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725 lines
31 KiB
725 lines
31 KiB
2 years ago
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<?php
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// This file is part of Moodle - http://moodle.org/
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//
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// Moodle is free software: you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation, either version 3 of the License, or
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// (at your option) any later version.
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//
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// Moodle is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with Moodle. If not, see <http://www.gnu.org/licenses/>.
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/**
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* Unit tests for evaluation, training and prediction.
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*
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* @package core_analytics
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* @copyright 2017 David Monllaó {@link http://www.davidmonllao.com}
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* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
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*/
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defined('MOODLE_INTERNAL') || die();
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global $CFG;
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require_once(__DIR__ . '/fixtures/test_indicator_max.php');
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require_once(__DIR__ . '/fixtures/test_indicator_min.php');
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require_once(__DIR__ . '/fixtures/test_indicator_null.php');
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require_once(__DIR__ . '/fixtures/test_indicator_fullname.php');
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require_once(__DIR__ . '/fixtures/test_indicator_random.php');
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require_once(__DIR__ . '/fixtures/test_target_shortname.php');
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require_once(__DIR__ . '/fixtures/test_static_target_shortname.php');
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require_once(__DIR__ . '/../../course/lib.php');
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/**
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* Unit tests for evaluation, training and prediction.
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*
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* @package core_analytics
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* @copyright 2017 David Monllaó {@link http://www.davidmonllao.com}
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* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
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*/
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class core_analytics_prediction_testcase extends advanced_testcase {
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/**
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* test_static_prediction
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*
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* @return void
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*/
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public function test_static_prediction() {
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global $DB;
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$this->resetAfterTest(true);
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$this->setAdminuser();
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$model = $this->add_perfect_model('test_static_target_shortname');
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$model->enable('\core\analytics\time_splitting\no_splitting');
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$this->assertEquals(1, $model->is_enabled());
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$this->assertEquals(1, $model->is_trained());
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// No training for static models.
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$results = $model->train();
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$trainedsamples = $DB->get_records('analytics_train_samples', array('modelid' => $model->get_id()));
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$this->assertEmpty($trainedsamples);
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$this->assertEmpty($DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'trained')));
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// Now we create 2 hidden courses (only hidden courses are getting predictions).
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$courseparams = array('shortname' => 'aaaaaa', 'fullname' => 'aaaaaa', 'visible' => 0);
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$course1 = $this->getDataGenerator()->create_course($courseparams);
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$courseparams = array('shortname' => 'bbbbbb', 'fullname' => 'bbbbbb', 'visible' => 0);
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$course2 = $this->getDataGenerator()->create_course($courseparams);
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$result = $model->predict();
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// Var $course1 predictions should be 1 == 'a', $course2 predictions should be 0 == 'b'.
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$correct = array($course1->id => 1, $course2->id => 0);
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foreach ($result->predictions as $uniquesampleid => $predictiondata) {
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list($sampleid, $rangeindex) = $model->get_time_splitting()->infer_sample_info($uniquesampleid);
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// The range index is not important here, both ranges prediction will be the same.
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$this->assertEquals($correct[$sampleid], $predictiondata->prediction);
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}
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// 1 range for each analysable.
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$predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));
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$this->assertCount(2, $predictedranges);
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// 2 predictions for each range.
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$this->assertEquals(2, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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// No new generated records as there are no new courses available.
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$model->predict();
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$predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));
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$this->assertCount(2, $predictedranges);
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$this->assertEquals(2, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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}
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/**
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* test_ml_training_and_prediction
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*
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* @dataProvider provider_ml_training_and_prediction
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* @param string $timesplittingid
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* @param int $predictedrangeindex
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* @param int $nranges
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* @param string $predictionsprocessorclass
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* @return void
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*/
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public function test_ml_training_and_prediction($timesplittingid, $predictedrangeindex, $nranges, $predictionsprocessorclass) {
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global $DB;
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$this->resetAfterTest(true);
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$this->setAdminuser();
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set_config('enabled_stores', 'logstore_standard', 'tool_log');
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// Generate training data.
