Python predictions processor. More...

Inheritance diagram for mlbackend_python\processor:

Public Member Functions
	__construct ()
	The constructor.

	classify ($uniqueid, stored_file $dataset, $outputdir)
	Classifies the provided dataset samples. More...

	clear_model ($uniqueid, $modelversionoutputdir)
	Delete the model version output directory. More...

	delete_output_dir ($modeloutputdir, $uniqueid)
	Delete the model output directory. More...

	estimate ($uniqueid, stored_file $dataset, $outputdir)
	Estimates linear values for the provided dataset samples. More...

	evaluate_classification ($uniqueid, $maxdeviation, $niterations, stored_file $dataset, $outputdir, $trainedmodeldir)
	Evaluates this processor classification model using the provided supervised learning dataset. More...

	evaluate_regression ($uniqueid, $maxdeviation, $niterations, stored_file $dataset, $outputdir, $trainedmodeldir)
	Evaluates this processor regression model using the provided supervised learning dataset. More...

	export (string $uniqueid, string $modeldir)
	Exports the machine learning model. More...

	import (string $uniqueid, string $modeldir, string $importdir)
	Imports the provided machine learning model. More...

	is_ready ()
	Is the plugin ready to be used?. More...

	train_classification ($uniqueid, stored_file $dataset, $outputdir)
	Trains a machine learning algorithm with the provided dataset. More...

	train_regression ($uniqueid, stored_file $dataset, $outputdir)
	Train this processor regression model using the provided supervised learning dataset. More...

Static Public Member Functions
static	check_pip_package_version ($actual, $required=self::REQUIRED_PIP_PACKAGE_VERSION)
	Check that the given package version can be used and return the error status. More...

Public Attributes
const	REQUIRED_PIP_PACKAGE_VERSION = '2.6.4'
	The required version of the python package that performs all calculations.

Protected Member Functions
	add_extra_result_info (\stdClass $resultobj)
	Adds extra information to results info. More...

	exec_command (string $modulename, array $params, string $errorlangstr)
	Executes the specified module. More...

	get_file_path (\stored_file $file)
	Returns the path to the dataset file. More...

	is_python_server_ready ()
	Checks if the server can be accessed. More...

	is_webserver_ready ()
	Checks if the python package is available in the web server executing this script. More...

	server_request ($url, string $method, array $requestparams, ?array $options=null)
	Sends a request to the python ML server. More...

Protected Attributes
string	$host
	Remote server host.

string	$password
	Server password for $this->username.

string	$pathtopython
	The path to the Python bin.

int	$port
	Remote server port.

bool	$secure
	Whether to use http or https.

string	$username
	Server username.

bool	$useserver
	The python package is installed in a server.

Detailed Description

Python predictions processor.

Copyright: 2016 David Monllao

License: http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later

Member Function Documentation

◆ add_extra_result_info()

mlbackend_python\processor::add_extra_result_info ( \stdClass $resultobj )

protected

Adds extra information to results info.

Parameters

stdClass $resultobj

Return values

stdClass

◆ check_pip_package_version()

static mlbackend_python\processor::check_pip_package_version	(	$actual,
		$required = `self::REQUIRED_PIP_PACKAGE_VERSION`
	)

static

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/.

Parameters

string	$actual	The actual Python package version
string	$required	The required version of the package

Return values

int	-1 = actual version is too low, 1 = actual version too high, 0 = actual version is ok

◆ classify()

mlbackend_python\processor::classify	(		$uniqueid,
		stored_file	$dataset,
			$outputdir
	)

Classifies the provided dataset samples.

Parameters

string	$uniqueid
stored_file	$dataset
string	$outputdir

Return values

stdClass

Implements core_analytics\classifier.

◆ clear_model()

mlbackend_python\processor::clear_model	(	$uniqueid,
		$modelversionoutputdir
	)

Delete the model version output directory.

Exceptions

moodle_exception

Parameters

string	$uniqueid
string	$modelversionoutputdir

Return values

null

Implements core_analytics\predictor.

◆ delete_output_dir()

mlbackend_python\processor::delete_output_dir	(	$modeloutputdir,
		$uniqueid
	)

Delete the model output directory.

Exceptions

moodle_exception

Parameters

string	$modeloutputdir
string	$uniqueid

Return values

null

Implements core_analytics\predictor.

◆ estimate()

mlbackend_python\processor::estimate	(		$uniqueid,
		stored_file	$dataset,
			$outputdir
	)

Estimates linear values for the provided dataset samples.

