public function mlparamEvaluationAction()
{
$rows = $categories = $series = '';
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists('parameval', $_GET) || count($_GET) == 2 && array_key_exists('current_model', $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mlparameval');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$money = Utils::read_params('money', $where_configs);
$paramEval = isset($_GET['parameval']) && $_GET['parameval'] != '' ? $_GET['parameval'] : 'maps';
$minExecs = isset($_GET['minexecs']) ? $_GET['minexecs'] : -1;
$minExecsFilter = "";
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
$where_configs = str_replace("`id_cluster`", "e.`id_cluster`", $where_configs);
if ($minExecs > 0) {
$minExecsFilter = "HAVING COUNT(*) > {$minExecs}";
}
$filter_execs = DBUtils::getFilterExecs();
$options = Utils::getFilterOptions($db);
$paramOptions = array();
foreach ($options[$paramEval] as $option) {
if ($paramEval == 'id_cluster') {
$paramOptions[] = $option['name'];
} else {
if ($paramEval == 'comp') {
$paramOptions[] = Utils::getCompressionName($option[$paramEval]);
} else {
if ($paramEval == 'net') {
$paramOptions[] = Utils::getNetworkName($option[$paramEval]);
} else {
if ($paramEval == 'disk') {
$paramOptions[] = Utils::getDisksName($option[$paramEval]);
} else {
$paramOptions[] = $option[$paramEval];
}
}
}
}
}
$benchOptions = $db->get_rows("SELECT DISTINCT bench FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs} GROUP BY {$paramEval}, bench order by {$paramEval}");
// get the result rows
$query = "SELECT count(*) as count, {$paramEval}, e.id_exec, exec as conf, bench, " . "exe_time, avg(exe_time) avg_exe_time, min(exe_time) min_exe_time " . "from execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs}" . "GROUP BY {$paramEval}, bench {$minExecsFilter} order by bench,{$paramEval}";
$rows = $db->get_rows($query);
if (!$rows) {
throw new \Exception("No results for query!");
}
$arrayBenchs = array();
foreach ($paramOptions as $param) {
foreach ($benchOptions as $bench) {
$arrayBenchs[$bench['bench']][$param] = null;
$arrayBenchs[$bench['bench']][$param]['y'] = 0;
$arrayBenchs[$bench['bench']][$param]['count'] = 0;
}
}
$series = array();
$bench = '';
foreach ($rows as $row) {
if ($paramEval == 'comp') {
$row[$paramEval] = Utils::getCompressionName($row['comp']);
} else {
if ($paramEval == 'id_cluster') {
$row[$paramEval] = Utils::getClusterName($row[$paramEval], $db);
} else {
if ($paramEval == 'net') {
$row[$paramEval] = Utils::getNetworkName($row['net']);
} else {
if ($paramEval == 'disk') {
$row[$paramEval] = Utils::getDisksName($row['disk']);
} else {
if ($paramEval == 'iofilebuf') {
$row[$paramEval] /= 1024;
}
}
}
}
}
$arrayBenchs[$row['bench']][$row[$paramEval]]['y'] = round((int) $row['avg_exe_time'], 2);
$arrayBenchs[$row['bench']][$row[$paramEval]]['count'] = (int) $row['count'];
}
// ----------------------------------------------------
// Add predictions to the series
// ----------------------------------------------------
$jsonData = $jsonHeader = "[]";
$instance = "";
$arrayBenchs_pred = array();
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, true, $db);
$model_info = MLUtils::generateModelInfo($param_names, $params, true, $db);
$instances = MLUtils::generateInstances($param_names, $params, true, $db);
// model for filling
$possible_models = $possible_models_id = array();
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = "";
if (array_key_exists('current_model', $_GET) && in_array($_GET['current_model'], $possible_models_id)) {
$current_model = $_GET['current_model'];
}
if (!empty($possible_models_id)) {
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 predictions p, 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 . "-parameval";
$query_cache = "SELECT count(*) as total FROM trees WHERE id_learner = '" . $current_model . "' AND model = '" . $model_info . "'";
$is_cached_mysql = $dbml->query($query_cache);
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$ret_data = null;
if (!$is_cached) {
// Call to MLFindAttributes, to fetch data
$_GET['pass'] = 2;
$_GET['unseen'] = 1;
$_GET['current_model'] = $current_model;
$mlfa1 = new MLFindAttributesController();
$mlfa1->container = $this->container;
$ret_data = $mlfa1->mlfindattributesAction();
if ($ret_data == 1) {
$must_wait = "YES";
$jsonData = $jsonHeader = '[]';
} else {
$is_cached_mysql = $dbml->query($query_cache);
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
}
}
if ($is_cached) {
$must_wait = 'NO';
$query = "SELECT count(*) as count, {$paramEval}, bench, exe_time, avg(pred_time) avg_pred_time, min(pred_time) min_pred_time " . "FROM predictions e WHERE e.id_learner = '" . $current_model . "' {$filter_execs} {$where_configs}" . "GROUP BY {$paramEval}, bench {$minExecsFilter} order by bench, {$paramEval}";
$result = $dbml->query($query);
// Initialize array
foreach ($paramOptions as $param) {
foreach ($benchOptions as $bench) {
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param] = null;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param]['y'] = 0;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param]['count'] = 0;
}
}
foreach ($result as $row) {
$bench_n = $row['bench'] . '_pred';
$class = $row[$paramEval];
if ($paramEval == 'comp') {
$value = Utils::getCompressionName($class);
} else {
if ($paramEval == 'id_cluster') {
$value = Utils::getClusterName($class, $db);
} else {
if ($paramEval == 'net') {
$value = Utils::getNetworkName($class);
} else {
if ($paramEval == 'disk') {
$value = Utils::getDisksName($class);
} else {
if ($paramEval == 'iofilebuf') {
$value = $class / 1024;
} else {
$value = $class;
}
}
}
}
}
if (!in_array($value, $paramOptions)) {
$paramOptions[] = $value;
foreach ($benchOptions as $bench) {
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value] = null;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value]['y'] = 0;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value]['count'] = 0;
$arrayBenchs[$bench['bench']][$value] = null;
$arrayBenchs[$bench['bench']][$value]['y'] = 0;
$arrayBenchs[$bench['bench']][$value]['count'] = 0;
}
}
$arrayBenchs_pred[$bench_n][$value]['y'] = (int) $row['avg_pred_time'];
$arrayBenchs_pred[$bench_n][$value]['count'] = (int) $row['count'];
}
}
}
// ----------------------------------------------------
// END - Add predictions to the series
// ----------------------------------------------------
asort($paramOptions);
foreach ($arrayBenchs as $key => $arrayBench) {
$caregories = '';
$data_a = null;
$data_p = null;
foreach ($paramOptions as $param) {
if ($arrayBenchs[$key][$param]['count'] > 0 && empty($arrayBenchs_pred) || !empty($arrayBenchs_pred) && ($arrayBenchs_pred[$key . '_pred'][$param]['count'] > 0 || $arrayBenchs[$key][$param]['count'] > 0)) {
$data_a[] = $arrayBenchs[$key][$param];
if (!empty($arrayBenchs_pred)) {
$data_p[] = $arrayBenchs_pred[$key . '_pred'][$param];
}
$categories = $categories . "'{$param} " . Utils::getParamevalUnit($paramEval) . "',";
// FIXME - Redundant n times performed... don't care now
}
}
$series[] = array('name' => $key, 'data' => $data_a);
if (!empty($arrayBenchs_pred)) {
$series[] = array('name' => $key . '_pred', 'data' => $data_p);
}
}
$series = json_encode($series);
if (!empty($arrayBenchs_pred)) {
$colors = "['#7cb5ec','#9cd5fc','#434348','#636368','#90ed7d','#b0fd9d','#f7a35c','#f7c37c','#8085e9','#a0a5f9','#f15c80','#f17ca0','#e4d354','#f4f374','#8085e8','#a0a5f8','#8d4653','#ad6673','#91e8e1','#b1f8f1']";
} else {
$colors = "['#7cb5ec','#434348','#90ed7d','#f7a35c','#8085e9','#f15c80','#e4d354','#8085e8','#8d4653','#91e8e1']";
}
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$series = $jsonHeader = $colors = '[]';
$instance = $current_model = '';
$possible_models = $possible_models_id = array();
$must_wait = 'NO';
}
