Proyectos de Subversion Moodle

<?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/>.

namespace core_analytics;

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

global $CFG;

require_once(__DIR__ . '/fixtures/test_indicator_max.php');

require_once(__DIR__ . '/fixtures/test_indicator_min.php');

require_once(__DIR__ . '/fixtures/test_indicator_null.php');

require_once(__DIR__ . '/fixtures/test_indicator_fullname.php');

require_once(__DIR__ . '/fixtures/test_indicator_random.php');

require_once(__DIR__ . '/fixtures/test_indicator_multiclass.php');

require_once(__DIR__ . '/fixtures/test_target_shortname.php');

require_once(__DIR__ . '/fixtures/test_target_shortname_multiclass.php');

require_once(__DIR__ . '/fixtures/test_static_target_shortname.php');

require_once(__DIR__ . '/../../course/lib.php');

use core_analytics\tests\mlbackend_helper_trait;

/**

 * Unit tests for evaluation, training and prediction.

 *

 * NOTE: in order to execute this test using a separate server for the

 *       python ML backend you need to define these variables in your config.php file:

 *

 * define('TEST_MLBACKEND_PYTHON_HOST', '127.0.0.1');

 * define('TEST_MLBACKEND_PYTHON_PORT', 5000);

 * define('TEST_MLBACKEND_PYTHON_USERNAME', 'default');

 * define('TEST_MLBACKEND_PYTHON_PASSWORD', 'sshhhh');

 *

 * @package   core_analytics

 * @copyright 2017 David Monllaó {@link http://www.davidmonllao.com}

 * @license   http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later

 */

final class prediction_test extends \advanced_testcase {

    use mlbackend_helper_trait;

    /**

     * Purge all the mlbackend outputs.

     *

     * This is done automatically for mlbackends using the web server dataroot but

     * other mlbackends may store files elsewhere and these files need to be removed.

     *

     * @return null

     */

    public function tearDown(): void {

        $this->setAdminUser();

        $models = \core_analytics\manager::get_all_models();

        foreach ($models as $model) {

            $model->delete();

        }

        parent::tearDown();

    }

    /**

     * test_static_prediction

     *

     * @return void

     */

    public function test_static_prediction(): void {

        global $DB;

        $this->resetAfterTest(true);

        $this->setAdminuser();

        $model = $this->add_perfect_model('test_static_target_shortname');

        $model->enable('\core\analytics\time_splitting\no_splitting');

        $this->assertEquals(1, $model->is_enabled());

        $this->assertEquals(1, $model->is_trained());

        // No training for static models.

        $results = $model->train();

        $trainedsamples = $DB->get_records('analytics_train_samples', array('modelid' => $model->get_id()));

        $this->assertEmpty($trainedsamples);

        $this->assertEmpty($DB->count_records('analytics_used_files',

            array('modelid' => $model->get_id(), 'action' => 'trained')));

        // Now we create 2 hidden courses (only hidden courses are getting predictions).

        $courseparams = array('shortname' => 'aaaaaa', 'fullname' => 'aaaaaa', 'visible' => 0);

        $course1 = $this->getDataGenerator()->create_course($courseparams);

        $courseparams = array('shortname' => 'bbbbbb', 'fullname' => 'bbbbbb', 'visible' => 0);

        $course2 = $this->getDataGenerator()->create_course($courseparams);

        $result = $model->predict();

        // Var $course1 predictions should be 1 == 'a', $course2 predictions should be 0 == 'b'.

        $correct = array($course1->id => 1, $course2->id => 0);

        foreach ($result->predictions as $uniquesampleid => $predictiondata) {

            list($sampleid, $rangeindex) = $model->get_time_splitting()->infer_sample_info($uniquesampleid);

            // The range index is not important here, both ranges prediction will be the same.

            $this->assertEquals($correct[$sampleid], $predictiondata->prediction);

        }

        // 1 range for each analysable.

        $predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));

        $this->assertCount(2, $predictedranges);

        // 2 predictions for each range.

        $this->assertEquals(2, $DB->count_records('analytics_predictions',

            array('modelid' => $model->get_id())));

        // No new generated records as there are no new courses available.