Exceptions

new	coding_exception

Parameters

string	$uniqueid
stored_file	$dataset
mixed	$outputdir

Return values

void

Implements core_analytics\regressor.

◆ evaluate_classification()

mlbackend_python\processor::evaluate_classification	(		$uniqueid,
			$maxdeviation,
			$niterations,
		stored_file	$dataset,
			$outputdir,
			$trainedmodeldir
	)

Evaluates this processor classification model using the provided supervised learning dataset.

Parameters

string	$uniqueid
float	$maxdeviation
int	$niterations
stored_file	$dataset
string	$outputdir
string	$trainedmodeldir

Return values

stdClass

Implements core_analytics\classifier.

◆ evaluate_regression()

mlbackend_python\processor::evaluate_regression	(		$uniqueid,
			$maxdeviation,
			$niterations,
		stored_file	$dataset,
			$outputdir,
			$trainedmodeldir
	)

Evaluates this processor regression model using the provided supervised learning dataset.

Exceptions

new	coding_exception

Parameters

string	$uniqueid
float	$maxdeviation
int	$niterations
stored_file	$dataset
string	$outputdir
string	$trainedmodeldir

Return values

stdClass

Implements core_analytics\regressor.

◆ exec_command()

mlbackend_python\processor::exec_command	(	string	$modulename,
		array	$params,
		string	$errorlangstr
	)

protected

Executes the specified module.

Parameters

string	$modulename
array	$params
string	$errorlangstr

Return values

array [0] is the result body and [1] the exit code.

◆ export()

mlbackend_python\processor::export	(	string	$uniqueid,
		string	$modeldir
	)

Exports the machine learning model.

Exceptions

moodle_exception

Parameters

string	$uniqueid	The model unique id
string	$modeldir	The directory that contains the trained model.

Return values

string The path to the directory that contains the exported model.

Implements core_analytics\packable.

◆ get_file_path()

mlbackend_python\processor::get_file_path ( \stored_file $file )

protected

Returns the path to the dataset file.

Parameters

stored_file $file

Return values

string

◆ import()

mlbackend_python\processor::import	(	string	$uniqueid,
		string	$modeldir,
		string	$importdir
	)

Imports the provided machine learning model.

Parameters

string	$uniqueid	The model unique id
string	$modeldir	The directory that will contain the trained model.
string	$importdir	The directory that contains the files to import.

Return values

bool Success

Implements core_analytics\packable.

◆ is_python_server_ready()

mlbackend_python\processor::is_python_server_ready ( )

protected

Checks if the server can be accessed.

Return values

bool|string True or an error string.

◆ is_ready()

mlbackend_python\processor::is_ready ( )

Is the plugin ready to be used?.

Return values

bool|string Returns true on success, a string detailing the error otherwise

Implements core_analytics\predictor.

◆ is_webserver_ready()

mlbackend_python\processor::is_webserver_ready ( )

protected

Checks if the python package is available in the web server executing this script.

Return values

bool|string Returns true on success, a string detailing the error otherwise

◆ server_request()

mlbackend_python\processor::server_request	(		$url,
		string	$method,
		array	$requestparams,
		?array	$options = `null`
	)

protected

Sends a request to the python ML server.

Parameters

moodle_url	$url	The requested url in the python ML server
string	$method	The curl method to use
array	$requestparams	Curl request params
array \| null	$options	Curl request options

Return values

array [0] for the response body and [1] for the http code

◆ train_classification()

mlbackend_python\processor::train_classification	(		$uniqueid,
		stored_file	$dataset,
			$outputdir
	)

Trains a machine learning algorithm with the provided dataset.

Parameters

string	$uniqueid
stored_file	$dataset
string	$outputdir

Return values

stdClass

Implements core_analytics\classifier.

◆ train_regression()

mlbackend_python\processor::train_regression	(		$uniqueid,
		stored_file	$dataset,
			$outputdir
	)

Train this processor regression model using the provided supervised learning dataset.

Exceptions

new	coding_exception

Parameters

string	$uniqueid
stored_file	$dataset
string	$outputdir

Return values

stdClass

Implements core_analytics\regressor.

The documentation for this class was generated from the following file:

lib/mlbackend/python/classes/processor.php

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

◆ add_extra_result_info()

◆ check_pip_package_version()

◆ classify()

◆ clear_model()

◆ delete_output_dir()

◆ estimate()

◆ evaluate_classification()

◆ evaluate_regression()

◆ exec_command()

◆ export()

◆ get_file_path()

◆ import()

◆ is_python_server_ready()

◆ is_ready()

◆ is_webserver_ready()

◆ server_request()

◆ train_classification()

◆ train_regression()