echo $this->container->getTwig()->render('mltemplate/mlconfigperf.html.twig', array('selected' => 'mlparameval', 'title' => 'Improvement of Hadoop Execution by SW and HW Configurations', 'categories' => $categories, 'series' => $series, 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'money' => $money, 'paramEval' => $paramEval, 'instance' => $instance, 'models' => '<li>' . implode('</li><li>', $possible_models) . '</li>', 'models_id' => $possible_models_id, 'current_model' => $current_model, 'gammacolors' => $colors, 'must_wait' => $must_wait, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function mlminconfigsAction()
{
$jsonData = array();
$message = $instance = '';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists('learn', $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mlminconfigs');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$learn_param = array_key_exists('learn', $_GET) ? $_GET['learn'] : 'regtree';
$unrestricted = array_key_exists('umodel', $_GET) && $_GET['umodel'] == 1;
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("`id_cluster`", "e.`id_cluster`", $where_configs);
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, $unrestricted, $db);
// Used only as indicator in the WEB
$model_info = MLUtils::generateModelInfo($param_names, $params, $unrestricted, $db);
$config = $model_info . ' ' . $learn_param . ' ' . ($unrestricted ? 'U' : 'R') . ' minconfs';
$learn_options = 'saveall=' . md5($config);
if ($learn_param == 'regtree') {
$learn_method = 'aloja_regtree';
$learn_options .= ':prange=0,20000';
} else {
if ($learn_param == 'nneighbours') {
$learn_method = 'aloja_nneighbors';
$learn_options .= ':kparam=3';
} else {
if ($learn_param == 'nnet') {
$learn_method = 'aloja_nnet';
$learn_options .= ':prange=0,20000';
} else {
if ($learn_param == 'polyreg') {
$learn_method = 'aloja_linreg';
$learn_options .= ':ppoly=3:prange=0,20000';
}
}
}
}
$cache_ds = getcwd() . '/cache/query/' . md5($config) . '-cache.csv';
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM learners WHERE id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM minconfigs WHERE id_minconfigs = '" . md5($config . 'R') . "' AND id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $is_cached && $tmp_result['num'] > 0;
$in_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.fin');
// Create Models and Predictions
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', 'name' => 'Cl.Name', 'datanodes' => 'Datanodes', 'headnodes' => 'Headnodes', 'vm_OS' => 'VM.OS', 'vm_cores' => 'VM.Cores', 'vm_RAM' => 'VM.RAM', 'provider' => 'Provider', 'vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type');
$headers = array_keys($header_names);
$names = array_values($header_names);
// dump the result to csv
$query = "SELECT " . implode(",", $headers) . " FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE e.valid = TRUE AND bench_type = 'HiBench' AND bench NOT LIKE 'prep_%' AND e.exe_time > 100" . $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($row), ',', '"');
}
// run the R processor
exec('cd ' . getcwd() . '/cache/query; touch ' . md5($config) . '.lock');
$command = getcwd() . '/resources/queue -c "cd ' . getcwd() . '/cache/query; ../../resources/aloja_cli.r -d ' . $cache_ds . ' -m ' . $learn_method . ' -p ' . $learn_options . ' >/dev/null 2>&1 && ';
$command = $command . '../../resources/aloja_cli.r -m aloja_minimal_instances -l ' . md5($config) . ' -p saveall=' . md5($config . 'R') . ':kmax=200 >/dev/null 2>&1; rm -f ' . md5($config) . '.lock; touch ' . md5($config) . '.fin" >/dev/null 2>&1 &';
exec($command);
}
$in_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.lock');
if ($in_process) {
$jsonData = $jsonHeader = $configs = '[]';
$must_wait = "YES";
$max_x = $max_y = 0;
} else {
$must_wait = "NO";
// Save learning model to DB, with predictions
$is_cached_mysql = $dbml->query("SELECT id_learner FROM learners WHERE id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
if ($tmp_result['id_learner'] != md5($config)) {
// register model to DB
$query = "INSERT INTO learners (id_learner,instance,model,algorithm)";
$query = $query . " VALUES ('" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "','" . $learn_param . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving model into DB');
}
// read results of the CSV and dump to DB
foreach (array("tt", "tv", "tr") as $value) {
if (($handle = fopen(getcwd() . '/cache/query/' . md5($config) . '-' . $value . '.csv', 'r')) !== FALSE) {
$header = fgetcsv($handle, 1000, ",");
$token = 0;
$query = "INSERT INTO predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,name,datanodes,headnodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,pred_time,id_learner,instance,predict_code) VALUES ";
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$specific_instance = implode(",", array_slice($data, 2, 19));
$specific_data = implode(",", $data);
$specific_data = preg_replace('/,Cmp(\\d+),/', ',${1},', $specific_data);
$specific_data = preg_replace('/,Cl(\\d+),/', ',${1},', $specific_data);
$specific_data = str_replace(",", "','", $specific_data);
$query_var = "SELECT count(*) as num FROM predictions WHERE instance = '" . $specific_instance . "' AND id_learner = '" . md5($config) . "'";
$result = $dbml->query($query_var);
$row = $result->fetch();
// Insert instance values
if ($row['num'] == 0) {
if ($token != 0) {
$query = $query . ",";
}
$token = 1;
$query = $query . "('" . $specific_data . "','" . md5($config) . "','" . $specific_instance . "','" . ($value == 'tt' ? 3 : ($value == 'tv' ? 2 : 1)) . "') ";
}
}
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving into DB');
}
fclose($handle);
}
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.csv');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.dat');
}
// Save minconfigs to DB, with props and centers
$is_cached_mysql = $dbml->query("SELECT id_minconfigs FROM minconfigs WHERE id_minconfigs = '" . md5($config . 'R') . "'");
$tmp_result = $is_cached_mysql->fetch();
if ($tmp_result['id_minconfigs'] != md5($config . 'R')) {
// register minconfigs to DB
$query = "INSERT INTO minconfigs (id_minconfigs,id_learner,instance,model)";
$query = $query . " VALUES ('" . md5($config . 'R') . "','" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving minconfis into DB');
}
$clusters = array();
// Save results of the CSV - MAE or RAE
if (file_exists(getcwd() . '/cache/query/' . md5($config . 'R') . '-raes.csv')) {
$error_file = 'raes.csv';
} else {
$error_file = 'maes.csv';
}
$handle = fopen(getcwd() . '/cache/query/' . md5($config . 'R') . '-' . $error_file, 'r');
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$cluster = (int) $data[0];
if ($error_file == 'raes.csv') {
$error_mae = 'NULL';
$error_rae = (double) $data[1];
}
if ($error_file == 'maes.csv') {
$error_mae = (double) $data[1];
$error_rae = 'NULL';
}
// register minconfigs_props to DB
$query = "INSERT INTO minconfigs_props (id_minconfigs,cluster,MAE,RAE)";
$query = $query . " VALUES ('" . md5($config . 'R') . "','" . $cluster . "','" . $error_mae . "','" . $error_rae . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving minconfis into DB');
}
$clusters[] = $cluster;
}
fclose($handle);
// Save results of the CSV - Configs
$handle_sizes = fopen(getcwd() . '/cache/query/' . md5($config . 'R') . '-sizes.csv', 'r');
foreach ($clusters as $cluster) {
// Get supports from sizes
$sizes = fgetcsv($handle_sizes, 1000, ",");
// Get clusters
$handle = fopen(getcwd() . '/cache/query/' . md5($config . 'R') . '-dsk' . $cluster . '.csv', 'r');
$header = fgetcsv($handle, 1000, ",");
$i = 0;
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$subdata = array_slice($data, 0, 12);
$specific_data = implode(',', $subdata);
$specific_data = preg_replace('/,Cmp(\\d+),/', ',${1},', $specific_data);
$specific_data = preg_replace('/,Cl(\\d+),/', ',${1},', $specific_data);
$specific_data = preg_replace('/,Cl(\\d+)/', ',${1}', $specific_data);
$specific_data = str_replace(",", "','", $specific_data);
