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445 lines
14 KiB
445 lines
14 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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* Python predictions processor
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*
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* @package mlbackend_python
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* @copyright 2016 David Monllao {@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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namespace mlbackend_python;
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defined('MOODLE_INTERNAL') || die();
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/**
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* Python predictions processor.
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*
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* @package mlbackend_python
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* @copyright 2016 David Monllao {@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 processor implements \core_analytics\classifier, \core_analytics\regressor, \core_analytics\packable {
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/**
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* The required version of the python package that performs all calculations.
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*/
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const REQUIRED_PIP_PACKAGE_VERSION = '1.0.0';
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/**
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* The path to the Python bin.
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*
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* @var string
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*/
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protected $pathtopython;
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/**
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* The constructor.
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*/
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public function __construct() {
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global $CFG;
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// Set the python location if there is a value.
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if (!empty($CFG->pathtopython)) {
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$this->pathtopython = $CFG->pathtopython;
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}
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}
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/**
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* Is the plugin ready to be used?.
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*
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* @return bool|string Returns true on success, a string detailing the error otherwise
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*/
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public function is_ready() {
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if (empty($this->pathtopython)) {
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$settingurl = new \moodle_url('/admin/settings.php', array('section' => 'systempaths'));
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return get_string('pythonpathnotdefined', 'mlbackend_python', $settingurl->out());
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}
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// Check the installed pip package version.
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$cmd = "{$this->pathtopython} -m moodlemlbackend.version";
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$output = null;
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$exitcode = null;
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// Execute it sending the standard error to $output.
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$result = exec($cmd . ' 2>&1', $output, $exitcode);
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$vercheck = self::check_pip_package_version($result);
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if ($vercheck === 0) {
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return true;
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}
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if ($exitcode != 0) {
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return get_string('pythonpackagenotinstalled', 'mlbackend_python', $cmd);
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}
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if ($result) {
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$a = [
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'installed' => $result,
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'required' => self::REQUIRED_PIP_PACKAGE_VERSION,
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];
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if ($vercheck < 0) {
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return get_string('packageinstalledshouldbe', 'mlbackend_python', $a);
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} else if ($vercheck > 0) {
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return get_string('packageinstalledtoohigh', 'mlbackend_python', $a);
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}
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}
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return get_string('pythonpackagenotinstalled', 'mlbackend_python', $cmd);
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}
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/**
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* Delete the model version output directory.
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*
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* @param string $uniqueid
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* @param string $modelversionoutputdir
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* @return null
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*/
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public function clear_model($uniqueid, $modelversionoutputdir) {
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remove_dir($modelversionoutputdir);
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}
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/**
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* Delete the model output directory.
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*
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* @param string $modeloutputdir
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* @return null
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*/
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public function delete_output_dir($modeloutputdir) {
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remove_dir($modeloutputdir);
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}
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/**
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* Trains a machine learning algorithm with the provided dataset.
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*
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* @param string $uniqueid
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* @param \stored_file $dataset
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* @param string $outputdir
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* @return \stdClass
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*/
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public function train_classification($uniqueid, \stored_file $dataset, $outputdir) {
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// Obtain the physical route to the file.
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$datasetpath = $this->get_file_path($dataset);
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$cmd = "{$this->pathtopython} -m moodlemlbackend.training " .
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escapeshellarg($uniqueid) . ' ' .
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escapeshellarg($outputdir) . ' ' .
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escapeshellarg($datasetpath);
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if (!PHPUNIT_TEST && CLI_SCRIPT) {
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debugging($cmd, DEBUG_DEVELOPER);
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}
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$output = null;
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$exitcode = null;
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$result = exec($cmd, $output, $exitcode);
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if (!$result) {
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throw new \moodle_exception('errornopredictresults', 'analytics');
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}
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if (!$resultobj = json_decode($result)) {
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throw new \moodle_exception('errorpredictwrongformat', 'analytics', '', json_last_error_msg());
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}
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if ($exitcode != 0) {
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if (!empty($resultobj->errors)) {
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$errors = $resultobj->errors;
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if (is_array($errors)) {
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$errors = implode(', ', $errors);
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}
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} else if (!empty($resultobj->info)) {
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// Show info if no errors are returned.