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$ncourses = 10;
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$this->generate_courses($ncourses);
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// We repeat the test for all prediction processors.
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$predictionsprocessor = \core_analytics\manager::get_predictions_processor($predictionsprocessorclass, false);
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if ($predictionsprocessor->is_ready() !== true) {
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$this->markTestSkipped('Skipping ' . $predictionsprocessorclass . ' as the predictor is not ready.');
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}
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$model = $this->add_perfect_model();
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$model->update(true, false, $timesplittingid, get_class($predictionsprocessor));
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// No samples trained yet.
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$this->assertEquals(0, $DB->count_records('analytics_train_samples', array('modelid' => $model->get_id())));
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$results = $model->train();
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$this->assertEquals(1, $model->is_enabled());
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$this->assertEquals(1, $model->is_trained());
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// 20 courses * the 3 model indicators * the number of time ranges of this time splitting method.
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$indicatorcalc = 20 * 3 * $nranges;
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$this->assertEquals($indicatorcalc, $DB->count_records('analytics_indicator_calc'));
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// 1 training file was created.
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$trainedsamples = $DB->get_records('analytics_train_samples', array('modelid' => $model->get_id()));
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$this->assertCount(1, $trainedsamples);
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$samples = json_decode(reset($trainedsamples)->sampleids, true);
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$this->assertCount($ncourses * 2, $samples);
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$this->assertEquals(1, $DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'trained')));
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// Check that analysable files for training are stored under labelled filearea.
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$fs = get_file_storage();
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$this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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$this->assertEmpty($fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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$params = [
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'startdate' => mktime(0, 0, 0, 10, 24, 2015),
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'enddate' => mktime(0, 0, 0, 2, 24, 2016),
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];
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$courseparams = $params + array('shortname' => 'aaaaaa', 'fullname' => 'aaaaaa', 'visible' => 0);
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$course1 = $this->getDataGenerator()->create_course($courseparams);
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$courseparams = $params + array('shortname' => 'bbbbbb', 'fullname' => 'bbbbbb', 'visible' => 0);
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$course2 = $this->getDataGenerator()->create_course($courseparams);
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// They will not be skipped for prediction though.
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$result = $model->predict();
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// Var $course1 predictions should be 1 == 'a', $course2 predictions should be 0 == 'b'.
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$correct = array($course1->id => 1, $course2->id => 0);
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foreach ($result->predictions as $uniquesampleid => $predictiondata) {
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list($sampleid, $rangeindex) = $model->get_time_splitting()->infer_sample_info($uniquesampleid);
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// The range index is not important here, both ranges prediction will be the same.
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$this->assertEquals($correct[$sampleid], $predictiondata->prediction);
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}
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// 1 range will be predicted.
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$predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));
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$this->assertCount(1, $predictedranges);
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foreach ($predictedranges as $predictedrange) {
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$this->assertEquals($predictedrangeindex, $predictedrange->rangeindex);
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$sampleids = json_decode($predictedrange->sampleids, true);
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$this->assertCount(2, $sampleids);
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$this->assertContains($course1->id, $sampleids);
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$this->assertContains($course2->id, $sampleids);
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}
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$this->assertEquals(1, $DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'predicted')));
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// 2 predictions.
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$this->assertEquals(2, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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// Check that analysable files to get predictions are stored under unlabelled filearea.
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$this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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$this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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// No new generated files nor records as there are no new courses available.
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$model->predict();
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$predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));
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$this->assertCount(1, $predictedranges);
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foreach ($predictedranges as $predictedrange) {
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$this->assertEquals($predictedrangeindex, $predictedrange->rangeindex);
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}
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$this->assertEquals(1, $DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'predicted')));
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$this->assertEquals(2, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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// New samples that can be used for prediction.
