|
|
1295 |
$target = $this->get_target();
|
|
|
1296 |
return $target::uses_insights();
|
|
|
1297 |
}
|
|
|
1298 |
|
|
|
1299 |
/**
|
|
|
1300 |
* Whether predictions exist for this context.
|
|
|
1301 |
*
|
|
|
1302 |
* @param \context $context
|
|
|
1303 |
* @return bool
|
|
|
1304 |
*/
|
|
|
1305 |
public function predictions_exist(\context $context) {
|
|
|
1306 |
global $DB;
|
|
|
1307 |
|
|
|
1308 |
// Filters out previous predictions keeping only the last time range one.
|
|
|
1309 |
$select = "modelid = :modelid AND contextid = :contextid";
|
|
|
1310 |
$params = array('modelid' => $this->model->id, 'contextid' => $context->id);
|
|
|
1311 |
return $DB->record_exists_select('analytics_predictions', $select, $params);
|
|
|
1312 |
}
|
|
|
1313 |
|
|
|
1314 |
/**
|
|
|
1315 |
* Gets the predictions for this context.
|
|
|
1316 |
*
|
|
|
1317 |
* @param \context $context
|
|
|
1318 |
* @param bool $skiphidden Skip hidden predictions
|
|
|
1319 |
* @param int $page The page of results to fetch. False for all results.
|
|
|
1320 |
* @param int $perpage The max number of results to fetch. Ignored if $page is false.
|
|
|
1321 |
* @return array($total, \core_analytics\prediction[])
|
|
|
1322 |
*/
|
|
|
1323 |
public function get_predictions(\context $context, $skiphidden = true, $page = false, $perpage = 100) {
|
|
|
1324 |
global $DB, $USER;
|
|
|
1325 |
|
|
|
1326 |
\core_analytics\manager::check_can_list_insights($context);
|
|
|
1327 |
|
|
|
1328 |
// Filters out previous predictions keeping only the last time range one.
|
|
|
1329 |
$sql = "SELECT ap.*
|
|
|
1330 |
FROM {analytics_predictions} ap
|
|
|
1331 |
JOIN (
|
|
|
1332 |
SELECT sampleid, max(rangeindex) AS rangeindex
|
|
|
1333 |
FROM {analytics_predictions}
|
|
|
1334 |
WHERE modelid = :modelidsubap and contextid = :contextidsubap
|
|
|
1335 |
GROUP BY sampleid
|
|
|
1336 |
) apsub
|
|
|
1337 |
ON ap.sampleid = apsub.sampleid AND ap.rangeindex = apsub.rangeindex
|
|
|
1338 |
WHERE ap.modelid = :modelid and ap.contextid = :contextid";
|
|
|
1339 |
|
|
|
1340 |
$params = array('modelid' => $this->model->id, 'contextid' => $context->id,
|
|
|
1341 |
'modelidsubap' => $this->model->id, 'contextidsubap' => $context->id);
|
|
|
1342 |
|
|
|
1343 |
if ($skiphidden) {
|
|
|
1344 |
$sql .= " AND NOT EXISTS (
|
|
|
1345 |
SELECT 1
|
|
|
1346 |
FROM {analytics_prediction_actions} apa
|
|
|
1347 |
WHERE apa.predictionid = ap.id AND apa.userid = :userid AND
|
|
|
1348 |
(apa.actionname = :fixed OR apa.actionname = :notuseful OR
|
|
|
1349 |
apa.actionname = :useful OR apa.actionname = :notapplicable OR
|
|
|
1350 |
apa.actionname = :incorrectlyflagged)
|
|
|
1351 |
)";
|
|
|
1352 |
$params['userid'] = $USER->id;
|
|
|
1353 |
$params['fixed'] = \core_analytics\prediction::ACTION_FIXED;
|
|
|
1354 |
$params['notuseful'] = \core_analytics\prediction::ACTION_NOT_USEFUL;
|
|
|
1355 |
$params['useful'] = \core_analytics\prediction::ACTION_USEFUL;
|
|
|
1356 |
$params['notapplicable'] = \core_analytics\prediction::ACTION_NOT_APPLICABLE;
|
|
|
1357 |
$params['incorrectlyflagged'] = \core_analytics\prediction::ACTION_INCORRECTLY_FLAGGED;
|
|
|
1358 |
}
|
|
|
1359 |
|
|
|
1360 |
$sql .= " ORDER BY ap.timecreated DESC";
|
|
|
1361 |
if (!$predictions = $DB->get_records_sql($sql, $params)) {
|
|
|
1362 |
return array();
|
|
|
1363 |
}
|
|
|
1364 |
|
|
|
1365 |
// Get predicted samples' ids.
|
|
|
1366 |
$sampleids = array_map(function($prediction) {
|
|
|
1367 |
return $prediction->sampleid;
|
|
|
1368 |
}, $predictions);
|
|
|
1369 |
|
|
|
1370 |
list($unused, $samplesdata) = $this->get_samples($sampleids);
|
|
|
1371 |
|
|
|
1372 |
$current = 0;
|
|
|
1373 |
|
|
|
1374 |
if ($page !== false) {
|
|
|
1375 |
$offset = $page * $perpage;
|
|
|
1376 |
$limit = $offset + $perpage;
|
|
|
1377 |
}
|
|
|
1378 |
|
|
|
1379 |
foreach ($predictions as $predictionid => $predictiondata) {
|
|
|
1380 |
|
|
|
1381 |
$sampleid = $predictiondata->sampleid;
|
|
|
1382 |
|
|
|
1383 |
// Filter out predictions which samples are not available anymore.