        $model->predict();

        $predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));

        $this->assertCount(2, $predictedranges);

        $this->assertEquals(2, $DB->count_records('analytics_predictions',

            array('modelid' => $model->get_id())));

    }

    /**

     * test_model_contexts

     */

    public function test_model_contexts(): void {

        global $DB;

        $this->resetAfterTest(true);

        $this->setAdminuser();

        $misc = $DB->get_record('course_categories', ['name' => get_string('defaultcategoryname')]);

        $miscctx = \context_coursecat::instance($misc->id);

        $category = $this->getDataGenerator()->create_category();

        $categoryctx = \context_coursecat::instance($category->id);

        // One course per category.

        $courseparams = array('shortname' => 'aaaaaa', 'fullname' => 'aaaaaa', 'visible' => 0,

            'category' => $category->id);

        $course1 = $this->getDataGenerator()->create_course($courseparams);

        $course1ctx = \context_course::instance($course1->id);

        $courseparams = array('shortname' => 'bbbbbb', 'fullname' => 'bbbbbb', 'visible' => 0,

            'category' => $misc->id);

        $course2 = $this->getDataGenerator()->create_course($courseparams);

        $model = $this->add_perfect_model('test_static_target_shortname');

        // Just 1 category.

        $model->update(true, false, '\core\analytics\time_splitting\no_splitting', false, [$categoryctx->id]);

        $this->assertCount(1, $model->predict()->predictions);

        // Now with 2 categories.

        $model->update(true, false, false, false, [$categoryctx->id, $miscctx->id]);

        // The courses in the new category are processed.

        $this->assertCount(1, $model->predict()->predictions);

        // Clear the predictions generated by the model and predict() again.

        $model->clear();

        $this->assertCount(2, $model->predict()->predictions);

        // Course context restriction.

        $model->update(true, false, '\core\analytics\time_splitting\no_splitting', false, [$course1ctx->id]);

        // Nothing new as the course was already analysed.

        $result = $model->predict();

        $this->assertTrue(empty($result->predictions));

        $model->clear();

        $this->assertCount(1, $model->predict()->predictions);

    }

    /**

     * test_ml_training_and_prediction

     *

     * @dataProvider provider_ml_training_and_prediction

     * @param string $timesplittingid

     * @param int $predictedrangeindex

     * @param int $nranges

     * @param string $predictionsprocessorclass

     * @param array $forcedconfig

     * @return void

     */

    public function test_ml_training_and_prediction($timesplittingid, $predictedrangeindex, $nranges, $predictionsprocessorclass,

            $forcedconfig): void {

        global $DB;

        $this->resetAfterTest(true);

        $this->set_forced_config($forcedconfig);

        $predictionsprocessor = $this->is_predictions_processor_ready($predictionsprocessorclass);

        $this->setAdminuser();

        set_config('enabled_stores', 'logstore_standard', 'tool_log');

        // Generate training data.

        $ncourses = 10;

        $this->generate_courses($ncourses);

        $model = $this->add_perfect_model();

        $model->update(true, false, $timesplittingid, get_class($predictionsprocessor));

        // No samples trained yet.

        $this->assertEquals(0, $DB->count_records('analytics_train_samples', array('modelid' => $model->get_id())));

        $results = $model->train();

        $this->assertEquals(1, $model->is_enabled());

        $this->assertEquals(1, $model->is_trained());

        // 20 courses * the 3 model indicators * the number of time ranges of this time splitting method.

        $indicatorcalc = 20 * 3 * $nranges;

        $this->assertEquals($indicatorcalc, $DB->count_records('analytics_indicator_calc'));

        // 1 training file was created.

        $trainedsamples = $DB->get_records('analytics_train_samples', array('modelid' => $model->get_id()));

        $this->assertCount(1, $trainedsamples);

        $samples = json_decode(reset($trainedsamples)->sampleids, true);

        $this->assertCount($ncourses * 2, $samples);

        $this->assertEquals(1, $DB->count_records('analytics_used_files',

            array('modelid' => $model->get_id(), 'action' => 'trained')));

        // Check that analysable files for training are stored under labelled filearea.

        $fs = get_file_storage();

        $this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        $this->assertEmpty($fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        $params = [

            'startdate' => mktime(0, 0, 0, 10, 24, 2015),

            'enddate' => mktime(0, 0, 0, 2, 24, 2016),

        ];

        $courseparams = $params + array('shortname' => 'aaaaaa', 'fullname' => 'aaaaaa', 'visible' => 0);

        $course1 = $this->getDataGenerator()->create_course($courseparams);

        $courseparams = $params + array('shortname' => 'bbbbbb', 'fullname' => 'bbbbbb', 'visible' => 0);

        $course2 = $this->getDataGenerator()->create_course($courseparams);

        // They will not be skipped for prediction though.

