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			{{Template:Migrated\|newDocId=/docs/apis/subsystems/analytics/}}
	== Summary ==		== Summary ==

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2021-07-14T13:27:11Z
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     abstract public function get_all_samples(\core_analytics\analysable $analysable);</code>      abstract public function get_all_samples(\core_analytics\analysable $analysable);</code>



<code php> <syntaxhighlight lang="php">    /**
  
    /** 
      * This function returns the samples data from a list of sample ids.       * This function returns the samples data from a list of sample ids.
      *       *
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     public function processes_user_data();</code>      public function processes_user_data();</code>



<code php> <syntaxhighlight lang="php">    /**
  
    /** 
      * SQL JOIN from a sample to users table.       * SQL JOIN from a sample to users table.
      *       *
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The first thing a target must define is the analyser class that it will use. The analyser class is specified in '''get_analyser_class'''. The first thing a target must define is the analyser class that it will use. The analyser class is specified in '''get_analyser_class'''.
<code php> <syntaxhighlight lang="php">    /**
  
    /** 
      * Returns the analyser class that should be used along with this target.       * Returns the analyser class that should be used along with this target.
      *       *
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'''is_valid_analysable''' and '''is_valid_sample''' are used to discard elements that are not valid for your target. '''is_valid_analysable''' and '''is_valid_sample''' are used to discard elements that are not valid for your target.
<code php> <syntaxhighlight lang="php">    /**
  
    /** 
      * Allows the target to verify that the analysable is a good candidate.       * Allows the target to verify that the analysable is a good candidate.
      *       *
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You can implement a '''always_update_analysis_time()''' method so analysable elements' timeanalysed is only updated when analysable elements have been successfully evaluated. It is useful for lightweight targets. You can implement a '''always_update_analysis_time()''' method so analysable elements' timeanalysed is only updated when analysable elements have been successfully evaluated. It is useful for lightweight targets.
<code php> <syntaxhighlight lang="php"> /**
  
 /** 
      * Update the last analysis time on analysable processed or always.       * Update the last analysis time on analysable processed or always.
      *       *
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You can also override the URL to the insight with get_insight_context_url(): You can also override the URL to the insight with get_insight_context_url():
<code php> <syntaxhighlight lang="php"> /**
  
 /** 
      * URL to the insight.       * URL to the insight.
      *       *
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'''define_ranges''' is the main method to implement and its values mostly depend on the current analysable element (available in '''$this->analysable'''). An array of time ranges should be returned, each of these ranges should contain 3 attributes: A start time ('start') and an end time ('end') that will be passed to indicators so they can limit the amount of activity logs they read; the 3rd attribute is 'time', which value will determine when the range will be executed. '''define_ranges''' is the main method to implement and its values mostly depend on the current analysable element (available in '''$this->analysable'''). An array of time ranges should be returned, each of these ranges should contain 3 attributes: A start time ('start') and an end time ('end') that will be passed to indicators so they can limit the amount of activity logs they read; the 3rd attribute is 'time', which value will determine when the range will be executed.
<code php> <syntaxhighlight lang="php">    /**
  
    /** 
      * Define the time splitting methods ranges.       * Define the time splitting methods ranges.
      *       *
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A name and description should also be specified: A name and description should also be specified:
<code php> <syntaxhighlight lang="php">    /**
  
    /** 
      * Returns a lang_string object representing the name for the time splitting method.       * Returns a lang_string object representing the name for the time splitting method.
      *       *



Mudrd8mz: Fix typos
2019-07-01T06:38:53Z
Fix typos

				
				
				
				
				
				← Older revision
				Revision as of 06:38, 1 July 2019
				
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==== Analysis interval (core_analytics\local\time_splitting\base) ==== ==== Analysis interval (core_analytics\local\time_splitting\base) ====



Analyisis intervals are used to define when the analytics API will train the predictions processor and when it will generate predictions. As explained above in [[Analytics_API#Analysis_intervals]], they define time ranges based on analysable elements start and end timestamps. Analysis intervals are used to define when the analytics API will train the predictions processor and when it will generate predictions. As explained above in [[Analytics_API#Analysis_intervals]], they define time ranges based on analysable elements start and end timestamps.