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$courseparams = $params + array('shortname' => 'cccccc', 'fullname' => 'cccccc', 'visible' => 0);
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$course3 = $this->getDataGenerator()->create_course($courseparams);
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$courseparams = $params + array('shortname' => 'dddddd', 'fullname' => 'dddddd', 'visible' => 0);
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$course4 = $this->getDataGenerator()->create_course($courseparams);
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$result = $model->predict();
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$predictedranges = $DB->get_records('analytics_predict_samples', array('modelid' => $model->get_id()));
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$this->assertCount(1, $predictedranges);
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foreach ($predictedranges as $predictedrange) {
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$this->assertEquals($predictedrangeindex, $predictedrange->rangeindex);
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$sampleids = json_decode($predictedrange->sampleids, true);
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$this->assertCount(4, $sampleids);
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$this->assertContains($course1->id, $sampleids);
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$this->assertContains($course2->id, $sampleids);
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$this->assertContains($course3->id, $sampleids);
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$this->assertContains($course4->id, $sampleids);
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}
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$this->assertEquals(2, $DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'predicted')));
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$this->assertEquals(4, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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$this->assertCount(1, $fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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$this->assertCount(2, $fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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// New visible course (for training).
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$course5 = $this->getDataGenerator()->create_course(array('shortname' => 'aaa', 'fullname' => 'aa'));
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$course6 = $this->getDataGenerator()->create_course();
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$result = $model->train();
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$this->assertEquals(2, $DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'trained')));
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$this->assertCount(2, $fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::LABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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$this->assertCount(2, $fs->get_directory_files(\context_system::instance()->id, 'analytics',
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\core_analytics\dataset_manager::UNLABELLED_FILEAREA, $model->get_id(), '/analysable/', true, false));
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set_config('enabled_stores', '', 'tool_log');
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get_log_manager(true);
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}
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/**
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* provider_ml_training_and_prediction
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*
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* @return array
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*/
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public function provider_ml_training_and_prediction() {
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$cases = array(
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'no_splitting' => array('\core\analytics\time_splitting\no_splitting', 0, 1),
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'quarters' => array('\core\analytics\time_splitting\quarters', 3, 4)
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);
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// We need to test all system prediction processors.
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return $this->add_prediction_processors($cases);
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}
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/**
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* test_ml_export_import
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*
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* @param string $predictionsprocessorclass The class name
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* @dataProvider provider_ml_processors
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*/
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public function test_ml_export_import($predictionsprocessorclass) {
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$this->resetAfterTest(true);
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$this->setAdminuser();
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set_config('enabled_stores', 'logstore_standard', 'tool_log');
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// Generate training data.
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$ncourses = 10;
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$this->generate_courses($ncourses);
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// We repeat the test for all prediction processors.
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$predictionsprocessor = \core_analytics\manager::get_predictions_processor($predictionsprocessorclass, false);
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if ($predictionsprocessor->is_ready() !== true) {
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$this->markTestSkipped('Skipping ' . $predictionsprocessorclass . ' as the predictor is not ready.');
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}
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$model = $this->add_perfect_model();
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$model->update(true, false, '\core\analytics\time_splitting\quarters', get_class($predictionsprocessor));
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$model->train();
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$this->assertTrue($model->trained_locally());
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$this->generate_courses(10, ['visible' => 0]);
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$originalresults = $model->predict();
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$zipfilename = 'model-zip-' . microtime() . '.zip';
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$zipfilepath = $model->export_model($zipfilename);
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$modelconfig = new \core_analytics\model_config();
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list($modelconfig, $mlbackend) = $modelconfig->extract_import_contents($zipfilepath);
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$this->assertNotFalse($mlbackend);
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|
|
$importmodel = \core_analytics\model::import_model($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 $predictionsprocessorclass
|
||
|
|
* @return void
|
||
|
|
*/
|
||
|
|
public function test_ml_classifiers_return($success, $nsamples, $classes, $predictionsprocessorclass) {
|
||
|
|
$this->resetAfterTest();
|
||
|
|
|
||
|
|
$predictionsprocessor = \core_analytics\manager::get_predictions_processor($predictionsprocessorclass, false);
|
||
|
|
if ($predictionsprocessor->is_ready() !== true) {
|
||
|
|
$this->markTestSkipped('Skipping ' . $predictionsprocessorclass . ' as the predictor is not ready.');
|
||
|
|
}
|
||
|
|
|
||
|
|
if ($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();
|
||
|
|
$result = $predictionsprocessor->train_classification('whatever', $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);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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);
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* Basic test to check that prediction processors work as expected.
