mirror of
https://github.com/moodle/moodle.git
synced 2025-01-18 22:08:20 +01:00
1121 lines
38 KiB
PHP
1121 lines
38 KiB
PHP
<?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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const OK = 0;
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const GENERAL_ERROR = 1;
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const NO_DATASET = 2;
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const EVALUATE_LOW_SCORE = 4;
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const EVALUATE_NOT_ENOUGH_DATA = 8;
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const ANALYSE_INPROGRESS = 2;
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const ANALYSE_REJECTED_RANGE_PROCESSOR = 4;
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const ANALYSABLE_STATUS_INVALID_FOR_RANGEPROCESSORS = 8;
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const ANALYSABLE_STATUS_INVALID_FOR_TARGET = 16;
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const MIN_SCORE = 0.7;
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const ACCEPTED_DEVIATION = 0.05;
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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\local\indicator\base[]
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*/
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protected $indicators = 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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* __construct
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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' => $model));
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if (!$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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* get_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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* get_model_obj
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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 $this->model;
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}
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/**
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* get_target
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*
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* @return \core_analytics\local\target\base
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*/
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public function get_target() {
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if ($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 $this->target;
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}
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/**
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* get_indicators
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*
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* @return \core_analytics\local\indicator\base[]
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*/
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public function get_indicators() {
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if ($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('evaluation' => 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 $indicators;
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}
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/**
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* get_analyser
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*
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* @return \core_analytics\local\analyser\base
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*/
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public function get_analyser() {
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if ($this->analyser !== null) {
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return $this->analyser;
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}
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// Default initialisation with no options.
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$this->init_analyser();
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return $this->analyser;
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}
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/**
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* init_analyser
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*
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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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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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$timesplittings = array($timesplitting->get_id() => $timesplitting);
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} else {
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$timesplittings = \core_analytics\manager::get_enabled_time_splitting_methods();
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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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$classname = $target->get_analyser_class();
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if (!class_exists($classname)) {
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throw \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, $options);
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}
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/**
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* get_time_splitting
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*
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* @return \core_analytics\local\time_splitting\base
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*/
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public function get_time_splitting() {
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if (empty($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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* 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 $timesplittingid The time splitting method id (its fully qualified class name)
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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, $timesplittingid = false) {
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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 = $target->get_id();
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$modelobj->indicators = json_encode($indicatorclasses);
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$modelobj->version = $now;
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$modelobj->timecreated = $now;
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$modelobj->timemodified = $now;
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$modelobj->usermodified = $USER->id;
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$id = $DB->insert_record('analytics_models', $modelobj);
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// Get db defaults.
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$modelobj = $DB->get_record('analytics_models', array('id' => $id), '*', MUST_EXIST);
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$model = new static($modelobj);
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if ($timesplittingid) {
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$model->enable($timesplittingid);
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}
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if ($model->is_static()) {
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$model->mark_as_trained();
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}
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return $model;
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}
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/**
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* update
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*
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* @param int|bool $enabled
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* @param \core_analytics\local\indicator\base[] $indicators
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* @param string $timesplittingid
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* @return void
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*/
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public function update($enabled, $indicators, $timesplittingid = '') {
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global $USER, $DB;
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$now = time();
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$indicatorclasses = self::indicator_classes($indicators);
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$indicatorsstr = json_encode($indicatorclasses);
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if ($this->model->timesplitting !== $timesplittingid ||
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$this->model->indicators !== $indicatorsstr) {
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// We update the version of the model so different time splittings are not mixed up.
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$this->model->version = $now;
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// Delete generated predictions.
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$this->clear_model();
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// Purge all generated files.
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\core_analytics\dataset_manager::clear_model_files($this->model->id);
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// Reset trained flag.
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$this->model->trained = 0;
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}
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$this->model->enabled = intval($enabled);
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$this->model->indicators = $indicatorsstr;
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$this->model->timesplitting = $timesplittingid;
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$this->model->timemodified = $now;
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$this->model->usermodified = $USER->id;
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$DB->update_record('analytics_models', $this->model);
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// It needs to be reset (just in case, we may already used it).