        $result = $model->predict();

        // Var $course1 predictions should be 1 == 'a', $course2 predictions should be 0 == 'b'.

        $correct = array($course1->id => 1, $course2->id => 0);

        foreach ($result->predictions as $uniquesampleid => $predictiondata) {

            list($sampleid, $rangeindex) = $model->get_time_splitting()->infer_sample_info($uniquesampleid);

            // The range index is not important here, both ranges prediction will be the same.

            $this->assertEquals($correct[$sampleid], $predictiondata->prediction);

        }

        // 1 range will be predicted.

        $predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));

        $this->assertCount(1, $predictedranges);

        foreach ($predictedranges as $predictedrange) {

            $this->assertEquals($predictedrangeindex, $predictedrange->rangeindex);

            $sampleids = json_decode($predictedrange->sampleids, true);

            $this->assertCount(2, $sampleids);

            $this->assertContainsEquals($course1->id, $sampleids);

            $this->assertContainsEquals($course2->id, $sampleids);

        }

        $this->assertEquals(1, $DB->count_records('analytics_used_files',

            array('modelid' => $model->get_id(), 'action' => 'predicted')));

        // 2 predictions.

        $this->assertEquals(2, $DB->count_records('analytics_predictions',

            array('modelid' => $model->get_id())));

        // Check that analysable files to get predictions are stored under unlabelled filearea.

        $this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        $this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        // No new generated files nor records as there are no new courses available.

        $model->predict();

        $predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));

        $this->assertCount(1, $predictedranges);

        foreach ($predictedranges as $predictedrange) {

            $this->assertEquals($predictedrangeindex, $predictedrange->rangeindex);

        }

        $this->assertEquals(1, $DB->count_records('analytics_used_files',

            array('modelid' => $model->get_id(), 'action' => 'predicted')));

        $this->assertEquals(2, $DB->count_records('analytics_predictions',

            array('modelid' => $model->get_id())));

        // New samples that can be used for prediction.

        $courseparams = $params + array('shortname' => 'cccccc', 'fullname' => 'cccccc', 'visible' => 0);

        $course3 = $this->getDataGenerator()->create_course($courseparams);

        $courseparams = $params + array('shortname' => 'dddddd', 'fullname' => 'dddddd', 'visible' => 0);

        $course4 = $this->getDataGenerator()->create_course($courseparams);

        $result = $model->predict();

        $predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));

        $this->assertCount(1, $predictedranges);

        foreach ($predictedranges as $predictedrange) {

            $this->assertEquals($predictedrangeindex, $predictedrange->rangeindex);

            $sampleids = json_decode($predictedrange->sampleids, true);

            $this->assertCount(4, $sampleids);

            $this->assertContainsEquals($course1->id, $sampleids);

            $this->assertContainsEquals($course2->id, $sampleids);

            $this->assertContainsEquals($course3->id, $sampleids);

            $this->assertContainsEquals($course4->id, $sampleids);

        }

        $this->assertEquals(2, $DB->count_records('analytics_used_files',

            array('modelid' => $model->get_id(), 'action' => 'predicted')));

        $this->assertEquals(4, $DB->count_records('analytics_predictions',

            array('modelid' => $model->get_id())));

        $this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        $this->assertCount(2, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        // New visible course (for training).

        $course5 = $this->getDataGenerator()->create_course(array('shortname' => 'aaa', 'fullname' => 'aa'));

        $course6 = $this->getDataGenerator()->create_course();

        $result = $model->train();

        $this->assertEquals(2, $DB->count_records('analytics_used_files',

            array('modelid' => $model->get_id(), 'action' => 'trained')));

        $this->assertCount(2, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        $this->assertCount(2, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        // Confirm that the files associated to the model are deleted on clear and on delete. The ML backend deletion

        // processes will be triggered by these actions and any exception there would result in a failed test.