The base class is '''\core_analytics\local\time_splitting\base'''; if what you are after is to split the analysable duration in equal parts or in cumulative parts you can extend '''\core_analytics\local\time_splitting\equal_parts''' or '''\core_analytics\local\time_splitting\accumulative_parts''' instead. The base class is '''\core_analytics\local\time_splitting\base'''; if what you are after is to split the analysable duration in equal parts or in cumulative parts you can extend '''\core_analytics\local\time_splitting\equal_parts''' or '''\core_analytics\local\time_splitting\accumulative_parts''' instead.
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'''Note''': As of Moodle 3.7, analyisis intervals can now overwrite the following: '''Note''': As of Moodle 3.7, analysis intervals can now overwrite the following:
  <code php>valid_for_evaluation()</code>.     <code php>valid_for_evaluation()</code>.  
You can return false if the time-splitting method can not be used to evaluate prediction models or if it does not make sense to evaluate prediction models with it, as for example upcoming_periodic children classes. You can return false if the time-splitting method can not be used to evaluate prediction models or if it does not make sense to evaluate prediction models with it, as for example upcoming_periodic children classes.





Mudrd8mz: Undo revision 56201 by Mudrd8mz (talk)
2019-07-01T06:37:36Z
Undo revision 56201 by Mudrd8mz (talk)
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Mudrd8mz: Fix typos
2019-07-01T06:36:47Z
Fix typos
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Emdalton1: /* Create the model */ updates per MDL-64783
2019-06-29T01:32:44Z
Create the model:  updates per MDL-64783

				
				
				
				
				
				← Older revision
				Revision as of 01:32, 29 June 2019
				
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=== Create the model === === Create the model ===



To add a new model to the system, it must be defined in a PHP file. Normally this is done as part of  install.php or upgrade.php for a plugin that contains the new model and components. However, it is also possible to execute the necessary commands in a standalone PHP file that references the Moodle config.php. To add a new model to the system, it must be defined in a PHP file. Plugins and core subsystems declare default prediction models by describing them in their db/analytics.php file. Models should not be created manually via the db/install.php file any more. (It is also possible to execute the necessary commands in a standalone PHP file that references the Moodle config.php.)



To create the model, specify at least its target and, optionally, a set of indicators and a time splitting method: To create the model, specify at least its target and, optionally, a set of indicators and a time splitting method:



     // Instantiate the target: classify users as spammers      <code php>
// Instantiate the target: classify users as spammers
     $target = \core_analytics\manager::get_target('\mod_yours\analytics\target\spammer_users');      $target = \core_analytics\manager::get_target('\mod_yours\analytics\target\spammer_users');



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     // Create the model.      // Create the model.
     // Note that the 3rd and 4rd arguments (the time splitting method and the predictions processor) are optional. The 4th argument is available from Moodle 3.6 onwards.      // Note that the 3rd and 4rd arguments (the time splitting method and the predictions processor) are optional. The 4th argument is available from Moodle 3.6 onwards.
     $model = \core_analytics\model::create($target, $indicators, '\core\analytics\time_splitting\single_range', '\mlbackend_python\processor');      $model = \core_analytics\model::create($target, $indicators, '\core\analytics\time_splitting\single_range', '\mlbackend_python\processor');</code>



Models are disabled by default because you may be interested in evaluating how good the model is at predicting before enabling them. You can enable models using Moodle UI or the analytics API: Models are disabled by default because you may be interested in evaluating how good the model is at predicting before enabling them. You can enable models using Moodle UI or the analytics API:
       
     $model->enable();      <code php>
$model->enable();</code>



=== Indicators === === Indicators ===



Emdalton1: /* Analyser (core_analytics\local\analyser\base) */ code tags, updates per MDL-64783
2019-06-29T01:29:49Z
Analyser (core_analytics\local\analyser\base):  code tags, updates per MDL-64783

				
				
				
				
				
				← Older revision
				Revision as of 01:29, 29 June 2019
				
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==== Analyser (core_analytics\local\analyser\base) ==== ==== Analyser (core_analytics\local\analyser\base) ====



'''get_analysables''' returns the whole list of analysable elements in the site. Each model will later be able to discard analysables that do not match their expectations. ''e.g. if your model is only interested in quizzes with a time close the analyser will return all quizzes, your model will exclude the ones without a time close. This approach is supposed to make analysers more reusable.''   The get_analysables() method has been deprecated in favour of a new '''get_analysables_iterator()'''  for performance reasons. This method returns the whole list of analysable elements in the site. Each model will later be able to discard analysables that do not match their expectations. ''e.g. if your model is only interested in quizzes with a time close the analyser will return all quizzes, your model will exclude the ones without a time close. This approach is supposed to make analysers more reusable.''  



