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"]);
}
}
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));
}
}
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;
}
}
}
