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Merge branch 'MDL-59265_master' of git://github.com/dmonllao/moodle

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This commit is contained in:
Eloy Lafuente (stronk7)
2017-08-29 01:16:42 +02:00
parent 685e99cabe 381c670894
commit 25f24df793
21 changed files with 388 additions and 84 deletions
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analytics/classes/classifier.php
+69
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@@ -0,0 +1,69 @@
<?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/>.
/**
* Classifier interface.
*
* @package core_analytics
* @copyright 2017 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
namespace core_analytics;
defined('MOODLE_INTERNAL') || die();
/**
* Classifier interface.
*
* @package core_analytics
* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
interface classifier extends predictor {
/**
* Train this processor classification model using the provided supervised learning dataset.
*
* @param string $uniqueid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function train_classification($uniqueid, \stored_file $dataset, $outputdir);
/**
* 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);
/**
* 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 $outputdir
* @return \stdClass
*/
public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir);
}
analytics/classes/local/analyser/base.php
+27
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@@ -469,6 +469,9 @@ abstract class base {
return $result;
}
// Add target metadata.
$this->add_target_metadata($data);
// Write all calculated data to a file.
$file = $dataset->store($data);
@@ -636,4 +639,28 @@ abstract class base {
$DB->insert_record('analytics_predict_samples', $predictionrange);
}
}
/**
* Adds target metadata to the dataset.
*
* @param array $data
* @return void
*/
protected function add_target_metadata(&$data) {
$data[0][] = 'targetcolumn';
$data[1][] = $this->analysabletarget->get_id();
if ($this->analysabletarget->is_linear()) {
$data[0][] = 'targettype';
$data[1][] = 'linear';
$data[0][] = 'targetmin';
$data[1][] = $this->analysabletarget::get_min_value();
$data[0][] = 'targetmax';
$data[1][] = $this->analysabletarget::get_max_value();
} else {
$data[0][] = 'targettype';
$data[1][] = 'discrete';
$data[0][] = 'targetclasses';
$data[1][] = json_encode($this->analysabletarget::get_classes());
}
}
}
analytics/classes/local/indicator/binary.php
+17
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@@ -46,6 +46,23 @@ abstract class binary extends discrete {
return array(0);
}
/**
* It should always be displayed.
*
* Binary values have no subtypes by default, please overwrite if
* your indicator is adding extra features.
*
* @param float $value
* @param string $subtype
* @return bool
*/
public function should_be_displayed($value, $subtype) {
if ($subtype != false) {
return false;
}
return true;
}
/**
* get_display_value
*
analytics/classes/local/indicator/discrete.php
+1 -1
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@@ -85,7 +85,7 @@ abstract class discrete extends base {
*/
public function get_display_value($value, $subtype = false) {
$displayvalue = array_search($subtype, static::get_classes());
$displayvalue = array_search($subtype, static::get_classes(), false);
debugging('Please overwrite \core_analytics\local\indicator\discrete::get_display_value to show something ' .
'different than the default "' . $displayvalue . '"', DEBUG_DEVELOPER);
analytics/classes/local/indicator/linear.php
+1 -1
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@@ -63,7 +63,7 @@ abstract class linear extends base {
}
/**
* should_be_displayed
* Show only the main feature.
*
* @param float $value
* @param string $subtype
analytics/classes/local/target/base.php
+1 -1
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@@ -231,7 +231,7 @@ abstract class base extends \core_analytics\calculable {
*/
protected function min_prediction_score() {
// The default minimum discards predictions with a low score.
return \core_analytics\model::MIN_SCORE;
return \core_analytics\model::PREDICTION_MIN_SCORE;
}
/**
analytics/classes/local/target/binary.php
+1 -1
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@@ -78,7 +78,7 @@ abstract class binary extends discrete {
throw new \moodle_exception('errorpredictionformat', 'analytics');
}
if (in_array($value, $this->ignored_predicted_classes())) {
if (in_array($value, $this->ignored_predicted_classes(), false)) {
// Just in case, if it is ignored the prediction should not even be recorded but if it would, it is ignored now,
// which should mean that is it nothing serious.
return self::OUTCOME_VERY_POSITIVE;
analytics/classes/local/target/discrete.php
+12 -12
View File
@@ -42,17 +42,18 @@ abstract class discrete extends base {
*/
public function is_linear() {
// Not supported yet.
throw new \coding_exception('Sorry, this version\'s prediction processors only support targets with binary values.');
throw new \coding_exception('Sorry, this version\'s prediction processors only support targets with binary values.' .