     /**      <code php>
/**
      * Returns the list of analysable elements available on the site.       * Returns the list of analysable elements available on the site.
      *       *
      * @return \core_analytics\analysable[] Array of analysable elements using the analysable id as array key.       * A relatively complex SQL query should be set so that we take into account which analysable elements
     * have already been processed and the order in which they have been processed. Helper methods are available
     * to ease to implementation of get_analysables_iterator: get_iterator_sql and order_sql.
     *
     * @param string|null $action 'prediction', 'training' or null if no specific action needed.
     * @return \Iterator
      */       */
     abstract public function get_analysables();      public function get_analysables_iterator(?string $action = null)</code>






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* The data included here is cached in PHP static vars; on one hand this reduces the amount of db queries indicators need to perform. On the other hand, if not well balanced, it can lead to PHP memory issues.    * The data included here is cached in PHP static vars; on one hand this reduces the amount of db queries indicators need to perform. On the other hand, if not well balanced, it can lead to PHP memory issues.   



     /**      <code php>
/**
      * This function returns this analysable list of samples.       * This function returns this analysable list of samples.
      *       *
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      * @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)       * @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)
      */       */
     abstract protected function get_all_samples(\core_analytics\analysable $analysable);      abstract public function get_all_samples(\core_analytics\analysable $analysable);</code>



<code php>



     /**      /**
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      * @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)       * @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)
      */       */
     abstract public function get_samples($sampleids);      abstract public function get_samples($sampleids);</code>



'''get_sample_analysable''' method is executing during prediction: '''get_sample_analysable''' method is executing during prediction:



     /**      <code php>
/**
      * Returns the analysable of a sample.       * Returns the analysable of a sample.
      *       *
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      * @return \core_analytics\analysable       * @return \core_analytics\analysable
      */       */
     abstract public function get_sample_analysable($sampleid);      abstract public function get_sample_analysable($sampleid);</code>



The sample origin is the moodle database table that uses the sample id as primary key. The sample origin is the moodle database table that uses the sample id as primary key.



     /**      <code php>
/**
      * Returns the sample's origin in moodle database.       * Returns the sample's origin in moodle database.
      *       *
      * @return string       * @return string
      */       */
     abstract public function get_samples_origin();      abstract public function get_samples_origin();</code>



'''sample_access_context''' associates a context to a sampleid. This is important because this sample predictions will only be available for users with ''moodle/analytics:listinsights'' capability in that context. '''sample_access_context''' associates a context to a sampleid. This is important because this sample predictions will only be available for users with ''moodle/analytics:listinsights'' capability in that context.



     /**      <code php>
/**
      * Returns the context of a sample.       * Returns the context of a sample.
      *       *
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      * @return \context       * @return \context
      */       */
     abstract public function sample_access_context($sampleid);      abstract public function sample_access_context($sampleid);</code>



'''sample_description''' is used to display samples in ''Insights'' report: '''sample_description''' is used to display samples in ''Insights'' report:



     /**      <code php>
/**
      * Describes a sample with a description summary and a \renderable (an image for example)       * Describes a sample with a description summary and a \renderable (an image for example)
      *       *
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      * @return array array(string, \renderable)       * @return array array(string, \renderable)
      */       */
     abstract public function sample_description($sampleid, $contextid, $sampledata);      abstract public function sample_description($sampleid, $contextid, $sampledata);</code>



'''processes_user_data''' and '''join_sample_user''' methods are used by the analytics implementation of the privacy API. You only need to overwrite them if your analyser deals with user data. They are used to export and delete user data that is stored in analytics database tables: '''processes_user_data''' and '''join_sample_user''' methods are used by the analytics implementation of the privacy API. You only need to overwrite them if your analyser deals with user data. They are used to export and delete user data that is stored in analytics database tables:



     /**      <code php>
/**
      * Whether the plugin needs user data clearing or not.       * Whether the plugin needs user data clearing or not.
      *       *
      * @return bool       * @return bool
      */       */
     public function processes_user_data();      public function processes_user_data();</code>
 
<code php>
 
     /**      /**
      * SQL JOIN from a sample to users table.       * SQL JOIN from a sample to users table.
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      * @return string       * @return string
      */       */
     public function join_sample_user($sampletablealias);      public function join_sample_user($sampletablealias);</code>
 
You can overwrite a '''new one_sample_per_analysable()''' method if the analysables your model is using only have one sample. The insights generated by models will then include the suggested actions in the notification.
 