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$this->uniqueid = null;
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}
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/**
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* Removes the model.
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*
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* @return void
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*/
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public static function delete() {
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global $DB;
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$this->clear_model();
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$DB->delete_record('analytics_models', array('id' => $this->model->id));
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}
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/**
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* Evaluates the model datasets.
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*
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* Model datasets should already be available in Moodle's filesystem.
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*
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* @param array $options
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* @return \stdClass[]
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*/
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public function evaluate($options = array()) {
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if ($this->is_static()) {
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$this->get_analyser()->add_log(get_string('noevaluationbasedassumptions', 'analytics'));
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$result = new \stdClass();
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$result->status = self::OK;
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return $result;
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}
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// Increase memory limit.
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$this->increase_memory();
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$options['evaluation'] = true;
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$this->init_analyser($options);
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if (empty($this->get_indicators())) {
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throw new \moodle_exception('errornoindicators', 'analytics');
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}
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// Before get_labelled_data call so we get an early exception if it is not ready.
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$predictor = \core_analytics\manager::get_predictions_processor();
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$datasets = $this->get_analyser()->get_labelled_data();
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// No datasets generated.
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if (empty($datasets)) {
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$result = new \stdClass();
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$result->status = self::NO_DATASET;
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$result->info = $this->get_analyser()->get_logs();
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return array($result);
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}
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if (!PHPUNIT_TEST && CLI_SCRIPT) {
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echo PHP_EOL . get_string('processingsitecontents', 'analytics') . PHP_EOL;
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}
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$results = array();
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foreach ($datasets as $timesplittingid => $dataset) {
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$timesplitting = \core_analytics\manager::get_time_splitting($timesplittingid);
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$result = new \stdClass();
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$dashestimesplittingid = str_replace('\\', '', $timesplittingid);
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$outputdir = $this->get_output_dir(array('evaluation', $dashestimesplittingid));
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// Evaluate the dataset, the deviation we accept in the results depends on the amount of iterations.
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$predictorresult = $predictor->evaluate($this->model->id, self::ACCEPTED_DEVIATION,
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self::EVALUATION_ITERATIONS, $dataset, $outputdir);
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$result->status = $predictorresult->status;
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$result->info = $predictorresult->info;
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if (isset($predictorresult->score)) {
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$result->score = $predictorresult->score;
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} else {
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// Prediction processors may return an error, default to 0 score in that case.
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$result->score = 0;
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}
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$dir = false;
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if (!empty($predictorresult->dir)) {
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$dir = $predictorresult->dir;
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}
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$result->logid = $this->log_result($timesplitting->get_id(), $result->score, $dir, $result->info);
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$results[$timesplitting->get_id()] = $result;
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}
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return $results;
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}
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/**
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* train
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*
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* @return \stdClass
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*/
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public function train() {
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global $DB;
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if ($this->is_static()) {
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$this->get_analyser()->add_log(get_string('notrainingbasedassumptions', 'analytics'));
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$result = new \stdClass();
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$result->status = self::OK;
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return $result;
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}
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// Increase memory limit.
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$this->increase_memory();
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if (!$this->is_enabled() || empty($this->model->timesplitting)) {
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throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
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}
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if (empty($this->get_indicators())) {
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throw new \moodle_exception('errornoindicators', 'analytics');
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}
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// Before get_labelled_data call so we get an early exception if it is not writable.
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$outputdir = $this->get_output_dir(array('execution'));
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// Before get_labelled_data call so we get an early exception if it is not ready.
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$predictor = \core_analytics\manager::get_predictions_processor();
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$datasets = $this->get_analyser()->get_labelled_data();
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// No training if no files have been provided.
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if (empty($datasets) || empty($datasets[$this->model->timesplitting])) {
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$result = new \stdClass();
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$result->status = self::NO_DATASET;
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$result->info = $this->get_analyser()->get_logs();
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return $result;
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}
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$samplesfile = $datasets[$this->model->timesplitting];
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// Train using the dataset.
