#Gocha#

<?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/>.

/**
 * Abstract base target.
 *
 * @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\target;

defined('MOODLE_INTERNAL') || die();

/**
     * This target have linear or discrete values.
     *
     * @return bool
     */
    abstract public function is_linear();

/**
     * Returns the analyser class that should be used along with this target.
     *
     * @return string The full class name as a string
     */
    abstract public function get_analyser_class();

/**
     * Allows the target to verify that the analysable is a good candidate.
     *
     * This method can be used as a quick way to discard invalid analysables.
     * e.g. Imagine that your analysable don't have students and you need them.
     *
     * @param \core_analytics\analysable $analysable
     * @param bool $fortraining
     * @return true|string
     */
    abstract public function is_valid_analysable(\core_analytics\analysable $analysable, $fortraining = true);

/**
     * Is this sample from the $analysable valid?
     *
     * @param int $sampleid
     * @param \core_analytics\analysable $analysable
     * @param bool $fortraining
     * @return bool
     */
    abstract public function is_valid_sample($sampleid, \core_analytics\analysable $analysable, $fortraining = true);

/**
     * Calculates this target for the provided samples.
     *
     * In case there are no values to return or the provided sample is not applicable just return null.
     *
     * @param int $sampleid
     * @param \core_analytics\analysable $analysable
     * @param int|false $starttime Limit calculations to start time
     * @param int|false $endtime Limit calculations to end time
     * @return float|null
     */
    abstract protected function calculate_sample($sampleid, \core_analytics\analysable $analysable, $starttime = false, $endtime = false);

/**
     * Is this target generating insights?
     *
     * Defaults to true.
     *
     * @return bool
     */
    public static function uses_insights() {
        return true;
    }

/**
     * Based on facts (processed by machine learning backends) by default.
     *
     * @return bool
     */
    public static function based_on_assumptions() {
        return false;
    }

/**
     * Suggested actions for a user.
     *
     * @param \core_analytics\prediction $prediction
     * @param bool $includedetailsaction
     * @return \core_analytics\prediction_action[]
     */
    public function prediction_actions(\core_analytics\prediction $prediction, $includedetailsaction = false) {
        global $PAGE;

$predictionid = $prediction->get_prediction_data()->id;

$PAGE->requires->js_call_amd('report_insights/actions', 'init', array($predictionid));

$actions = array();

if ($includedetailsaction) {

$predictionurl = new \moodle_url('/report/insights/prediction.php',
                array('id' => $predictionid));

$actions[] = new \core_analytics\prediction_action(\core_analytics\prediction::ACTION_PREDICTION_DETAILS, $prediction,
                $predictionurl, new \pix_icon('t/preview', get_string('viewprediction', 'analytics')),
                get_string('viewprediction', 'analytics'));
        }

// Flag as fixed / solved.
        $fixedattrs = array(
            'data-prediction-id' => $predictionid,
            'data-prediction-methodname' => 'report_insights_set_fixed_prediction'
        );
        $actions[] = new \core_analytics\prediction_action(\core_analytics\prediction::ACTION_FIXED,
            $prediction, new \moodle_url(''), new \pix_icon('t/check', get_string('fixedack', 'analytics')),
            get_string('fixedack', 'analytics'), false, $fixedattrs);

// Flag as not useful.
        $notusefulattrs = array(
            'data-prediction-id' => $predictionid,
            'data-prediction-methodname' => 'report_insights_set_notuseful_prediction'
        );
        $actions[] = new \core_analytics\prediction_action(\core_analytics\prediction::ACTION_NOT_USEFUL,
            $prediction, new \moodle_url(''), new \pix_icon('t/delete', get_string('notuseful', 'analytics')),
            get_string('notuseful', 'analytics'), false, $notusefulattrs);

return $actions;
    }

/**
     * Callback to execute once a prediction has been returned from the predictions processor.
     *
     * Note that the analytics_predictions db record is not yet inserted.
     *
     * @param int $modelid
     * @param int $sampleid
     * @param int $rangeindex
     * @param \context $samplecontext
     * @param float|int $prediction
     * @param float $predictionscore
     * @return void
     */
    public function prediction_callback($modelid, $sampleid, $rangeindex, \context $samplecontext, $prediction, $predictionscore) {
        return;
    }

/**
     * Generates insights notifications
     *
     * @param int $modelid
     * @param \context[] $samplecontexts
     * @return void
     */
    public function generate_insight_notifications($modelid, $samplecontexts) {

foreach ($samplecontexts as $context) {

$insightinfo = new \stdClass();
            $insightinfo->insightname = $this->get_name();
            $insightinfo->contextname = $context->get_context_name();
            $subject = get_string('insightmessagesubject', 'analytics', $insightinfo);

$users = $this->get_insights_users($context);

if (!$coursecontext = $context->get_course_context(false)) {
                $coursecontext = \context_course::instance(SITEID);
            }

foreach ($users as $user) {

$message = new \core\message\message();
                $message->component = 'moodle';
                $message->name = 'insights';