// register minconfigs_props to DB
$query = "INSERT INTO minconfigs_centers (id_minconfigs,cluster,id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,support)";
$query = $query . " VALUES ('" . md5($config . 'R') . "','" . $cluster . "','" . $specific_data . "','" . $sizes[$i++] . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving centers into DB');
}
}
fclose($handle);
}
fclose($handle_sizes);
// 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);
$query = "INSERT INTO model_storage (id_hash,type,file) VALUES ('" . md5($config) . "','learner','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file model into DB');
}
$filemodel = getcwd() . '/cache/query/' . md5($config . 'R') . '-object.rds';
$fp = fopen($filemodel, 'r');
$content = fread($fp, filesize($filemodel));
$content = addslashes($content);
fclose($fp);
$query = "INSERT INTO model_storage (id_hash,type,file) VALUES ('" . md5($config . 'R') . "','minconf','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file minconf into DB');
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config . 'R') . '*.csv');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config . 'R') . '*.rds');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.rds');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '.fin');
}
// Retrieve minconfig progression results from DB
$header = "id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,support";
$header_array = explode(",", $header);
$last_y = 9000000000000000.0;
$configs = '[';
$query = "SELECT cluster, MAE, RAE FROM minconfigs_props WHERE id_minconfigs='" . md5($config . 'R') . "'";
$result = $dbml->query($query);
foreach ($result as $row) {
// Retrieve minconfig progression results from DB
if ((double) $row['MAE'] > 0) {
$error = (double) $row['MAE'];
} else {
$error = (double) $row['RAE'];
}
$cluster = (int) $row['cluster'];
$new_val = array();
$new_val['x'] = $cluster;
if ($error > $last_y) {
$new_val['y'] = $last_y;
} else {
$last_y = $new_val['y'] = $error;
}
$jsonData[] = $new_val;
// Retrieve minconfig centers from DB
$query_2 = "SELECT " . $header . " FROM minconfigs_centers WHERE id_minconfigs='" . md5($config . 'R') . "' AND cluster='" . $cluster . "'";
$result_2 = $dbml->query($query_2);
$jsonConfig = '[';
foreach ($result_2 as $row_2) {
$values = '';
foreach ($header_array as $ha) {
$values = $values . ($values != '' ? ',' : '') . '\'' . $row_2[$ha] . '\'';
}
$jsonConfig = $jsonConfig . ($jsonConfig != '[' ? ',' : '') . '[' . $values . ']';
}
$jsonConfig = $jsonConfig . ']';
$configs = $configs . ($configs != '[' ? ',' : '') . $jsonConfig;
}
$configs = $configs . ']';
$jsonData = json_encode($jsonData);
$jsonHeader = '[{title:""},{title:"Est.Time"},{title:"Benchmark"},{title:"Network"},{title:"Disk"},{title:"Maps"},{title:"IO.SF"},{title:"Replicas"},{title:"IO.FBuf"},{title:"Compression"},{title:"Blk.Size"},{title:"Main Ref. Cluster"},{title:"Support"}]';
$is_cached_mysql = $dbml->query("SELECT MAX(cluster) as mcluster, MAX(MAE) as mmae, MAX(RAE) as mrae FROM minconfigs_props WHERE id_minconfigs='" . md5($config . 'R') . "'");
$tmp_result = $is_cached_mysql->fetch();
$max_x = (double) $tmp_result['mmae'] > 0 ? (double) $tmp_result['mmae'] : (double) $tmp_result['mrae'];
$max_y = (double) $tmp_result['mcluster'];
}
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonData = $jsonHeader = $configs = '[]';
$max_x = $max_y = 0;
$must_wait = 'NO';
}
echo $this->container->getTwig()->render('mltemplate/mlminconfigs.html.twig', array('selected' => 'mlminconfigs', 'jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'configs' => $configs, 'max_p' => min(array($max_x, $max_y)), 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'message' => $message, 'instance' => $instance, 'id_learner' => md5($config), 'id_minconf' => md5($config . 'R'), 'model_info' => $model_info, 'unrestricted' => $unrestricted, 'learn' => $learn_param, 'must_wait' => $must_wait, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function mlfindattributesAction()
{
$instance = $message = $tree_descriptor = $model_html = '';
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists("current_model", $_GET) || count($_GET) == 2 && array_key_exists("dump", $_GET) || count($_GET) == 2 && array_key_exists("tree", $_GET) || count($_GET) == 2 && array_key_exists("pass", $_GET) || count($_GET) == 3 && array_key_exists("dump", $_GET) && array_key_exists("current_model", $_GET) || count($_GET) == 3 && array_key_exists("tree", $_GET) && array_key_exists("current_model", $_GET) || count($_GET) == 3 && array_key_exists("tree", $_GET) && array_key_exists("current_model", $_GET) || count($_GET) == 3 && array_key_exists("pass", $_GET) && array_key_exists("current_model", $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mlfindattributes');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$unseen = array_key_exists('unseen', $_GET) && $_GET['unseen'] == 1;
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
$jsonData = $jsonHeader = "[]";
$mae = $rae = 0;
// compose instance
$model_info = MLUtils::generateModelInfo($param_names, $params, $unseen, $db);
$instance = MLUtils::generateSimpleInstance($param_names, $params, $unseen, $db);
$instances = MLUtils::generateInstances($param_names, $params, $unseen, $db);
// Model for filling
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = "";
if (array_key_exists('current_model', $_GET) && !is_null($possible_models_id) && in_array($_GET['current_model'], $possible_models_id)) {
$current_model = $_GET['current_model'];
}
if (!empty($possible_models_id)) {
$other_models = array();
$result = $dbml->query("SELECT id_learner FROM learners WHERE id_learner NOT IN ('" . implode("','", $possible_models_id) . "')");
foreach ($result as $row) {
$other_models[] = $row['id_learner'];
}
$result = $dbml->query("SELECT id_learner, model, algorithm, CASE WHEN `id_learner` IN ('" . implode("','", $possible_models_id) . "') THEN 'COMPATIBLE' ELSE 'NOT MATCHED' END AS compatible FROM learners");
foreach ($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 predictions p, 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 . '-' . $current_model . '-' . ($unseen ? 'U' : 'R');
$is_cached_mysql = $dbml->query("SELECT count(*) as total FROM trees WHERE id_findattrs = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$tmp_file = md5($config) . '.tmp';
$in_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.lock');
$finished_process = $in_process && (int) shell_exec('ls ' . getcwd() . '/cache/query/' . md5($config) . '-*.lock | wc -w ') == count($instances);
if (!$in_process && !$finished_process && !$is_cached) {
// Retrieve file model from DB
$query = "SELECT file FROM 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);
// Run the predictor
exec('cd ' . getcwd() . '/cache/query ; touch ' . md5($config) . '.lock ; rm -f ' . $tmp_file);
$count = 1;
foreach ($instances as $inst) {
exec(getcwd() . '/resources/queue -d -c "cd ' . getcwd() . '/cache/query ; ../../resources/aloja_cli.r -m aloja_predict_instance -l ' . $current_model . ' -p inst_predict=\'' . $inst . '\' -v | grep -v \'Prediction\' >>' . $tmp_file . ' 2>/dev/null; touch ' . md5($config) . '-' . $count++ . '.lock" >/dev/null 2>&1 &');
}
}
$finished_process = (int) shell_exec('ls ' . getcwd() . '/cache/query/' . md5($config) . '-*.lock | wc -w ') == count($instances);
if ($finished_process && !$is_cached) {
// Read results and dump to DB
$i = 0;
$token = 0;
$token_i = 0;
$query = "INSERT IGNORE INTO predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,name,datanodes,headnodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,pred_time,id_learner,instance,predict_code) VALUES ";
if (($handle = fopen(getcwd() . '/cache/query/' . $tmp_file, "r")) !== FALSE) {
while (($line = fgets($handle, 1000)) !== FALSE && $i < 1000) {
if ($line == '') {
break;
}