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$predictorresult = $predictor->train($this->get_unique_id(), $samplesfile, $outputdir);
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$result = new \stdClass();
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$result->status = $predictorresult->status;
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$result->info = $predictorresult->info;
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$this->flag_file_as_used($samplesfile, 'trained');
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// Mark the model as trained if it wasn't.
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if ($this->model->trained == false) {
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$this->mark_as_trained();
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}
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return $result;
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}
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/**
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* predict
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*
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* @return \stdClass
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*/
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public function predict() {
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global $DB;
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// Increase memory limit.
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$this->increase_memory();
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if (!$this->is_enabled() || empty($this->model->timesplitting)) {
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throw new \moodle_exception('invalidtimesplitting', 'analytics', '', $this->model->id);
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}
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if (empty($this->get_indicators())) {
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throw new \moodle_exception('errornoindicators', 'analytics');
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}
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// Before get_unlabelled_data call so we get an early exception if it is not writable.
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$outputdir = $this->get_output_dir(array('execution'));
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// Before get_unlabelled_data call so we get an early exception if it is not ready.
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if (!$this->is_static()) {
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$predictor = \core_analytics\manager::get_predictions_processor();
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}
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$samplesdata = $this->get_analyser()->get_unlabelled_data();
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// Get the prediction samples file.
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if (empty($samplesdata) || empty($samplesdata[$this->model->timesplitting])) {
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$result = new \stdClass();
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$result->status = self::NO_DATASET;
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$result->info = $this->get_analyser()->get_logs();
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return $result;
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}
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$samplesfile = $samplesdata[$this->model->timesplitting];
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// We need to throw an exception if we are trying to predict stuff that was already predicted.
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$params = array('modelid' => $this->model->id, 'fileid' => $samplesfile->get_id(), 'action' => 'predicted');
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if ($predicted = $DB->get_record('analytics_used_files', $params)) {
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throw new \moodle_exception('erroralreadypredict', 'analytics', '', $samplesfile->get_id());
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}
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|
|
$indicatorcalculations = \core_analytics\dataset_manager::get_structured_data($samplesfile);
|
|
|
|
// Prepare the results object.
|
|
$result = new \stdClass();
|
|
|
|
if ($this->is_static()) {
|
|
// Prediction based on assumptions.
|
|
$result->status = \core_analytics\model::OK;
|
|
$result->info = [];
|
|
$result->predictions = $this->get_static_predictions($indicatorcalculations);
|
|
|
|
} else {
|
|
// Defer the prediction to the machine learning backend.
|
|
$predictorresult = $predictor->predict($this->get_unique_id(), $samplesfile, $outputdir);
|
|
|
|
$result->status = $predictorresult->status;
|
|
$result->info = $predictorresult->info;
|
|
$result->predictions = array();
|
|
if ($predictorresult->predictions) {
|
|
foreach ($predictorresult->predictions as $sampleinfo) {
|
|
|
|
// We parse each prediction
|
|
switch (count($sampleinfo)) {
|
|
case 1:
|
|
// For whatever reason the predictions processor could not process this sample, we
|
|
// skip it and do nothing with it.
|
|
debugging($this->model->id . ' model predictions processor could not process the sample with id ' .
|
|
$sampleinfo[0], DEBUG_DEVELOPER);
|
|
continue;
|
|
case 2:
|
|
// Prediction processors that do not return a prediction score will have the maximum prediction
|
|
// score.
|
|
list($uniquesampleid, $prediction) = $sampleinfo;
|
|
$predictionscore = 1;
|
|
break;
|
|
case 3:
|
|
list($uniquesampleid, $prediction, $predictionscore) = $sampleinfo;
|
|
break;
|
|
default:
|
|
break;
|
|
}
|
|
$predictiondata = (object)['prediction' => $prediction, 'predictionscore' => $predictionscore];
|
|
$result->predictions[$uniquesampleid] = $predictiondata;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Here we will store all predictions' contexts, this will be used to limit which users will see those predictions.
