function predict($input_data, $by_name = true, $method = MultiVote::PLURALITY_CODE, $with_confidence = false, $add_confidence = false, $add_distribution = false, $add_count = false, $add_median = false, $add_min = false, $add_max = false, $options = null, $missing_strategy = Tree::LAST_PREDICTION, $median = false)
{
/*
Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
3 - threshold filtered vote / doesn't apply:
THRESHOLD_CODE
*/
if (count($this->models_splits) > 1) {
$votes = new MultiVote(array());
$models = array();
$api = $this->api;
$order = 0;
foreach ($this->models_splits as $model_split) {
$models = array();
foreach ($model_split as $model_id) {
array_push($models, $api::retrieve_resource($model_id, $api::ONLY_MODEL));
}
$multi_model = new MultiModel($models, $this->api);
$votes_split = $multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max);
if ($median) {
foreach ($votes_split->predictions as $prediction) {
$prediction['prediction'] = $prediction['median'];
}
}
$votes->extend($votes_split->predictions);
}
#return $votes->combine($method, $with_confidence, $options);
} else {
# When only one group of models is found you use the
# corresponding multimodel to predict
$votes_split = $this->multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max);
$votes = new MultiVote($votes_split->predictions);
if ($median) {
$new_predictions = array();
foreach ($votes->predictions as $prediction) {
$prediction->prediction = $prediction->median;
array_push($new_predictions, $prediction);
}
$votes->predictions = $new_predictions;
}
}
return $votes->combine($method, $with_confidence, $add_confidence, $add_distribution, $add_count, $add_median, $add_min, $add_max, $options);
}
function _predict($input_data, $by_name = true, $method = MultiVote::PLURALITY_CODE, $with_confidence = false, $add_confidence = false, $add_distribution = false, $add_count = false, $add_median = false, $add_unused_fields = false, $add_min = false, $add_max = false, $options = null, $missing_strategy = Tree::LAST_PREDICTION, $median = false)
{
/*
Makes a prediction based on the prediction made by every model.
:param input_data: Test data to be used as input
:param by_name: Boolean that is set to true if field_names (as
alternative to field ids) are used in the
input_data dict
:param method: numeric key code for the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
3 - threshold filtered vote / doesn't apply:
THRESHOLD_CODE
The following parameter causes the result to be returned as a list
:param add_confidence: Adds confidence to the prediction
:param add_distribution: Adds the predicted node's distribution to the prediction
:param add_count: Adds the predicted nodes' instances to the prediction
:param add_median: Adds the median of the predicted nodes' distribution
to the prediction
:param add_min: Boolean, if true adds the minimum value in the
prediction's distribution (for regressions only)
:param add_max: Boolean, if true adds the maximum value in the
prediction's distribution (for regressions only)
:param add_unused_fields: Boolean, if true adds the information about
the fields in the input_data that are not
being used in the model as predictors.
:param options: Options to be used in threshold filtered votes.
:param missing_strategy: numeric key for the individual model's
prediction method. See the model predict
method.
:param median: Uses the median of each individual model's predicted
node as individual prediction for the specified
combination method.
*/
if (count($this->models_splits) > 1) {
$votes = new MultiVote(array());
$models = array();
$api = $this->api;
$order = 0;
foreach ($this->models_splits as $model_split) {
$models = array();
foreach ($model_split as $model_id) {
array_push($models, $api::retrieve_resource($model_id, $api::ONLY_MODEL));
}
$multi_model = new MultiModel($models, $this->api);
$votes_split = $multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max, $add_unused_fields);
if ($median) {
foreach ($votes_split->predictions as $prediction) {
$prediction['prediction'] = $prediction['median'];
}
}
$votes->extend($votes_split->predictions);
}
#return $votes->combine($method, $with_confidence, $options);
} else {
# When only one group of models is found you use the
# corresponding multimodel to predict
$votes_split = $this->multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max, $add_unused_fields);
$votes = new MultiVote($votes_split->predictions);
if ($median) {
$new_predictions = array();
foreach ($votes->predictions as $prediction) {
$prediction->prediction = $prediction->median;
array_push($new_predictions, $prediction);
}
$votes->predictions = $new_predictions;
}
}
$result = $votes->combine($method, $with_confidence, $add_confidence, $add_distribution, $add_count, $add_median, $add_min, $add_max, $options);
if ($add_unused_fields) {
$unused_fields = array_unique(array_keys($input_data));
foreach ($votes->predictions as $index => $prediction) {
$unused_fields = array_intersect($unused_fields, array_unique($prediction->unused_fields));
}
if (!is_array($result)) {
$result = array("prediction" => $result);
}
$result['unused_fields'] = $unused_fields;
}
return $result;
}