    <code php>
/**
     * Just one sample per analysable.
     *
     * @return bool
     */
    public static function one_sample_per_analysable() {
        return true;
    }</code>



==== Indicator (core_analytics\local\indicator\base) ==== ==== Indicator (core_analytics\local\indicator\base) ====

← Older revision		Revision as of 13:03, 14 July 2021
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	abstract public function get_all_samples(\core_analytics\analysable $analysable);</code>		abstract public function get_all_samples(\core_analytics\analysable $analysable);</code>

	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* This function returns the samples data from a list of sample ids.		* This function returns the samples data from a list of sample ids.
	*		*
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	public function processes_user_data();</code>		public function processes_user_data();</code>

	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* SQL JOIN from a sample to users table.		* SQL JOIN from a sample to users table.
	*		*
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	The first thing a target must define is the analyser class that it will use. The analyser class is specified in '''get_analyser_class'''.		The first thing a target must define is the analyser class that it will use. The analyser class is specified in '''get_analyser_class'''.
	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* Returns the analyser class that should be used along with this target.		* Returns the analyser class that should be used along with this target.
	*		*
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	'''is_valid_analysable''' and '''is_valid_sample''' are used to discard elements that are not valid for your target.		'''is_valid_analysable''' and '''is_valid_sample''' are used to discard elements that are not valid for your target.
	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* Allows the target to verify that the analysable is a good candidate.		* Allows the target to verify that the analysable is a good candidate.
	*		*
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	You can implement a '''always_update_analysis_time()''' method so analysable elements' timeanalysed is only updated when analysable elements have been successfully evaluated. It is useful for lightweight targets.		You can implement a '''always_update_analysis_time()''' method so analysable elements' timeanalysed is only updated when analysable elements have been successfully evaluated. It is useful for lightweight targets.
	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* Update the last analysis time on analysable processed or always.		* Update the last analysis time on analysable processed or always.
	*		*
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	You can also override the URL to the insight with get_insight_context_url():		You can also override the URL to the insight with get_insight_context_url():
	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* URL to the insight.		* URL to the insight.
	*		*
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	'''define_ranges''' is the main method to implement and its values mostly depend on the current analysable element (available in '''$this->analysable'''). An array of time ranges should be returned, each of these ranges should contain 3 attributes: A start time ('start') and an end time ('end') that will be passed to indicators so they can limit the amount of activity logs they read; the 3rd attribute is 'time', which value will determine when the range will be executed.		'''define_ranges''' is the main method to implement and its values mostly depend on the current analysable element (available in '''$this->analysable'''). An array of time ranges should be returned, each of these ranges should contain 3 attributes: A start time ('start') and an end time ('end') that will be passed to indicators so they can limit the amount of activity logs they read; the 3rd attribute is 'time', which value will determine when the range will be executed.
	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* Define the time splitting methods ranges.		* Define the time splitting methods ranges.
	*		*
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	A name and description should also be specified:		A name and description should also be specified:
	<~~code~~ php>		<syntaxhighlight lang="php"> /**

	/**
	* Returns a lang_string object representing the name for the time splitting method.		* Returns a lang_string object representing the name for the time splitting method.
	*		*

← Older revision		Revision as of 06:38, 1 July 2019
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	==== Analysis interval (core_analytics\local\time_splitting\base) ====		==== Analysis interval (core_analytics\local\time_splitting\base) ====

	~~Analyisis~~ intervals are used to define when the analytics API will train the predictions processor and when it will generate predictions. As explained above in [[Analytics_API#Analysis_intervals]], they define time ranges based on analysable elements start and end timestamps.		Analysis intervals are used to define when the analytics API will train the predictions processor and when it will generate predictions. As explained above in [[Analytics_API#Analysis_intervals]], they define time ranges based on analysable elements start and end timestamps.

