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<?php
// This file is part of Moodle - http://moodle.org/
//
// Moodle is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// Moodle is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Moodle. If not, see <http://www.gnu.org/licenses/>.
/**
* Base time splitting method.
*
* @package core_analytics
* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
namespace core_analytics\local\time_splitting;
defined('MOODLE_INTERNAL') || die();
/**
* Base time splitting method.
*
* @package core_analytics
* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
abstract class base {
/**
* @var string
*/
protected $id;
/**
* The model id.
*
* @var int
*/
protected $modelid;
/**
* @var \core_analytics\analysable
*/
protected $analysable;
/**
* @var array
*/
protected $ranges = [];
/**
* Define the time splitting methods ranges.
*
* 'time' value defines when predictions are executed, their values will be compared with
* the current time in ready_to_predict. The ranges should be sorted by 'time' in
* ascending order.
*
* @return array('start' => time(), 'end' => time(), 'time' => time())
*/
abstract protected function define_ranges();
/**
* Returns a lang_string object representing the name for the time splitting method.
*
* Used as column identificator.
*
* If there is a corresponding '_help' string this will be shown as well.
*
* @return \lang_string
*/
public static abstract function get_name() : \lang_string;
/**
* Returns the time splitting method id.
*
* @return string
*/
public function get_id() {
return '\\' . get_class($this);
}
/**
* Assigns the analysable and updates the time ranges according to the analysable start and end dates.
*
* @param \core_analytics\analysable $analysable
* @return void
*/
public function set_analysable(\core_analytics\analysable $analysable) {
$this->analysable = $analysable;
$this->ranges = $this->define_ranges();
$this->validate_ranges();
}
/**
* Assigns the model id to this time-splitting method it case it needs it.
*
* @param int $modelid
*/
public function set_modelid(int $modelid) {
$this->modelid = $modelid;
}
/**
* get_analysable
*
* @return \core_analytics\analysable
*/
public function get_analysable() {
return $this->analysable;
}
/**
* Returns whether the course can be processed by this time splitting method or not.
*
* @param \core_analytics\analysable $analysable
* @return bool
*/
public function is_valid_analysable(\core_analytics\analysable $analysable) {
return true;
}
/**
* Should we predict this time range now?
*
* @param array $range
* @return bool
*/
public function ready_to_predict($range) {
if ($range['time'] <= time()) {
return true;
}
return false;
}
/**
* Should we use this time range for training?
*
* @param array $range
* @return bool
*/
public function ready_to_train($range) {
$now = time();
if ($range['time'] <= $now && $range['end'] <= $now) {
return true;
}
return false;
}
/**
* Returns the ranges used by this time splitting method.
*
* @return array
*/
public function get_all_ranges() {
return $this->ranges;
}
/**
* By default all ranges are for training.
*
* @return array
*/
public function get_training_ranges() {
return $this->ranges;
}
/**
* Returns the distinct range indexes in this time splitting method.
*
* @return int[]
*/
public function get_distinct_ranges() {
if ($this->include_range_info_in_training_data()) {
return array_keys($this->ranges);
} else {
return [0];
}
}
/**
* Returns the most recent range that can be used to predict.
*
* This method is only called when calculating predictions.
*
* @return array
*/
public function get_most_recent_prediction_range() {
$ranges = $this->get_all_ranges();
// Opposite order as we are interested in the last range that can be used for prediction.
krsort($ranges);
// We already provided the analysable to the time splitting method, there is no need to feed it back.
foreach ($ranges as $rangeindex => $range) {
if ($this->ready_to_predict($range)) {
// We need to maintain the same indexes.
return array($rangeindex => $range);
}
}
return array();
}
/**
* Returns range data by its index.
*
* @param int $rangeindex
* @return array|false Range data or false if the index is not part of the existing ranges.
*/
public function get_range_by_index($rangeindex) {
if (!isset($this->ranges[$rangeindex])) {
return false;
}
return $this->ranges[$rangeindex];
}
/**
* Generates a unique sample id (sample in a range index).
*
* @param int $sampleid
* @param int $rangeindex
* @return string
*/
public final function append_rangeindex($sampleid, $rangeindex) {
return $sampleid . '-' . $rangeindex;
}
/**
* Returns the sample id and the range index from a uniquesampleid.
*
* @param string $uniquesampleid
* @return array array($sampleid, $rangeindex)
*/
public final function infer_sample_info($uniquesampleid) {
return explode('-', $uniquesampleid);
}
/**
* Whether to include the range index in the training data or not.
*
* By default, we consider that the different time ranges included in a time splitting method may not be
* compatible between them (i.e. the indicators calculated at the end of the course can easily
* differ from indicators calculated at the beginning of the course). So we include the range index as
* one of the variables that the machine learning backend uses to generate predictions.
*
* If the indicators calculated using the different time ranges available in this time splitting method
* are comparable you can overwrite this method to return false.
*
* Note that:
* - This is only relevant for models whose predictions are not based on assumptions
* (i.e. the ones using a machine learning backend to generate predictions).
* - The ranges can only be included in the training data when
* we know the final number of ranges the time splitting method will have. E.g.
* We can not know the final number of ranges of a 'daily' time splitting method
* as we will have one new range every day.
* @return bool
*/
public function include_range_info_in_training_data() {
return true;
}
/**
* Whether to cache or not the indicator calculations.
*
* Indicator calculations are stored to be reused across models. The calculations
* are indexed by the calculation start and end time, and these times depend on the
* time-splitting method. You should overwrite this method and return false if the time
* frames generated by your time-splitting method are unique and / or can hardly be
* reused by further models.
*
* @return bool
*/
public function cache_indicator_calculations(): bool {
return true;
}
/**
* Is this method valid to evaluate prediction models?
*
* @return bool
*/
public function valid_for_evaluation(): bool {
return true;
}
/**
* Validates the time splitting method ranges.
*
* @throws \coding_exception
* @return void
*/
protected function validate_ranges() {
foreach ($this->ranges as $key => $range) {
if (!isset($this->ranges[$key]['start']) || !isset($this->ranges[$key]['end']) ||
!isset($this->ranges[$key]['time'])) {
throw new \coding_exception($this->get_id() . ' time splitting method "' . $key .
'" range is not fully defined. We need a start timestamp and an end timestamp.');
}
}
}
}