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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/>.
/**
* 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
*/
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.
*
* @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
*/
class core_analytics_prediction_testcase 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() {
$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']);
$miscctx = \context_coursecat::instance($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);
$this->assertContains($course1->id, $sampleids);
$this->assertContains($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);
$this->assertContains($course1->id, $sampleids);
$this->assertContains($course2->id, $sampleids);
$this->assertContains($course3->id, $sampleids);
$this->assertContains($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')->setMethods(['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;
}
}