        $model->clear();

        $this->assertEquals(0, $DB->count_records('analytics_used_files',

            array('modelid' => $model->get_id(), 'action' => 'trained')));

        $this->assertCount(0, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        $this->assertCount(0, $fs->get_directory_files(\context_system::instance()->id, 'analytics',

            \core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));

        $model->delete();

        set_config('enabled_stores', '', 'tool_log');

        get_log_manager(true);

    }

    /**

     * provider_ml_training_and_prediction

     *

     * @return array

     */

    public static function provider_ml_training_and_prediction(): array {

        $cases = array(

            'no_splitting' => array('\core\analytics\time_splitting\no_splitting', 0, 1),

            'quarters' => array('\core\analytics\time_splitting\quarters', 3, 4)

        );

        // We need to test all system prediction processors.

        return static::add_prediction_processors($cases);

    }

    /**

     * test_ml_export_import

     *

     * @param string $predictionsprocessorclass The class name

     * @param array $forcedconfig

     * @dataProvider provider_ml_processors

     */

    public function test_ml_export_import($predictionsprocessorclass, $forcedconfig): void {

        $this->resetAfterTest(true);

        $this->set_forced_config($forcedconfig);

        $predictionsprocessor = $this->is_predictions_processor_ready($predictionsprocessorclass);

        $this->setAdminuser();

        set_config('enabled_stores', 'logstore_standard', 'tool_log');

        // Generate training data.

        $ncourses = 10;

        $this->generate_courses($ncourses);

        $model = $this->add_perfect_model();

        $model->update(true, false, '\core\analytics\time_splitting\quarters', get_class($predictionsprocessor));

        $model->train();

        $this->assertTrue($model->trained_locally());

        $this->generate_courses(10, ['visible' => 0]);

        $originalresults = $model->predict();

        $zipfilename = 'model-zip-' . microtime() . '.zip';

        $zipfilepath = $model->export_model($zipfilename);

        $modelconfig = new \core_analytics\model_config();

        list($modelconfig, $mlbackend) = $modelconfig->extract_import_contents($zipfilepath);

        $this->assertNotFalse($mlbackend);

        $importmodel = \core_analytics\model::import_model($zipfilepath);

        $importmodel->enable();

        // Now predict using the imported model without prior training.

        $importedmodelresults = $importmodel->predict();

        foreach ($originalresults->predictions as $sampleid => $prediction) {

            $this->assertEquals($importedmodelresults->predictions[$sampleid]->prediction, $prediction->prediction);

        }

        $this->assertFalse($importmodel->trained_locally());

        $zipfilename = 'model-zip-' . microtime() . '.zip';

        $zipfilepath = $model->export_model($zipfilename, false);

        $modelconfig = new \core_analytics\model_config();

        list($modelconfig, $mlbackend) = $modelconfig->extract_import_contents($zipfilepath);

        $this->assertFalse($mlbackend);

        set_config('enabled_stores', '', 'tool_log');

        get_log_manager(true);

    }

    /**

     * provider_ml_processors

     *

     * @return array

     */

    public static function provider_ml_processors(): array {

        $cases = [

            'case' => [],

        ];

        // We need to test all system prediction processors.

        return static::add_prediction_processors($cases);

    }

    /**

     * Test the system classifiers returns.

     *

     * This test checks that all mlbackend plugins in the system are able to return proper status codes

     * even under weird situations.

     *

     * @dataProvider provider_ml_classifiers_return

     * @param int $success

     * @param int $nsamples

     * @param int $classes

     * @param string $predictionsprocessorclass

     * @param array $forcedconfig

     * @return void

     */

    public function test_ml_classifiers_return($success, $nsamples, $classes, $predictionsprocessorclass, $forcedconfig): void {

        $this->resetAfterTest();

        $this->set_forced_config($forcedconfig);

        $predictionsprocessor = $this->is_predictions_processor_ready($predictionsprocessorclass);

        if ($nsamples % count($classes) != 0) {

            throw new \coding_exception('The number of samples should be divisible by the number of classes');

        }

        $samplesperclass = $nsamples / count($classes);

        // Metadata (we pass 2 classes even if $classes only provides 1 class samples as we want to test

        // what the backend does in this case.

        $dataset = "nfeatures,targetclasses,targettype" . PHP_EOL;

        $dataset .= "3,\"[0,1]\",\"discrete\"" . PHP_EOL;

        // Headers.

