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MDL-59265 analytics: Rename machine learning backend method

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- Method names renamed to avoid interface changes once
  we support regression and unsupervised learning
- Adding regressor interface even if not implemente
- predictor interface comments expanded
- Differentiate model's required accuracy from predictions quality
- Add missing get_callback_boundary call
- Updated datasets' metadata to allow 3rd parties to code
  regressors themselves
- Add missing option to exception message
- Include target data into the dataset regardless of being a prediction
  dataset or a training dataset
- Explicit in_array and array_search non-strict calls
- Overwrite discrete should_be_displayed implementation with the binary one
- Overwrite no_teacher get_display_value as it would otherwise look
  wrong
- Other minor fixes
This commit is contained in:
David Monllao 2017-08-14 10:59:03 +02:00
parent b8fe16cd7c
commit 5c5cb3ee15
15 changed files with 265 additions and 51 deletions
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26
analytics/classes/model.php
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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);