' You can write your own and overwrite this method though.');
}
/**
* Is the provided class one of this target valid classes?
*
* @param string $class
* @param mixed $class
* @return bool
*/
protected static function is_a_class($class) {
return (in_array($class, static::get_classes()));
return (in_array($class, static::get_classes(), false));
}
/**
@@ -99,7 +100,7 @@ abstract class discrete extends base {
throw new \moodle_exception('errorpredictionformat', 'analytics');
}
if (in_array($value, $this->ignored_predicted_classes())) {
if (in_array($value, $this->ignored_predicted_classes(), false)) {
// Just in case, if it is ignored the prediction should not even be recorded.
return self::OUTCOME_OK;
}
@@ -138,15 +139,16 @@ abstract class discrete extends base {
* Returns the predicted classes that will be ignored.
*
* Better be keen to add more than less classes here, the callback is always able to discard some classes. As an example
* a target with classes 'grade 0-3', 'grade 3-6', 'grade 6-8' and 'grade 8-10' is interested in flagging both 'grade 0-3'
* and 'grade 3-6'. On the other hand, a target like dropout risk with classes 'yes', 'no' may just be interested in 'yes'.
* a target with classes 'grade 0-3', 'grade 3-6', 'grade 6-8' and 'grade 8-10' is interested in flagging both 'grade 6-8'
* and 'grade 8-10' as ignored. On the other hand, a target like dropout risk with classes 'yes', 'no' may just be
* interested in 'yes'.
*
* @return array List of values that will be ignored (array keys are ignored).
*/
protected function ignored_predicted_classes() {
// Coding exception as this will only be called if this target have non-linear values.
throw new \coding_exception('Overwrite ignored_predicted_classes() and return an array with the classes that triggers ' .
'the callback');
throw new \coding_exception('Overwrite ignored_predicted_classes() and return an array with the classes that should not ' .
'trigger the callback');
}
/**
@@ -162,10 +164,8 @@ abstract class discrete extends base {
return false;
}
if (!$this->is_linear()) {
if (in_array($predictedvalue, $this->ignored_predicted_classes())) {
return false;
}
if (in_array($predictedvalue, $this->ignored_predicted_classes())) {
return false;
}
return true;
analytics/classes/local/target/linear.php
+27 -4
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@@ -42,7 +42,8 @@ abstract class linear extends base {
*/
public function is_linear() {
// Not supported yet.
throw new \coding_exception('Sorry, this version\'s prediction processors only support targets with binary values.');
throw new \coding_exception('Sorry, this version\'s prediction processors only support targets with binary values.' .
' You can write your own and overwrite this method though.');
}
/**
@@ -52,7 +53,7 @@ abstract class linear extends base {
* @param string $ignoredsubtype
* @return int
*/
public function get_calculated_outcome($value, $ignoredsubtype = false) {
public function get_calculation_outcome($value, $ignoredsubtype = false) {
// This is very generic, targets will probably be interested in overwriting this.
$diff = static::get_max_value() - static::get_min_value();
@@ -67,7 +68,7 @@ abstract class linear extends base {
*
* @return float
*/
protected static function get_max_value() {
public static function get_max_value() {
// Coding exception as this will only be called if this target have linear values.
throw new \coding_exception('Overwrite get_max_value() and return the target max value');
}
@@ -77,11 +78,33 @@ abstract class linear extends base {
*
* @return float
*/
protected static function get_min_value() {
public static function get_min_value() {
// Coding exception as this will only be called if this target have linear values.
throw new \coding_exception('Overwrite get_min_value() and return the target min value');
}
/**
* Should the model callback be triggered?
*
* @param mixed $predictedvalue
* @param float $predictionscore
* @return bool
*/
public function triggers_callback($predictedvalue, $predictionscore) {
if (!parent::triggers_callback($predictedvalue, $predictionscore)) {
return false;
}
// People may not want to set a boundary.
$boundary = $this->get_callback_boundary();
if (!empty($boundary) && floatval($predictedvalue) < $boundary) {
return false;
}
return true;
}
/**
* Returns the minimum value that triggers the callback.
*
analytics/classes/local/time_splitting/base.php
+1 -4
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@@ -371,12 +371,9 @@ abstract class base {
$metadata = array(
'timesplitting' => $this->get_id(),
// If no target the first column is the sampleid, if target the last column is the target.
// This will need to be updated when we support unsupervised learning models.
'nfeatures' => count(current($dataset)) - 1
);
if ($target) {
$metadata['targetclasses'] = json_encode($target::get_classes());
$metadata['targettype'] = ($target->is_linear()) ? 'linear' : 'discrete';
}
// The first 2 samples will be used to store metadata about the dataset.
