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);
}
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 mlnewconfigsAction()
{
$jsonData = $jsonHeader = $configs = $jsonNewconfs = $jsonNewconfsHeader = '[]';
$message = $instance = $config = $model_info = $slice_info = '';
$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();
$where_configs = '';
// FIXME - This must be counted BEFORE building filters, as filters inject rubbish in GET when there are no parameters...
$instructions = count($_GET) <= 1;
// Where_Configs and Manual Presets
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists('learn', $_GET)) {
$_GET['id_cluster'] = $params['id_cluster'] = array('3', '5', '8');
$where_configs .= ' AND id_cluster IN (3,5,8)';
//$_GET['bench'] = $params['bench'] = array('terasort'); $where_configs .= ' AND bench IN ("terasort")';
//$_GET['disk'] = $params['disk'] = array('HDD','SSD'); $where_configs .= ' AND disk IN ("HDD","SSD")';
$_GET['blk_size'] = $params['blk_size'] = array('64', '128', '256');
$where_configs .= ' AND blk_size IN ("64","128","256")';
$_GET['iofilebuf'] = $params['iofilebuf'] = array('32768', '65536', '131072');
$where_configs .= ' AND iofilebuf IN ("32768","65536","131072")';
$_GET['comp'] = $params['comp'] = array('0');
$where_configs .= ' AND comp IN ("0")';
$_GET['replication'] = $params['replication'] = array('1');
$where_configs .= ' AND replication IN ("1")';
//$_GET['hadoop_version'] = $params['hadoop_version'] = array('1','1.03','2'); $where_configs .= ' AND hadoop_version IN ("1","1.03","2")';
//$_GET['bench_type'] = $params['bench_type'] = array('HiBench'); $where_configs .= ' AND bench_type IN ("HiBench")';
$_GET['datanodes'] = $params['datanodes'] = array('3');
// $where_configs .= ' AND datanodes = 3';
$_GET['vm_OS'] = $params['vm_OS'] = array('linux');
// $where_configs .= ' AND vm_OS = "linux"';
$_GET['vm_size'] = $params['vm_size'] = array('SYS-6027R-72RF');
// $where_configs .= ' AND vm_size = "SYS-6027R-72RF"';
$_GET['vm_cores'] = $params['vm_cores'] = array('12');
// $where_configs .= ' AND vm_cores = 12';
$_GET['vm_RAM'] = $params['vm_RAM'] = array('128');
// $where_configs .= ' AND vm_RAM = 128';
$_GET['type'] = $params['type'] = array('On-premise');
// $where_configs .= ' AND type = "On-premise"';
$_GET['provider'] = $params['provider'] = array('on-premise');
// $where_configs .= ' AND provider = "on-premise"';
$_GET['datefrom'] = $params['datefrom'] = '';
$_GET['dateto'] = $params['dateto'] = '';
$_GET['maxexetime'] = $params['maxexetime'] = 20000;
$_GET['minexetime'] = $params['minexetime'] = 1;
} else {
$param_names_whereconfig = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'bench_type', 'hadoop_version', 'datasize', 'scale_factor');
foreach ($param_names_whereconfig as $p) {
MLNewconfigsController::add_where_configs($p, $where_configs);
}
$where_configs .= (isset($_GET['datefrom']) && $_GET['datefrom'] != '' ? ' AND start_time >= ' . $_GET['datefrom'] : '') . (isset($_GET['dateto']) && $_GET['dateto'] != '' ? ' AND end_time <= ' . $_GET['dateto'] : '') . (isset($_GET['minexetime']) && $_GET['minexetime'] != '' ? ' AND exe_time >= ' . $_GET['minexetime'] : '') . (isset($_GET['maxexetime']) && $_GET['maxexetime'] != '' ? ' AND exe_time <= ' . $_GET['maxexetime'] : '') . (isset($_GET['valid']) ? ' AND valid = ' . $_GET['valid'] : '') . (isset($_GET['filter']) ? ' AND filter = ' . $_GET['filter'] : '');
}
// Real fetching of parameters
$params = array();
$param_names = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'vm_OS', 'vm_cores', 'vm_RAM', 'provider', 'vm_size', 'type', 'bench_type', 'hadoop_version', 'datasize', 'scale_factor');