|
|
$samplecontexts = array();
|
|
|
|
if ($result->predictions) {
|
|
foreach ($result->predictions as $uniquesampleid => $prediction) {
|
|
|
|
if ($this->get_target()->triggers_callback($prediction->prediction, $prediction->predictionscore)) {
|
|
|
|
// The unique sample id contains both the sampleid and the rangeindex.
|
|
list($sampleid, $rangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
|
|
|
|
// Store the predicted values.
|
|
$samplecontext = $this->save_prediction($sampleid, $rangeindex, $prediction->prediction, $prediction->predictionscore,
|
|
json_encode($indicatorcalculations[$uniquesampleid]));
|
|
|
|
// Also store all samples context to later generate insights or whatever action the target wants to perform.
|
|
$samplecontexts[$samplecontext->id] = $samplecontext;
|
|
|
|
$this->get_target()->prediction_callback($this->model->id, $sampleid, $rangeindex, $samplecontext,
|
|
$prediction->prediction, $prediction->predictionscore);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!empty($samplecontexts)) {
|
|
// Notify the target that all predictions have been processed.
|
|
$this->get_target()->generate_insights($this->model->id, $samplecontexts);
|
|
|
|
// Aggressive invalidation, the cost of filling up the cache is not high.
|
|
$cache = \cache::make('core', 'modelswithpredictions');
|
|
foreach ($samplecontexts as $context) {
|
|
$cache->delete($context->id);
|
|
}
|
|
}
|
|
|
|
$this->flag_file_as_used($samplesfile, 'predicted');
|
|
|
|
return $result;
|
|
}
|
|
|
|
/**
|
|
* get_static_predictions
|
|
*
|
|
* @param array $indicatorcalculations
|
|
* @return \stdClass[]
|
|
*/
|
|
protected function get_static_predictions(&$indicatorcalculations) {
|
|
|
|
// Group samples by analysable for \core_analytics\local\target::calculate.
|
|
$analysables = array();
|
|
// List all sampleids together.
|
|
$sampleids = array();
|
|
|
|
foreach ($indicatorcalculations as $uniquesampleid => $indicators) {
|
|
list($sampleid, $rangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
|
|
|
|
$analysable = $this->get_analyser()->get_sample_analysable($sampleid);
|
|
$analysableclass = get_class($analysable);
|
|
if (empty($analysables[$analysableclass])) {
|
|
$analysables[$analysableclass] = array();
|
|
}
|
|
if (empty($analysables[$analysableclass][$rangeindex])) {
|
|
$analysables[$analysableclass][$rangeindex] = (object)[
|
|
'analysable' => $analysable,
|
|
'indicatorsdata' => array(),
|
|
'sampleids' => array()
|
|
];
|
|
}
|
|
// Using the sampleid as a key so we can easily merge indicators data later.
|
|
$analysables[$analysableclass][$rangeindex]->indicatorsdata[$sampleid] = $indicators;
|
|
// We could use indicatorsdata keys but the amount of redundant data is not that big and leaves code below cleaner.
|
|
$analysables[$analysableclass][$rangeindex]->sampleids[$sampleid] = $sampleid;
|
|
|
|
// Accumulate sample ids to get all their associated data in 1 single db query (analyser::get_samples).
|
|
$sampleids[$sampleid] = $sampleid;
|
|
}
|
|
|
|
// Get all samples data.
|
|
list($sampleids, $samplesdata) = $this->get_analyser()->get_samples($sampleids);
|
|
|
|
// Calculate the targets.
|
|
$calculations = array();
|
|
foreach ($analysables as $analysableclass => $rangedata) {
|
|
foreach ($rangedata as $rangeindex => $data) {
|
|
|
|
// Attach samples data and calculated indicators data.
|
|
$this->get_target()->clear_sample_data();
|
|
$this->get_target()->add_sample_data($samplesdata);
|
|
$this->get_target()->add_sample_data($data->indicatorsdata);
|
|
|
|
// Append new elements (we can not get duplicated because sample-analysable relation is N-1).
