<?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/>.

/**
 * Python predictions processor
 *
 * @package   mlbackend_python
 * @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
 * @license   http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
 */

namespace mlbackend_python;

defined('MOODLE_INTERNAL') || die();

/**
 * Python predictions processor.
 *
 * @package   mlbackend_python
 * @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
 * @license   http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
 */
class processor implements  \core_analytics\classifier, \core_analytics\regressor, \core_analytics\packable {

    /**
     * The required version of the python package that performs all calculations.
     */
    const REQUIRED_PIP_PACKAGE_VERSION = '1.0.0';

    /**
     * The path to the Python bin.
     *
     * @var string
     */
    protected $pathtopython;

    /**
     * The constructor.
     */
    public function __construct() {
        global $CFG;

        // Set the python location if there is a value.
        if (!empty($CFG->pathtopython)) {
            $this->pathtopython = $CFG->pathtopython;
        }
    }

    /**
     * Is the plugin ready to be used?.
     *
     * @return bool|string Returns true on success, a string detailing the error otherwise
     */
    public function is_ready() {
        if (empty($this->pathtopython)) {
            $settingurl = new \moodle_url('/admin/settings.php', array('section' => 'systempaths'));
            return get_string('pythonpathnotdefined', 'mlbackend_python', $settingurl->out());
        }

        // Check the installed pip package version.
        $cmd = "{$this->pathtopython} -m moodlemlbackend.version";

        $output = null;
        $exitcode = null;
        // Execute it sending the standard error to $output.
        $result = exec($cmd . ' 2>&1', $output, $exitcode);

        $vercheck = self::check_pip_package_version($result);

        if ($vercheck === 0) {
            return true;
        }

        if ($exitcode != 0) {
            return get_string('pythonpackagenotinstalled', 'mlbackend_python', $cmd);
        }

        if ($result) {
            $a = [
                'installed' => $result,
                'required' => self::REQUIRED_PIP_PACKAGE_VERSION,
            ];

            if ($vercheck < 0) {
                return get_string('packageinstalledshouldbe', 'mlbackend_python', $a);

            } else if ($vercheck > 0) {
                return get_string('packageinstalledtoohigh', 'mlbackend_python', $a);
            }
        }

        return get_string('pythonpackagenotinstalled', 'mlbackend_python', $cmd);
    }

    /**
     * Delete the model version output directory.
     *
     * @param string $uniqueid
     * @param string $modelversionoutputdir
     * @return null
     */
    public function clear_model($uniqueid, $modelversionoutputdir) {
        remove_dir($modelversionoutputdir);
    }

    /**
     * Delete the model output directory.
     *
     * @param string $modeloutputdir
     * @return null
     */
    public function delete_output_dir($modeloutputdir) {
        remove_dir($modeloutputdir);
    }

    /**
     * Trains a machine learning algorithm with the provided dataset.
     *
     * @param string $uniqueid
     * @param \stored_file $dataset
     * @param string $outputdir
     * @return \stdClass
     */
    public function train_classification($uniqueid, \stored_file $dataset, $outputdir) {

        // Obtain the physical route to the file.
        $datasetpath = $this->get_file_path($dataset);

        $cmd = "{$this->pathtopython} -m moodlemlbackend.training " .
            escapeshellarg($uniqueid) . ' ' .
            escapeshellarg($outputdir) . ' ' .
            escapeshellarg($datasetpath);

        if (!PHPUNIT_TEST && CLI_SCRIPT) {
            debugging($cmd, DEBUG_DEVELOPER);
        }

        $output = null;
        $exitcode = null;
        $result = exec($cmd, $output, $exitcode);

        if (!$result) {
            throw new \moodle_exception('errornopredictresults', 'analytics');
        }

        if (!$resultobj = json_decode($result)) {
            throw new \moodle_exception('errorpredictwrongformat', 'analytics', '', json_last_error_msg());
        }

        if ($exitcode != 0) {
            if (!empty($resultobj->errors)) {
                $errors = $resultobj->errors;
                if (is_array($errors)) {
                    $errors = implode(', ', $errors);
                }
            } else if (!empty($resultobj->info)) {
                // Show info if no errors are returned.
                $errors = $resultobj->info;
                if (is_array($errors)) {
                    $errors = implode(', ', $errors);
                }
            }
            $resultobj->info = array(get_string('errorpredictionsprocessor', 'analytics', $errors));
        }

        return $resultobj;
    }

    /**
     * Classifies the provided dataset samples.
     *
     * @param string $uniqueid
     * @param \stored_file $dataset
     * @param string $outputdir
     * @return \stdClass
     */
    public function classify($uniqueid, \stored_file $dataset, $outputdir) {

        // Obtain the physical route to the file.
        $datasetpath = $this->get_file_path($dataset);

        $cmd = "{$this->pathtopython} -m moodlemlbackend.prediction " .
            escapeshellarg($uniqueid) . ' ' .
            escapeshellarg($outputdir) . ' ' .
            escapeshellarg($datasetpath);

        if (!PHPUNIT_TEST && CLI_SCRIPT) {
            debugging($cmd, DEBUG_DEVELOPER);
        }