// Order is important
foreach ($param_names as $p) {
$params[$p] = MLNewconfigsController::read_params($p);
sort($params[$p]);
}
$params_additional = array();
$param_names_additional = array('datefrom', 'dateto', 'minexetime', 'maxexetime', 'valid', 'filter');
// Order is important
foreach ($param_names_additional as $p) {
$params_additional[$p] = MLNewconfigsController::read_params($p);
}
$learn_param = array_key_exists('learn', $_GET) ? $_GET['learn'] : 'regtree';
$param_id_cluster = $params['id_cluster'];
unset($params['id_cluster']);
// Exclude the param from now on
$where_configs = str_replace("AND .", "AND ", $where_configs);
// Semi-Dummy Filters (For ModelInfo and SimpleInstance)
$this->buildFilters(array('learn' => array('type' => 'selectOne', 'default' => array('regtree'), 'label' => 'Learning method: ', 'generateChoices' => function () {
return array('regtree', 'nneighbours', 'nnet', 'polyreg');
}, 'beautifier' => function ($value) {
$labels = array('regtree' => 'Regression Tree', 'nneighbours' => 'k-NN', 'nnet' => 'NNets', 'polyreg' => 'PolyReg-3');
return $labels[$value];
}, 'parseFunction' => function () {
$choice = isset($_GET['learn']) ? $_GET['learn'] : array('regtree');
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'minexetime' => array('default' => 1), 'maxexetime' => array('default' => 20000), 'datefrom' => array('default' => ''), 'dateto' => array('default' => ''), 'valid' => array('default' => 1), 'filter' => array('default' => 1), 'prepares' => array('default' => 0)));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true, 'filters' => array('learn'))));
if ($instructions) {
MLUtils::getIndexNewconfs($jsonNewconfs, $jsonNewconfsHeader, $dbml);
$params['id_cluster'] = $param_id_cluster;
$return_params = array('selected' => 'mlnewconfigs', 'instructions' => 'YES', 'jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'configs' => $configs, 'newconfs' => $jsonNewconfs, 'header_newconfs' => $jsonNewconfsHeader, 'must_wait' => $must_wait, 'options' => MLNewconfigsController::getFilterOptions($db));
foreach ($param_names as $p) {
$return_params[$p] = $params[$p];
}
foreach ($param_names_additional as $p) {
$return_params[$p] = $params_additional[$p];
}
echo $this->container->getTwig()->render('mltemplate/mlnewconfigs.html.twig', $return_params);
return;
}
// compose instance
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, true, true);
$param_names_aux = array_diff($param_names, array('id_cluster'));
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names_aux, $params, true, true);
$instances = MLUtils::completeInstances($this->filters, array($instance), $param_names, $params, $db);
$slice_info = MLUtils::generateDatasliceInfo($this->filters, $param_names_additional, $params_additional);
$config = $model_info . ' ' . $learn_param . ' ' . $slice_info . ' newminconfs';
if ($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';
}
}
}
}
$cache_ds = getcwd() . '/cache/ml/' . md5($config) . '-cache.csv';
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM aloja_ml.learners WHERE id_learner = '" . md5($config . "M") . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM aloja_ml.minconfigs WHERE id_minconfigs = '" . md5($config . 'R') . "' AND id_learner = '" . md5($config . "M") . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $is_cached && $tmp_result['num'] > 0;