|
|
|
1384 |
if (empty($samplesdata[$sampleid])) {
|
|
|
1385 |
unset($predictions[$predictionid]);
|
|
|
1386 |
continue;
|
|
|
1387 |
}
|
|
|
1388 |
|
|
|
1389 |
// Return paginated dataset - we cannot paginate in the DB because we post filter the list.
|
|
|
1390 |
if ($page === false || ($current >= $offset && $current < $limit)) {
|
|
|
1391 |
// Replace \stdClass object by \core_analytics\prediction objects.
|
|
|
1392 |
$prediction = new \core_analytics\prediction($predictiondata, $samplesdata[$sampleid]);
|
|
|
1393 |
$predictions[$predictionid] = $prediction;
|
|
|
1394 |
} else {
|
|
|
1395 |
unset($predictions[$predictionid]);
|
|
|
1396 |
}
|
|
|
1397 |
|
|
|
1398 |
$current++;
|
|
|
1399 |
}
|
|
|
1400 |
|
|
|
1401 |
if (empty($predictions)) {
|
|
|
1402 |
return array();
|
|
|
1403 |
}
|
|
|
1404 |
|
|
|
1405 |
return [$current, $predictions];
|
|
|
1406 |
}
|
|
|
1407 |
|
|
|
1408 |
/**
|
|
|
1409 |
* Returns the actions executed by users on the predictions.
|
|
|
1410 |
*
|
|
|
1411 |
* @param \context|null $context
|
|
|
1412 |
* @return \moodle_recordset
|
|
|
1413 |
*/
|
|
|
1414 |
public function get_prediction_actions(?\context $context): \moodle_recordset {
|
|
|
1415 |
global $DB;
|
|
|
1416 |
|
|
|
1417 |
$sql = "SELECT apa.id, apa.predictionid, apa.userid, apa.actionname, apa.timecreated,
|
|
|
1418 |
ap.contextid, ap.sampleid, ap.rangeindex, ap.prediction, ap.predictionscore
|
|
|
1419 |
FROM {analytics_prediction_actions} apa
|
|
|
1420 |
JOIN {analytics_predictions} ap ON ap.id = apa.predictionid
|
|
|
1421 |
WHERE ap.modelid = :modelid";
|
|
|
1422 |
$params = ['modelid' => $this->model->id];
|
|
|
1423 |
|
|
|
1424 |
if ($context) {
|
|
|
1425 |
$sql .= " AND ap.contextid = :contextid";
|
|
|
1426 |
$params['contextid'] = $context->id;
|
|
|
1427 |
}
|
|
|
1428 |
|
|
|
1429 |
return $DB->get_recordset_sql($sql, $params);
|
|
|
1430 |
}
|
|
|
1431 |
|
|
|
1432 |
/**
|
|
|
1433 |
* Returns the sample data of a prediction.
|
|
|
1434 |
*
|
|
|
1435 |
* @param \stdClass $predictionobj
|
|
|
1436 |
* @return array
|
|
|
1437 |
*/
|
|
|
1438 |
public function prediction_sample_data($predictionobj) {
|
|
|
1439 |
|
|
|
1440 |
list($unused, $samplesdata) = $this->get_samples(array($predictionobj->sampleid));
|
|
|
1441 |
|
|
|
1442 |
if (empty($samplesdata[$predictionobj->sampleid])) {
|
|
|
1443 |
throw new \moodle_exception('errorsamplenotavailable', 'analytics');
|
|
|
1444 |
}
|
|
|
1445 |
|
|
|
1446 |
return $samplesdata[$predictionobj->sampleid];
|
|
|
1447 |
}
|
|
|
1448 |
|
|
|
1449 |
/**
|
|
|
1450 |
* Returns the samples data of the provided predictions.
|
|
|
1451 |
*
|
|
|
1452 |
* @param \stdClass[] $predictionrecords
|
|
|
1453 |
* @return array
|
|
|
1454 |
*/
|
|
|
1455 |
public function predictions_sample_data(array $predictionrecords): array {
|
|
|
1456 |
|
|
|
1457 |
$sampleids = [];
|
|
|
1458 |
foreach ($predictionrecords as $predictionobj) {
|
|
|
1459 |
$sampleids[] = $predictionobj->sampleid;
|
|
|
1460 |
}
|
|
|
1461 |
list($sampleids, $samplesdata) = $this->get_analyser()->get_samples($sampleids);
|
|
|
1462 |
|
|
|
1463 |
return $samplesdata;
|
|
|
1464 |
}
|
|
|
1465 |
|
|
|
1466 |
/**
|
|
|
1467 |
* Appends the calculation info to the samples data.
