public function test_scenario2()
{
$data = array(array('filename' => 'data/grades.csv', 'input_data' => array('Tutorial' => 99.47, 'Midterm' => 53.12, 'TakeHome' => 87.95999999999999), 'prediction' => 50));
foreach ($data as $item) {
print "I create a data source uploading a " . $item["filename"] . " file\n";
$source = self::$api->create_source($item["filename"], $options = array('name' => 'local_test_source'));
$this->assertEquals(BigMLRequest::HTTP_CREATED, $source->code);
$this->assertEquals(1, $source->object->status->code);
print "check local source is ready\n";
$resource = self::$api->_check_resource($source->resource, null, 20000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
print "create dataset with local source\n";
$dataset = self::$api->create_dataset($source->resource);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $dataset->code);
$this->assertEquals(BigMLRequest::QUEUED, $dataset->object->status->code);
$model = self::$api->create_model($dataset->resource);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model->code);
print "check model is ready\n";
$resource = self::$api->_check_resource($model->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
$list_of_models = array(self::$api->get_model($model->resource));
print "I create a local multi model\n";
$local_multimodel = new MultiModel($list_of_models);
print "I create a batch prediction\n";
$batch_predict = $local_multimodel->batch_predict(array($item["input_data"]), null, true, false, Tree::LAST_PREDICTION, null, false, true);
$this->assertEquals(current($batch_predict)->predictions[0]["prediction"][0], $item["prediction"]);
}
}
function predict($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);
}
public function test_scenario2()
{
$data = array(array('filename' => 'data/iris.csv', 'params' => array("tags" => array("mytag"), 'missing_splits' => false), 'data_input' => array(array("petal width" => 0.5), array("petal length" => 6, "petal width" => 2)), 'tag' => 'mytag', 'predictions' => array("Iris-setosa", "Iris-virginica")));
foreach ($data as $item) {
print "\nSuccessfully creating a local batch prediction from a multi model\n";
print "I create a data source uploading a " . $item["filename"] . " file\n";
$source = self::$api->create_source($item["filename"], $options = array('name' => 'local_test_source', 'project' => self::$project->resource));
$this->assertEquals(BigMLRequest::HTTP_CREATED, $source->code);
$this->assertEquals(1, $source->object->status->code);
print "And I wait until the source is ready\n";
$resource = self::$api->_check_resource($source->resource, null, 20000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
print "And I create dataset with local source\n";
$dataset = self::$api->create_dataset($source->resource);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $dataset->code);
$this->assertEquals(BigMLRequest::QUEUED, $dataset->object->status->code);
print "And I wait until the dataset is ready " . $dataset->resource . " \n";
$resource = self::$api->_check_resource($dataset->resource, null, 20000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
$list_of_models = array();
print "And I create model with params " . json_encode($item["params"]) . "\n";
$model_1 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_1->code);
print "And I wait until the is ready\n";
$resource = self::$api->_check_resource($model_1->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_1->resource));
print "And I create model with params " . json_encode($item["params"]) . "\n";
$model_2 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_2->code);
print "And I wait until the is ready\n";
$resource = self::$api->_check_resource($model_2->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_2->resource));
print "And I create model with params " . json_encode($item["params"]) . "\n";
$model_3 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_3->code);
print "And I wait until the is ready\n";
$resource = self::$api->_check_resource($model_3->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_3->resource));
print "And I create a local multi model\n";
$local_multimodel = new MultiModel($list_of_models);
print "When I create a batch multimodel prediction for " . json_encode($item["data_input"]) . " \n";
$predictions = $local_multimodel->batch_predict($item["data_input"], null, array('to_file' => false));
$i = 0;
print "Then the predictions are " . json_encode($item["predictions"]) . "\n";
foreach ($predictions as $multivote) {
foreach ($multivote->predictions as $prediction) {
$this->assertEquals($prediction["prediction"], $item["predictions"][$i]);
}
$i += 1;
}
$this->assertEquals($i, count($predictions));
}
}
function _predict($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;
}
public function test_scenario2()
{
$data = array(array('filename' => 'data/iris.csv', 'params' => array("tags" => array("mytag"), 'missing_splits' => false), 'data_input' => array(array("petal width" => 0.5), array("petal length" => 6, "petal width" => 2)), 'tag' => 'mytag', 'predictions' => array("Iris-setosa", "Iris-virginica")));
foreach ($data as $item) {
