public function mloutliersAction()
{
$jsonData = $jsonWarns = $jsonOuts = array();
$message = $instance = $jsonHeader = $jsonTable = $model_html = $config = $model_info = '';
$possible_models = $possible_models_id = $other_models = array();
$jsonResolutions = $jsonResolutionsHeader = '[]';
$max_x = $max_y = 0;
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain'], $this->container->get('config')['mysql_user'], $this->container->get('config')['mysql_pwd']);
$dbml->setAttribute(\PDO::ATTR_ERRMODE, \PDO::ERRMODE_EXCEPTION);
$dbml->setAttribute(\PDO::ATTR_EMULATE_PREPARES, false);
$db = $this->container->getDBUtils();
// FIXME - This must be counted BEFORE building filters, as filters inject rubbish in GET when there are no parameters...
$instructions = count($_GET) <= 1;
if (array_key_exists('dump', $_GET)) {
$dump = $_GET["dump"];
unset($_GET["dump"]);
}
if (array_key_exists('register', $_GET)) {
$register = $_GET["register"];
unset($_GET["register"]);
}
$this->buildFilters(array('current_model' => array('type' => 'selectOne', 'default' => null, 'label' => 'Model to use: ', 'generateChoices' => function () {
return array();
}, 'parseFunction' => function () {
$choice = isset($_GET['current_model']) ? $_GET['current_model'] : array("");
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'sigma' => array('type' => 'inputNumber', 'default' => 1, 'label' => 'Sigmas: ', 'parseFunction' => function () {
$choice = isset($_GET['sigma']) ? $_GET['sigma'] : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'max' => 3, 'min' => 1, 'filterGroup' => 'MLearning'), 'minexetime' => array('default' => 0), 'valid' => array('default' => 0), 'filter' => array('default' => 0), 'prepares' => array('default' => 1)));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true, 'filters' => array('current_model', 'sigma'))));
$params = array();
$param_names = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'vm_OS', 'vm_cores', 'vm_RAM', 'provider', 'vm_size', 'type', 'bench_type', 'hadoop_version');
// Order is important
$params = $this->filters->getFiltersSelectedChoices($param_names);
foreach ($param_names as $p) {
if (!is_null($params[$p]) && is_array($params[$p])) {
sort($params[$p]);
}
}
$params_additional = array();
$param_names_additional = array('datefrom', 'dateto', 'minexetime', 'maxexetime', 'valid', 'filter');
// Order is important
$params_additional = $this->filters->getFiltersSelectedChoices($param_names_additional);
$param_variables = $this->filters->getFiltersSelectedChoices(array('current_model', 'sigma'));
$param_current_model = $param_variables['current_model'];
$sigma_param = $param_variables['sigma'];
$where_configs = $this->filters->getWhereclause();
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names, $params, true);
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, true);
$slice_info = MLUtils::generateDatasliceInfo($this->filters, $param_names_additional, $params_additional);
// model for filling
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = '';
if (!is_null($possible_models_id) && in_array($param_current_model, $possible_models_id)) {
$current_model = $param_current_model;
}
// Other models for filling
$where_models = '';
if (!empty($possible_models_id)) {
$where_models = " WHERE id_learner NOT IN ('" . implode("','", $possible_models_id) . "')";
}
$result = $dbml->query("SELECT id_learner FROM aloja_ml.learners" . $where_models);
foreach ($result as $row) {
$other_models[] = $row['id_learner'];
}
if ($instructions) {
$result = $dbml->query("SELECT id_learner, model, algorithm FROM aloja_ml.learners");
foreach ($result as $row) {
$model_html = $model_html . "<li>" . $row['id_learner'] . " => " . $row['algorithm'] . " : " . $row['model'] . "</li>";
}
MLUtils::getIndexOutExps($jsonResolutions, $jsonResolutionsHeader, $dbml);
$this->filters->setCurrentChoices('current_model', array_merge($possible_models_id, array('---Other models---'), $other_models));
return $this->render('mltemplate/mloutliers.html.twig', array('outexps' => $jsonResolutions, 'header_outexps' => $jsonResolutionsHeader, 'jsonData' => '[]', 'jsonWarns' => '[]', 'jsonOuts' => '[]', 'jsonHeader' => '[]', 'jsonTable' => '[]', 'max_p' => 0, 'models' => $model_html, 'instructions' => 'YES'));
}
if (!empty($possible_models_id)) {