|
|
$range = $this->get_time_splitting()->get_range_by_index($rangeindex);
|
|
$calculations = $this->get_target()->calculate($data->sampleids, $data->analysable, $range['start'], $range['end']);
|
|
|
|
// Missing $indicatorcalculations values in $calculations are caused by is_valid_sample. We need to remove
|
|
// these $uniquesampleid from $indicatorcalculations because otherwise they will be stored as calculated
|
|
// by self::save_prediction.
|
|
$indicatorcalculations = array_filter($indicatorcalculations, function($indicators, $uniquesampleid) use ($calculations) {
|
|
list($sampleid, $rangeindex) = $this->get_time_splitting()->infer_sample_info($uniquesampleid);
|
|
if (!isset($calculations[$sampleid])) {
|
|
debugging($uniquesampleid . ' discarded by is_valid_sample');
|
|
return false;
|
|
}
|
|
return true;
|
|
}, ARRAY_FILTER_USE_BOTH);
|
|
|
|
foreach ($calculations as $sampleid => $value) {
|
|
|
|
$uniquesampleid = $this->get_time_splitting()->append_rangeindex($sampleid, $rangeindex);
|
|
|
|
// Null means that the target couldn't calculate the sample, we also remove them from $indicatorcalculations.
|
|
if (is_null($calculations[$sampleid])) {
|
|
debugging($uniquesampleid . ' discarded by is_valid_sample');
|
|
unset($indicatorcalculations[$uniquesampleid]);
|
|
continue;
|
|
}
|
|
|
|
// Even if static predictions are based on assumptions we flag them as 100% because they are 100%
|
|
// true according to what the developer defined.
|
|
$predictions[$uniquesampleid] = (object)['prediction' => $value, 'predictionscore' => 1];
|
|
}
|
|
}
|
|
}
|
|
return $predictions;
|
|
}
|
|
|
|
/**
|
|
* save_prediction
|
|
*
|
|
* @param int $sampleid
|
|
* @param int $rangeindex
|
|
* @param int $prediction
|
|
* @param float $predictionscore
|
|
* @param string $calculations
|
|
* @return \context
|
|
*/
|
|
protected function save_prediction($sampleid, $rangeindex, $prediction, $predictionscore, $calculations) {
|
|
global $DB;
|
|
|
|
$context = $this->get_analyser()->sample_access_context($sampleid);
|
|
|
|
$record = new \stdClass();
|
|
$record->modelid = $this->model->id;
|
|
$record->contextid = $context->id;
|
|
$record->sampleid = $sampleid;
|
|
$record->rangeindex = $rangeindex;
|
|
$record->prediction = $prediction;
|
|
$record->predictionscore = $predictionscore;
|
|
$record->calculations = $calculations;
|
|
$record->timecreated = time();
|
|
$DB->insert_record('analytics_predictions', $record);
|
|
|
|
return $context;
|
|
}
|
|
|
|
/**
|
|
* enable
|
|
*
|
|
* @param string $timesplittingid
|
|
* @return void
|
|
*/
|
|
public function enable($timesplittingid = false) {
|
|
global $DB;
|
|
|
|
$now = time();
|
|
|
|
if ($timesplittingid && $timesplittingid !== $this->model->timesplitting) {
|
|
|
|
if (!\core_analytics\manager::is_valid($timesplittingid, '\core_analytics\local\time_splitting\base')) {
|
|
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
|
|
}
|
|
|
|
if (substr($timesplittingid, 0, 1) !== '\\') {
|
|
throw new \moodle_exception('errorinvalidtimesplitting', 'analytics');
|
|
}
|
|
|
|
$this->model->timesplitting = $timesplittingid;
|
|
$this->model->version = $now;
|
|
}
|
|
$this->model->enabled = 1;
|
|
$this->model->timemodified = $now;
|
|
|
|
// We don't always update timemodified intentionally as we reserve it for target, indicators or timesplitting updates.
|
|
$DB->update_record('analytics_models', $this->model);
|
|
|
|
// It needs to be reset (just in case, we may already used it).
