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381 lines
16 KiB
PHP
381 lines
16 KiB
PHP
<?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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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_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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/**
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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_ranges', array('modelid' => $model->get_id()));
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$this->assertCount(2, $predictedranges);
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$this->assertEquals(1, $DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'predicted')));
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// 2 predictions for each range.
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$this->assertEquals(2, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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// No new generated files nor records as there are no new courses available.
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$model->predict();
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$predictedranges = $DB->get_records('analytics_predict_ranges', array('modelid' => $model->get_id()));
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$this->assertCount(2, $predictedranges);
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$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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}
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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 $npredictedranges
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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, $npredictedranges, $predictionsprocessorclass) {
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global $DB;
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$this->resetAfterTest(true);
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$this->setAdminuser();
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set_config('enabled_stores', 'logstore_standard', 'tool_log');
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$ncourses = 10;
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// Generate training data.
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$params = array(
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'startdate' => mktime(0, 0, 0, 10, 24, 2015),
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'enddate' => mktime(0, 0, 0, 2, 24, 2016),
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);
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for ($i = 0; $i < $ncourses; $i++) {
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$name = 'a' . random_string(10);
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$courseparams = array('shortname' => $name, 'fullname' => $name) + $params;
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$this->getDataGenerator()->create_course($courseparams);
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}
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for ($i = 0; $i < $ncourses; $i++) {
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$name = 'b' . random_string(10);
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$courseparams = array('shortname' => $name, 'fullname' => $name) + $params;
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$this->getDataGenerator()->create_course($courseparams);
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}
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// We repeat the test for all prediction processors.
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$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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set_config('predictionsprocessor', $predictionsprocessorclass, 'analytics');
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$model = $this->add_perfect_model();
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$model->enable($timesplittingid);
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// No samples trained yet.
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$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, $model->is_trained());
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// 1 training file was created.
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$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' => $model->get_id(), 'action' => 'trained')));
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$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], $predictiondata->prediction);
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}
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// 2 ranges will be predicted.
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$predictedranges = $DB->get_records('analytics_predict_ranges', array('modelid' => $model->get_id()));
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$this->assertCount($npredictedranges, $predictedranges);
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$this->assertEquals(1, $DB->count_records('analytics_used_files',
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array('modelid' => $model->get_id(), 'action' => 'predicted')));
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// 2 predictions for each range.
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$this->assertEquals(2 * $npredictedranges, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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// No new generated files nor records as there are no new courses available.
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$model->predict();
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$predictedranges = $DB->get_records('analytics_predict_ranges', array('modelid' => $model->get_id()));
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$this->assertCount($npredictedranges, $predictedranges);
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$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 * $npredictedranges, $DB->count_records('analytics_predictions',
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array('modelid' => $model->get_id())));
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}
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/**
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* provider_ml_training_and_prediction
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*
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* @return array
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*/
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public function provider_ml_training_and_prediction() {
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$cases = array(
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'no_splitting' => array('\core\analytics\time_splitting\no_splitting', 1),
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'quarters' => array('\core\analytics\time_splitting\quarters', 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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* Basic test to check that prediction processors work as expected.
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*
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* @dataProvider provider_ml_test_evaluation
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* @param string $modelquality
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* @param int $ncourses
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* @param array $expected
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* @param string $predictionsprocessorclass
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* @return void
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*/
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public function test_ml_evaluation($modelquality, $ncourses, $expected, $predictionsprocessorclass) {
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$this->resetAfterTest(true);
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$this->setAdminuser();
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set_config('enabled_stores', 'logstore_standard', 'tool_log');
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$sometimesplittings = '\core\analytics\time_splitting\weekly,' .
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'\core\analytics\time_splitting\single_range,' .
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'\core\analytics\time_splitting\quarters';
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set_config('timesplittings', $sometimesplittings, 'analytics');
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if ($modelquality === 'perfect') {
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$model = $this->add_perfect_model();
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} else if ($modelquality === 'random') {
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$model = $this->add_random_model();
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} else {
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throw new \coding_exception('Only perfect and random accepted as $modelquality values');
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}
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// Generate training data.