$result = $dbml->query("SELECT id_learner, model, algorithm, CASE WHEN `id_learner` IN ('" . implode("','", $possible_models_id) . "') THEN 'COMPATIBLE' ELSE 'NOT MATCHED' END AS compatible FROM aloja_ml.learners");
foreach ($result as $row) {
$model_html = $model_html . "<li>" . $row['id_learner'] . " => " . $row['algorithm'] . " : " . $row['compatible'] . " : " . $row['model'] . "</li>";
}
if ($current_model == "") {
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE, p.id_learner FROM aloja_ml.predictions p, aloja_ml.learners l WHERE l.id_learner = p.id_learner AND p.id_learner IN ('" . implode("','", $possible_models_id) . "') AND predict_code > 0 ORDER BY MAE LIMIT 1";
$result = $dbml->query($query);
$row = $result->fetch();
$current_model = $row['id_learner'];
}
$config = $instance . '-' . $current_model . '-' . $sigma_param . ' ' . $slice_info . '-outliers';
$is_cached_mysql = $dbml->query("SELECT count(*) as total FROM aloja_ml.resolutions WHERE id_resolution = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$cache_ds = getcwd() . '/cache/query/' . md5($config) . '-cache.csv';
$in_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '-resolutions.csv');
if (!$is_cached && !$in_process && !$finished_process) {
// get headers for csv
$header_names = array('id_exec' => 'ID', 'bench' => 'Benchmark', 'exe_time' => 'Exe.Time', 'net' => 'Net', 'disk' => 'Disk', 'maps' => 'Maps', 'iosf' => 'IO.SFac', 'replication' => 'Rep', 'iofilebuf' => 'IO.FBuf', 'comp' => 'Comp', 'blk_size' => 'Blk.size', 'e.id_cluster' => 'Cluster', 'datanodes' => 'Datanodes', 'vm_OS' => 'VM.OS', 'vm_cores' => 'VM.Cores', 'vm_RAM' => 'VM.RAM', 'provider' => 'Provider', 'vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type', 'hadoop_version' => 'Hadoop.Version');
$headers = array_keys($header_names);
$names = array_values($header_names);
// dump the result to csv
$query = "SELECT " . implode(",", $headers) . " FROM aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE hadoop_version IS NOT NULL" . $where_configs . ";";
$rows = $db->get_rows($query);
if (empty($rows)) {
throw new \Exception('No data matches with your critteria.');
}
$fp = fopen($cache_ds, 'w');
fputcsv($fp, $names, ',', '"');
foreach ($rows as $row) {
$row['id_cluster'] = "Cl" . $row['id_cluster'];
// Cluster is numerically codified...
$row['comp'] = "Cmp" . $row['comp'];
// Compression is numerically codified...
fputcsv($fp, array_values($row), ',', '"');
}
// Retrieve file model from DB
$query = "SELECT file FROM aloja_ml.model_storage WHERE id_hash='" . $current_model . "' AND type='learner';";
$result = $dbml->query($query);
$row = $result->fetch();
$content = $row['file'];
$filemodel = getcwd() . '/cache/query/' . $current_model . '-object.rds';
$fp = fopen($filemodel, 'w');
fwrite($fp, $content);
fclose($fp);
// launch query
exec('cd ' . getcwd() . '/cache/query ; touch ' . md5($config) . '.lock');
exec(getcwd() . '/resources/queue -c "cd ' . getcwd() . '/cache/query ; ' . getcwd() . '/resources/aloja_cli.r -m aloja_outlier_dataset -d ' . $cache_ds . ' -l ' . $current_model . ' -p sigma=' . $sigma_param . ':hdistance=3:saveall=' . md5($config) . ' > /dev/null 2>&1 ; rm -f ' . md5($config) . '.lock" > /dev/null 2>&1 &');
}
$finished_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '-resolutions.csv');
if ($finished_process && !$is_cached) {
if (($handle = fopen(getcwd() . '/cache/query/' . md5($config) . '-resolutions.csv', 'r')) !== FALSE) {
$header = fgetcsv($handle, 1000, ",");
$token = 0;
$query = "REPLACE INTO aloja_ml.resolutions (id_resolution,id_learner,id_exec,instance,model,dataslice,sigma,outlier_code,predicted,observed) VALUES ";
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$resolution = $data[0];
$pred_value = (int) $data[1] >= 100 ? (int) $data[1] : 100;
$exec_value = (int) $data[2];
$selected_instance_pre = preg_replace('/\\s+/', '', $data[3]);
$selected_instance_pre = str_replace(':', ',', $selected_instance_pre);
$specific_id = $data[4];
if ($token > 0) {
$query = $query . ",";
}
$token = 1;
$query = $query . "('" . md5($config) . "','" . $current_model . "','" . $specific_id . "','" . $selected_instance_pre . "','" . $model_info . "','" . $slice_info . "','" . $sigma_param . "','" . $resolution . "','" . $pred_value . "','" . $exec_value . "') ";
}
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving tree into DB');
}
}
// Store file model to DB