// Fetch Real Value
$inst_aux = preg_split("/\\s+/", $line);
$query_var = "SELECT AVG(exe_time) as AVG, id_exec, outlier FROM predictions WHERE instance = '" . $inst_aux[1] . "' AND predict_code > 0";
$result = $dbml->query($query_var);
$row = $result->fetch();
$realexecval = is_null($row['AVG']) || $row['outlier'] == 2 ? 0 : $row['AVG'];
$realid_exec = is_null($row['id_exec']) || $row['outlier'] == 2 ? 0 : $row['id_exec'];
$query_var = "SELECT count(*) as num FROM predictions WHERE instance = '" . $inst_aux[1] . "' AND id_learner = '" . $current_model . "'";
$result = $dbml->query($query_var);
$row = $result->fetch();
// Insert instance values
if ($row['num'] == 0) {
$token_i = 1;
$selected_instance = preg_replace('/,Cmp(\\d+),/', ',${1},', $inst_aux[1]);
$selected_instance = preg_replace('/,Cl(\\d+),/', ',${1},', $selected_instance);
if ($token > 0) {
$query = $query . ",";
}
$token = 1;
$query = $query . "('" . $realid_exec . "','" . $realexecval . "','" . str_replace(",", "','", $selected_instance) . "','" . $inst_aux[2] . "','" . $current_model . "','" . $inst_aux[1] . "','0') ";
}
$i++;
if ($i % 100 == 0 && $token_i > 0) {
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving into DB');
}
$query = "INSERT IGNORE INTO predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,name,datanodes,headnodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,pred_time,id_learner,instance,predict_code) VALUES ";
$token = 0;
$token_i = 0;
}
}
if ($token_i > 0) {
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving into DB');
}
}
// Descriptive Tree
$tree_descriptor = shell_exec(getcwd() . '/resources/aloja_cli.r -m aloja_representative_tree -p method=ordered:dump_file="' . getcwd() . '/cache/query/' . $tmp_file . '":output="html" -v 2> /dev/null');
$tree_descriptor = substr($tree_descriptor, 5, -2);
$query = "INSERT INTO trees (id_findattrs,id_learner,instance,model,tree_code) VALUES ('" . md5($config) . "','" . $current_model . "','" . $instance . "','" . $model_info . "','" . $tree_descriptor . "')";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving tree into DB');
}
// remove remaining locks
shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.lock');
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '.tmp');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . $current_model . '-object.rds');
$is_cached = true;
}
fclose($handle);
}
if (!$is_cached) {
$jsonData = $jsonHeader = $jsonColumns = $jsonColor = '[]';
$must_wait = 'YES';
if (isset($_GET['dump'])) {
$dbml = null;
echo "1";
exit(0);
}
if (isset($_GET['pass'])) {
$dbml = null;
return "1";
}
} else {
if (isset($_GET['pass']) && $_GET['pass'] == 2) {
$dbml = null;
return "2";
}
// Fetch results and compose JSON
$header = array('Benchmark', 'Net', 'Disk', 'Maps', 'IO.SFS', 'Rep', 'IO.FBuf', 'Comp', 'Blk.Size', 'Cluster', 'Cl.Name', 'Datanodes', 'Headnodes', 'VM.OS', 'VM.Cores', 'VM.RAM', 'Provider', 'VM.Size', 'Type', 'Bench.Type', 'Prediction', 'Observed');
$jsonHeader = '[{title:""}';
foreach ($header as $title) {
$jsonHeader = $jsonHeader . ',{title:"' . $title . '"}';
}
$jsonHeader = $jsonHeader . ']';
$query = "SELECT @i:=@i+1 as num, instance, AVG(pred_time) as pred_time, AVG(exe_time) as exe_time FROM predictions, (SELECT @i:=0) d WHERE id_learner='" . $current_model . "' " . $where_configs . " GROUP BY instance";
$result = $dbml->query($query);
$jsonData = '[';
foreach ($result as $row) {
if ($jsonData != '[') {
$jsonData = $jsonData . ',';
}
$jsonData = $jsonData . "['" . $row['num'] . "','" . str_replace(",", "','", $row['instance']) . "','" . $row['pred_time'] . "','" . $row['exe_time'] . "']";
}
$jsonData = $jsonData . ']';
foreach (range(1, 33) as $value) {
$jsonData = str_replace('Cmp' . $value, Utils::getCompressionName($value), $jsonData);
}
// Fetch MAE & RAE values
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE FROM predictions WHERE id_learner='" . md5($config) . "' AND predict_code > 0";
$result = $dbml->query($query);
$row = $result->fetch();
$mae = $row['MAE'];
$rae = $row['RAE'];
// Dump case
if (isset($_GET['dump'])) {
echo "ID" . str_replace(array("[", "]", "{title:\"", "\"}"), array('', '', ''), $jsonHeader) . "\n";
echo str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonData);
$dbml = null;
exit(0);
}
if (isset($_GET['pass']) && $_GET['pass'] == 1) {
$retval = "ID" . str_replace(array("[", "]", "{title:\"", "\"}"), array('', '', ''), $jsonHeader) . "\n";
$retval .= str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonData);
$dbml = null;
return $retval;
}
// Display Descriptive Tree
$query = "SELECT tree_code FROM trees WHERE id_findattrs = '" . md5($config) . "'";
$result = $dbml->query($query);
$row = $result->fetch();
$tree_descriptor = $row['tree_code'];
}
} else {
$message = "There are no prediction models trained for such parameters. Train at least one model in 'ML Prediction' section.";
if (isset($_GET['dump'])) {
echo "-1";
exit(0);
}
if (isset($_GET['pass'])) {
return "-1";
}
$config = "";
$possible_models = $possible_models_id = array("None");
}
$dbml = null;
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonData = $jsonHeader = "[]";
$instance = $instances = $possible_models_id = "";
$possible_models = $possible_models_id = $other_models = array();
$must_wait = 'NO';
$mae = $rae = 0;
$dbml = null;
if (isset($_GET['pass'])) {
return "-2";
}
}
echo $this->container->getTwig()->render('mltemplate/mlfindattributes.html.twig', array('selected' => 'mlfindattributes', 'instance' => $instance, 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'models' => $model_html, 'models_id' => $possible_models_id, 'other_models_id' => $other_models, 'current_model' => $current_model, 'message' => $message, 'mae' => $mae, 'rae' => $rae, 'must_wait' => $must_wait, 'instance' => $instance, 'instances' => implode("<br/>", $instances), 'model_info' => $model_info, 'id_findattr' => md5($config), 'unseen' => $unseen, 'tree' => isset($_GET['tree']) ? "true" : "false", 'tree_descriptor' => $tree_descriptor, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function mlcrossvar3dfaAction()
{
$jsonData = array();
$message = $instance = $possible_models_id = '';
$maxx = $minx = $maxy = $miny = $maxz = $minz = 0;
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists('current_model', $_GET) || count($_GET) == 3 && array_key_exists('variable1', $_GET) && array_key_exists('variable2', $_GET) || count($_GET) == 4 && array_key_exists('current_model', $_GET) && array_key_exists('variable1', $_GET) && array_key_exists('variable2', $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mlcrossvar3dfa');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$cross_var1 = array_key_exists('variable1', $_GET) ? $_GET['variable1'] : 'maps';
$cross_var2 = array_key_exists('variable2', $_GET) ? $_GET['variable2'] : 'net';
$unseen = array_key_exists('unseen', $_GET) && $_GET['unseen'] == 1;
$where_configs = str_replace("AND .", "AND ", $where_configs);
$cross_var1 = str_replace("id_cluster", "e.id_cluster", $cross_var1);
$cross_var2 = str_replace("id_cluster", "e.id_cluster", $cross_var2);
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, $unseen, $db);
$model_info = MLUtils::generateModelInfo($param_names, $params, $unseen, $db);
// Model for filling
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = "";
if (array_key_exists('current_model', $_GET) && in_array($_GET['current_model'], $possible_models_id)) {
$current_model = $_GET['current_model'];
}
// Call to MLFindAttributes, to fetch data
$_GET['pass'] = 1;
$_GET['unseen'] = $unseen;
$mlfa1 = new MLFindAttributesController();
$mlfa1->container = $this->container;
$ret_data = $mlfa1->mlfindattributesAction();
$rows = null;
if ($ret_data == 1) {
$must_wait = "YES";
$jsonData = '[]';
$categories1 = $categories2 = "''";
} else {
if ($ret_data == -1) {
$must_wait = "NO";
$jsonData = '[]';
$categories1 = $categories2 = "''";