|
|
$this->uniqueid = null;
|
|
}
|
|
|
|
/**
|
|
* is_static
|
|
*
|
|
* @return bool
|
|
*/
|
|
public function is_static() {
|
|
return (bool)$this->get_target()->based_on_assumptions();
|
|
}
|
|
|
|
/**
|
|
* is_enabled
|
|
*
|
|
* @return bool
|
|
*/
|
|
public function is_enabled() {
|
|
return (bool)$this->model->enabled;
|
|
}
|
|
|
|
/**
|
|
* is_trained
|
|
*
|
|
* @return bool
|
|
*/
|
|
public function is_trained() {
|
|
// Models which targets are based on assumptions do not need training.
|
|
return (bool)$this->model->trained || $this->is_static();
|
|
}
|
|
|
|
/**
|
|
* mark_as_trained
|
|
*
|
|
* @return void
|
|
*/
|
|
public function mark_as_trained() {
|
|
global $DB;
|
|
|
|
$this->model->trained = 1;
|
|
$DB->update_record('analytics_models', $this->model);
|
|
}
|
|
|
|
/**
|
|
* get_predictions_contexts
|
|
*
|
|
* @return \stdClass[]
|
|
*/
|
|
public function get_predictions_contexts() {
|
|
global $DB;
|
|
|
|
$sql = "SELECT DISTINCT contextid FROM {analytics_predictions} WHERE modelid = ?";
|
|
return $DB->get_records_sql($sql, array($this->model->id));
|
|
}
|
|
|
|
/**
|
|
* Has this model generated predictions?
|
|
*
|
|
* We don't check analytics_predictions table because targets have the ability to
|
|
* ignore some predicted values, if that is the case predictions are not even stored
|
|
* in db.
|
|
*
|
|
* @return bool
|
|
*/
|
|
public function any_prediction_obtained() {
|
|
global $DB;
|
|
return $DB->record_exists('analytics_predict_ranges',
|
|
array('modelid' => $this->model->id, 'timesplitting' => $this->model->timesplitting));
|
|
}
|
|
|
|
/**
|
|
* Whether this model generates insights or not (defined by the model's target).
|
|
*
|
|
* @return bool
|
|
*/
|
|
public function uses_insights() {
|
|
$target = $this->get_target();
|
|
return $target::uses_insights();
|
|
}
|
|
|
|
/**
|
|
* Whether predictions exist for this context.
|
|
*
|
|
* @param \context $context
|
|
* @return bool
|
|
*/
|
|
public function predictions_exist(\context $context) {
|
|
global $DB;
|
|
|
|
// Filters out previous predictions keeping only the last time range one.
|
|
$select = "modelid = :modelid AND contextid = :contextid";
|
|
$params = array('modelid' => $this->model->id, 'contextid' => $context->id);
|
|
return $DB->record_exists_select('analytics_predictions', $select, $params);
|
|
}
|
|
|
|
/**
|
|
* Gets the predictions for this context.
|
|
*
|
|
* @param \context $context
|
|
* @return \core_analytics\prediction[]
|
|
*/
|
|
public function get_predictions(\context $context) {
|
|
global $DB;
|
|
|
|
// Filters out previous predictions keeping only the last time range one.
|
|
$sql = "SELECT tip.*
|
|
FROM {analytics_predictions} tip
|
|
JOIN (
|
|
SELECT sampleid, max(rangeindex) AS rangeindex
|
|
FROM {analytics_predictions}
|
|
WHERE modelid = ? and contextid = ?
|
|
GROUP BY sampleid
|
|
) tipsub
|
|
ON tip.sampleid = tipsub.sampleid AND tip.rangeindex = tipsub.rangeindex
|
|
WHERE tip.modelid = ? and tip.contextid = ?";
|
|
$params = array($this->model->id, $context->id, $this->model->id, $context->id);
|
|
if (!$predictions = $DB->get_records_sql($sql, $params)) {
|
|
return array();
|
|
}
|
|
|
|
// Get predicted samples' ids.
|
|
$sampleids = array_map(function($prediction) {
|
|
return $prediction->sampleid;
|
|
}, $predictions);
|
|
|
|
list($unused, $samplesdata) = $this->get_analyser()->get_samples($sampleids);
|
|
|
|
// Add samples data as part of each prediction.