        $dataset .= "feature1,feature2,feature3,target" . PHP_EOL;

        foreach ($classes as $class) {

            for ($i = 0; $i < $samplesperclass; $i++) {

                $dataset .= "1,0,1,$class" . PHP_EOL;

            }

        }

        $trainingfile = array(

            'contextid' => \context_system::instance()->id,

            'component' => 'analytics',

            'filearea' => 'labelled',

            'itemid' => 123,

            'filepath' => '/',

            'filename' => 'whocares.csv'

        );

        $fs = get_file_storage();

        $dataset = $fs->create_file_from_string($trainingfile, $dataset);

        // Training should work correctly if at least 1 sample of each class is included.

        $dir = make_request_directory();

        $modeluniqueid = 'whatever' . microtime();

        $result = $predictionsprocessor->train_classification($modeluniqueid, $dataset, $dir);

        switch ($success) {

            case 'yes':

                $this->assertEquals(\core_analytics\model::OK, $result->status);

                break;

            case 'no':

                $this->assertNotEquals(\core_analytics\model::OK, $result->status);

                break;

            case 'maybe':

            default:

                // We just check that an object is returned so we don't have an empty check,

                // what we really want to check is that an exception was not thrown.

                $this->assertInstanceOf(\stdClass::class, $result);

        }

        // Purge the directory used in this test (useful in case the mlbackend is storing files

        // somewhere out of the default moodledata/models dir.

        $predictionsprocessor->delete_output_dir($dir, $modeluniqueid);

    }

    /**

     * test_ml_classifiers_return provider

     *

     * We can not be very specific here as test_ml_classifiers_return only checks that

     * mlbackend plugins behave and expected and control properly backend errors even

     * under weird situations.

     *

     * @return array

     */

    public static function provider_ml_classifiers_return(): array {

        // Using verbose options as the first argument for readability.

        $cases = array(

            '1-samples' => array('maybe', 1, [0]),

            '2-samples-same-class' => array('maybe', 2, [0]),

            '2-samples-different-classes' => array('yes', 2, [0, 1]),

            '4-samples-different-classes' => array('yes', 4, [0, 1])

        );

        // We need to test all system prediction processors.

        return static::add_prediction_processors($cases);

    }

    /**

     * Tests correct multi-classification.

     *

     * @dataProvider provider_test_multi_classifier

     * @param string $timesplittingid

     * @param string $predictionsprocessorclass

     * @param array|null $forcedconfig

     * @throws coding_exception

     * @throws moodle_exception

     */

    public function test_ml_multi_classifier($timesplittingid, $predictionsprocessorclass, $forcedconfig): void {

        global $DB;

        $this->resetAfterTest(true);

        $this->setAdminuser();

        set_config('enabled_stores', 'logstore_standard', 'tool_log');

        $this->set_forced_config($forcedconfig);

        $predictionsprocessor = \core_analytics\manager::get_predictions_processor($predictionsprocessorclass, false);

        if ($predictionsprocessor->is_ready() !== true) {

            $this->markTestSkipped('Skipping ' . $predictionsprocessorclass . ' as the predictor is not ready.');

        }

        // Generate training courses.

        $ncourses = 5;

        $this->generate_courses(ncourses: $ncourses, ismulticlass: true);

        $model = $this->add_multiclass_model();

        $model->update(true, false, $timesplittingid, get_class($predictionsprocessor));

        $results = $model->train();

        $params = [

            'startdate' => mktime(0, 0, 0, 10, 24, 2015),

            'enddate' => mktime(0, 0, 0, 2, 24, 2016),

        ];

        $courseparams = $params + array('shortname' => 'aaaaaa', 'fullname' => 'aaaaaa', 'visible' => 0);

        $course1 = $this->getDataGenerator()->create_course($courseparams);

        $courseparams = $params + array('shortname' => 'bbbbbb', 'fullname' => 'bbbbbb', 'visible' => 0);

        $course2 = $this->getDataGenerator()->create_course($courseparams);

        $courseparams = $params + array('shortname' => 'cccccc', 'fullname' => 'cccccc', 'visible' => 0);

        $course3 = $this->getDataGenerator()->create_course($courseparams);

        // They will not be skipped for prediction though.

        $result = $model->predict();

        // The $course1 predictions should be 0 == 'a', $course2 should be 1 == 'b' and $course3 should be 2 == 'c'.

        $correct = array($course1->id => 0, $course2->id => 1, $course3->id => 2);

        foreach ($result->predictions as $uniquesampleid => $predictiondata) {

            list($sampleid, $rangeindex) = $model->get_time_splitting()->infer_sample_info($uniquesampleid);

            // The range index is not important here, both ranges prediction will be the same.