$filemodel = getcwd() . '/cache/query/' . md5($config) . '-object.rds';
$fp = fopen($filemodel, 'r');
$content = fread($fp, filesize($filemodel));
$content = addslashes($content);
fclose($fp);
$query = "INSERT INTO aloja_ml.model_storage (id_hash,type,file) VALUES ('" . md5($config) . "','resolution','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file resolution into DB');
}
// Remove temporary files
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '-*.csv');
$is_cached = true;
}
if (!$is_cached) {
$jsonData = $jsonOuts = $jsonWarns = $jsonHeader = $jsonTable = '[]';
$must_wait = 'YES';
if (isset($dump)) {
echo "1";
exit(0);
}
} else {
$must_wait = 'NO';
$query = "SELECT predicted, observed, outlier_code, id_exec, instance FROM aloja_ml.resolutions WHERE id_resolution = '" . md5($config) . "' LIMIT 5000";
// FIXME - CLUMSY PATCH FOR BYPASS THE BUG FROM HIGHCHARTS... REMEMBER TO ERASE THIS LINE WHEN THE BUG IS SOLVED
$result = $dbml->query($query);
foreach ($result as $row) {
$entry = array('x' => (int) $row['predicted'], 'y' => (int) $row['observed'], 'name' => $row['instance'], 'id' => (int) $row['id_exec']);
if ($row['outlier_code'] == 0) {
$jsonData[] = $entry;
}
if ($row['outlier_code'] == 1) {
$jsonWarns[] = $entry;
}
if ($row['outlier_code'] == 2) {
$jsonOuts[] = $entry;
}
$jsonTable .= ($jsonTable == '' ? '' : ',') . '["' . ($row['outlier_code'] == 0 ? 'Legitimate' : ($row['outlier_code'] == 1 ? 'Warning' : 'Outlier')) . '","' . $row['predicted'] . '","' . $row['observed'] . '","' . str_replace(",", "\",\"", $row['instance']) . '","' . $row['id_exec'] . '"]';
}
$query_var = "SELECT MAX(predicted) as max_x, MAX(observed) as max_y FROM aloja_ml.resolutions WHERE id_resolution = '" . md5($config) . "' LIMIT 5000";
$result = $dbml->query($query_var);
$row = $result->fetch();
$max_x = $row['max_x'];
$max_y = $row['max_y'];
$header = array('Prediction', 'Observed', 'Benchmark', 'Net', 'Disk', 'Maps', 'IO.SFS', 'Rep', 'IO.FBuf', 'Comp', 'Blk.Size', 'Cluster', 'Datanodes', 'VM.OS', 'VM.Cores', 'VM.RAM', 'Provider', 'VM.Size', 'Type', 'Bench.Type', 'Version', 'ID');
$jsonHeader = '[{title:""}';
foreach ($header as $title) {
$jsonHeader = $jsonHeader . ',{title:"' . $title . '"}';
}
$jsonHeader = $jsonHeader . ']';
$jsonData = json_encode($jsonData);
$jsonWarns = json_encode($jsonWarns);
$jsonOuts = json_encode($jsonOuts);
$jsonTable = '[' . $jsonTable . ']';
// Dump case
if (isset($dump)) {
echo str_replace(array("[", "]", "{title:\"", "\"}"), array('', '', ''), $jsonHeader) . "\n";
echo str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonOuts);
echo str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonWarns);
echo str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonData);
exit(0);
}
// Register case
if (isset($register)) {
// Update the predictions table
$query_var = "UPDATE aloja_ml.predictions as p, aloja_ml.resolutions as r\n\t\t\t\t\t\t\t\tSET p.outlier = r.outlier_code\n\t\t\t\t\t\t\t\tWHERE r.id_exec = p.id_exec\n\t\t\t\t\t\t\t\t\tAND r.id_resolution = '" . md5($config) . "'\n\t\t\t\t\t\t\t\t\tAND p.id_learner = '" . $current_model . "'";
if ($dbml->query($query_var) === FALSE) {
throw new \Exception('Error when updating aloja_ml.predictions in DB');
}
}
}
} else {
throw new \Exception('There are no prediction models trained for such parameters. Train at least one model in "ML Prediction" section.');
}
$dbml = null;
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage());
$jsonData = $jsonOuts = $jsonWarns = $jsonHeader = $jsonTable = '[]';
$must_wait = "NO";
$dbml = null;
}
$return_params = array('jsonData' => $jsonData, 'jsonWarns' => $jsonWarns, 'jsonOuts' => $jsonOuts, 'jsonHeader' => $jsonHeader, 'jsonTable' => $jsonTable, 'max_p' => min(array($max_x, $max_y)), 'outexps' => $jsonResolutions, 'header_outexps' => $jsonResolutionsHeader, 'must_wait' => $must_wait, 'models' => $model_html, 'models_id' => $possible_models_id, 'other_models_id' => $other_models, 'current_model' => $current_model, 'resolution_id' => md5($config), 'model_info' => $model_info, 'slice_info' => $slice_info, 'sigma' => $sigma_param, 'message' => $message, 'instance' => $instance);
$this->filters->setCurrentChoices('current_model', array_merge($possible_models_id, array('---Other models---'), $other_models));
return $this->render('mltemplate/mloutliers.html.twig', $return_params);
}