$metadatacolumns = [];
analytics/classes/model.php
+23 -5
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@@ -80,6 +80,11 @@ class model {
*/
const MIN_SCORE = 0.7;
/**
* Minimum prediction confidence (from 0 to 1) to accept a prediction as reliable enough.
*/
const PREDICTION_MIN_SCORE = 0.6;
/**
* Maximum standard deviation between different evaluation repetitions to consider that evaluation results are stable.
*/
@@ -524,8 +529,13 @@ class model {
$outputdir = $this->get_output_dir(array('evaluation', $dashestimesplittingid));
// Evaluate the dataset, the deviation we accept in the results depends on the amount of iterations.
$predictorresult = $predictor->evaluate($this->model->id, self::ACCEPTED_DEVIATION,
if ($this->get_target()->is_linear()) {
$predictorresult = $predictor->evaluate_regression($this->get_unique_id(), self::ACCEPTED_DEVIATION,
self::EVALUATION_ITERATIONS, $dataset, $outputdir);
} else {
$predictorresult = $predictor->evaluate_classification($this->get_unique_id(), self::ACCEPTED_DEVIATION,
self::EVALUATION_ITERATIONS, $dataset, $outputdir);
}
$result->status = $predictorresult->status;
$result->info = $predictorresult->info;
@@ -599,7 +609,11 @@ class model {
$samplesfile = $datasets[$this->model->timesplitting];
// Train using the dataset.
$predictorresult = $predictor->train($this->get_unique_id(), $samplesfile, $outputdir);
if ($this->get_target()->is_linear()) {
$predictorresult = $predictor->train_regression($this->get_unique_id(), $samplesfile, $outputdir);
} else {
$predictorresult = $predictor->train_classification($this->get_unique_id(), $samplesfile, $outputdir);
}
$result = new \stdClass();
$result->status = $predictorresult->status;
@@ -678,8 +692,12 @@ class model {
$result->predictions = $this->get_static_predictions($indicatorcalculations);
} else {
// Prediction process runs on the machine learning backend.
$predictorresult = $predictor->predict($this->get_unique_id(), $samplesfile, $outputdir);
// Estimation and classification processes run on the machine learning backend side.
if ($this->get_target()->is_linear()) {
$predictorresult = $predictor->estimate($this->get_unique_id(), $samplesfile, $outputdir);
} else {
$predictorresult = $predictor->classify($this->get_unique_id(), $samplesfile, $outputdir);
}
$result->status = $predictorresult->status;
$result->info = $predictorresult->info;
$result->predictions = $this->format_predictor_predictions($predictorresult);
@@ -1194,7 +1212,7 @@ class model {
// Generate a unique id for this site, this model and this time splitting method, considering the last time
// that the model target and indicators were updated.
$ids = array($CFG->wwwroot, $CFG->dirroot, $CFG->prefix, $this->model->id, $this->model->version);
$ids = array($CFG->wwwroot, $CFG->prefix, $this->model->id, $this->model->version);
$this->uniqueid = sha1(implode('$$', $ids));
return $this->uniqueid;
analytics/classes/predictor.php
-32
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@@ -41,36 +41,4 @@ interface predictor {
* @return bool
*/
public function is_ready();
/**
* Train the provided dataset.
*
* @param int $modelid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function train($modelid, \stored_file $dataset, $outputdir);
/**
* Predict the provided dataset samples.
*
* @param int $modelid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function predict($modelid, \stored_file $dataset, $outputdir);
/**
* evaluate
*
* @param int $modelid
* @param float $maxdeviation
* @param int $niterations
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function evaluate($modelid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir);
}
analytics/classes/regressor.php
+69
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@@ -0,0 +1,69 @@
<?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/>.
/**
* Regressors interface.
*
* @package core_analytics
* @copyright 2017 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
namespace core_analytics;
defined('MOODLE_INTERNAL') || die();
/**
* Regressors interface.
*
* @package core_analytics
* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
interface regressor extends predictor {
/**
* Train this processor regression model using the provided supervised learning dataset.
*
* @param string $uniqueid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function train_regression($uniqueid, \stored_file $dataset, $outputdir);
/**
* Estimates linear values for the provided dataset samples.
*
* @param string $uniqueid
* @param \stored_file $dataset
* @param mixed $outputdir
* @return void
*/
public function estimate($uniqueid, \stored_file $dataset, $outputdir);
/**
* 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 $outputdir
* @return \stdClass
*/
public function evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir);
}
course/classes/analytics/indicator/no_teacher.php
+17
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@@ -64,6 +64,23 @@ class no_teacher extends \core_analytics\local\indicator\binary {
return array('context', 'course');
}
/**
* Reversed because the indicator is in 'negative' and the max returned value means teacher present.