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$errors = $resultobj->info;
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if (is_array($errors)) {
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$errors = implode(', ', $errors);
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}
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}
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$resultobj->info = array(get_string('errorpredictionsprocessor', 'analytics', $errors));
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}
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return $resultobj;
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}
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/**
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* Classifies the provided dataset samples.
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*
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* @param string $uniqueid
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* @param \stored_file $dataset
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* @param string $outputdir
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* @return \stdClass
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*/
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public function classify($uniqueid, \stored_file $dataset, $outputdir) {
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// Obtain the physical route to the file.
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$datasetpath = $this->get_file_path($dataset);
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$cmd = "{$this->pathtopython} -m moodlemlbackend.prediction " .
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escapeshellarg($uniqueid) . ' ' .
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escapeshellarg($outputdir) . ' ' .
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escapeshellarg($datasetpath);
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if (!PHPUNIT_TEST && CLI_SCRIPT) {
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debugging($cmd, DEBUG_DEVELOPER);
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}
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$output = null;
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$exitcode = null;
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$result = exec($cmd, $output, $exitcode);
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if (!$result) {
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throw new \moodle_exception('errornopredictresults', 'analytics');
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}
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if (!$resultobj = json_decode($result)) {
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throw new \moodle_exception('errorpredictwrongformat', 'analytics', '', json_last_error_msg());
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}
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if ($exitcode != 0) {
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if (!empty($resultobj->errors)) {
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$errors = $resultobj->errors;
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if (is_array($errors)) {
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$errors = implode(', ', $errors);
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}
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} else if (!empty($resultobj->info)) {
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// Show info if no errors are returned.
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$errors = $resultobj->info;
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if (is_array($errors)) {
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$errors = implode(', ', $errors);
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}
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}
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$resultobj->info = array(get_string('errorpredictionsprocessor', 'analytics', $errors));
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}
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return $resultobj;
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}
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/**
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* Evaluates this processor classification model using the provided supervised learning dataset.
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*
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* @param string $uniqueid
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* @param float $maxdeviation
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* @param int $niterations
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* @param \stored_file $dataset
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* @param string $outputdir
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* @param string $trainedmodeldir
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* @return \stdClass
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*/
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public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,
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$outputdir, $trainedmodeldir) {
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// Obtain the physical route to the file.
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$datasetpath = $this->get_file_path($dataset);
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$cmd = "{$this->pathtopython} -m moodlemlbackend.evaluation " .
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escapeshellarg($uniqueid) . ' ' .
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escapeshellarg($outputdir) . ' ' .
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escapeshellarg($datasetpath) . ' ' .
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escapeshellarg(\core_analytics\model::MIN_SCORE) . ' ' .
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escapeshellarg($maxdeviation) . ' ' .
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escapeshellarg($niterations);
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if ($trainedmodeldir) {
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$cmd .= ' ' . escapeshellarg($trainedmodeldir);
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}
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if (!PHPUNIT_TEST && CLI_SCRIPT) {
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debugging($cmd, DEBUG_DEVELOPER);
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}
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$output = null;
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$exitcode = null;
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$result = exec($cmd, $output, $exitcode);
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if (!$result) {
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throw new \moodle_exception('errornopredictresults', 'analytics');
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}
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if (!$resultobj = json_decode($result)) {
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throw new \moodle_exception('errorpredictwrongformat', 'analytics', '', json_last_error_msg());
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}
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return $resultobj;
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}
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/**
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* Exports the machine learning model.
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*
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* @throws \moodle_exception
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* @param string $uniqueid The model unique id
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* @param string $modeldir The directory that contains the trained model.
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* @return string The path to the directory that contains the exported model.
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*/
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public function export(string $uniqueid, string $modeldir) : string {
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// We include an exporttmpdir as we want to be sure that the file is not deleted after the
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// python process finishes.
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$exporttmpdir = make_request_directory('mlbackend_python_export');
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$cmd = "{$this->pathtopython} -m moodlemlbackend.export " .
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escapeshellarg($uniqueid) . ' ' .
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escapeshellarg($modeldir) . ' ' .