|
|
foreach ($predictions as $predictionid => $predictiondata) {
|
|
|
|
$sampleid = $predictiondata->sampleid;
|
|
|
|
// Filter out predictions which samples are not available anymore.
|
|
if (empty($samplesdata[$sampleid])) {
|
|
unset($predictions[$predictionid]);
|
|
continue;
|
|
}
|
|
|
|
// Replace stdClass object by \core_analytics\prediction objects.
|
|
$prediction = new \core_analytics\prediction($predictiondata, $samplesdata[$sampleid]);
|
|
|
|
$predictions[$predictionid] = $prediction;
|
|
}
|
|
|
|
return $predictions;
|
|
}
|
|
|
|
/**
|
|
* prediction_sample_data
|
|
*
|
|
* @param \stdClass $predictionobj
|
|
* @return array
|
|
*/
|
|
public function prediction_sample_data($predictionobj) {
|
|
|
|
list($unused, $samplesdata) = $this->get_analyser()->get_samples(array($predictionobj->sampleid));
|
|
|
|
if (empty($samplesdata[$predictionobj->sampleid])) {
|
|
throw new \moodle_exception('errorsamplenotavailable', 'analytics');
|
|
}
|
|
|
|
return $samplesdata[$predictionobj->sampleid];
|
|
}
|
|
|
|
/**
|
|
* prediction_sample_description
|
|
*
|
|
* @param \core_analytics\prediction $prediction
|
|
* @return array 2 elements: list(string, \renderable)
|
|
*/
|
|
public function prediction_sample_description(\core_analytics\prediction $prediction) {
|
|
return $this->get_analyser()->sample_description($prediction->get_prediction_data()->sampleid,
|
|
$prediction->get_prediction_data()->contextid, $prediction->get_sample_data());
|
|
}
|
|
|
|
/**
|
|
* Returns the output directory for prediction processors.
|
|
*
|
|
* Directory structure as follows:
|
|
* - Evaluation runs:
|
|
* models/$model->id/$model->version/evaluation/$model->timesplitting
|
|
* - Training & prediction runs:
|
|
* models/$model->id/$model->version/execution
|
|
*
|
|
* @param array $subdirs
|
|
* @return string
|
|
*/
|
|
protected function get_output_dir($subdirs = array()) {
|
|
global $CFG;
|
|
|
|
$subdirstr = '';
|
|
foreach ($subdirs as $subdir) {
|
|
$subdirstr .= DIRECTORY_SEPARATOR . $subdir;
|
|
}
|
|
|
|
$outputdir = get_config('analytics', 'modeloutputdir');
|
|
if (empty($outputdir)) {
|
|
// Apply default value.
|
|
$outputdir = rtrim($CFG->dataroot, '/') . DIRECTORY_SEPARATOR . 'models';
|
|
}
|
|
|
|
// Append model id and version + subdirs.
|
|
$outputdir .= DIRECTORY_SEPARATOR . $this->model->id . DIRECTORY_SEPARATOR . $this->model->version . $subdirstr;
|
|
|
|
make_writable_directory($outputdir);
|
|
|
|
return $outputdir;
|
|
}
|
|
|
|
/**
|
|
* get_unique_id
|
|
*
|
|
* @return string
|
|
*/
|
|
public function get_unique_id() {
|
|
global $CFG;
|
|
|
|
if (!is_null($this->uniqueid)) {
|
|
return $this->uniqueid;
|
|
}
|
|
|
|
// Generate a unique id for this site, this model and this time splitting method, considering the last time
|
|
// that the model target and indicators were updated.
|
|
$ids = array($CFG->wwwroot, $CFG->dirroot, $CFG->prefix, $this->model->id, $this->model->version);
|
|
$this->uniqueid = sha1(implode('$$', $ids));
|
|
|
|
return $this->uniqueid;
|
|
}
|
|
|
|
/**
|
|
* Exports the model data.