        $output = null;
        $exitcode = null;
        $result = exec($cmd, $output, $exitcode);

        if (!$result) {
            throw new \moodle_exception('errornopredictresults', 'analytics');
        }

        if (!$resultobj = json_decode($result)) {
            throw new \moodle_exception('errorpredictwrongformat', 'analytics', '', json_last_error_msg());
        }

        if ($exitcode != 0) {
            if (!empty($resultobj->errors)) {
                $errors = $resultobj->errors;
                if (is_array($errors)) {
                    $errors = implode(', ', $errors);
                }
            } else if (!empty($resultobj->info)) {
                // Show info if no errors are returned.
                $errors = $resultobj->info;
                if (is_array($errors)) {
                    $errors = implode(', ', $errors);
                }
            }
            $resultobj->info = array(get_string('errorpredictionsprocessor', 'analytics', $errors));
        }

        return $resultobj;
    }

    /**
     * Evaluates this processor classification model using the provided supervised learning dataset.
     *
     * @param string $uniqueid
     * @param float $maxdeviation
     * @param int $niterations
     * @param \stored_file $dataset
     * @param string $outputdir
     * @param  string $trainedmodeldir
     * @return \stdClass
     */
    public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,
            $outputdir, $trainedmodeldir) {

        // Obtain the physical route to the file.
        $datasetpath = $this->get_file_path($dataset);

        $cmd = "{$this->pathtopython} -m moodlemlbackend.evaluation " .
            escapeshellarg($uniqueid) . ' ' .
            escapeshellarg($outputdir) . ' ' .
            escapeshellarg($datasetpath) . ' ' .
            escapeshellarg(\core_analytics\model::MIN_SCORE) . ' ' .
            escapeshellarg($maxdeviation) . ' ' .
            escapeshellarg($niterations);

        if ($trainedmodeldir) {
            $cmd .= ' ' . escapeshellarg($trainedmodeldir);
        }

        if (!PHPUNIT_TEST && CLI_SCRIPT) {
            debugging($cmd, DEBUG_DEVELOPER);
        }

        $output = null;
        $exitcode = null;
        $result = exec($cmd, $output, $exitcode);

        if (!$result) {
            throw new \moodle_exception('errornopredictresults', 'analytics');
        }

        if (!$resultobj = json_decode($result)) {
            throw new \moodle_exception('errorpredictwrongformat', 'analytics', '', json_last_error_msg());
        }

        return $resultobj;
    }

    /**
     * Exports the machine learning model.
     *
     * @throws \moodle_exception
     * @param  string $uniqueid  The model unique id
     * @param  string $modeldir  The directory that contains the trained model.
     * @return string            The path to the directory that contains the exported model.
     */
    public function export(string $uniqueid, string $modeldir) : string {

        // We include an exporttmpdir as we want to be sure that the file is not deleted after the
        // python process finishes.
        $exporttmpdir = make_request_directory('mlbackend_python_export');

        $cmd = "{$this->pathtopython} -m moodlemlbackend.export " .
            escapeshellarg($uniqueid) . ' ' .
            escapeshellarg($modeldir) . ' ' .
            escapeshellarg($exporttmpdir);

        if (!PHPUNIT_TEST && CLI_SCRIPT) {
            debugging($cmd, DEBUG_DEVELOPER);
        }

        $output = null;
        $exitcode = null;
        $exportdir = exec($cmd, $output, $exitcode);

        if ($exitcode != 0) {
            throw new \moodle_exception('errorexportmodelresult', 'analytics');
        }

        if (!$exportdir) {
            throw new \moodle_exception('errorexportmodelresult', 'analytics');
        }

        return $exportdir;
    }

    /**
     * Imports the provided machine learning model.
     *
     * @param  string $uniqueid The model unique id
     * @param  string $modeldir  The directory that will contain the trained model.
     * @param  string $importdir The directory that contains the files to import.
     * @return bool Success
     */
    public function import(string $uniqueid, string $modeldir, string $importdir) : bool {

        $cmd = "{$this->pathtopython} -m moodlemlbackend.import " .
            escapeshellarg($uniqueid) . ' ' .
            escapeshellarg($modeldir) . ' ' .
            escapeshellarg($importdir);

        if (!PHPUNIT_TEST && CLI_SCRIPT) {
            debugging($cmd, DEBUG_DEVELOPER);
        }

        $output = null;
        $exitcode = null;
        $success = exec($cmd, $output, $exitcode);

        if ($exitcode != 0) {
            throw new \moodle_exception('errorimportmodelresult', 'analytics');
        }

        if (!$success) {
            throw new \moodle_exception('errorimportmodelresult', 'analytics');
        }

        return $success;
    }

    /**
     * Train this processor regression model using the provided supervised learning dataset.
     *
     * @throws new \coding_exception
     * @param string $uniqueid
     * @param \stored_file $dataset
     * @param string $outputdir
     * @return \stdClass
     */
    public function train_regression($uniqueid, \stored_file $dataset, $outputdir) {
        throw new \coding_exception('This predictor does not support regression yet.');
    }

    /**
     * Estimates linear values for the provided dataset samples.
     *
     * @throws new \coding_exception
     * @param string $uniqueid
     * @param \stored_file $dataset
     * @param mixed $outputdir
     * @return void
     */
    public function estimate($uniqueid, \stored_file $dataset, $outputdir) {
        throw new \coding_exception('This predictor does not support regression yet.');
    }

    /**
     * Evaluates this processor regression model using the provided supervised learning dataset.
     *
     * @throws new \coding_exception
     * @param string $uniqueid
     * @param float $maxdeviation
     * @param int $niterations
     * @param \stored_file $dataset
     * @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;
    }
}