|
|
|
1468 |
*
|
|
|
1469 |
* @param \stdClass[] $predictionrecords
|
|
|
1470 |
* @param array $samplesdata
|
|
|
1471 |
* @return array
|
|
|
1472 |
*/
|
|
|
1473 |
public function append_calculations_info(array $predictionrecords, array $samplesdata): array {
|
|
|
1474 |
|
|
|
1475 |
if ($extrainfo = calculation_info::pull_info($predictionrecords)) {
|
|
|
1476 |
foreach ($samplesdata as $sampleid => $data) {
|
|
|
1477 |
// The extra info come prefixed by extra: so we will not have overwrites here.
|
|
|
1478 |
$samplesdata[$sampleid] = $samplesdata[$sampleid] + $extrainfo[$sampleid];
|
|
|
1479 |
}
|
|
|
1480 |
}
|
|
|
1481 |
return $samplesdata;
|
|
|
1482 |
}
|
|
|
1483 |
|
|
|
1484 |
/**
|
|
|
1485 |
* Returns the description of a sample
|
|
|
1486 |
*
|
|
|
1487 |
* @param \core_analytics\prediction $prediction
|
|
|
1488 |
* @return array 2 elements: list(string, \renderable)
|
|
|
1489 |
*/
|
|
|
1490 |
public function prediction_sample_description(\core_analytics\prediction $prediction) {
|
|
|
1491 |
return $this->get_analyser()->sample_description($prediction->get_prediction_data()->sampleid,
|
|
|
1492 |
$prediction->get_prediction_data()->contextid, $prediction->get_sample_data());
|
|
|
1493 |
}
|
|
|
1494 |
|
|
|
1495 |
/**
|
|
|
1496 |
* Returns the default output directory for prediction processors
|
|
|
1497 |
*
|
|
|
1498 |
* @return string
|
|
|
1499 |
*/
|
|
|
1500 |
public static function default_output_dir(): string {
|
|
|
1501 |
global $CFG;
|
|
|
1502 |
|
|
|
1503 |
return $CFG->dataroot . DIRECTORY_SEPARATOR . 'models';
|
|
|
1504 |
}
|
|
|
1505 |
|
|
|
1506 |
/**
|
|
|
1507 |
* Returns the output directory for prediction processors.
|
|
|
1508 |
*
|
|
|
1509 |
* Directory structure as follows:
|
|
|
1510 |
* - Evaluation runs:
|
|
|
1511 |
* models/$model->id/$model->version/evaluation/$model->timesplitting
|
|
|
1512 |
* - Training & prediction runs:
|
|
|
1513 |
* models/$model->id/$model->version/execution
|
|
|
1514 |
*
|
|
|
1515 |
* @param array $subdirs
|
|
|
1516 |
* @param bool $onlymodelid Preference over $subdirs
|
|
|
1517 |
* @return string
|
|
|
1518 |
*/
|
|
|
1519 |
public function get_output_dir($subdirs = array(), $onlymodelid = false) {
|
|
|
1520 |
$subdirstr = '';
|
|
|
1521 |
foreach ($subdirs as $subdir) {
|
|
|
1522 |
$subdirstr .= DIRECTORY_SEPARATOR . $subdir;
|
|
|
1523 |
}
|
|
|
1524 |
|
|
|
1525 |
$outputdir = get_config('analytics', 'modeloutputdir');
|
|
|
1526 |
if (empty($outputdir)) {
|
|
|
1527 |
// Apply default value.
|
|
|
1528 |
$outputdir = self::default_output_dir();
|
|
|
1529 |
}
|
|
|
1530 |
|
|
|
1531 |
// Append model id.
|
|
|
1532 |
$outputdir .= DIRECTORY_SEPARATOR . $this->model->id;
|
|
|
1533 |
if (!$onlymodelid) {
|
|
|
1534 |
// Append version + subdirs.
|
|
|
1535 |
$outputdir .= DIRECTORY_SEPARATOR . $this->model->version . $subdirstr;
|
|
|
1536 |
}
|
|
|
1537 |
|
|
|
1538 |
make_writable_directory($outputdir);
|
|
|
1539 |
|
|
|
1540 |
return $outputdir;
|
|
|
1541 |
}
|
|
|
1542 |
|
|
|
1543 |
/**
|
|
|
1544 |
* Returns a unique id for this model.
|
|
|
1545 |
*
|
|
|
1546 |
* This id should be unique for this site.
|
|
|
1547 |
*
|
|
|
1548 |
* @return string
|
|
|
1549 |
*/
|
|
|
1550 |
public function get_unique_id() {
|
|
|
1551 |
global $CFG;
|
|
|
1552 |
|
|
|
1553 |
if (!is_null($this->uniqueid)) {
|
|
|
1554 |
return $this->uniqueid;
|
|
|
1555 |
}
|
|
|
1556 |
|
|
|
1557 |
// Generate a unique id for this site, this model and this time splitting method, considering the last time
|
|
|
1558 |
// that the model target and indicators were updated.
|
|
|
1559 |
$ids = array($CFG->wwwroot, $CFG->prefix, $this->model->id, $this->model->version);
|
|
|
1560 |
$this->uniqueid = sha1(implode('$$', $ids));
|
|
|
1561 |
|
|
|
1562 |
return $this->uniqueid;
|
|
|
1563 |
}
|
|
|
1564 |
|
|
|
1565 |
/**
|
|
|
1566 |
* Exports the model data for displaying it in a template.