$message->userfrom = get_admin();
                $message->userto = $user;

$insighturl = new \moodle_url('/report/insights/insights.php?modelid=' . $modelid . '&contextid=' . $context->id);
                $message->subject = $subject;
                // Same than the subject.
                $message->contexturlname = $message->subject;
                $message->courseid = $coursecontext->instanceid;

$message->fullmessage = get_string('insightinfomessage', 'analytics', $insighturl->out(false));
                $message->fullmessageformat = FORMAT_PLAIN;
                $message->fullmessagehtml = get_string('insightinfomessagehtml', 'analytics', $insighturl->out());
                $message->smallmessage = get_string('insightinfomessage', 'analytics', $insighturl->out(false));
                $message->contexturl = $insighturl->out(false);

message_send($message);
            }
        }

}

/**
     * Returns the list of users that will receive insights notifications.
     *
     * Feel free to overwrite if you need to but keep in mind that moodle/analytics:listinsights
     * capability is required to access the list of insights.
     *
     * @param \context $context
     * @return array
     */
    protected function get_insights_users(\context $context) {
        if ($context->contextlevel >= CONTEXT_COURSE) {
            // At course level or below only enrolled users although this is not ideal for
            // teachers assigned at category level.
            $users = get_enrolled_users($context, 'moodle/analytics:listinsights');
        } else {
            $users = get_users_by_capability($context, 'moodle/analytics:listinsights');
        }
        return $users;
    }

/**
     * Returns an instance of the child class.
     *
     * Useful to reset cached data.
     *
     * @return \core_analytics\base\target
     */
    public static function instance() {
        return new static();
    }

/**
     * Defines a boundary to ignore predictions below the specified prediction score.
     *
     * Value should go from 0 to 1.
     *
     * @return float
     */
    protected function min_prediction_score() {
        // The default minimum discards predictions with a low score.
        return \core_analytics\model::PREDICTION_MIN_SCORE;
    }

/**
     * This method determines if a prediction is interesing for the model or not.
     *
     * @param mixed $predictedvalue
     * @param float $predictionscore
     * @return bool
     */
    public function triggers_callback($predictedvalue, $predictionscore) {

$minscore = floatval($this->min_prediction_score());
        if ($minscore < 0) {
            debugging(get_class($this) . ' minimum prediction score is below 0, please update it to a value between 0 and 1.');
        } else if ($minscore > 1) {
            debugging(get_class($this) . ' minimum prediction score is above 1, please update it to a value between 0 and 1.');
        }

// We need to consider that targets may not have a min score.
        if (!empty($minscore) && floatval($predictionscore) < $minscore) {
            return false;
        }

return true;
    }

/**
     * Calculates the target.
     *
     * Returns an array of values which size matches $sampleids size.
     *
     * Rows with null values will be skipped as invalid by time splitting methods.
     *
     * @param array $sampleids
     * @param \core_analytics\analysable $analysable
     * @param int $starttime
     * @param int $endtime
     * @return array The format to follow is [userid] = scalar|null
     */
    public function calculate($sampleids, \core_analytics\analysable $analysable, $starttime = false, $endtime = false) {

if (!PHPUNIT_TEST && CLI_SCRIPT) {
            echo '.';
        }

$calculations = [];
        foreach ($sampleids as $sampleid => $unusedsampleid) {

// No time limits when calculating the target to train models.
            $calculatedvalue = $this->calculate_sample($sampleid, $analysable, $starttime, $endtime);

if (!is_null($calculatedvalue)) {
                if ($this->is_linear() &&
                        ($calculatedvalue > static::get_max_value() || $calculatedvalue < static::get_min_value())) {
                    throw new \coding_exception('Calculated values should be higher than ' . static::get_min_value() .
                        ' and lower than ' . static::get_max_value() . '. ' . $calculatedvalue . ' received');
                } else if (!$this->is_linear() && static::is_a_class($calculatedvalue) === false) {
                    throw new \coding_exception('Calculated values should be one of the target classes (' .
                        json_encode(static::get_classes()) . '). ' . $calculatedvalue . ' received');
                }
            }
            $calculations[$sampleid] = $calculatedvalue;
        }
        return $calculations;
    }

/**
     * Filters out invalid samples for training.
     *
     * @param int[] $sampleids
     * @param \core_analytics\analysable $analysable
     * @param bool $fortraining
     * @return void
     */
    public function filter_out_invalid_samples(&$sampleids, \core_analytics\analysable $analysable, $fortraining = true) {
        foreach ($sampleids as $sampleid => $unusedsampleid) {
            if (!$this->is_valid_sample($sampleid, $analysable, $fortraining)) {
                // Skip it and remove the sample from the list of calculated samples.
                unset($sampleids[$sampleid]);
            }
        }
    }
}