	The base class is '''\core_analytics\local\time_splitting\base'''; if what you are after is to split the analysable duration in equal parts or in cumulative parts you can extend '''\core_analytics\local\time_splitting\equal_parts''' or '''\core_analytics\local\time_splitting\accumulative_parts''' instead.		The base class is '''\core_analytics\local\time_splitting\base'''; if what you are after is to split the analysable duration in equal parts or in cumulative parts you can extend '''\core_analytics\local\time_splitting\equal_parts''' or '''\core_analytics\local\time_splitting\accumulative_parts''' instead.
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	'''Note''': As of Moodle 3.7, ~~analyisis~~ intervals can now overwrite the following:		'''Note''': As of Moodle 3.7, analysis intervals can now overwrite the following:
	<code php>valid_for_evaluation()</code>.		<code php>valid_for_evaluation()</code>.
	You can return false if the time-splitting method can not be used to evaluate prediction models or if it does not make sense to evaluate prediction models with it, as for example upcoming_periodic children classes.		You can return false if the time-splitting method can not be used to evaluate prediction models or if it does not make sense to evaluate prediction models with it, as for example upcoming_periodic children classes.

← Older revision		Revision as of 01:32, 29 June 2019
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	=== Create the model ===		=== Create the model ===

	To add a new model to the system, it must be defined in a PHP file. ~~Normally this is done as part of install~~.php ~~or upgrade~~.php ~~for a plugin that contains the new model and components~~. ~~However, it~~ is also possible to execute the necessary commands in a standalone PHP file that references the Moodle config.php.		To add a new model to the system, it must be defined in a PHP file. Plugins and core subsystems declare default prediction models by describing them in their db/analytics.php file. Models should not be created manually via the db/install.php file any more. (It is also possible to execute the necessary commands in a standalone PHP file that references the Moodle config.php.)

	To create the model, specify at least its target and, optionally, a set of indicators and a time splitting method:		To create the model, specify at least its target and, optionally, a set of indicators and a time splitting method:

	// Instantiate the target: classify users as spammers		<code php>
			// Instantiate the target: classify users as spammers
	$target = \core_analytics\manager::get_target('\mod_yours\analytics\target\spammer_users');		$target = \core_analytics\manager::get_target('\mod_yours\analytics\target\spammer_users');

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	// Create the model.		// Create the model.
	// Note that the 3rd and 4rd arguments (the time splitting method and the predictions processor) are optional. The 4th argument is available from Moodle 3.6 onwards.		// Note that the 3rd and 4rd arguments (the time splitting method and the predictions processor) are optional. The 4th argument is available from Moodle 3.6 onwards.
	$model = \core_analytics\model::create($target, $indicators, '\core\analytics\time_splitting\single_range', '\mlbackend_python\processor');		$model = \core_analytics\model::create($target, $indicators, '\core\analytics\time_splitting\single_range', '\mlbackend_python\processor');</code>

	Models are disabled by default because you may be interested in evaluating how good the model is at predicting before enabling them. You can enable models using Moodle UI or the analytics API:		Models are disabled by default because you may be interested in evaluating how good the model is at predicting before enabling them. You can enable models using Moodle UI or the analytics API:

	$model->enable();		<code php>
			$model->enable();</code>

	=== Indicators ===		=== Indicators ===

← Older revision		Revision as of 01:29, 29 June 2019
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	==== Analyser (core_analytics\local\analyser\base) ====		==== Analyser (core_analytics\local\analyser\base) ====

	'''~~get_analysables~~''' returns the whole list of analysable elements in the site. Each model will later be able to discard analysables that do not match their expectations. ''e.g. if your model is only interested in quizzes with a time close the analyser will return all quizzes, your model will exclude the ones without a time close. This approach is supposed to make analysers more reusable.''		The get_analysables() method has been deprecated in favour of a new '''get_analysables_iterator()''' for performance reasons. This method returns the whole list of analysable elements in the site. Each model will later be able to discard analysables that do not match their expectations. ''e.g. if your model is only interested in quizzes with a time close the analyser will return all quizzes, your model will exclude the ones without a time close. This approach is supposed to make analysers more reusable.''

	/**		<code php>
			/**
	* Returns the list of analysable elements available on the site.		* Returns the list of analysable elements available on the site.
	*		*
	* ~~@return \core_analytics\analysable[] Array of~~ analysable elements ~~using~~ the ~~analysable id as array key~~.		* A relatively complex SQL query should be set so that we take into account which analysable elements
			* have already been processed and the order in which they have been processed. Helper methods are available
			* to ease to implementation of get_analysables_iterator: get_iterator_sql and order_sql.
			*
			* @param string\|null $action 'prediction', 'training' or null if no specific action needed.
			* @return \Iterator
	*/		*/
	~~abstract~~ public function ~~get_analysables~~();		public function get_analysables_iterator(?string $action = null)</code>


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	* The data included here is cached in PHP static vars; on one hand this reduces the amount of db queries indicators need to perform. On the other hand, if not well balanced, it can lead to PHP memory issues.		* The data included here is cached in PHP static vars; on one hand this reduces the amount of db queries indicators need to perform. On the other hand, if not well balanced, it can lead to PHP memory issues.

