David Monllao
5c5cb3ee15
- Method names renamed to avoid interface changes once we support regression and unsupervised learning - Adding regressor interface even if not implemente - predictor interface comments expanded - Differentiate model's required accuracy from predictions quality - Add missing get_callback_boundary call - Updated datasets' metadata to allow 3rd parties to code regressors themselves - Add missing option to exception message - Include target data into the dataset regardless of being a prediction dataset or a training dataset - Explicit in_array and array_search non-strict calls - Overwrite discrete should_be_displayed implementation with the binary one - Overwrite no_teacher get_display_value as it would otherwise look wrong - Other minor fixes
110 lines
3.3 KiB
PHP
110 lines
3.3 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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* Predictions processor interface.
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*
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* @package core_analytics
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* @copyright 2017 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 core_analytics;
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defined('MOODLE_INTERNAL') || die();
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/**
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* Predictors interface.
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*
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* @package core_analytics
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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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interface predictor {
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/**
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* Is it ready to predict?
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*
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* @return bool
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*/
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public function is_ready();
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/**
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* Train 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 \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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/**
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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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/**
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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 $outputdir
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* @return \stdClass
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*/
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public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir);
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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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* @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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/**
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* Estimates linear values for 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 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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/**
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* Evaluates this processor regression 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 $outputdir
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* @return \stdClass
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*/
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public function evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir);
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}
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