$in_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/ml/' . md5($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', 'e.net' => 'Net', 'e.disk' => 'Disk', 'maps' => 'Maps', 'iosf' => 'IO.SFac', 'replication' => 'Rep', 'iofilebuf' => 'IO.FBuf', 'comp' => 'Comp', 'blk_size' => 'Blk.size', 'datanodes' => 'Datanodes', 'c.vm_OS' => 'VM.OS', 'c.vm_cores' => 'VM.Cores', 'c.vm_RAM' => 'VM.RAM', 'c.provider' => 'Provider', 'c.vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type', 'hadoop_version' => 'Hadoop.Version', 'IFNULL(datasize,0)' => 'Datasize', 'scale_factor' => 'Scale.Factor');
$added_names = array('maxtxkbs' => 'Net.maxtxKB.s', 'maxrxkbs' => 'Net.maxrxKB.s', 'maxtxpcks' => 'Net.maxtxPck.s', 'maxrxpcks' => 'Net.maxrxPck.s', 'maxtxcmps' => 'Net.maxtxCmp.s', 'maxrxcmps' => 'Net.maxrxCmp.s', 'maxrxmscts' => 'Net.maxrxmsct.s', 'maxtps' => 'Disk.maxtps', 'maxsvctm' => 'Disk.maxsvctm', 'maxrds' => 'Disk.maxrd.s', 'maxwrs' => 'Disk.maxwr.s', 'maxrqsz' => 'Disk.maxrqsz', 'maxqusz' => 'Disk.maxqusz', 'maxawait' => 'Disk.maxawait', 'maxutil' => 'Disk.maxutil');
// dump the result to csv
$query = "SELECT " . implode(",", array_keys($header_names)) . ",\n\t\t\t\t\tn.maxtxkbs, n.maxrxkbs, n.maxtxpcks, n.maxrxpcks, n.maxtxcmps, n.maxrxcmps, n.maxrxmscts,\n\t\t\t\t\td.maxtps, d.maxsvctm, d.maxrds, d.maxwrs, d.maxrqsz, d.maxqusz, d.maxawait, d.maxutil\n\t\t\t\t\tFROM aloja2.execs AS e LEFT JOIN aloja2.clusters AS c ON e.id_cluster = c.id_cluster,\n\t\t\t\t\t(\n\t\t\t\t\t SELECT MAX(n1.`maxtxkB/s`) AS maxtxkbs, MAX(n1.`maxrxkB/s`) AS maxrxkbs,\n\t\t\t\t\t MAX(n1.`maxtxpck/s`) AS maxtxpcks, MAX(n1.`maxrxpck/s`) AS maxrxpcks,\n\t\t\t\t\t MAX(n1.`maxtxcmp/s`) AS maxtxcmps, MAX(n1.`maxrxcmp/s`) AS maxrxcmps,\n\t\t\t\t\t MAX(n1.`maxrxmcst/s`) AS maxrxmscts,\n\t\t\t\t\t e1.net AS net, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t FROM aloja2.precal_network_metrics AS n1,\n\t\t\t\t\t aloja2.execs AS e1 LEFT JOIN aloja2.clusters AS c1 ON e1.id_cluster = c1.id_cluster\n\t\t\t\t\t WHERE e1.id_exec = n1.id_exec\n\t\t\t\t\t GROUP BY e1.net, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t) AS n,\n\t\t\t\t\t(\n\t\t\t\t\t SELECT MAX(d1.maxtps) AS maxtps, MAX(d1.maxsvctm) as maxsvctm,\n\t\t\t\t\t MAX(d1.`maxrd_sec/s`) as maxrds, MAX(d1.`maxwr_sec/s`) as maxwrs,\n\t\t\t\t\t MAX(d1.maxrq_sz) as maxrqsz, MAX(d1.maxqu_sz) as maxqusz,\n\t\t\t\t\t MAX(d1.maxawait) as maxawait, MAX(d1.`max%util`) as maxutil,\n\t\t\t\t\t e2.disk AS disk, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t FROM aloja2.precal_disk_metrics AS d1,\n\t\t\t\t\t aloja2.execs AS e2 LEFT JOIN aloja2.clusters AS c1 ON e2.id_cluster = c1.id_cluster\n\t\t\t\t\t WHERE e2.id_exec = d1.id_exec\n\t\t\t\t\t GROUP BY e2.disk, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t) AS d\n\t\t\t\t\tWHERE e.net = n.net AND c.vm_cores = n.vm_cores AND c.vm_RAM = n.vm_RAM AND c.vm_size = n.vm_size\n\t\t\t\t\tAND c.vm_OS = n.vm_OS AND c.provider = n.provider AND e.disk = d.disk AND c.vm_cores = d.vm_cores\n\t\t\t\t\tAND c.vm_RAM = d.vm_RAM AND c.vm_size = d.vm_size AND c.vm_OS = d.vm_OS AND c.provider = d.provider\n\t\t\t\t\tAND 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($fp, array_values(array_merge($header_names, $added_names)), ',', '"');
foreach ($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
$vin = "Benchmark,Net,Disk,Maps,IO.SFac,Rep,IO.FBuf,Comp,Blk.size,Datanodes,VM.OS,VM.Cores,VM.RAM,Provider,VM.Size,Type,Bench.Type,Hadoop.Version,Datasize,Scale.Factor,Net.maxtxKB.s,Net.maxrxKB.s,Net.maxtxPck.s,Net.maxrxPck.s,Net.maxtxCmp.s,Net.maxrxCmp.s,Net.maxrxmsct.s,Disk.maxtps,Disk.maxsvctm,Disk.maxrd.s,Disk.maxwr.s,Disk.maxrqsz,Disk.maxqusz,Disk.maxawait,Disk.maxutil";
exec('cd ' . getcwd() . '/cache/ml; touch ' . md5($config) . '.lock');