*
* @param float $value
* @param string $subtype
* @return string
*/
public function get_display_value($value, $subtype = false) {
// No subtypes for binary values by default.
if ($value == -1) {
return get_string('yes');
} else if ($value == 1) {
return get_string('no');
}
}
/**
* calculate_sample
*
lib/db/install.xml
+2 -2
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@@ -1,5 +1,5 @@
<?xml version="1.0" encoding="UTF-8" ?>
<XMLDB PATH="lib/db" VERSION="20170801" COMMENT="XMLDB file for core Moodle tables"
<XMLDB PATH="lib/db" VERSION="20170814" COMMENT="XMLDB file for core Moodle tables"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:noNamespaceSchemaLocation="../../lib/xmldb/xmldb.xsd"
>
@@ -3610,7 +3610,7 @@
<FIELD NAME="contextid" TYPE="int" LENGTH="10" NOTNULL="true" SEQUENCE="false"/>
<FIELD NAME="sampleid" TYPE="int" LENGTH="10" NOTNULL="true" SEQUENCE="false"/>
<FIELD NAME="rangeindex" TYPE="int" LENGTH="5" NOTNULL="true" SEQUENCE="false"/>
<FIELD NAME="prediction" TYPE="int" LENGTH="2" NOTNULL="true" SEQUENCE="false"/>
<FIELD NAME="prediction" TYPE="number" LENGTH="10" NOTNULL="true" SEQUENCE="false" DECIMALS="2"/>
<FIELD NAME="predictionscore" TYPE="number" LENGTH="10" NOTNULL="true" SEQUENCE="false" DECIMALS="5"/>
<FIELD NAME="calculations" TYPE="text" NOTNULL="true" SEQUENCE="false"/>
<FIELD NAME="timecreated" TYPE="int" LENGTH="10" NOTNULL="true" DEFAULT="0" SEQUENCE="false"/>
lib/db/upgrade.php
+13
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@@ -2423,5 +2423,18 @@ function xmldb_main_upgrade($oldversion) {
upgrade_main_savepoint(true, 2017082500.00);
}
if ($oldversion < 2017082800.00) {
// Changing type of field prediction on table analytics_predictions to number.
$table = new xmldb_table('analytics_predictions');
$field = new xmldb_field('prediction', XMLDB_TYPE_NUMBER, '10, 2', null, XMLDB_NOTNULL, null, null, 'rangeindex');
// Launch change of type for field prediction.
$dbman->change_field_type($table, $field);
// Main savepoint reached.
upgrade_main_savepoint(true, 2017082800.00);
}
return true;
}
lib/mlbackend/php/classes/processor.php
+48 -7
View File
@@ -38,7 +38,7 @@ use Phpml\ModelManager;
* @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\predictor {
class processor implements \core_analytics\classifier, \core_analytics\regressor {
/**
* Size of training / prediction batches.
@@ -73,14 +73,14 @@ class processor implements \core_analytics\predictor {
}
/**
* Trains a machine learning algorithm with the provided training set.
* Train this processor classification model using the provided supervised learning dataset.
*
* @param string $uniqueid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function train($uniqueid, \stored_file $dataset, $outputdir) {
public function train_classification($uniqueid, \stored_file $dataset, $outputdir) {
// Output directory is already unique to the model.
$modelfilepath = $outputdir . DIRECTORY_SEPARATOR . self::MODEL_FILENAME;
@@ -134,14 +134,14 @@ class processor implements \core_analytics\predictor {
}
/**
* Predicts the provided samples
* Classifies the provided dataset samples.
*
* @param string $uniqueid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function predict($uniqueid, \stored_file $dataset, $outputdir) {
public function classify($uniqueid, \stored_file $dataset, $outputdir) {
// Output directory is already unique to the model.
$modelfilepath = $outputdir . DIRECTORY_SEPARATOR . self::MODEL_FILENAME;
@@ -199,7 +199,7 @@ class processor implements \core_analytics\predictor {
}
/**
* Evaluates the provided dataset.
* 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
@@ -216,7 +216,7 @@ class processor implements \core_analytics\predictor {
* @param string $outputdir
* @return \stdClass
*/
public function evaluate($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
$fh = $dataset->get_content_file_handle();
// The first lines are var names and the second one values.
