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/lib/mlbackend/php/classes/ -> processor.php (source)
Php predictions processor
| Copyright: | 2016 David Monllao {@link http://www.davidmonllao.com} |
| License: | http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later |
| File Size: | 551 lines (20 kb) |
| Included or required: | 0 times |
| Referenced: | 0 times |
| Includes or requires: | 0 files |
Defines 1 class
processor:: (16 methods):
is_ready()
clear_model()
delete_output_dir()
train_classification()
classify()
evaluate_classification()
get_evaluation_result_object()
load_classifier()
train_regression()
estimate()
evaluate_regression()
export()
import()
get_model_filepath()
extract_metadata()
instantiate_algorithm()
PHP predictions processor.
| is_ready() X-Ref |
| Checks if the processor is ready to use. return: bool |
| clear_model($uniqueid, $modelversionoutputdir) X-Ref |
| Delete the stored models. param: string $uniqueid param: string $modelversionoutputdir return: null |
| delete_output_dir($modeloutputdir, $uniqueid) X-Ref |
| Delete the output directory. param: string $modeloutputdir param: string $uniqueid return: null |
| train_classification($uniqueid, \stored_file $dataset, $outputdir) X-Ref |
| Train this processor classification model using the provided supervised learning dataset. param: string $uniqueid param: \stored_file $dataset param: string $outputdir return: \stdClass |
| classify($uniqueid, \stored_file $dataset, $outputdir) X-Ref |
| Classifies the provided dataset samples. param: string $uniqueid param: \stored_file $dataset param: string $outputdir return: \stdClass |
| evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,$outputdir, $trainedmodeldir) X-Ref |
| Evaluates this processor classification model using the provided supervised learning dataset. During evaluation we need to shuffle the evaluation dataset samples to detect deviated results, if the dataset is massive we can not load everything into memory. We know that 2GB is the minimum memory limit we should have (\core_analytics\model::heavy_duty_mode), if we substract the memory that we already consumed and the memory that Phpml algorithms will need we should still have at least 500MB of memory, which should be enough to evaluate a model. In any case this is a robust solution that will work for all sites but it should minimize memory limit problems. Site admins can still set $CFG->mlbackend_php_no_evaluation_limits to true to skip this 500MB limit. param: string $uniqueid param: float $maxdeviation param: int $niterations param: \stored_file $dataset param: string $outputdir param: string $trainedmodeldir return: \stdClass |
| get_evaluation_result_object(\stored_file $dataset, $scores, $maxdeviation) X-Ref |
| Returns the results objects from all evaluations. param: \stored_file $dataset param: array $scores param: float $maxdeviation return: \stdClass |
| load_classifier($outputdir) X-Ref |
| Loads the pre-trained classifier. param: string $outputdir return: \Phpml\Classification\Linear\LogisticRegression |
| train_regression($uniqueid, \stored_file $dataset, $outputdir) X-Ref |
| Train this processor regression model using the provided supervised learning dataset. param: string $uniqueid param: \stored_file $dataset param: string $outputdir return: \stdClass |
| estimate($uniqueid, \stored_file $dataset, $outputdir) X-Ref |
| Estimates linear values for the provided dataset samples. param: string $uniqueid param: \stored_file $dataset param: mixed $outputdir return: void |
| evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,$outputdir, $trainedmodeldir) X-Ref |
| Evaluates this processor regression 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 |
| export(string $uniqueid, string $modeldir) X-Ref |
| Exports the machine learning model. 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. |
| import(string $uniqueid, string $modeldir, string $importdir) X-Ref |
| 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 |
| get_model_filepath(string $modeldir) X-Ref |
| Returns the path to the serialised model file in the provided directory. param: string $modeldir The model directory return: string The model file |
| extract_metadata($fh) X-Ref |
| Extracts metadata from the dataset file. The file poiter should be located at the top of the file. param: resource $fh return: array |
| instantiate_algorithm() X-Ref |
| Instantiates the ML algorithm. return: \Phpml\Classification\Linear\LogisticRegression |