$command = getcwd() . '/resources/queue -c "cd ' . getcwd() . '/cache/ml; ../../resources/aloja_cli.r -d ' . $cache_ds . ' -m ' . $learn_method . ' -p ' . $learn_options . ':saveall=' . md5($config . "F") . ':vin=\'' . $vin . '\' >debug1.txt 2>&1 && ';
$count = 1;
foreach ($instances as $inst) {
$command = $command . '../../resources/aloja_cli.r -m aloja_predict_instance -l ' . md5($config . "F") . ' -p inst_predict=\'' . $inst . '\':saveall=' . md5($config . "D") . '-' . $count++ . ':vin=\'' . $vin . '\' >>debug2.txt 2>&1 && ';
}
$command = $command . ' head -1 ' . md5($config . "D") . '-1-dataset.data >' . md5($config . "D") . '-dataset.data 2>>debug2-1.txt && ';
$command = $command . ' cat ' . md5($config . "D") . '*-dataset.data >' . md5($config . "D") . '-aux.data 2>>debug2-1.txt && ';
$command = $command . ' grep -v "ID" ' . md5($config . "D") . '*-aux.data >>' . md5($config . "D") . '-dataset.data 2>>debug2-1.txt && ';
$command = $command . '../../resources/aloja_cli.r -d ' . md5($config . "D") . '-dataset.data -m ' . $learn_method . ' -p ' . $learn_options . ':saveall=' . md5($config . "M") . ':vin=\'' . $vin . '\' >debug3.txt 2>&1 && ';
$command = $command . '../../resources/aloja_cli.r -m aloja_minimal_instances -l ' . md5($config . "M") . ' -p saveall=' . md5($config . 'R') . ':kmax=200 >debug4.txt 2>&1; rm -f ' . md5($config) . '.lock; touch ' . md5($config) . '.fin" >debug4.tmp 2>&1 &';
exec($command);
sleep(2);
}
$in_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.lock');
if ($in_process) {
$must_wait = "YES";
throw new \Exception('WAIT');
}
$learners = array();
$learners[] = md5($config . "F");
$learners[] = md5($config . "M");
foreach ($learners as $learner_1) {
// Save learning model to DB, with predictions
$is_cached_mysql = $dbml->query("SELECT id_learner FROM aloja_ml.learners WHERE id_learner = '" . $learner_1 . "'");
$tmp_result = $is_cached_mysql->fetch();
if ($tmp_result['id_learner'] != $learner_1) {
// register model to DB
$query = "INSERT IGNORE INTO aloja_ml.learners (id_learner,instance,model,algorithm,dataslice)";
$query = $query . " VALUES ('" . $learner_1 . "','" . $instance . "','" . substr($model_info, 1) . "','" . $learn_param . "','" . $slice_info . "');";
if ($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/ml/' . $learner_1 . '-' . $value . '.csv', 'r')) !== FALSE) {
$header = fgetcsv($handle, 1000, ",");
$token = 0;
$query = "INSERT IGNORE INTO aloja_ml.predictions (\n\t\t\t\t\t\t\t\tid_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,\n\t\t\t\t\t\t\t\tdatanodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,hadoop_version,\n\t\t\t\t\t\t\t\tdatasize,scale_factor,\n\t\t\t\t\t\t\t\tnet_maxtxkbs,net_maxrxkbs,net_maxtxpcks,net_maxrxpcks,net_maxtxcmps,net_maxrxcmps,net_maxrxmscts,\n\t\t\t\t\t\t\t\tdisk_maxtps,disk_maxsvctm,disk_maxrds,disk_maxwrs,disk_maxrqsz,disk_maxqusz,disk_maxawait, disk_maxutil,\n\t\t\t\t\t\t\t\tpred_time,id_learner,instance,predict_code) VALUES ";
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$specific_instance = implode(",", array_slice($data, 2, 35));
$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 aloja_ml.predictions WHERE instance = '" . $specific_instance . "' AND id_learner = '" . $learner_1 . "'";
$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 . "','" . $learner_1 . "','" . $specific_instance . "','" . ($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/ml/' . $learner_1 . '*.{dat,csv}');
}
}
// Save minconfigs to DB, with props and centers
$is_cached_mysql = $dbml->query("SELECT id_minconfigs FROM aloja_ml.minconfigs WHERE id_minconfigs = '" . md5($config . 'R') . "'");
$tmp_result = $is_cached_mysql->fetch();
if ($tmp_result['id_minconfigs'] != md5($config . 'R')) {