|
|
*
|
|
* @return \stdClass
|
|
*/
|
|
public function export() {
|
|
$data = clone $this->model;
|
|
$data->target = $this->get_target()->get_name();
|
|
|
|
if ($timesplitting = $this->get_time_splitting()) {
|
|
$data->timesplitting = $timesplitting->get_name();
|
|
}
|
|
|
|
$data->indicators = array();
|
|
foreach ($this->get_indicators() as $indicator) {
|
|
$data->indicators[] = $indicator->get_name();
|
|
}
|
|
return $data;
|
|
}
|
|
|
|
/**
|
|
* flag_file_as_used
|
|
*
|
|
* @param \stored_file $file
|
|
* @param string $action
|
|
* @return void
|
|
*/
|
|
protected function flag_file_as_used(\stored_file $file, $action) {
|
|
global $DB;
|
|
|
|
$usedfile = new \stdClass();
|
|
$usedfile->modelid = $this->model->id;
|
|
$usedfile->fileid = $file->get_id();
|
|
$usedfile->action = $action;
|
|
$usedfile->time = time();
|
|
$DB->insert_record('analytics_used_files', $usedfile);
|
|
}
|
|
|
|
/**
|
|
* log_result
|
|
*
|
|
* @param string $timesplittingid
|
|
* @param float $score
|
|
* @param string $dir
|
|
* @param array $info
|
|
* @return int The inserted log id
|
|
*/
|
|
protected function log_result($timesplittingid, $score, $dir = false, $info = false) {
|
|
global $DB, $USER;
|
|
|
|
$log = new \stdClass();
|
|
$log->modelid = $this->get_id();
|
|
$log->version = $this->model->version;
|
|
$log->target = $this->model->target;
|
|
$log->indicators = $this->model->indicators;
|
|
$log->timesplitting = $timesplittingid;
|
|
$log->dir = $dir;
|
|
if ($info) {
|
|
// Ensure it is not an associative array.
|
|
$log->info = json_encode(array_values($info));
|
|
}
|
|
$log->score = $score;
|
|
$log->timecreated = time();
|
|
$log->usermodified = $USER->id;
|
|
|
|
return $DB->insert_record('analytics_models_log', $log);
|
|
}
|
|
|
|
/**
|
|
* Utility method to return indicator class names from a list of indicator objects
|
|
*
|
|
* @param \core_analytics\local\indicator\base[] $indicators
|
|
* @return string[]
|
|
*/
|
|
private static function indicator_classes($indicators) {
|
|
|
|
// What we want to check and store are the indicator classes not the keys.
|
|
$indicatorclasses = array();
|
|
foreach ($indicators as $indicator) {
|
|
if (!\core_analytics\manager::is_valid($indicator, '\core_analytics\local\indicator\base')) {
|
|
if (!is_object($indicator) && !is_scalar($indicator)) {
|
|
$indicator = strval($indicator);
|
|
} else if (is_object($indicator)) {
|
|
$indicator = get_class($indicator);
|
|
}
|
|
throw new \moodle_exception('errorinvalidindicator', 'analytics', '', $indicator);
|
|
}
|
|
$indicatorclasses[] = $indicator->get_id();
|
|
}
|
|
|
|
return $indicatorclasses;
|
|
}
|
|
|
|
/**
|
|
* Clears the model training and prediction data.
|
|
*
|
|
* Executed after updating model critical elements like the time splitting method
|
|
* or the indicators.
|
|
*
|
|
* @return void
|
|
*/
|
|
private function clear_model() {
|
|
global $DB;
|
|
|
|
$DB->delete_records('analytics_predict_ranges', array('modelid' => $this->model->id));
|
|
$DB->delete_records('analytics_predictions', array('modelid' => $this->model->id));
|
|
$DB->delete_records('analytics_train_samples', array('modelid' => $this->model->id));
|
|
$DB->delete_records('analytics_used_files', array('modelid' => $this->model->id));
|
|
|
|
$cache = \cache::make('core', 'modelswithpredictions');
|
|
$result = $cache->purge();
|
|
}
|
|
|
|
private function increase_memory() {
|
|
if (ini_get('memory_limit') != -1) {
|
|
raise_memory_limit(MEMORY_HUGE);
|
|
}
|
|
}
|
|
|
|
}
|