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escapeshellarg($exporttmpdir);
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if (!PHPUNIT_TEST && CLI_SCRIPT) {
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debugging($cmd, DEBUG_DEVELOPER);
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}
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$output = null;
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$exitcode = null;
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$exportdir = exec($cmd, $output, $exitcode);
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if ($exitcode != 0) {
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throw new \moodle_exception('errorexportmodelresult', 'analytics');
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}
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if (!$exportdir) {
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throw new \moodle_exception('errorexportmodelresult', 'analytics');
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}
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return $exportdir;
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}
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/**
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* Imports the provided machine learning model.
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*
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* @param string $uniqueid The model unique id
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* @param string $modeldir The directory that will contain the trained model.
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* @param string $importdir The directory that contains the files to import.
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* @return bool Success
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*/
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public function import(string $uniqueid, string $modeldir, string $importdir) : bool {
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$cmd = "{$this->pathtopython} -m moodlemlbackend.import " .
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escapeshellarg($uniqueid) . ' ' .
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escapeshellarg($modeldir) . ' ' .
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escapeshellarg($importdir);
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if (!PHPUNIT_TEST && CLI_SCRIPT) {
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debugging($cmd, DEBUG_DEVELOPER);
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}
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$output = null;
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$exitcode = null;
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$success = exec($cmd, $output, $exitcode);
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if ($exitcode != 0) {
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throw new \moodle_exception('errorimportmodelresult', 'analytics');
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}
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if (!$success) {
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throw new \moodle_exception('errorimportmodelresult', 'analytics');
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}
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return $success;
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}
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/**
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* Train this processor regression model using the provided supervised learning dataset.
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*
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* @throws new \coding_exception
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* @param string $uniqueid
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* @param \stored_file $dataset
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* @param string $outputdir
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* @return \stdClass
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*/
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public function train_regression($uniqueid, \stored_file $dataset, $outputdir) {
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throw new \coding_exception('This predictor does not support regression yet.');
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}
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/**
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* Estimates linear values for the provided dataset samples.
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*
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* @throws new \coding_exception
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* @param string $uniqueid
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* @param \stored_file $dataset
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* @param mixed $outputdir
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* @return void
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*/
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public function estimate($uniqueid, \stored_file $dataset, $outputdir) {
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throw new \coding_exception('This predictor does not support regression yet.');
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}
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/**
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* Evaluates this processor regression model using the provided supervised learning dataset.
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||
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*
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||
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* @throws new \coding_exception
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||
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* @param string $uniqueid
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* @param float $maxdeviation
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* @param int $niterations
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* @param \stored_file $dataset
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|
|
* @param string $outputdir
|
||
|
|
* @param string $trainedmodeldir
|
||
|
|
* @return \stdClass
|
||
|
|
*/
|
||
|
|
public function evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,
|
||
|
|
$outputdir, $trainedmodeldir) {
|
||
|
|
throw new \coding_exception('This predictor does not support regression yet.');
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* Returns the path to the dataset file.
|
||
|
|
*
|
||
|
|
* @param \stored_file $file
|
||
|
|
* @return string
|
||
|
|
*/
|
||
|
|
protected function get_file_path(\stored_file $file) {
|
||
|
|
// From moodle filesystem to the local file system.
|
||
|
|
// This is not ideal, but there is no read access to moodle filesystem files.
|
||
|
|
return $file->copy_content_to_temp('core_analytics');
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* Check that the given package version can be used and return the error status.
|
||
|
|
*
|
||
|
|
* When evaluating the version, we assume the sematic versioning scheme as described at
|
||
|
|
* https://semver.org/.
|
||
|
|
*
|
||
|
|
* @param string $actual The actual Python package version
|
||
|
|
* @param string $required The required version of the package
|
||
|
|
* @return int -1 = actual version is too low, 1 = actual version too high, 0 = actual version is ok
|
||
|
|
*/
|
||
|
|
public static function check_pip_package_version($actual, $required = self::REQUIRED_PIP_PACKAGE_VERSION) {
|
||
|
|
|
||
|
|
if (empty($actual)) {
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (version_compare($actual, $required, '<')) {
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
$parts = explode('.', $required);
|
||
|
|
$requiredapiver = reset($parts);
|
||
|
|
|
||
|
|
$parts = explode('.', $actual);
|
||
|
|
$actualapiver = reset($parts);
|
||
|
|
|
||
|
|
if ($requiredapiver > 0 || $actualapiver > 1) {
|
||
|
|
if (version_compare($actual, $requiredapiver + 1, '>=')) {
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
}
|