// register minconfigs to DB
$query = "INSERT IGNORE INTO aloja_ml.minconfigs (id_minconfigs,id_learner,instance,model,is_new)";
$query = $query . " VALUES ('" . md5($config . 'R') . "','" . md5($config . 'M') . "','" . $instance . "','" . substr($model_info, 1) . "','1');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving minconfis into DB');
}
$clusters = array();
// Save results of the CSV - MAE or RAE
if (file_exists(getcwd() . '/cache/ml/' . md5($config . 'R') . '-raes.csv')) {
$error_file = 'raes.csv';
} else {
$error_file = 'maes.csv';
}
$handle = fopen(getcwd() . '/cache/ml/' . md5($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 aloja_ml.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
$handle_sizes = fopen(getcwd() . '/cache/ml/' . md5($config . 'R') . '-sizes.csv', 'r');
foreach ($clusters as $cluster) {
// Get supports from sizes
$sizes = fgetcsv($handle_sizes, 1000, ",");
// Get clusters
$handle = fopen(getcwd() . '/cache/ml/' . md5($config . 'R') . '-dsk' . $cluster . '.csv', 'r');
$header = fgetcsv($handle, 1000, ",");
$i = 0;
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$subdata1 = array_slice($data, 0, 11);
$subdata2 = array_slice($data, 18, 4);
$specific_data = implode(',', array_merge($subdata1, $subdata2));
$specific_data = preg_replace('/,Cmp(\\d+),/', ',${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 aloja_ml.minconfigs_centers (id_minconfigs,cluster,id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,bench_type,hadoop_version,datasize,scale_factor,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
$filemodel = getcwd() . '/cache/ml/' . md5($config . 'F') . '-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 . 'F') . "','learner','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file model into DB');
}
$filemodel = getcwd() . '/cache/ml/' . md5($config . 'M') . '-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 . 'M') . "','learner','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file model into DB');
}
$filemodel = getcwd() . '/cache/ml/' . md5($config . 'R') . '-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 . 'R') . "','minconf','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file minconf into DB');
}
// Remove temporal files
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'R') . '*.rds');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'R') . '*.dat');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'R') . '*.csv');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'D') . '*.csv');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'D') . '*.dat');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'D') . '*.data');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'F') . '*.rds');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'F') . '*.csv');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'F') . '*.dat');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'M') . '*.rds');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'M') . '*.csv');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'M') . '*.dat');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '*.csv');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '*.dat');
exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '*.fin');
}
// Retrieve minconfig progression results from DB
$header = "id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,bench_type,hadoop_version,datasize,scale_factor,support";
$header_array = explode(",", $header);
$last_y = 9000000000000000.0;
$configs = '[';
$jsonData = array();