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$params = array(
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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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for ($i = 0; $i < $ncourses; $i++) {
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$name = 'a' . random_string(10);
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$params = array('shortname' => $name, 'fullname' => $name) + $params;
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$this->getDataGenerator()->create_course($params);
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}
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for ($i = 0; $i < $ncourses; $i++) {
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$name = 'b' . random_string(10);
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$params = array('shortname' => $name, 'fullname' => $name) + $params;
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$this->getDataGenerator()->create_course($params);
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}
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// We repeat the test for all prediction processors.
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$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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set_config('predictionsprocessor', $predictionsprocessorclass, 'analytics');
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$results = $model->evaluate();
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// We check that the returned status includes at least $expectedcode code.
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foreach ($results as $timesplitting => $result) {
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$message = 'The returned status code ' . $result->status . ' should include ' . $expected[$timesplitting];
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$filtered = $result->status & $expected[$timesplitting];
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$this->assertEquals($expected[$timesplitting], $filtered, $message);
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}
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}
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/**
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* provider_ml_test_evaluation
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*
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* @return array
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*/
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public function provider_ml_test_evaluation() {
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$notenoughandlowscore = \core_analytics\model::EVALUATE_NOT_ENOUGH_DATA + \core_analytics\model::EVALUATE_LOW_SCORE;
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$cases = array(
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'bad-and-no-enough-data' => array(
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'modelquality' => 'random',
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'ncourses' => 5,
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'expectedresults' => array(
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// The course duration is too much to be processed by in weekly basis.
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'\core\analytics\time_splitting\weekly' => \core_analytics\model::NO_DATASET,
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// 10 samples is not enough to process anything.
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'\core\analytics\time_splitting\single_range' => $notenoughandlowscore,
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'\core\analytics\time_splitting\quarters' => $notenoughandlowscore,
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)
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),
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'bad' => array(
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'modelquality' => 'random',
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'ncourses' => 50,
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'expectedresults' => array(
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// The course duration is too much to be processed by in weekly basis.
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'\core\analytics\time_splitting\weekly' => \core_analytics\model::NO_DATASET,
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'\core\analytics\time_splitting\single_range' => \core_analytics\model::EVALUATE_LOW_SCORE,
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'\core\analytics\time_splitting\quarters' => \core_analytics\model::EVALUATE_LOW_SCORE,
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)
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),
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'good' => array(
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'modelquality' => 'perfect',
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'ncourses' => 50,
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'expectedresults' => array(
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// The course duration is too much to be processed by in weekly basis.
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'\core\analytics\time_splitting\weekly' => \core_analytics\model::NO_DATASET,
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'\core\analytics\time_splitting\single_range' => \core_analytics\model::OK,
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'\core\analytics\time_splitting\quarters' => \core_analytics\model::OK,
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)
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)
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);
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return $this->add_prediction_processors($cases);
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}
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/**
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* add_random_model
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*
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* @return \core_analytics\model
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*/
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protected function add_random_model() {
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$target = \core_analytics\manager::get_target('test_target_shortname');
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$indicators = array('test_indicator_max', 'test_indicator_min', 'test_indicator_random');
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foreach ($indicators as $key => $indicator) {
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$indicators[$key] = \core_analytics\manager::get_indicator($indicator);
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}
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$model = \core_analytics\model::create($target, $indicators);
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// To load db defaults as well.
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return new \core_analytics\model($model->get_id());
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}
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/**
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* add_perfect_model
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*
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* @param string $targetclass
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* @return \core_analytics\model
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*/
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protected function add_perfect_model($targetclass = 'test_target_shortname') {
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$target = \core_analytics\manager::get_target($targetclass);
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$indicators = array('test_indicator_max', 'test_indicator_min', 'test_indicator_fullname');
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foreach ($indicators as $key => $indicator) {
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$indicators[$key] = \core_analytics\manager::get_indicator($indicator);
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}
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$model = \core_analytics\model::create($target, $indicators);
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// To load db defaults as well.
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return new \core_analytics\model($model->get_id());
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}
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/**
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* add_prediction_processors
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*
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* @param array $cases
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* @return array
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*/
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protected function add_prediction_processors($cases) {
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$return = array();
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// We need to test all system prediction processors.
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$predictionprocessors = \core_analytics\manager::get_all_prediction_processors();
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foreach ($predictionprocessors as $classfullname => $unused) {
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foreach ($cases as $key => $case) {
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$newkey = $key . '-' . $classfullname;
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$return[$newkey] = $case + array('predictionsprocessorclass' => $classfullname);
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}
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}
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return $return;
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}
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}
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