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MDL-59988 analytics: Files marked as used only if valid

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- Basic unit test for minimum machine learning backends requirements
- Warning return messages now include not enough data
- Clear models when the predictions processor is changed
- Refined the name of a couple of constants / methods
This commit is contained in:
David Monllao
2017-10-13 12:24:17 +02:00
parent 9a316f3367
commit 325b3bdd8e
11 changed files with 165 additions and 34 deletions
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lib/mlbackend/php/classes/processor.php
+15 -4
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@@ -129,16 +129,27 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
$samples[] = array_slice($sampledata, 0, $metadata['nfeatures']);
$targets[] = intval($data[$metadata['nfeatures']]);
if (count($samples) === self::BATCH_SIZE) {
$nsamples = count($samples);
if ($nsamples === self::BATCH_SIZE) {
// Training it batches to avoid running out of memory.
$classifier->partialTrain($samples, $targets, array(0, 1));
$samples = array();
$targets = array();
}
if (empty($morethan1sample) && $nsamples > 1) {
$morethan1sample = true;
}
}
fclose($fh);
if (empty($morethan1sample)) {
$resultobj = new \stdClass();
$resultobj->status = \core_analytics\model::NO_DATASET;
$resultobj->info = array();
return $resultobj;
}
// Train the remaining samples.
if ($samples) {
$classifier->partialTrain($samples, $targets, array(0, 1));
@@ -288,7 +299,7 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
}
if (!empty($notenoughdata)) {
$resultobj = new \stdClass();
$resultobj->status = \core_analytics\model::EVALUATE_NOT_ENOUGH_DATA;
$resultobj->status = \core_analytics\model::NOT_ENOUGH_DATA;
$resultobj->score = 0;
$resultobj->info = array(get_string('errornotenoughdata', 'mlbackend_php'));
return $resultobj;
@@ -350,7 +361,7 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
// If each iteration results varied too much we need more data to confirm that this is a valid model.
if ($modeldev > $maxdeviation) {
$resultobj->status = $resultobj->status + \core_analytics\model::EVALUATE_NOT_ENOUGH_DATA;
$resultobj->status = $resultobj->status + \core_analytics\model::NOT_ENOUGH_DATA;
$a = new \stdClass();
$a->deviation = $modeldev;
$a->accepteddeviation = $maxdeviation;
@@ -358,7 +369,7 @@ class processor implements \core_analytics\classifier, \core_analytics\regressor
}
if ($resultobj->score < \core_analytics\model::MIN_SCORE) {
$resultobj->status = $resultobj->status + \core_analytics\model::EVALUATE_LOW_SCORE;
$resultobj->status = $resultobj->status + \core_analytics\model::LOW_SCORE;
$a = new \stdClass();
$a->score = $resultobj->score;
$a->minscore = \core_analytics\model::MIN_SCORE;