|
|
|
1567 |
*
|
|
|
1568 |
* @param \renderer_base $output The renderer to use for exporting
|
|
|
1569 |
* @return \stdClass
|
|
|
1570 |
*/
|
|
|
1571 |
public function export(\renderer_base $output) {
|
|
|
1572 |
|
|
|
1573 |
\core_analytics\manager::check_can_manage_models();
|
|
|
1574 |
|
|
|
1575 |
$data = clone $this->model;
|
|
|
1576 |
|
|
|
1577 |
$data->modelname = format_string($this->get_name());
|
|
|
1578 |
$data->name = $this->inplace_editable_name()->export_for_template($output);
|
|
|
1579 |
$data->target = $this->get_target()->get_name();
|
|
|
1580 |
$data->targetclass = $this->get_target()->get_id();
|
|
|
1581 |
|
|
|
1582 |
if ($timesplitting = $this->get_time_splitting()) {
|
|
|
1583 |
$data->timesplitting = $timesplitting->get_name();
|
|
|
1584 |
}
|
|
|
1585 |
|
|
|
1586 |
$data->indicators = array();
|
|
|
1587 |
foreach ($this->get_indicators() as $indicator) {
|
|
|
1588 |
$data->indicators[] = $indicator->get_name();
|
|
|
1589 |
}
|
|
|
1590 |
return $data;
|
|
|
1591 |
}
|
|
|
1592 |
|
|
|
1593 |
/**
|
|
|
1594 |
* Exports the model data to a zip file.
|
|
|
1595 |
*
|
|
|
1596 |
* @param string $zipfilename
|
|
|
1597 |
* @param bool $includeweights Include the model weights if available
|
|
|
1598 |
* @return string Zip file path
|
|
|
1599 |
*/
|
|
|
1600 |
public function export_model(string $zipfilename, bool $includeweights = true): string {
|
|
|
1601 |
|
|
|
1602 |
\core_analytics\manager::check_can_manage_models();
|
|
|
1603 |
|
|
|
1604 |
$modelconfig = new model_config($this);
|
|
|
1605 |
return $modelconfig->export($zipfilename, $includeweights);
|
|
|
1606 |
}
|
|
|
1607 |
|
|
|
1608 |
/**
|
|
|
1609 |
* Imports the provided model.
|
|
|
1610 |
*
|
|
|
1611 |
* Note that this method assumes that model_config::check_dependencies has already been called.
|
|
|
1612 |
*
|
|
|
1613 |
* @throws \moodle_exception
|
|
|
1614 |
* @param string $zipfilepath Zip file path
|
|
|
1615 |
* @return \core_analytics\model
|
|
|
1616 |
*/
|
|
|
1617 |
public static function import_model(string $zipfilepath): \core_analytics\model {
|
|
|
1618 |
|
|
|
1619 |
\core_analytics\manager::check_can_manage_models();
|
|
|
1620 |
|
|
|
1621 |
$modelconfig = new \core_analytics\model_config();
|
|
|
1622 |
return $modelconfig->import($zipfilepath);
|
|
|
1623 |
}
|
|
|
1624 |
|
|
|
1625 |
/**
|
|
|
1626 |
* Can this model be exported?
|
|
|
1627 |
*
|
|
|
1628 |
* @return bool
|
|
|
1629 |
*/
|
|
|
1630 |
public function can_export_configuration(): bool {
|
|
|
1631 |
|
|
|
1632 |
if (empty($this->model->timesplitting)) {
|
|
|
1633 |
return false;
|
|
|
1634 |
}
|
|
|
1635 |
if (!$this->get_indicators()) {
|
|
|
1636 |
return false;
|
|
|
1637 |
}
|
|
|
1638 |
|
|
|
1639 |
if ($this->is_static()) {
|
|
|
1640 |
return false;
|
|
|
1641 |
}
|
|
|
1642 |
|
|
|
1643 |
return true;
|
|
|
1644 |
}
|
|
|
1645 |
|
|
|
1646 |
/**
|
|
|
1647 |
* Returns the model logs data.
|
|
|
1648 |
*
|
|
|
1649 |
* @param int $limitfrom
|
|
|
1650 |
* @param int $limitnum
|
|
|
1651 |
* @return \stdClass[]
|
|
|
1652 |
*/
|
|
|
1653 |
public function get_logs($limitfrom = 0, $limitnum = 0) {
|
|
|
1654 |
global $DB;
|
|
|
1655 |
|
|
|
1656 |
\core_analytics\manager::check_can_manage_models();
|
|
|
1657 |
|
|
|
1658 |
return $DB->get_records('analytics_models_log', array('modelid' => $this->get_id()), 'timecreated DESC', '*',
|
|
|
1659 |
$limitfrom, $limitnum);
|
|
|
1660 |
}
|
|
|
1661 |
|
|
|
1662 |
/**
|
|
|
1663 |
* Merges all training data files into one and returns it.