print "I create a data source uploading a " . $item["filename"] . " file\n";
$source = self::$api->create_source($item["filename"], $options = array('name' => 'local_test_source'));
$this->assertEquals(BigMLRequest::HTTP_CREATED, $source->code);
$this->assertEquals(1, $source->object->status->code);
print "check local source is ready\n";
$resource = self::$api->_check_resource($source->resource, null, 20000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
print "create dataset with local source\n";
$dataset = self::$api->create_dataset($source->resource);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $dataset->code);
$this->assertEquals(BigMLRequest::QUEUED, $dataset->object->status->code);
print "check the dataset is ready " . $dataset->resource . " \n";
$resource = self::$api->_check_resource($dataset->resource, null, 20000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
$list_of_models = array();
print "create model_1\n";
$model_1 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_1->code);
print "check model_1 is ready\n";
$resource = self::$api->_check_resource($model_1->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_1->resource));
print "create model_2\n";
$model_2 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_2->code);
print "check model_2 is ready\n";
$resource = self::$api->_check_resource($model_2->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_2->resource));
print "create model_3\n";
$model_3 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_3->code);
print "check model_3 is ready\n";
$resource = self::$api->_check_resource($model_3->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_3->resource));
print "I create a local multi model\n";
$local_multimodel = new MultiModel($list_of_models);
print "I create a prediction\n";
$predictions = $local_multimodel->batch_predict($item["data_input"], null, true, false, Tree::LAST_PREDICTION, null, false, false);
$i = 0;
foreach ($predictions as $multivote) {
foreach ($multivote->predictions as $prediction) {
$this->assertEquals($prediction["prediction"], $item["predictions"][$i]);
}
$i += 1;
}
$this->assertEquals($i, count($predictions));
}
}
public function test_scenario1()
{
$data = array(array('filename' => 'data/iris.csv', 'params' => array("tags" => array("mytag"), 'missing_splits' => false), 'data_input' => array(array("petal width" => 0.5), array("petal length" => 6, "petal width" => 2), array("petal length" => 4, "petal width" => 1.5)), 'tag' => 'mytag', 'path' => 'tmp/', 'predictions' => array("Iris-setosa", "Iris-virginica", "Iris-versicolor")));
foreach ($data as $item) {
print "I create a data source uploading a " . $item["filename"] . " file\n";
$source = self::$api->create_source($item["filename"], $options = array('name' => 'local_test_source'));
$this->assertEquals(BigMLRequest::HTTP_CREATED, $source->code);
$this->assertEquals(1, $source->object->status->code);
print "check local source is ready\n";
$resource = self::$api->_check_resource($source->resource, null, 20000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
print "create dataset with local source\n";
$dataset = self::$api->create_dataset($source->resource);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $dataset->code);
$this->assertEquals(BigMLRequest::QUEUED, $dataset->object->status->code);
print "check the dataset is ready " . $dataset->resource . " \n";
$resource = self::$api->_check_resource($dataset->resource, null, 20000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
print "create model_1\n";
$model_1 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_1->code);
$list_of_models = array();
print "check model_1 is ready\n";
$resource = self::$api->_check_resource($model_1->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_1->resource));
print "create model_2\n";
$model_2 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_2->code);
print "check model_2 is ready\n";
$resource = self::$api->_check_resource($model_2->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_2->resource));
print "create model_3\n";
$model_3 = self::$api->create_model($dataset->resource, $item["params"]);
$this->assertEquals(BigMLRequest::HTTP_CREATED, $model_3->code);
print "check model_3 is ready\n";
$resource = self::$api->_check_resource($model_3->resource, null, 3000, 30);
$this->assertEquals(BigMLRequest::FINISHED, $resource["status"]);
array_push($list_of_models, self::$api->get_model($model_3->resource));
print "I create a local multi model\n";
$local_multimodel = new MultiModel($list_of_models);
if (!is_dir($item["path"])) {
mkdir($item["path"]);
}
print "I create a batch prediction\n";
$batch_predict = $local_multimodel->batch_predict($item["data_input"], $item["path"]);
print "I combine the votes in\n";
$votes = $local_multimodel->batch_votes($item["path"]);
print "test the plurarity combined prediction\n";
$i = 0;
foreach ($votes as $vote) {
$this->assertEquals($item["predictions"][$i], $vote->combine());
$i += 1;
}
print "test the confidence weighted prediction\n";
$i = 0;
foreach ($votes as $vote) {
$this->assertEquals($item["predictions"][$i], $vote->combine(1));
$i += 1;
}
}
}