$message = "There are no prediction models trained for such parameters. Train at least one model in 'ML Prediction' section. [" . $instance . "]";
} else {
// Get stuff from the DB
$query = "SELECT " . $cross_var1 . " AS V1, " . $cross_var2 . " AS V2, AVG(p.pred_time) as V3, p.instance\n\t\t\t\t\tFROM predictions as p\n\t\t\t\t\tWHERE p.id_learner " . ($current_model != '' ? "='" . $current_model . "'" : "IN (SELECT id_learner FROM trees WHERE model='" . $model_info . "')") . $where_configs . "\n\t\t\t\t\tGROUP BY p.instance\n\t\t\t\t\tORDER BY RAND() LIMIT 5000;";
// FIXME - CLUMPSY PATCH FOR BYPASS THE BUG FROM HIGHCHARTS... REMEMBER TO ERASE THIS LINE WHEN THE BUG IS SOLVED
$rows = $dbml->query($query);
if (empty($rows)) {
throw new \Exception('No data matches with your critteria.');
}
}
}
if ($must_wait == "NO") {
$map_var1 = $map_var2 = array();
$count_var1 = $count_var2 = 0;
$categories1 = $categories2 = '';
$var1_categorical = in_array($cross_var1, array("net", "disk", "bench", "vm_OS", "provider", "vm_size", "type", "bench_type"));
$var2_categorical = in_array($cross_var2, array("net", "disk", "bench", "vm_OS", "provider", "vm_size", "type", "bench_type"));
foreach ($rows as $row) {
$entry = array();
if ($var1_categorical) {
if (!array_key_exists($row['V1'], $map_var1)) {
$map_var1[$row['V1']] = $count_var1++;
$categories1 = $categories1 . ($categories1 != '' ? "," : "") . "\"" . $row['V1'] . "\"";
}
$entry['y'] = $map_var1[$row['V1']] * (rand(990, 1010) / 1000);
} else {
$entry['y'] = (int) $row['V1'] * (rand(990, 1010) / 1000);
}
if ($entry['y'] > $maxy) {
$maxy = $entry['y'];
}
if ($entry['y'] < $miny) {
$miny = $entry['y'];
}
if ($var2_categorical) {
if (!array_key_exists($row['V2'], $map_var2)) {
$map_var2[$row['V2']] = $count_var2++;
$categories2 = $categories2 . ($categories2 != '' ? "," : "") . "\"" . $row['V2'] . "\"";
}
$entry['x'] = $map_var2[$row['V2']] * (rand(990, 1010) / 1000);
} else {
$entry['x'] = (int) $row['V2'] * (rand(990, 1010) / 1000);
}
if ($entry['x'] > $maxx) {
$maxx = $entry['x'];
}
if ($entry['x'] < $minx) {
$minx = $entry['x'];
}
$entry['z'] = -1 * (int) $row['V3'] * (rand(990, 1010) / 1000);
if ($entry['z'] > $maxz) {
$maxz = $entry['z'];
}
if ($entry['z'] < $minz) {
$minz = $entry['z'];
}
$entry['name'] = $row['instance'];
//$row['V1']." - ".$row['V2']." - ".max(100,(int)$row['V3']);
$jsonData[] = $entry;
}
$jsonData = json_encode($jsonData);
if ($categories1 != '') {
$categories1 = "[" . $categories1 . "]";
} else {
$categories1 = "''";
}
if ($categories2 != '') {
$categories2 = "[" . $categories2 . "]";
} else {
$categories2 = "''";
}
}
$dbml = null;
$cross_var1 = str_replace("e.id_cluster", "id_cluster", $cross_var1);
$cross_var2 = str_replace("e.id_cluster", "id_cluster", $cross_var2);
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonData = '[]';
$cross_var1 = $cross_var2 = '';
$categories1 = $categories2 = '';
$maxx = $minx = $maxy = $miny = $maxz = $minz = 0;
$must_wait = "NO";
$dbml = null;
$possible_models = $possible_models_id = array();
}
echo $this->container->getTwig()->render('mltemplate/mlcrossvar3dfa.html.twig', array('selected' => 'mlcrossvar3dfa', 'jsonData' => $jsonData, 'variable1' => $cross_var1, 'variable2' => $cross_var2, 'categories1' => $categories1, 'categories2' => $categories2, 'maxx' => $maxx, 'minx' => $minx, 'maxy' => $maxy, 'miny' => $miny, 'maxz' => $maxz, 'minz' => $minz, 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'message' => $message, 'instance' => $instance, 'model_info' => $model_info, 'current_model' => $current_model, 'unseen' => $unseen, 'models' => '<li>' . implode('</li><li>', $possible_models) . '</li>', 'models_id' => $possible_models_id, 'must_wait' => $must_wait, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function mldatacollapseAction()
{
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists("current_model", $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mldatacollapse');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$unseen = array_key_exists('unseen', $_GET) && $_GET['unseen'] == 1;
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
$dims1 = (empty($params['nets']) ? '' : 'Net,') . (empty($params['disks']) ? '' : 'Disk,') . (empty($params['blk_sizes']) ? '' : 'Blk.size,') . (empty($params['comps']) ? '' : 'Comp,');
$dims1 = $dims1 . (empty($params['id_clusters']) ? '' : 'Cluster,') . (empty($params['mapss']) ? '' : 'Maps,') . (empty($params['replications']) ? '' : 'Rep,') . (empty($params['iosfs']) ? '' : 'IO.SFac,') . (empty($params['iofilebufs']) ? '' : 'IO.FBuf');
if (substr($dims1, -1) == ',') {
$dims1 = substr($dims1, 0, -1);
}
$dims2 = "Benchmark";
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, $unseen, $db);
$model_info = MLUtils::generateModelInfo($param_names, $params, $unseen, $db);
// select model for filling
$possible_models = $possible_models_id = array();
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = '';
/* if (array_key_exists('current_model',$_GET) && in_array($_GET['current_model'],$possible_models_id)) $current_model = $_GET['current_model']; // FIXME - Needs re-think logic
if ($current_model == '')
{
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE, p.id_learner FROM predictions p, learners l WHERE l.id_learner = p.id_learner AND p.id_learner IN ('".implode("','",$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;
$learning_model = '';
if ($current_model != '' && file_exists(getcwd() . '/cache/query/' . $current_model . '-object.rds')) {
$learning_model = ':model_name=' . $current_model . ':inst_general="' . $instance . '"';
}
$config = $dims1 . '-' . $dims2 . '-' . $current_model . '-' . $model_info;
// 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', 'name' => 'Cl.Name', 'datanodes' => 'Datanodes', 'headnodes' => 'Headnodes', 'vm_OS' => 'VM.OS', 'vm_cores' => 'VM.Cores', 'vm_RAM' => 'VM.RAM', 'provider' => 'Provider', 'vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type');
$headers = array_keys($header_names);
$names = array_values($header_names);
$dims1_array = explode(",", $dims1);
$dims1_query = '';
$dims1_title = $dims1_concat = '';
foreach ($dims1_array as $d1value) {
$dims1_query = $dims1_query . ($dims1_query == '' ? '' : ',') . array_search($d1value, $header_names);
$dims1_title = $dims1_title . ($dims1_title == '' ? '' : ':') . array_search($d1value, $header_names);
$dims1_concat = $dims1_concat . ($dims1_concat == '' ? '' : ',":",') . array_search($d1value, $header_names);
}
$query = "SELECT distinct bench FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE e.valid = TRUE AND e.exe_time > 100" . $where_configs . " ORDER BY bench;";
$rows = $db->get_rows($query);
if (empty($rows)) {
throw new \Exception('No data matches with your critteria.');
}
$table = array();
$jsonHeader = '[{title:"' . $dims1_title . '"}';
foreach ($rows as $row) {
$jsonHeader = $jsonHeader . ',{title:"' . $row['bench'] . '"}';
$table[$row['bench']] = array();
}
$jsonHeader = $jsonHeader . ']';
$query = "SELECT CONCAT(" . $dims1_concat . ") as dim1, bench, avg(exe_time) as avg_exe_time FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE e.valid = TRUE AND e.exe_time > 100" . $where_configs . " GROUP BY bench," . $dims1_query . " ORDER BY dim1,bench;";
$rows = $db->get_rows($query);
if (empty($rows)) {
throw new \Exception('No data matches with your critteria.');
}
foreach ($rows as $row) {
$table[$row['bench']][$row['dim1']] = (int) $row['avg_exe_time'];
}
$row_ids = array();
foreach ($table as $bmk) {
foreach ($bmk as $key => $value) {
$row_ids[] = $key;
}
}
$row_ids = array_unique($row_ids);
$tableColor = array();
foreach ($table as $bmk => $values) {
$tableColor[$bmk] = array();
foreach ($row_ids as $rid) {
if (!array_key_exists($rid, $table[$bmk])) {
$table[$bmk][$rid] = 0;
$tableColor[$bmk][$rid] = 0;
} else {
$tableColor[$bmk][$rid] = 1;
}
}
}
$jsonData = '[';
$jsonColor = '[';