|
|
|
1664 |
*
|
|
|
1665 |
* @return \stored_file|false
|
|
|
1666 |
*/
|
|
|
1667 |
public function get_training_data() {
|
|
|
1668 |
|
|
|
1669 |
\core_analytics\manager::check_can_manage_models();
|
|
|
1670 |
|
|
|
1671 |
$timesplittingid = $this->get_time_splitting()->get_id();
|
|
|
1672 |
return \core_analytics\dataset_manager::export_training_data($this->get_id(), $timesplittingid);
|
|
|
1673 |
}
|
|
|
1674 |
|
|
|
1675 |
/**
|
|
|
1676 |
* Has the model been trained using data from this site?
|
|
|
1677 |
*
|
|
|
1678 |
* This method is useful to determine if a trained model can be evaluated as
|
|
|
1679 |
* we can not use the same data for training and for evaluation.
|
|
|
1680 |
*
|
|
|
1681 |
* @return bool
|
|
|
1682 |
*/
|
|
|
1683 |
public function trained_locally(): bool {
|
|
|
1684 |
global $DB;
|
|
|
1685 |
|
|
|
1686 |
if (!$this->is_trained() || $this->is_static()) {
|
|
|
1687 |
// Early exit.
|
|
|
1688 |
return false;
|
|
|
1689 |
}
|
|
|
1690 |
|
|
|
1691 |
if ($DB->record_exists('analytics_train_samples', ['modelid' => $this->model->id])) {
|
|
|
1692 |
return true;
|
|
|
1693 |
}
|
|
|
1694 |
|
|
|
1695 |
return false;
|
|
|
1696 |
}
|
|
|
1697 |
|
|
|
1698 |
/**
|
|
|
1699 |
* Flag the provided file as used for training or prediction.
|
|
|
1700 |
*
|
|
|
1701 |
* @param \stored_file $file
|
|
|
1702 |
* @param string $action
|
|
|
1703 |
* @return void
|
|
|
1704 |
*/
|
|
|
1705 |
protected function flag_file_as_used(\stored_file $file, $action) {
|
|
|
1706 |
global $DB;
|
|
|
1707 |
|
|
|
1708 |
$usedfile = new \stdClass();
|
|
|
1709 |
$usedfile->modelid = $this->model->id;
|
|
|
1710 |
$usedfile->fileid = $file->get_id();
|
|
|
1711 |
$usedfile->action = $action;
|
|
|
1712 |
$usedfile->time = time();
|
|
|
1713 |
$DB->insert_record('analytics_used_files', $usedfile);
|
|
|
1714 |
}
|
|
|
1715 |
|
|
|
1716 |
/**
|
|
|
1717 |
* Log the evaluation results in the database.
|
|
|
1718 |
*
|
|
|
1719 |
* @param string $timesplittingid
|
|
|
1720 |
* @param float $score
|
|
|
1721 |
* @param string $dir
|
|
|
1722 |
* @param array $info
|
|
|
1723 |
* @param string $evaluationmode
|
|
|
1724 |
* @return int The inserted log id
|
|
|
1725 |
*/
|
|
|
1726 |
protected function log_result($timesplittingid, $score, $dir = false, $info = false, $evaluationmode = 'configuration') {
|
|
|
1727 |
global $DB, $USER;
|
|
|
1728 |
|
|
|
1729 |
$log = new \stdClass();
|
|
|
1730 |
$log->modelid = $this->get_id();
|
|
|
1731 |
$log->version = $this->model->version;
|
|
|
1732 |
$log->evaluationmode = $evaluationmode;
|
|
|
1733 |
$log->target = $this->model->target;
|
|
|
1734 |
$log->indicators = $this->model->indicators;
|
|
|
1735 |
$log->timesplitting = $timesplittingid;
|
|
|
1736 |
$log->dir = $dir;
|
|
|
1737 |
if ($info) {
|
|
|
1738 |
// Ensure it is not an associative array.
|
|
|
1739 |
$log->info = json_encode(array_values($info));
|
|
|
1740 |
}
|
|
|
1741 |
$log->score = $score;
|
|
|
1742 |
$log->timecreated = time();
|
|
|
1743 |
$log->usermodified = $USER->id;
|
|
|
1744 |
|
|
|
1745 |
return $DB->insert_record('analytics_models_log', $log);
|
|
|
1746 |
}
|
|
|
1747 |
|
|
|
1748 |
/**
|
|
|
1749 |
* Utility method to return indicator class names from a list of indicator objects
|
|
|
1750 |
*
|
|
|
1751 |
* @param \core_analytics\local\indicator\base[] $indicators
|
|
|
1752 |
* @return string[]
|
|
|
1753 |
*/
|
|
|
1754 |
private static function indicator_classes($indicators) {
|
|
|
1755 |
|
|
|
1756 |
// What we want to check and store are the indicator classes not the keys.
|
|
|
1757 |
$indicatorclasses = array();
|
|
|
1758 |
foreach ($indicators as $indicator) {
|
|
|
1759 |
if (!\core_analytics\manager::is_valid($indicator, '\core_analytics\local\indicator\base')) {
|
|
|
1760 |
if (!is_object($indicator) && !is_scalar($indicator)) {
|
|
|
1761 |
$indicator = strval($indicator);
|
|
|
1762 |
} else if (is_object($indicator)) {
|
|
|
1763 |
$indicator = '\\' . get_class($indicator);
|
|
|
1764 |
}
|
|
|
1765 |
throw new \moodle_exception('errorinvalidindicator', 'analytics', '', $indicator);
|
|
|
1766 |
}
|
|
|
1767 |
$indicatorclasses[] = $indicator->get_id();
|
|
|
1768 |
}
|
|
|
1769 |
|
|
|
1770 |
return $indicatorclasses;
|
|
|
1771 |
}
|
|
|
1772 |
|
|
|
1773 |
/**
|
|
|
1774 |
* Clears the model training and prediction data.
