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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/>. 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'); /** * 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 MonllaĆ³ {@link http://www.davidmonllao.com} * @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later */ class prediction_test extends \advanced_testcase { /** * 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(); } } /** * test_static_prediction * * @return void */ public function test_static_prediction() { 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() { global $DB; $this->resetAfterTest(true); $this->setAdminuser();< $misc = $DB->get_record('course_categories', ['name' => 'Miscellaneous']);> $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) { 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 function provider_ml_training_and_prediction() { $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 $this->add_prediction_processors($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) { $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 function provider_ml_processors() { $cases = [ 'case' => [], ]; // We need to test all system prediction processors. return $this->add_prediction_processors($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) { $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 function provider_ml_classifiers_return() { // 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 $this->add_prediction_processors($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) { 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; $this->generate_courses_multiclass($ncourses); $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 function provider_test_multi_classifier() { $cases = array( 'notimesplitting' => array('\core\analytics\time_splitting\no_splitting'), ); // Add all system prediction processors. return $this->add_prediction_processors($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) { $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) { $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() { 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() { $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 function provider_ml_test_evaluation_configuration() { $cases = array( 'bad' => array( 'modelquality' => 'random', 'ncourses' => 50, 'expectedresults' => 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, 'expectedresults' => array( '\core\analytics\time_splitting\single_range' => \core_analytics\model::OK, '\core\analytics\time_splitting\quarters' => \core_analytics\model::OK, ) ) ); return $this->add_prediction_processors($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()); } /** * Generates $ncourses courses * * @param int $ncourses The number of courses to be generated. * @param array $params Course params * @return null */ protected function generate_courses($ncourses, array $params = []) { $params = $params + [ 'startdate' => mktime(0, 0, 0, 10, 24, 2015), 'enddate' => mktime(0, 0, 0, 2, 24, 2016), ]; for ($i = 0; $i < $ncourses; $i++) { $name = 'a' . random_string(10); $courseparams = array('shortname' => $name, 'fullname' => $name) + $params; $this->getDataGenerator()->create_course($courseparams); } for ($i = 0; $i < $ncourses; $i++) { $name = 'b' . random_string(10); $courseparams = array('shortname' => $name, 'fullname' => $name) + $params; $this->getDataGenerator()->create_course($courseparams); } } /** * Generates ncourses for multi-classification * * @param int $ncourses The number of courses to be generated. * @param array $params Course params * @return null */ protected function generate_courses_multiclass($ncourses, array $params = []) { $params = $params + [ 'startdate' => mktime(0, 0, 0, 10, 24, 2015), 'enddate' => mktime(0, 0, 0, 2, 24, 2016), ]; for ($i = 0; $i < $ncourses; $i++) { $name = 'a' . random_string(10); $courseparams = array('shortname' => $name, 'fullname' => $name) + $params; $this->getDataGenerator()->create_course($courseparams); } for ($i = 0; $i < $ncourses; $i++) { $name = 'b' . random_string(10); $courseparams = array('shortname' => $name, 'fullname' => $name) + $params; $this->getDataGenerator()->create_course($courseparams); } for ($i = 0; $i < $ncourses; $i++) { $name = 'c' . random_string(10); $courseparams = array('shortname' => $name, 'fullname' => $name) + $params; $this->getDataGenerator()->create_course($courseparams); } } /** * 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 $cases * @return array */ protected function add_prediction_processors($cases) { $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 = ['predictionsprocessor' => $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 + ['predictionsprocessor' => $classfullname, 'forcedconfig' => $forcedconfig]; } } } return $return; } }