foreach ($row_ids as $rid) {
$jsonData = $jsonData . ($jsonData == '[' ? '' : ',') . '[\'' . $rid . '\'';
$jsonColor = $jsonColor . ($jsonColor == '[' ? '' : ',') . '[1';
foreach ($table as $bmk => $values) {
$jsonData = $jsonData . ',' . $table[$bmk][$rid];
$jsonColor = $jsonColor . ',' . $tableColor[$bmk][$rid];
}
$jsonData = $jsonData . ']';
$jsonColor = $jsonColor . ']';
}
$jsonData = $jsonData . ']';
$jsonColor = $jsonColor . ']';
$jsonColumns = '[';
for ($i = 1; $i <= count($table); $i++) {
if ($jsonColumns != '[') {
$jsonColumns = $jsonColumns . ',';
}
$jsonColumns = $jsonColumns . $i;
}
$jsonColumns = $jsonColumns . ']';
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonData = $jsonHeader = $jsonColumns = $jsonColor = '[]';
}
echo $this->container->getTwig()->render('mltemplate/mldatacollapse.html.twig', array('selected' => 'mldatacollapse', 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'jsonEncoded' => $jsonData, 'jsonHeader' => $jsonHeader, 'jsonColumns' => $jsonColumns, 'jsonColor' => $jsonColor, 'instance' => $instance, 'instance' => $instance, 'model_info' => $model_info, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function mlsummariesAction()
{
$displaydata = $message = '';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1) {
$preset = Utils::setDefaultPreset($db, 'mlsummaries');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$separate_feat = 'joined';
if (array_key_exists('feature', $_GET)) {
$separate_feat = $_GET['feature'];
}
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, true, $db);
$model_info = MLUtils::generateModelInfo($param_names, $params, true, $db);
$config = $model_info . ' ' . $separate_feat . ' SUMMARY';
$cache_ds = getcwd() . '/cache/query/' . md5($config) . '-cache.csv';
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM summaries WHERE id_summaries = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
if (!$is_cached) {
// 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', 'name' => 'Cl.Name', 'datanodes' => 'Datanodes', 'headnodes' => 'Headnodes', 'vm_OS' => 'VM.OS', 'vm_cores' => 'VM.Cores', 'vm_RAM' => 'VM.RAM', 'provider' => 'Provider', 'vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type');
$headers = array_keys($header_names);
$names = array_values($header_names);
// dump the result to csv
$query = "SELECT " . implode(",", $headers) . " FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE e.valid = TRUE AND e.exe_time > 100" . $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), ',', '"');
}
// launch query
$command = 'cd ' . getcwd() . '/cache/query; ../../resources/aloja_cli.r -m aloja_print_summaries -d ' . $cache_ds . ' -p ' . ($separate_feat != 'joined' ? 'sname=' . $separate_feat . ':' : '') . 'fprint=' . md5($config) . ':fwidth=1000:html=1';
#fwidth=135
$output = shell_exec($command);
// Save to DB
if (($handle = fopen(getcwd() . '/cache/query/' . md5($config) . '-summary.data', 'r')) !== FALSE) {
$displaydata = "";
while (($data = fgets($handle)) !== FALSE) {
$displaydata = $displaydata . $data;
}
fclose($handle);
$displaydata = str_replace('\'', '\\\'', $displaydata);
// register model to DB
$query = "INSERT INTO summaries (id_summaries,instance,model,summary)";
$query = $query . " VALUES ('" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "','" . $displaydata . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving model into DB');
}
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '-summary.data');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '-cache.csv');
}
// Read results of the DB
$is_cached_mysql = $dbml->query("SELECT summary FROM summaries WHERE id_summaries = '" . md5($config) . "' LIMIT 1");
$tmp_result = $is_cached_mysql->fetch();
$displaydata = $tmp_result['summary'];
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$displaydata = $separate_feat = '';
}
echo $this->container->getTwig()->render('mltemplate/mlsummaries.html.twig', array('selected' => 'mlsummaries', 'displaydata' => $displaydata, 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'feature' => $separate_feat, 'message' => $message, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function mloutliersAction()
{
$jsonData = $jsonWarns = $jsonOuts = array();
$message = $instance = $jsonHeader = $jsonTable = '';
$max_x = $max_y = 0;
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists('current_model', $_GET) || count($_GET) == 2 && array_key_exists('dump', $_GET) || count($_GET) == 2 && array_key_exists('register', $_GET) || count($_GET) == 3 && array_key_exists('dump', $_GET) && array_key_exists('current_model', $_GET) || count($_GET) == 3 && array_key_exists('register', $_GET) && array_key_exists('current_model', $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mloutliers');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$sigma_param = array_key_exists('sigma', $_GET) ? (int) $_GET['sigma'] : 1;
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, true, $db);
// Used only as indicator for WEB
$model_info = MLUtils::generateModelInfo($param_names, $params, true, $db);
// model for filling
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = "";
if (array_key_exists('current_model', $_GET) && in_array($_GET['current_model'], $possible_models_id)) {
$current_model = $_GET['current_model'];
}
if (!empty($possible_models_id)) {
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 predictions p, 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 . '-outliers';
$is_cached_mysql = $dbml->query("SELECT count(*) as total FROM resolutions WHERE id_resolution = '" . md5($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', 'name' => 'Cl.Name', 'datanodes' => 'Datanodes', 'headnodes' => 'Headnodes', 'vm_OS' => 'VM.OS', 'vm_cores' => 'VM.Cores', 'vm_RAM' => 'VM.RAM', 'provider' => 'Provider', 'vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type');
$headers = array_keys($header_names);
$names = array_values($header_names);
// dump the result to csv
$query = "SELECT " . implode(",", $headers) . " FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE e.valid = TRUE AND e.exe_time > 100" . $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 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;
$query = "REPLACE INTO resolutions (id_resolution,id_learner,id_exec,instance,model,sigma,outlier_code,predicted,observed) VALUES ";
while (($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 . "','" . $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);
$query = "INSERT INTO model_storage (id_hash,type,file) VALUES ('" . md5($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');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . $current_model . '-object.rds');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '-object.rds');
$is_cached = true;
}
if (!$is_cached) {
$jsonData = $jsonOuts = $jsonWarns = $jsonHeader = $jsonTable = '[]';
$must_wait = 'YES';
if (isset($_GET['dump'])) {
echo "1";
exit(0);
}
} else {
$must_wait = 'NO';
$query = "SELECT predicted, observed, outlier_code, id_exec, instance FROM resolutions WHERE id_resolution = '" . md5($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'] . '"]';
}
$query_var = "SELECT MAX(predicted) as max_x, MAX(observed) as max_y FROM resolutions WHERE id_resolution = '" . md5($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', 'Cl.Name', 'Datanodes', 'Headnodes', 'VM.OS', 'VM.Cores', 'VM.RAM', 'Provider', 'VM.Size', 'Type', 'Bench.Type', '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 = '[' . $jsonTable . ']';
// Dump case
if (isset($_GET['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", '', ''), $jsonData);
exit(0);
}
// Register case
if (isset($_GET['register'])) {
// Update the predictions table
$query_var = "UPDATE predictions as p, resolutions as r\n\t\t\t\t\t\t\t\tSET p.outlier = r.outlier_code\n\t\t\t\t\t\t\t\tWHERE r.id_exec = p.id_exec\n\t\t\t\t\t\t\t\t\tAND r.id_resolution = '" . md5($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 predictions in DB');