|
|
|
1775 |
*
|
|
|
1776 |
* Executed after updating model critical elements like the time splitting method
|
|
|
1777 |
* or the indicators.
|
|
|
1778 |
*
|
|
|
1779 |
* @return void
|
|
|
1780 |
*/
|
|
|
1781 |
public function clear() {
|
|
|
1782 |
global $DB, $USER;
|
|
|
1783 |
|
|
|
1784 |
\core_analytics\manager::check_can_manage_models();
|
|
|
1785 |
|
|
|
1786 |
// Delete current model version stored stuff.
|
|
|
1787 |
$predictor = $this->get_predictions_processor(false);
|
|
|
1788 |
if ($predictor->is_ready() !== true) {
|
|
|
1789 |
$predictorname = \core_analytics\manager::get_predictions_processor_name($predictor);
|
|
|
1790 |
debugging('Prediction processor ' . $predictorname . ' is not ready to be used. Model ' .
|
|
|
1791 |
$this->model->id . ' could not be cleared.');
|
|
|
1792 |
} else {
|
|
|
1793 |
$predictor->clear_model($this->get_unique_id(), $this->get_output_dir());
|
|
|
1794 |
}
|
|
|
1795 |
|
|
|
1796 |
$DB->delete_records_select('analytics_prediction_actions', "predictionid IN
|
|
|
1797 |
(SELECT id FROM {analytics_predictions} WHERE modelid = :modelid)", ['modelid' => $this->get_id()]);
|
|
|
1798 |
|
|
|
1799 |
$DB->delete_records('analytics_predictions', array('modelid' => $this->model->id));
|
|
|
1800 |
$DB->delete_records('analytics_predict_samples', array('modelid' => $this->model->id));
|
|
|
1801 |
$DB->delete_records('analytics_train_samples', array('modelid' => $this->model->id));
|
|
|
1802 |
$DB->delete_records('analytics_used_files', array('modelid' => $this->model->id));
|
|
|
1803 |
$DB->delete_records('analytics_used_analysables', array('modelid' => $this->model->id));
|
|
|
1804 |
|
|
|
1805 |
// Purge all generated files.
|
|
|
1806 |
\core_analytics\dataset_manager::clear_model_files($this->model->id);
|
|
|
1807 |
|
|
|
1808 |
// We don't expect people to clear models regularly and the cost of filling the cache is
|
|
|
1809 |
// 1 db read per context.
|
|
|
1810 |
$this->purge_insights_cache();
|
|
|
1811 |
|
|
|
1812 |
if (!$this->is_static()) {
|
|
|
1813 |
$this->model->trained = 0;
|
|
|
1814 |
}
|
|
|
1815 |
|
|
|
1816 |
$this->model->timemodified = time();
|
|
|
1817 |
$this->model->usermodified = $USER->id;
|
|
|
1818 |
$DB->update_record('analytics_models', $this->model);
|
|
|
1819 |
}
|
|
|
1820 |
|
|
|
1821 |
/**
|
|
|
1822 |
* Returns the name of the model.
|
|
|
1823 |
*
|
|
|
1824 |
* By default, models use their target's name as their own name. They can have their explicit name, too. In which
|
|
|
1825 |
* case, the explicit name is used instead of the default one.
|
|
|
1826 |
*
|
|
|
1827 |
* @return string|lang_string
|
|
|
1828 |
*/
|
|
|
1829 |
public function get_name() {
|
|
|
1830 |
|
|
|
1831 |
if (trim($this->model->name ?? '') === '') {
|
|
|
1832 |
return $this->get_target()->get_name();
|
|
|
1833 |
|
|
|
1834 |
} else {
|
|
|
1835 |
return $this->model->name;
|
|
|
1836 |
}
|
|
|
1837 |
}
|
|
|
1838 |
|
|
|
1839 |
/**
|
|
|
1840 |
* Renames the model to the given name.
|
|
|
1841 |
*
|
|
|
1842 |
* When given an empty string, the model falls back to using the associated target's name as its name.
|
|
|
1843 |
*
|
|
|
1844 |
* @param string $name The new name for the model, empty string for using the default name.
|
|
|
1845 |
*/
|
|
|
1846 |
public function rename(string $name) {
|
|
|
1847 |
global $DB, $USER;
|
|
|
1848 |
|
|
|
1849 |
$this->model->name = $name;
|
|
|
1850 |
$this->model->timemodified = time();
|
|
|
1851 |
$this->model->usermodified = $USER->id;
|
|
|
1852 |
|
|
|
1853 |
$DB->update_record('analytics_models', $this->model);
|
|
|
1854 |
}
|
|
|
1855 |
|
|
|
1856 |
/**
|
|
|
1857 |
* Returns an inplace editable element with the model's name.