            $this->assertEquals($correct[$sampleid], $predictiondata->prediction);

        }

        set_config('enabled_stores', '', 'tool_log');

        get_log_manager(true);

    }

    /**

     * Provider for the multi_classification test.

     *

     * @return array

     */

    public static function provider_test_multi_classifier(): array {

        $cases = array(

            'notimesplitting' => array('\core\analytics\time_splitting\no_splitting'),

        );

        // Add all system prediction processors.

        return static::add_prediction_processors($cases);

    }

    /**

     * Basic test to check that prediction processors work as expected.

     *

     * @coversNothing

     * @dataProvider provider_ml_test_evaluation_configuration

     * @param string $modelquality

     * @param int $ncourses

     * @param array $expected

     * @param string $predictionsprocessorclass

     * @param array $forcedconfig

     * @return void

     */

    public function test_ml_evaluation_configuration($modelquality, $ncourses, $expected, $predictionsprocessorclass,

            $forcedconfig): void {

        $this->resetAfterTest(true);

        $this->set_forced_config($forcedconfig);

        $predictionsprocessor = $this->is_predictions_processor_ready($predictionsprocessorclass);

        $this->setAdminuser();

        set_config('enabled_stores', 'logstore_standard', 'tool_log');

        $sometimesplittings = '\core\analytics\time_splitting\single_range,' .

            '\core\analytics\time_splitting\quarters';

        set_config('defaulttimesplittingsevaluation', $sometimesplittings, 'analytics');

        if ($modelquality === 'perfect') {

            $model = $this->add_perfect_model();

        } else if ($modelquality === 'random') {

            $model = $this->add_random_model();

        } else {

            throw new \coding_exception('Only perfect and random accepted as $modelquality values');

        }

        // Generate training data.

        $this->generate_courses($ncourses);

        $model->update(false, false, false, get_class($predictionsprocessor));

        $results = $model->evaluate();

        // We check that the returned status includes at least $expectedcode code.

        foreach ($results as $timesplitting => $result) {

            $message = 'The returned status code ' . $result->status . ' should include ' . $expected[$timesplitting];

            $filtered = $result->status & $expected[$timesplitting];

            $this->assertEquals($expected[$timesplitting], $filtered, $message);

            $options = ['evaluation' => true, 'reuseprevanalysed' => true];

            $result = new \core_analytics\local\analysis\result_file($model->get_id(), true, $options);

            $timesplittingobj = \core_analytics\manager::get_time_splitting($timesplitting);

            $analysable = new \core_analytics\site();

            $cachedanalysis = $result->retrieve_cached_result($timesplittingobj, $analysable);

            $this->assertInstanceOf(\stored_file::class, $cachedanalysis);

        }

        set_config('enabled_stores', '', 'tool_log');

        get_log_manager(true);

    }

    /**

     * Tests the evaluation of already trained models.

     *

     * @coversNothing

     * @dataProvider provider_ml_processors

     * @param  string $predictionsprocessorclass

     * @param array $forcedconfig

     * @return null

     */

    public function test_ml_evaluation_trained_model($predictionsprocessorclass, $forcedconfig): void {

        $this->resetAfterTest(true);

        $this->set_forced_config($forcedconfig);

        $predictionsprocessor = $this->is_predictions_processor_ready($predictionsprocessorclass);

        $this->setAdminuser();

        set_config('enabled_stores', 'logstore_standard', 'tool_log');

        $model = $this->add_perfect_model();

        // Generate training data.

        $this->generate_courses(50);

        $model->update(true, false, '\\core\\analytics\\time_splitting\\quarters', get_class($predictionsprocessor));

        $model->train();

        $zipfilename = 'model-zip-' . microtime() . '.zip';

        $zipfilepath = $model->export_model($zipfilename);

        $importmodel = \core_analytics\model::import_model($zipfilepath);

        $results = $importmodel->evaluate(['mode' => 'trainedmodel']);

        $this->assertEquals(0, $results['\\core\\analytics\\time_splitting\\quarters']->status);

        $this->assertEquals(1, $results['\\core\\analytics\\time_splitting\\quarters']->score);

        set_config('enabled_stores', '', 'tool_log');

        get_log_manager(true);

    }

    /**

     * test_read_indicator_calculations

     *

     * @return void

     */

    public function test_read_indicator_calculations(): void {

        global $DB;