}
}
}
} else {
$message = "There are no prediction models trained for such parameters. Train at least one model in 'ML Prediction' section.";
$must_wait = "NO";
}
$dbml = null;
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonData = $jsonOuts = $jsonWarns = $jsonHeader = $jsonTable = '[]';
$model = '';
$possible_models_id = $possible_models = array();
$dbml = null;
}
echo $this->container->getTwig()->render('mltemplate/mloutliers.html.twig', array('selected' => 'mloutliers', 'jsonData' => $jsonData, 'jsonWarns' => $jsonWarns, 'jsonOuts' => $jsonOuts, 'jsonHeader' => $jsonHeader, 'jsonTable' => $jsonTable, 'max_p' => min(array($max_x, $max_y)), 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'must_wait' => $must_wait, 'models' => '<li>' . implode('</li><li>', $possible_models) . '</li>', 'models_id' => $possible_models_id, 'current_model' => $current_model, 'resolution_id' => md5($config), 'sigma' => $sigma_param, 'message' => $message, 'instance' => $instance, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function nodesEvaluationAction()
{
$dbUtils = $this->container->getDBUtils();
$preset = null;
if (sizeof($_GET) <= 1) {
$preset = Utils::setDefaultPreset($dbUtils, 'Number of Nodes Evaluation');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
try {
$filter_execs = DBUtils::getFilterExecs();
$where_configs = '';
$datefrom = Utils::read_params('datefrom', $where_configs);
$dateto = Utils::read_params('dateto', $where_configs);
$benchs = Utils::read_params('benchs', $where_configs, true);
$nets = Utils::read_params('nets', $where_configs, true);
$disks = Utils::read_params('disks', $where_configs, true);
$blk_sizes = Utils::read_params('blk_sizes', $where_configs, true);
$comps = Utils::read_params('comps', $where_configs, true);
$id_clusters = Utils::read_params('id_clusters', $where_configs, true);
$mapss = Utils::read_params('mapss', $where_configs, true);
$replications = Utils::read_params('replications', $where_configs, true);
$iosfs = Utils::read_params('iosfs', $where_configs, true);
$iofilebufs = Utils::read_params('iofilebufs', $where_configs, true);
$money = Utils::read_params('money', $where_configs, true);
$datanodes = Utils::read_params('datanodess', $where_configs, true);
$benchtype = Utils::read_params('bench_types', $where_configs, true);
$vm_sizes = Utils::read_params('vm_sizes', $where_configs, true);
$vm_coress = Utils::read_params('vm_coress', $where_configs, true);
$vm_RAMs = Utils::read_params('vm_RAMs', $where_configs, true);
$hadoop_versions = Utils::read_params('hadoop_versions', $where_configs, true);
$types = Utils::read_params('types', $where_configs, true);
$filters = Utils::read_params('filters', $where_configs, true);
$allunchecked = isset($_GET['allunchecked']) ? $_GET['allunchecked'] : '';
$minexetime = Utils::read_params('minexetime', $where_configs, true);
$maxexetime = Utils::read_params('maxexetime', $where_configs, true);
$provider = Utils::read_params('providers', $where_configs, false);
$vm_OS = Utils::read_params('vm_OSs', $where_configs, false);
if (!$benchs) {
$where_configs .= 'AND bench IN (\'terasort\')';
}
/*
$selectedGroups = array();
if(isset($_GET['selected-groups']) && $_GET['selected-groups'] != "") {
$selectedGroups = explode(",",$_GET['selected-groups']);
}
else {
$selectedGroups[] = 'exec_type';
$selectedGroups[] = 'vm_OS';
}
$selectedString = implode(',',Utils::getStandardGroupBy($selectedGroups));
$query = "SELECT ".str_replace("execTable","e",str_replace("clusterTable","c",$selectedString)).",c.vm_size,(e.exe_time * (c.cost_hour / 3600)) as cost, e.*, c.*".
" FROM execs e JOIN clusters c USING (id_cluster) INNER JOIN (
SELECT ".str_replace("execTable","e2",str_replace("clusterTable","c2",$selectedString)).",c2.vm_size as vmsize,MIN(e2.exe_time) as minexe from execs e2 JOIN clusters c2 USING (id_cluster) WHERE 1 $where_configs GROUP BY ".str_replace("execTable","e2",str_replace("clusterTable","c2",$selectedString)).",c2.vm_size
) t ON";
$it = 0;
foreach(explode(',',$selectedString) as $group) {
if ($it != 0)
$query .= " AND";
$tableName = str_replace("execTable", "e", $group);
$tableName = str_replace("clusterTable", "c", $tableName);
$withoutPrefixes = str_replace(array("execTable.", "clusterTable."), '', $group);
$query .= " t.$withoutPrefixes = $tableName";
$it++;
}
$query .= " AND t.vmsize = c.vm_size WHERE 1 GROUP BY ".str_replace("execTable","e",str_replace("clusterTable","c",$selectedString)).",c.vm_size ORDER BY ".str_replace("execTable","e",str_replace("clusterTable","c",$selectedString)).",c.vm_size DESC;";
$execs = $dbUtils->get_rows($query);
*/
$execs = $dbUtils->get_rows("SELECT c.datanodes,e.exec_type,c.vm_OS,c.vm_size,(e.exe_time * (c.cost_hour/3600)) as cost,e.*,c.* FROM execs e JOIN clusters c USING (id_cluster) INNER JOIN ( SELECT c2.datanodes,e2.exec_type,c2.vm_OS,c2.vm_size as vmsize,MIN(e2.exe_time) as minexe from execs e2 JOIN clusters c2 USING (id_cluster) WHERE 1 {$where_configs} GROUP BY c2.datanodes,e2.exec_type,c2.vm_OS,c2.vm_size ) t ON t.minexe = e.exe_time AND t.datanodes = c.datanodes AND t.vmsize = c.vm_size WHERE 1 {$filter_execs} GROUP BY c.datanodes,e.exec_type,c.vm_OS,c.vm_size ORDER BY c.datanodes ASC,c.vm_OS,c.vm_size DESC;");
$vmSizes = array();
$categories = array();
$dataNodes = array();
$vmOS = array();
$execTypes = array();
foreach ($execs as &$exec) {
if (!isset($dataNodes[$exec['datanodes']])) {
$dataNodes[$exec['datanodes']] = 1;
$categories[] = $exec['datanodes'];
}
if (!isset($vmOS[$exec['vm_OS']])) {
$vmOS[$exec['vm_OS']] = 1;
}
if (!isset($execTypes[$exec['exec_type']])) {
$execTypes[$exec['exec_type']] = 1;
}
$vmSizes[$exec['vm_size']][$exec['exec_type']][$exec['vm_OS']][$exec['datanodes']] = array(round($exec['exe_time'], 2), round($exec['cost'], 2));
}
$i = 0;
$seriesColors = array('#7cb5ec', '#434348', '#90ed7d', '#f7a35c', '#8085e9', '#f15c80', '#e4d354', '#2b908f', '#f45b5b', '#91e8e1');
$series = array();
foreach ($vmSizes as $vmSize => $value) {
foreach ($execTypes as $execType => $typevalue) {
foreach ($vmOS as $OS => $osvalue) {
if (isset($vmSizes[$vmSize][$execType][$OS])) {
if ($i == sizeof($seriesColors)) {
$i = 0;
}
$costSeries = array('name' => "{$vmSize} {$execType} {$OS} Run cost", 'type' => 'spline', 'dashStyle' => 'longdash', 'yAxis' => 0, 'data' => array(), 'tooltip' => array('valueSuffix' => ' US$'), 'color' => $seriesColors[$i]);
$timeSeries = array('name' => "{$vmSize} {$execType} {$OS} Run execution time", 'type' => 'spline', 'yAxis' => 1, 'data' => array(), 'tooltip' => array('valueSuffix' => ' s'), 'color' => $seriesColors[$i++]);
foreach ($dataNodes as $datanodes => $dvalue) {
if (!isset($value[$execType][$OS][$datanodes])) {
$costSeries['data'][] = "null";
$timeSeries['data'][] = "null";
} else {
$costSeries['data'][] = $value[$execType][$OS][$datanodes][1];
$timeSeries['data'][] = $value[$execType][$OS][$datanodes][0];
}
}
$series[] = $timeSeries;
$series[] = $costSeries;
}
}
}
}
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
echo $this->container->getTwig()->render('nodeseval/nodes_evaluation.html.twig', array('selected' => 'Number of Nodes Evaluation', 'highcharts_js' => HighCharts::getHeader(), 'categories' => json_encode($categories), 'seriesData' => str_replace('"null"', 'null', json_encode($series)), 'options' => Utils::getFilterOptions($dbUtils), 'datefrom' => $datefrom, 'dateto' => $dateto, 'benchs' => $benchs, 'nets' => $nets, 'disks' => $disks, 'blk_sizes' => $blk_sizes, 'comps' => $comps, 'id_clusters' => $id_clusters, 'mapss' => $mapss, 'replications' => $replications, 'iosfs' => $iosfs, 'iofilebufs' => $iofilebufs, 'money' => $money, 'datanodess' => $datanodes, 'bench_types' => $benchtype, 'vm_sizes' => $vm_sizes, 'vm_coress' => $vm_coress, 'vm_RAMs' => $vm_RAMs, 'vm_OS' => $vm_OS, 'hadoop_versions' => $hadoop_versions, 'types' => $types, 'providers' => $provider, 'filters' => $filters, 'allunchecked' => $allunchecked, 'select_multiple_benchs' => false, 'minexetime' => $minexetime, 'maxexetime' => $maxexetime, 'preset' => $preset, 'selPreset' => $selPreset, 'select_multiple_benchs' => false));
