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MDL-64787 mlbackend: Evaluate trained models

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This commit is contained in:
David Monllaó
2019-03-07 20:43:30 +01:00
parent 74823933c8
commit bc82b89586
7 changed files with 133 additions and 31 deletions
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lib/mlbackend/php/classes/processor.php
+42 -17
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@@ -174,14 +174,7 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
*/
public function classify($uniqueid, \stored_file $dataset, $outputdir) {
$modelfilepath = $this->get_model_filepath($outputdir);
if (!file_exists($modelfilepath)) {
throw new \moodle_exception('errorcantloadmodel', 'mlbackend_php', '', $modelfilepath);
}
$modelmanager = new ModelManager();
$classifier = $modelmanager->restoreFromFile($modelfilepath);
$classifier = $this->load_classifier($outputdir);
$fh = $dataset->get_content_file_handle();
@@ -244,11 +237,19 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
* @param int $niterations
* @param \stored_file $dataset
* @param string $outputdir
* @param string $trainedmodeldir
* @return \stdClass
*/
public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,
$outputdir, $trainedmodeldir) {
$fh = $dataset->get_content_file_handle();
if ($trainedmodeldir) {
// We overwrite the number of iterations as the results will always be the same.
$niterations = 1;
$classifier = $this->load_classifier($trainedmodeldir);
}
// The first lines are var names and the second one values.
$metadata = $this->extract_metadata($fh);
@@ -308,15 +309,19 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
// Evaluate the model multiple times to confirm the results are not significantly random due to a short amount of data.
for ($i = 0; $i < $niterations; $i++) {
$classifier = new \Phpml\Classification\Linear\LogisticRegression(self::TRAIN_ITERATIONS, Normalizer::NORM_L2);
if (!$trainedmodeldir) {
$classifier = new \Phpml\Classification\Linear\LogisticRegression(self::TRAIN_ITERATIONS, Normalizer::NORM_L2);
// Split up the dataset in classifier and testing.
$data = new RandomSplit(new ArrayDataset($samples, $targets), 0.2);
// Split up the dataset in classifier and testing.
$data = new RandomSplit(new ArrayDataset($samples, $targets), 0.2);
$classifier->train($data->getTrainSamples(), $data->getTrainLabels());
$predictedlabels = $classifier->predict($data->getTestSamples());
$phis[] = $this->get_phi($data->getTestLabels(), $predictedlabels);
$classifier->train($data->getTrainSamples(), $data->getTrainLabels());
$predictedlabels = $classifier->predict($data->getTestSamples());
$phis[] = $this->get_phi($data->getTestLabels(), $predictedlabels);
} else {
$predictedlabels = $classifier->predict($samples);
$phis[] = $this->get_phi($targets, $predictedlabels);
}
}
// Let's fill the results changing the returned status code depending on the phi-related calculated metrics.
@@ -381,6 +386,24 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
return $resultobj;
}
/**
* Loads the pre-trained classifier.
*
* @throws \moodle_exception
* @param string $outputdir
* @return \Phpml\Classification\Linear\LogisticRegression
*/
protected function load_classifier($outputdir) {
$modelfilepath = $this->get_model_filepath($outputdir);
if (!file_exists($modelfilepath)) {
throw new \moodle_exception('errorcantloadmodel', 'mlbackend_php', '', $modelfilepath);
}
$modelmanager = new ModelManager();
return $modelmanager->restoreFromFile($modelfilepath);
}
/**
* Train this processor regression model using the provided supervised learning dataset.
*
@@ -416,9 +439,11 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
* @param int $niterations
* @param \stored_file $dataset
* @param string $outputdir
* @param string $trainedmodeldir
* @return \stdClass
*/
public function evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir) {
public function evaluate_regression($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,
$outputdir, $trainedmodeldir) {
throw new \coding_exception('This predictor does not support regression yet.');
}