|
|
|
1858 |
*
|
|
|
1859 |
* @return \core\output\inplace_editable
|
|
|
1860 |
*/
|
|
|
1861 |
public function inplace_editable_name() {
|
|
|
1862 |
|
|
|
1863 |
$displayname = format_string($this->get_name());
|
|
|
1864 |
|
|
|
1865 |
return new \core\output\inplace_editable('core_analytics', 'modelname', $this->model->id,
|
|
|
1866 |
has_capability('moodle/analytics:managemodels', \context_system::instance()), $displayname, $this->model->name);
|
|
|
1867 |
}
|
|
|
1868 |
|
|
|
1869 |
/**
|
|
|
1870 |
* Returns true if the time-splitting method used by this model is invalid for this model.
|
|
|
1871 |
* @return bool
|
|
|
1872 |
*/
|
|
|
1873 |
public function invalid_timesplitting_selected(): bool {
|
|
|
1874 |
$currenttimesplitting = $this->model->timesplitting;
|
|
|
1875 |
if (empty($currenttimesplitting)) {
|
|
|
1876 |
// Not set is different from invalid. This function is used to identify invalid
|
|
|
1877 |
// time-splittings.
|
|
|
1878 |
return false;
|
|
|
1879 |
}
|
|
|
1880 |
|
|
|
1881 |
$potentialtimesplittings = $this->get_potential_timesplittings();
|
|
|
1882 |
if ($currenttimesplitting && empty($potentialtimesplittings[$currenttimesplitting])) {
|
|
|
1883 |
return true;
|
|
|
1884 |
}
|
|
|
1885 |
|
|
|
1886 |
return false;
|
|
|
1887 |
}
|
|
|
1888 |
|
|
|
1889 |
/**
|
|
|
1890 |
* Adds the id from {analytics_predictions} db table to the prediction \stdClass objects.
|
|
|
1891 |
*
|
|
|
1892 |
* @param \stdClass[] $predictionrecords
|
|
|
1893 |
* @return \stdClass[] The prediction records including their ids in {analytics_predictions} db table.
|
|
|
1894 |
*/
|
|
|
1895 |
private function add_prediction_ids($predictionrecords) {
|
|
|
1896 |
global $DB;
|
|
|
1897 |
|
|
|
1898 |
$firstprediction = reset($predictionrecords);
|
|
|
1899 |
|
|
|
1900 |
$contextids = array_map(function($predictionobj) {
|
|
|
1901 |
return $predictionobj->contextid;
|
|
|
1902 |
}, $predictionrecords);
|
|
|
1903 |
|
|
|
1904 |
// Limited to 30000 records as a middle point between the ~65000 params limit in pgsql and the size limit for mysql which
|
|
|
1905 |
// can be increased if required up to a reasonable point.
|
|
|
1906 |
$chunks = array_chunk($contextids, 30000);
|
|
|
1907 |
foreach ($chunks as $contextidschunk) {
|
|
|
1908 |
list($contextsql, $contextparams) = $DB->get_in_or_equal($contextidschunk, SQL_PARAMS_NAMED);
|
|
|
1909 |
|
|
|
1910 |
// We select the fields that will allow us to map ids to $predictionrecords. Given that we already filter by modelid
|
|
|
1911 |
// we have enough with sampleid and rangeindex. The reason is that the sampleid relation to a site is N - 1.
|
|
|
1912 |
$fields = 'id, sampleid, rangeindex';
|
|
|
1913 |
|
|
|
1914 |
// We include the contextid and the timecreated filter to reduce the number of records in $dbpredictions. We can not
|
|
|
1915 |
// add as many OR conditions as records in $predictionrecords.
|
|
|
1916 |
$sql = "SELECT $fields
|
|
|
1917 |
FROM {analytics_predictions}
|
|
|
1918 |
WHERE modelid = :modelid
|
|
|
1919 |
AND contextid $contextsql
|
|
|
1920 |
AND timecreated >= :firsttimecreated";
|
|
|
1921 |
$params = $contextparams + ['modelid' => $this->model->id, 'firsttimecreated' => $firstprediction->timecreated];
|
|
|
1922 |
$dbpredictions = $DB->get_recordset_sql($sql, $params);
|
|
|
1923 |
foreach ($dbpredictions as $id => $dbprediction) {
|
|
|
1924 |
// The append_rangeindex implementation is the same regardless of the time splitting method in use.
|
|
|
1925 |
$uniqueid = $this->get_time_splitting()->append_rangeindex($dbprediction->sampleid, $dbprediction->rangeindex);
|
|
|
1926 |
$predictionrecords[$uniqueid]->id = $dbprediction->id;
|
|
|
1927 |
}
|
|
|
1928 |
}
|
|
|
1929 |
|
|
|
1930 |
return $predictionrecords;
|
|
|
1931 |
}
|
|
|
1932 |
|
|
|
1933 |
/**
|
|
|
1934 |
* Wrapper around analyser's get_samples to skip DB's max-number-of-params exception.
