File: /var/www/moodle/evaluaciones/moodle/analytics/tests/prediction_test.php
<?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.
*
* @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_fullname.php');
require_once(__DIR__ . '/fixtures/test_indicator_random.php');
require_once(__DIR__ . '/fixtures/test_target_shortname.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 {
/**
* 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);
$this->assertEquals(1, $DB->count_records('analytics_used_files',
array('modelid' => $model->get_id(), 'action' => 'predicted')));
// 2 predictions for each range.
$this->assertEquals(2, $DB->count_records('analytics_predictions',
array('modelid' => $model->get_id())));
// 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(2, $predictedranges);
$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())));
}
/**
* test_ml_training_and_prediction
*
* @dataProvider provider_ml_training_and_prediction
* @param string $timesplittingid
* @param int $predictedrangeindex
* @param int $nranges
* @param string $predictionsprocessorclass
* @return void
*/
public function test_ml_training_and_prediction($timesplittingid, $predictedrangeindex, $nranges, $predictionsprocessorclass) {
global $DB;
$this->resetAfterTest(true);
$this->setAdminuser();
set_config('enabled_stores', 'logstore_standard', 'tool_log');
$ncourses = 10;
// Generate training data.
$params = array(
'startdate' => mktime(0, 0, 0, 10, 24, 2015),
'enddate' => mktime(0, 0, 0, 2, 24, 2016),
);
for ($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);
}
// 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 = $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));
$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));
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 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
* @param string $modelquality
* @param int $ncourses
* @param array $expected
* @param string $predictionsprocessorclass
* @return void
*/
public function test_ml_evaluation($modelquality, $ncourses, $expected, $predictionsprocessorclass) {
$this->resetAfterTest(true);
$this->setAdminuser();
set_config('enabled_stores', 'logstore_standard', 'tool_log');
$sometimesplittings = '\core\analytics\time_splitting\weekly,' .
'\core\analytics\time_splitting\single_range,' .
'\core\analytics\time_splitting\quarters';
set_config('timesplittings', $sometimesplittings, 'analytics');
if ($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.
$params = array(
'startdate' => mktime(0, 0, 0, 10, 24, 2015),
'enddate' => mktime(0, 0, 0, 2, 24, 2016),
);
for ($i = 0; $i < $ncourses; $i++) {
$name = 'a' . random_string(10);
$params = array('shortname' => $name, 'fullname' => $name) + $params;
$this->getDataGenerator()->create_course($params);
}
for ($i = 0; $i < $ncourses; $i++) {
$name = 'b' . random_string(10);
$params = array('shortname' => $name, 'fullname' => $name) + $params;
$this->getDataGenerator()->create_course($params);
}
// 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);
}
/**
* 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 => 0.5);
$sampleids = array(111 => 111, 222 => 222);
list($values, $unused) = $indicator->calculate($sampleids, $sampleorigin, $starttime, $endtime, $existingcalcs);
}
/**
* test_not_null_samples
*/
public function test_not_null_samples() {
$this->resetAfterTest(true);
$classname = '\core\analytics\time_splitting\quarters';
$timesplitting = \core_analytics\manager::get_time_splitting($classname);
$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);
$indicator1 = $this->getMockBuilder('test_indicator_max')
->setMethods(['calculate_sample'])
->getMock();
$indicator1->method('calculate_sample')
->willReturn(null);
$indicator2 = \core_analytics\manager::get_indicator('test_indicator_min');
// Samples with at least 1 not null value are returned.
$params = array(
$samples,
'whatever',
array($indicator1, $indicator2),
$ranges
);
$dataset = phpunit_util::call_internal_method($timesplitting, 'calculate_indicators', $params, $classname);
$this->assertArrayHasKey('123-0', $dataset);
$this->assertArrayHasKey('123-1', $dataset);
$this->assertArrayHasKey('321-0', $dataset);
$this->assertArrayHasKey('321-1', $dataset);
// Samples with only null values are not returned.
$params = array(
$samples,
'whatever',
array($indicator1),
$ranges
);
$dataset = phpunit_util::call_internal_method($timesplitting, 'calculate_indicators', $params, $classname);
$this->assertArrayNotHasKey('123-0', $dataset);
$this->assertArrayNotHasKey('123-1', $dataset);
$this->assertArrayNotHasKey('321-0', $dataset);
$this->assertArrayNotHasKey('321-1', $dataset);
}
/**
* provider_ml_test_evaluation
*
* @return array
*/
public function provider_ml_test_evaluation() {
$cases = array(
'bad' => array(
'modelquality' => 'random',
'ncourses' => 50,
'expectedresults' => array(
// The course duration is too much to be processed by in weekly basis.
'\core\analytics\time_splitting\weekly' => \core_analytics\model::NO_DATASET,
'\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(
// The course duration is too much to be processed by in weekly basis.
'\core\analytics\time_splitting\weekly' => \core_analytics\model::NO_DATASET,
'\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());
}
/**
* 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;
}
}