@@ -351,6 +351,47 @@ class processor implements \core_analytics\predictor {
return $resultobj;
}
/**
* 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 $outputdir
* @return \stdClass
*/
public function evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
throw new \coding_exception('This predictor does not support regression yet.');
}
/**
* Returns the Phi correlation coefficient.
*
lib/mlbackend/python/classes/processor.php
+47 -6
View File
@@ -33,7 +33,7 @@ defined('MOODLE_INTERNAL') || die();
* @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\predictor {
class processor implements \core_analytics\classifier, \core_analytics\regressor {
/**
* The required version of the python package that performs all calculations.
@@ -79,7 +79,7 @@ class processor implements \core_analytics\predictor {
* @param string $outputdir
* @return \stdClass
*/
public function train($uniqueid, \stored_file $dataset, $outputdir) {
public function train_classification($uniqueid, \stored_file $dataset, $outputdir) {
// Obtain the physical route to the file.
$datasetpath = $this->get_file_path($dataset);
@@ -113,14 +113,14 @@ class processor implements \core_analytics\predictor {
}
/**
* Returns predictions for the provided dataset samples.
* Classifies the provided dataset samples.
*
* @param string $uniqueid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function predict($uniqueid, \stored_file $dataset, $outputdir) {
public function classify($uniqueid, \stored_file $dataset, $outputdir) {
// Obtain the physical route to the file.
$datasetpath = $this->get_file_path($dataset);
@@ -154,7 +154,7 @@ class processor implements \core_analytics\predictor {
}
/**
* Evaluates the provided dataset.
* Evaluates this processor classification model using the provided supervised learning dataset.
*
* @param string $uniqueid
* @param float $maxdeviation
@@ -163,7 +163,7 @@ class processor implements \core_analytics\predictor {
* @param string $outputdir
* @return \stdClass
*/
public function evaluate($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
// Obtain the physical route to the file.
$datasetpath = $this->get_file_path($dataset);
@@ -195,6 +195,47 @@ class processor implements \core_analytics\predictor {
return $resultobj;
}
/**
* 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 $outputdir
* @return \stdClass
*/
public function evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
throw new \coding_exception('This predictor does not support regression yet.');
}
/**
* Returns the path to the dataset file.
*
report/insights/classes/output/insight.php
+3 -3
View File
@@ -134,7 +134,7 @@ class insight implements \renderable, \templatable {
* Returns a CSS class from the calculated value outcome.
*
* @param \core_analytics\calculable $calculable
* @param mixed $value
* @param float $value
* @param string|false $subtype
* @return string
*/
@@ -159,8 +159,8 @@ class insight implements \renderable, \templatable {
default:
throw new \coding_exception('The outcome returned by ' . get_class($calculable) . '::get_calculation_outcome is ' .
'not one of the accepted values. Please use \core_analytics\calculable::OUTCOME_VERY_POSITIVE, ' .
'\core_analytics\calculable::OUTCOME_OK, \core_analytics\calculable::OUTCOME_NEGATIVE or ' .
'\core_analytics\calculable::OUTCOME_VERY_NEGATIVE');
'\core_analytics\calculable::OUTCOME_OK, \core_analytics\calculable::OUTCOME_NEGATIVE, ' .
'\core_analytics\calculable::OUTCOME_VERY_NEGATIVE or \core_analytics\calculable::OUTCOME_NEUTRAL');
}
return $style;
}
report/insights/classes/output/insights_list.php
+8 -4
View File
@@ -91,12 +91,16 @@ class insights_list implements \renderable, \templatable {
$total = 0;
if ($this->model->uses_insights()) {
list($total, $predictions) = $this->model->get_predictions($this->context, $this->page, $this->perpage);
$predictionsdata = $this->model->get_predictions($this->context, $this->page, $this->perpage);
$data->insights = array();
foreach ($predictions as $prediction) {
$insightrenderable = new \report_insights\output\insight($prediction, $this->model, true);
$data->insights[] = $insightrenderable->export_for_template($output);
if ($predictionsdata) {
list($total, $predictions) = $predictionsdata;
foreach ($predictions as $prediction) {
$insightrenderable = new \report_insights\output\insight($prediction, $this->model, true);
$data->insights[] = $insightrenderable->export_for_template($output);
}
}
if (empty($data->insights) && $this->page == 0) {
version.php
+1 -1
View File
@@ -29,7 +29,7 @@
defined('MOODLE_INTERNAL') || die();
$version = 2017082500.00; // YYYYMMDD = weekly release date of this DEV branch.
$version = 2017082800.00; // YYYYMMDD = weekly release date of this DEV branch.
// RR = release increments - 00 in DEV branches.
// .XX = incremental changes.