	/**		<code php>
			/**
	* This function returns this analysable list of samples.		* This function returns this analysable list of samples.
	*		*
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	* @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)		* @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)
	*/		*/
	abstract ~~protected~~ function get_all_samples(\core_analytics\analysable $analysable);		abstract public function get_all_samples(\core_analytics\analysable $analysable);</code>

			<code php>

	/**		/**
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	* @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)		* @return array array[0] = int[] (sampleids) and array[1] = array (samplesdata)
	*/		*/
	abstract public function get_samples($sampleids);		abstract public function get_samples($sampleids);</code>

	'''get_sample_analysable''' method is executing during prediction:		'''get_sample_analysable''' method is executing during prediction:

	/**		<code php>
			/**
	* Returns the analysable of a sample.		* Returns the analysable of a sample.
	*		*
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	* @return \core_analytics\analysable		* @return \core_analytics\analysable
	*/		*/
	abstract public function get_sample_analysable($sampleid);		abstract public function get_sample_analysable($sampleid);</code>

	The sample origin is the moodle database table that uses the sample id as primary key.		The sample origin is the moodle database table that uses the sample id as primary key.

	/**		<code php>
			/**
	* Returns the sample's origin in moodle database.		* Returns the sample's origin in moodle database.
	*		*
	* @return string		* @return string
	*/		*/
	abstract public function get_samples_origin();		abstract public function get_samples_origin();</code>

	'''sample_access_context''' associates a context to a sampleid. This is important because this sample predictions will only be available for users with ''moodle/analytics:listinsights'' capability in that context.		'''sample_access_context''' associates a context to a sampleid. This is important because this sample predictions will only be available for users with ''moodle/analytics:listinsights'' capability in that context.

	/**		<code php>
			/**
	* Returns the context of a sample.		* Returns the context of a sample.
	*		*
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	* @return \context		* @return \context
	*/		*/
	abstract public function sample_access_context($sampleid);		abstract public function sample_access_context($sampleid);</code>

	'''sample_description''' is used to display samples in ''Insights'' report:		'''sample_description''' is used to display samples in ''Insights'' report:

	/**		<code php>
			/**
	* Describes a sample with a description summary and a \renderable (an image for example)		* Describes a sample with a description summary and a \renderable (an image for example)
	*		*
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	* @return array array(string, \renderable)		* @return array array(string, \renderable)
	*/		*/
	abstract public function sample_description($sampleid, $contextid, $sampledata);		abstract public function sample_description($sampleid, $contextid, $sampledata);</code>

	'''processes_user_data''' and '''join_sample_user''' methods are used by the analytics implementation of the privacy API. You only need to overwrite them if your analyser deals with user data. They are used to export and delete user data that is stored in analytics database tables:		'''processes_user_data''' and '''join_sample_user''' methods are used by the analytics implementation of the privacy API. You only need to overwrite them if your analyser deals with user data. They are used to export and delete user data that is stored in analytics database tables:

	/**		<code php>
			/**
	* Whether the plugin needs user data clearing or not.		* Whether the plugin needs user data clearing or not.
	*		*
	* @return bool		* @return bool
	*/		*/
	public function processes_user_data();		public function processes_user_data();</code>

			<code php>

	/**		/**
	* SQL JOIN from a sample to users table.		* SQL JOIN from a sample to users table.
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	* @return string		* @return string
	*/		*/
	public function join_sample_user($sampletablealias);		public function join_sample_user($sampletablealias);</code>

			You can overwrite a '''new one_sample_per_analysable()''' method if the analysables your model is using only have one sample. The insights generated by models will then include the suggested actions in the notification.

			<code php>
			/**
			* Just one sample per analysable.
			*
			* @return bool
			*/
			public static function one_sample_per_analysable() {
			return true;
			}</code>

	==== Indicator (core_analytics\local\indicator\base) ====		==== Indicator (core_analytics\local\indicator\base) ====

Analytics API - Revision history

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