|
|
|
1935 |
*
|
|
|
1936 |
* @param array $sampleids
|
|
|
1937 |
* @return array
|
|
|
1938 |
*/
|
|
|
1939 |
public function get_samples(array $sampleids): array {
|
|
|
1940 |
|
|
|
1941 |
if (empty($sampleids)) {
|
|
|
1942 |
throw new \coding_exception('No sample ids provided');
|
|
|
1943 |
}
|
|
|
1944 |
|
|
|
1945 |
$chunksize = count($sampleids);
|
|
|
1946 |
|
|
|
1947 |
// We start with just 1 chunk, if it is too large for the db we split the list of sampleids in 2 and we
|
|
|
1948 |
// try again. We repeat this process until the chunk is small enough for the db engine to process. The
|
|
|
1949 |
// >= has been added in case there are other \dml_read_exceptions unrelated to the max number of params.
|
|
|
1950 |
while (empty($done) && $chunksize >= 1) {
|
|
|
1951 |
|
|
|
1952 |
$chunks = array_chunk($sampleids, $chunksize);
|
|
|
1953 |
$allsampleids = [];
|
|
|
1954 |
$allsamplesdata = [];
|
|
|
1955 |
|
|
|
1956 |
foreach ($chunks as $index => $chunk) {
|
|
|
1957 |
|
|
|
1958 |
try {
|
|
|
1959 |
list($chunksampleids, $chunksamplesdata) = $this->get_analyser()->get_samples($chunk);
|
|
|
1960 |
} catch (\dml_read_exception $e) {
|
|
|
1961 |
|
|
|
1962 |
// Reduce the chunksize, we use floor() so the $chunksize is always less than the previous $chunksize value.
|
|
|
1963 |
$chunksize = floor($chunksize / 2);
|
|
|
1964 |
break;
|
|
|
1965 |
}
|
|
|
1966 |
|
|
|
1967 |
// We can sum as these two arrays are indexed by sampleid and there are no collisions.
|
|
|
1968 |
$allsampleids = $allsampleids + $chunksampleids;
|
|
|
1969 |
$allsamplesdata = $allsamplesdata + $chunksamplesdata;
|
|
|
1970 |
|
|
|
1971 |
if ($index === count($chunks) - 1) {
|
|
|
1972 |
// We successfully processed all the samples in all chunks, we are done.
|
|
|
1973 |
$done = true;
|
|
|
1974 |
}
|
|
|
1975 |
}
|
|
|
1976 |
}
|
|
|
1977 |
|
|
|
1978 |
if (empty($done)) {
|
|
|
1979 |
if (!empty($e)) {
|
|
|
1980 |
// Throw the last exception we caught, the \dml_read_exception we have been catching is unrelated to the max number
|
|
|
1981 |
// of param's exception.
|
|
|
1982 |
throw new \dml_read_exception($e);
|
|
|
1983 |
} else {
|
|
|
1984 |
throw new \coding_exception('We should never reach this point, there is a bug in ' .
|
|
|
1985 |
'core_analytics\\model::get_samples\'s code');
|
|
|
1986 |
}
|
|
|
1987 |
}
|
|
|
1988 |
return [$allsampleids, $allsamplesdata];
|
|
|
1989 |
}
|
|
|
1990 |
|
|
|
1991 |
/**
|
|
|
1992 |
* Contexts where this model should be active.
|
|
|
1993 |
*
|
|
|
1994 |
* @return \context[] Empty array if there are no context restrictions.
|
|
|
1995 |
*/
|
|
|
1996 |
public function get_contexts() {
|
|
|
1997 |
if ($this->contexts !== null) {
|
|
|
1998 |
return $this->contexts;
|
|
|
1999 |
}
|
|
|
2000 |
|
|
|
2001 |
if (!$this->model->contextids) {
|
|
|
2002 |
$this->contexts = [];
|
|
|
2003 |
return $this->contexts;
|
|
|
2004 |
}
|
|
|
2005 |
$contextids = json_decode($this->model->contextids);
|
|
|
2006 |
|
|
|
2007 |
// We don't expect this list to be massive as contexts need to be selected manually using the edit model form.
|
|
|
2008 |
$this->contexts = array_map(function($contextid) {
|
|
|
2009 |
return \context::instance_by_id($contextid, IGNORE_MISSING);
|
|
|
2010 |
}, $contextids);
|
|
|
2011 |
|
|
|
2012 |
return $this->contexts;
|
|
|
2013 |
}
|
|
|
2014 |
|
|
|
2015 |
/**
|
|
|
2016 |
* Purges the insights cache.
|
|
|
2017 |
*/
|
|
|
2018 |
private function purge_insights_cache() {
|
|
|
2019 |
$cache = \cache::make('core', 'contextwithinsights');
|
|
|
2020 |
$cache->purge();
|
|
|
2021 |
}
|
|
|
2022 |
|
|
|
2023 |
/**
|
|
|
2024 |
* Increases system memory and time limits.
|
|
|
2025 |
*
|
|
|
2026 |
* @return void
|
|
|
2027 |
*/
|
|
|
2028 |
private function heavy_duty_mode() {
|
|
|
2029 |
if (ini_get('memory_limit') != -1) {
|
|
|
2030 |
raise_memory_limit(MEMORY_HUGE);
|
|
|
2031 |
}
|
|
|
2032 |
\core_php_time_limit::raise();
|
|
|
2033 |
}
|
|
|
2034 |
}
|