}
public function mlpredictionAction()
{
$jsonExecs = array();
$instance = $error_stats = '';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists("dump", $_GET) || count($_GET) == 2 && array_key_exists("pass", $_GET) || count($_GET) == 3 && array_key_exists("dump", $_GET) && array_key_exists("pass", $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mlprediction');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$learn_param = array_key_exists('learn', $_GET) ? $_GET['learn'] : 'regtree';
$unrestricted = array_key_exists('umodel', $_GET) && $_GET['umodel'] == 1;
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("`id_cluster`", "e.`id_cluster`", $where_configs);
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, $unrestricted, $db);
$model_info = MLUtils::generateModelInfo($param_names, $params, $unrestricted, $db);
$config = $model_info . ' ' . $learn_param . ' ' . ($unrestricted ? 'U' : 'R');
$learn_options = 'saveall=' . md5($config);
if ($learn_param == 'regtree') {
$learn_method = 'aloja_regtree';
$learn_options .= ':prange=0,20000';
} else {
if ($learn_param == 'nneighbours') {
$learn_method = 'aloja_nneighbors';
$learn_options .= ':kparam=3';
} else {
if ($learn_param == 'nnet') {
$learn_method = 'aloja_nnet';
$learn_options .= ':prange=0,20000';
} else {
if ($learn_param == 'polyreg') {
$learn_method = 'aloja_linreg';
$learn_options .= ':ppoly=3:prange=0,20000';
}
}
}
}
$cache_ds = getcwd() . '/cache/query/' . md5($config) . '-cache.csv';
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM learners WHERE id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
$in_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.fin');
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', 'name' => 'Cl.Name', 'datanodes' => 'Datanodes', 'headnodes' => 'Headnodes', 'vm_OS' => 'VM.OS', 'vm_cores' => 'VM.Cores', 'vm_RAM' => 'VM.RAM', 'provider' => 'Provider', 'vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type');
$headers = array_keys($header_names);
$names = array_values($header_names);
// dump the result to csv
$query = "SELECT " . implode(",", $headers) . " FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE e.valid = TRUE AND e.exe_time > 100" . $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($row), ',', '"');
}
// run the R processor
exec('cd ' . getcwd() . '/cache/query ; touch ' . getcwd() . '/cache/query/' . md5($config) . '.lock');
exec('cd ' . getcwd() . '/cache/query ; ' . getcwd() . '/resources/queue -c "' . getcwd() . '/resources/aloja_cli.r -d ' . $cache_ds . ' -m ' . $learn_method . ' -p ' . $learn_options . ' > /dev/null 2>&1; rm -f ' . getcwd() . '/cache/query/' . md5($config) . '.lock; touch ' . md5($config) . '.fin" > /dev/null 2>&1 -p 1 &');
}
$in_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.fin');
if ($in_process) {
$jsonExecs = "[]";
$must_wait = "YES";
$max_x = $max_y = 0;
if (isset($_GET['dump'])) {
echo "1";
exit(0);
}
if (isset($_GET['pass'])) {
return 1;
}
} else {
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM learners WHERE id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
if (!$is_cached) {
// register model to DB
$query = "INSERT IGNORE INTO learners (id_learner,instance,model,algorithm)";
$query = $query . " VALUES ('" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "','" . $learn_param . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving model into DB');
}
// read results of the CSV and dump to DB
foreach (array("tt", "tv", "tr") as $value) {
if (($handle = fopen(getcwd() . '/cache/query/' . md5($config) . '-' . $value . '.csv', 'r')) !== FALSE) {
$header = fgetcsv($handle, 1000, ",");
$token = 0;
$insertions = 0;
$query = "INSERT IGNORE INTO predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,name,datanodes,headnodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,pred_time,id_learner,instance,predict_code) VALUES ";
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$specific_instance = implode(",", array_slice($data, 2, 20));
$specific_data = implode(",", $data);
$specific_data = preg_replace('/,Cmp(\\d+),/', ',${1},', $specific_data);
$specific_data = preg_replace('/,Cl(\\d+),/', ',${1},', $specific_data);
$specific_data = str_replace(",", "','", $specific_data);
$query_var = "SELECT count(*) as num FROM predictions WHERE instance = '" . $specific_instance . "' AND id_learner = '" . md5($config) . "'";
$result = $dbml->query($query_var);
$row = $result->fetch();
// Insert instance values
if ($row['num'] == 0) {
if ($token != 0) {
$query = $query . ",";
}
$token = 1;
$insertions = 1;
$query = $query . "('" . $specific_data . "','" . md5($config) . "','" . $specific_instance . "','" . ($value == 'tt' ? 3 : ($value == 'tv' ? 2 : 1)) . "') ";
}
}
if ($insertions > 0) {
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving into DB');
}
}
fclose($handle);
}
}
// 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);
$query = "INSERT INTO model_storage (id_hash,type,file) VALUES ('" . md5($config) . "','learner','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file model into DB');
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.csv');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.fin');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.dat');
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.rds');
}
$must_wait = "NO";
$count = 0;
$max_x = $max_y = 0;
$error_stats = '';
$query = "SELECT exe_time, pred_time, instance FROM predictions WHERE id_learner='" . md5($config) . "' AND exe_time > 100 LIMIT 5000";
// FIXME - CLUMPSY PATCH FOR BYPASS THE BUG FROM HIGHCHARTS... REMEMBER TO ERASE THIS LIMIT WHEN THE BUG IS SOLVED
$result = $dbml->query($query);
foreach ($result as $row) {
$jsonExecs[$count]['y'] = (int) $row['exe_time'];
$jsonExecs[$count]['x'] = (int) $row['pred_time'];
$jsonExecs[$count]['mydata'] = $row['instance'];
if ((int) $row['exe_time'] > $max_y) {
$max_y = (int) $row['exe_time'];
}
if ((int) $row['pred_time'] > $max_x) {
$max_x = (int) $row['pred_time'];
}
$count++;
}
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE, predict_code FROM predictions WHERE id_learner='" . md5($config) . "' AND predict_code > 0 AND exe_time > 100 GROUP BY predict_code";
$result = $dbml->query($query);
foreach ($result as $row) {
$error_stats = $error_stats . 'Dataset: ' . ($row['predict_code'] == 1 ? 'tr' : ($row['predict_code'] == 2 ? 'tv' : 'tt')) . ' => MAE: ' . $row['MAE'] . ' RAE: ' . $row['RAE'] . '<br/>';
}
if (isset($_GET['dump'])) {
$data = json_encode($jsonExecs);
echo "Observed, Predicted, Execution\n";
echo str_replace(array('},{"y":', '"x":', '"mydata":', '[{"y":', '"}]'), array("\n", '', '', '', ''), $data);
exit(0);
}
if (isset($_GET['pass'])) {
$data = json_encode($jsonExecs);
$retval = "Observed, Predicted, Execution\n";
$retval = $retval . str_replace(array('},{"y":', '"x":', '"mydata":', '[{"y":', '"}]'), array("\n", '', '', '', ''), $data);
return $retval;
}
}
$dbml = null;
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonExecs = '[]';
$max_x = $max_y = 0;
$must_wait = 'NO';
$dbml = null;
}
echo $this->container->getTwig()->render('mltemplate/mlprediction.html.twig', array('selected' => 'mlprediction', 'jsonExecs' => json_encode($jsonExecs), 'max_p' => min(array($max_x, $max_y)), 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'unrestricted' => $unrestricted, 'learn' => $learn_param, 'must_wait' => $must_wait, 'instance' => $instance, 'model_info' => $model_info, 'id_learner' => md5($config), 'error_stats' => $error_stats, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}