        $this->resetAfterTest(true);

        $starttime = 123;

        $endtime = 321;

        $sampleorigin = 'whatever';

        $indicator = $this->getMockBuilder('test_indicator_max')->onlyMethods(['calculate_sample'])->getMock();

        $indicator->expects($this->never())->method('calculate_sample');

        $existingcalcs = array(111 => 1, 222 => -1);

        $sampleids = array(111 => 111, 222 => 222);

        list($values, $unused) = $indicator->calculate($sampleids, $sampleorigin, $starttime, $endtime, $existingcalcs);

    }

    /**

     * test_not_null_samples

     */

    public function test_not_null_samples(): void {

        $this->resetAfterTest(true);

        $timesplitting = \core_analytics\manager::get_time_splitting('\core\analytics\time_splitting\quarters');

        $timesplitting->set_analysable(new \core_analytics\site());

        $ranges = array(

            array('start' => 111, 'end' => 222, 'time' => 222),

            array('start' => 222, 'end' => 333, 'time' => 333)

        );

        $samples = array(123 => 123, 321 => 321);

        $target = \core_analytics\manager::get_target('test_target_shortname');

        $indicators = array('test_indicator_null', 'test_indicator_min');

        foreach ($indicators as $key => $indicator) {

            $indicators[$key] = \core_analytics\manager::get_indicator($indicator);

        }

        $model = \core_analytics\model::create($target, $indicators, '\core\analytics\time_splitting\no_splitting');

        $analyser = $model->get_analyser();

        $result = new \core_analytics\local\analysis\result_array($model->get_id(), false, $analyser->get_options());

        $analysis = new \core_analytics\analysis($analyser, false, $result);

        // Samples with at least 1 not null value are returned.

        $params = array(

            $timesplitting,

            $samples,

            $ranges

        );

        $dataset = \phpunit_util::call_internal_method($analysis, 'calculate_indicators', $params,

            '\core_analytics\analysis');

        $this->assertArrayHasKey('123-0', $dataset);

        $this->assertArrayHasKey('123-1', $dataset);

        $this->assertArrayHasKey('321-0', $dataset);

        $this->assertArrayHasKey('321-1', $dataset);

        $indicators = array('test_indicator_null');

        foreach ($indicators as $key => $indicator) {

            $indicators[$key] = \core_analytics\manager::get_indicator($indicator);

        }

        $model = \core_analytics\model::create($target, $indicators, '\core\analytics\time_splitting\no_splitting');

        $analyser = $model->get_analyser();

        $result = new \core_analytics\local\analysis\result_array($model->get_id(), false, $analyser->get_options());

        $analysis = new \core_analytics\analysis($analyser, false, $result);

        // Samples with only null values are not returned.

        $params = array(

            $timesplitting,

            $samples,

            $ranges

        );

        $dataset = \phpunit_util::call_internal_method($analysis, 'calculate_indicators', $params,

            '\core_analytics\analysis');

        $this->assertArrayNotHasKey('123-0', $dataset);

        $this->assertArrayNotHasKey('123-1', $dataset);

        $this->assertArrayNotHasKey('321-0', $dataset);

        $this->assertArrayNotHasKey('321-1', $dataset);

    }

    /**

     * provider_ml_test_evaluation_configuration

     *

     * @return array

     */

    public static function provider_ml_test_evaluation_configuration(): array {

        $cases = array(

            'bad' => array(

                'modelquality' => 'random',

                'ncourses' => 50,

                'expected' => array(

                    '\core\analytics\time_splitting\single_range' => \core_analytics\model::LOW_SCORE,

                    '\core\analytics\time_splitting\quarters' => \core_analytics\model::LOW_SCORE,

                )

            ),

            'good' => array(

                'modelquality' => 'perfect',

                'ncourses' => 50,

                'expected' => array(

                    '\core\analytics\time_splitting\single_range' => \core_analytics\model::OK,

                    '\core\analytics\time_splitting\quarters' => \core_analytics\model::OK,

                )

            )

        );

        return static::add_prediction_processors($cases);

    }

    /**

     * add_random_model

     *

     * @return \core_analytics\model

     */

    protected function add_random_model() {

        $target = \core_analytics\manager::get_target('test_target_shortname');

        $indicators = array('test_indicator_max', 'test_indicator_min', 'test_indicator_random');

        foreach ($indicators as $key => $indicator) {

            $indicators[$key] = \core_analytics\manager::get_indicator($indicator);