|
||
|
|
*
|
||
|
|
* @dataProvider provider_ml_test_evaluation_configuration
|
||
|
|
* @param string $modelquality
|
||
|
|
* @param int $ncourses
|
||
|
|
* @param array $expected
|
||
|
|
* @param string $predictionsprocessorclass
|
||
|
|
* @return void
|
||
|
|
*/
|
||
|
|
public function test_ml_evaluation_configuration($modelquality, $ncourses, $expected, $predictionsprocessorclass) {
|
||
|
|
$this->resetAfterTest(true);
|
||
|
|
$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);
|
||
|
|
|
||
|
|
// We repeat the test for all prediction processors.
|
||
|
|
$predictionsprocessor = \core_analytics\manager::get_predictions_processor($predictionsprocessorclass, false);
|
||
|
|
if ($predictionsprocessor->is_ready() !== true) {
|
||
|
|
$this->markTestSkipped('Skipping ' . $predictionsprocessorclass . ' as the predictor is not ready.');
|
||
|
|
}
|
||
|
|
|
||
|
|
$model->update(false, false, false, get_class($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);
|
||
|
|
}
|
||
|
|
|
||
|
|
set_config('enabled_stores', '', 'tool_log');
|
||
|
|
get_log_manager(true);
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* Tests the evaluation of already trained models.
|
||
|
|
*
|
||
|
|
* @dataProvider provider_ml_processors
|
||
|
|
* @param string $predictionsprocessorclass
|
||
|
|
* @return null
|
||
|
|
*/
|
||
|
|
public function test_ml_evaluation_trained_model($predictionsprocessorclass) {
|
||
|
|
$this->resetAfterTest(true);
|
||
|
|
$this->setAdminuser();
|
||
|
|
set_config('enabled_stores', 'logstore_standard', 'tool_log');
|
||
|
|
|
||
|
|
$model = $this->add_perfect_model();
|
||
|
|
|
||
|
|
// Generate training data.
|
||
|
|
$this->generate_courses(50);
|
||
|
|
|
||
|
|
// We repeat the test for all prediction processors.
|
||
|
|
$predictionsprocessor = \core_analytics\manager::get_predictions_processor($predictionsprocessorclass, false);
|
||
|
|
if ($predictionsprocessor->is_ready() !== true) {
|
||
|
|
$this->markTestSkipped('Skipping ' . $predictionsprocessorclass . ' as the predictor is not ready.');
|
||
|
|
}
|
||
|
|
|
||
|
|
$model->update(true, false, '\\core\\analytics\\time_splitting\\quarters', get_class($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 $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);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* add_prediction_processors
|
||
|
|
*
|
||
|
|
* @param array $cases
|
||
|
|
* @return array
|
||
|
|
*/
|
||
|
|
protected function add_prediction_processors($cases) {
|
||
|
|
|
||
|
|
$return = array();
|
||
|
|
|
||
|
|
// We need to test all system prediction processors.
|
||
|
|
$predictionprocessors = \core_analytics\manager::get_all_prediction_processors();
|
||
|
|
foreach ($predictionprocessors as $classfullname => $unused) {
|
||
|
|
foreach ($cases as $key => $case) {
|
||
|
|
$newkey = $key . '-' . $classfullname;
|
||
|
|
$return[$newkey] = $case + array('predictionsprocessorclass' => $classfullname);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
return $return;
|
||
|
|
}
|
||
|
|
}
|