        }

        $model = \core_analytics\model::create($target, $indicators);

        // To load db defaults as well.

        return new \core_analytics\model($model->get_id());

    }

    /**

     * add_perfect_model

     *

     * @param string $targetclass

     * @return \core_analytics\model

     */

    protected function add_perfect_model($targetclass = 'test_target_shortname') {

        $target = \core_analytics\manager::get_target($targetclass);

        $indicators = array('test_indicator_max', 'test_indicator_min', 'test_indicator_fullname');

        foreach ($indicators as $key => $indicator) {

            $indicators[$key] = \core_analytics\manager::get_indicator($indicator);

        }

        $model = \core_analytics\model::create($target, $indicators);

        // To load db defaults as well.

        return new \core_analytics\model($model->get_id());

    }

    /**

     * Generates model for multi-classification

     *

     * @param string $targetclass

     * @return \core_analytics\model

     * @throws coding_exception

     * @throws moodle_exception

     */

    public function add_multiclass_model($targetclass = 'test_target_shortname_multiclass') {

        $target = \core_analytics\manager::get_target($targetclass);

        $indicators = array('test_indicator_fullname', 'test_indicator_multiclass');

        foreach ($indicators as $key => $indicator) {

            $indicators[$key] = \core_analytics\manager::get_indicator($indicator);

        }

        $model = \core_analytics\model::create($target, $indicators);

        return new \core_analytics\model($model->get_id());

    }

    /**

     * Forces some configuration values.

     *

     * @param array $forcedconfig

     */

    protected function set_forced_config($forcedconfig) {

        \core_analytics\manager::reset_prediction_processors();

        if (empty($forcedconfig)) {

            return;

        }

        foreach ($forcedconfig as $pluginname => $pluginconfig) {

            foreach ($pluginconfig as $name => $value) {

                set_config($name, $value, $pluginname);

            }

        }

    }

    /**

     * Is the provided processor ready using the current configuration in the site?

     *

     * @param  string  $predictionsprocessorclass

     * @return \core_analytics\predictor

     */

    protected function is_predictions_processor_ready(string $predictionsprocessorclass) {

        // We repeat the test for all prediction processors.

        $predictionsprocessor = \core_analytics\manager::get_predictions_processor($predictionsprocessorclass, false);

        $ready = $predictionsprocessor->is_ready();

        if ($ready !== true) {

            $this->markTestSkipped('Skipping ' . $predictionsprocessorclass . ' as the predictor is not ready: ' . $ready);

        }

        return $predictionsprocessor;

    }

    /**

     * add_prediction_processors

     *

     * @param array $cases

     * @return array

     */

    protected static function add_prediction_processors($cases): array {

        $return = array();

        if (defined('TEST_MLBACKEND_PYTHON_HOST') && defined('TEST_MLBACKEND_PYTHON_PORT')

                && defined('TEST_MLBACKEND_PYTHON_USERNAME') && defined('TEST_MLBACKEND_PYTHON_USERNAME')) {

            $testpythonserver = true;

        }

        // We need to test all prediction processors in the system.

        $predictionprocessors = \core_analytics\manager::get_all_prediction_processors();

        foreach ($predictionprocessors as $classfullname => $predictionsprocessor) {

            foreach ($cases as $key => $case) {

                if (!$predictionsprocessor instanceof \mlbackend_python\processor || empty($testpythonserver)) {

                    $extraparams = ['predictionsprocessorclass' => $classfullname, 'forcedconfig' => null];

                    $return[$key . '-' . $classfullname] = $case + $extraparams;

                } else {

                    // We want the configuration to be forced during the test as things like importing models create new

                    // instances of ML backend processors during the process.

                    $forcedconfig = ['mlbackend_python' => ['useserver' => true, 'host' => TEST_MLBACKEND_PYTHON_HOST,

                        'port' => TEST_MLBACKEND_PYTHON_PORT, 'secure' => false, 'username' => TEST_MLBACKEND_PYTHON_USERNAME,

                        'password' => TEST_MLBACKEND_PYTHON_PASSWORD]];

                    $casekey = $key . '-' . $classfullname . '-server';

                    $return[$casekey] = $case + ['predictionsprocessorclass' => $classfullname, 'forcedconfig' => $forcedconfig];

                }

            }

        }

        return $return;

    }

}