$query = "SELECT cluster, MAE, RAE FROM aloja_ml.minconfigs_props WHERE id_minconfigs='" . md5($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 " . $header . " FROM aloja_ml.minconfigs_centers WHERE id_minconfigs='" . md5($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:"Bench.Type"},{title:"Hadoop.Ver"},{title:"Data.Size"},{title:"Scale.Factor"},{title:"Support"}]';
$query = "SELECT MAX(cluster) as mcluster, MAX(MAE) as mmae, MAX(RAE) as mrae FROM aloja_ml.minconfigs_props WHERE id_minconfigs='" . md5($config . 'R') . "'";
$is_cached_mysql = $dbml->query($query);
$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 $e) {
if ($e->getMessage() != "WAIT") {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
$jsonData = $jsonHeader = $configs = '[]';
}
$dbml = null;
$params['id_cluster'] = $param_id_cluster;
$return_params = array('selected' => 'mlnewconfigs', 'jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'configs' => $configs, 'newconfs' => $jsonNewconfs, 'header_newconfs' => $jsonNewconfsHeader, 'max_p' => min(array($max_x, $max_y)), 'instance' => $instance, 'id_newconf' => md5($config), 'id_newconf_first' => md5($config . 'F'), 'id_newconf_dataset' => md5($config . 'D'), 'id_newconf_model' => md5($config . 'M'), 'id_newconf_result' => md5($config . 'R'), 'model_info' => $model_info, 'slice_info' => $slice_info, 'learn' => $learn_param, 'must_wait' => $must_wait, 'options' => MLNewconfigsController::getFilterOptions($db));
foreach ($param_names as $p) {
$return_params[$p] = $params[$p];
}
foreach ($param_names_additional as $p) {
$return_params[$p] = $params_additional[$p];
}
echo $this->container->getTwig()->render('mltemplate/mlnewconfigs.html.twig', $return_params);
}
public function mlpredictionAction()
{
$jsonExecs = array();
$instance = $error_stats = '';
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();
if (array_key_exists('dump', $_GET)) {
$dump = $_GET["dump"];
unset($_GET["dump"]);
}
if (array_key_exists('pass', $_GET)) {
$pass = $_GET["pass"];
unset($_GET["pass"]);
}
$this->buildFilters(array('learn' => array('type' => 'selectOne', 'default' => array('regtree'), 'label' => 'Learning method: ', 'generateChoices' => function () {
return array('regtree', 'nneighbours', 'nnet', 'polyreg');
}, 'beautifier' => function ($value) {
$labels = array('regtree' => 'Regression Tree', 'nneighbours' => 'k-NN', 'nnet' => 'NNets', 'polyreg' => 'PolyReg-3');
return $labels[$value];
}, 'parseFunction' => function () {
$choice = isset($_GET['learn']) ? $_GET['learn'] : array('regtree');
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'umodel' => array('type' => 'checkbox', 'default' => 1, 'label' => 'Unrestricted to new values', 'parseFunction' => function () {
$choice = isset($_GET['submit']) && !isset($_GET['umodel']) ? 0 : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning')));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true, 'filters' => array('learn', 'umodel'))));
$where_configs = $this->filters->getWhereClause();
$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);
$learnParams = $this->filters->getFiltersSelectedChoices(array('learn', 'umodel'));
$learn_param = $learnParams['learn'];
$unrestricted = $learnParams['umodel'] ? true : false;
$where_configs = str_replace("id_cluster", "e.id_cluster", $where_configs);
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names, $params, $unrestricted);
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, $unrestricted);
$slice_info = MLUtils::generateDatasliceInfo($this->filters, $param_names_additional, $params_additional);
$config = $model_info . ' ' . $learn_param . ' ' . ($unrestricted ? 'U' : 'R') . ' ' . $slice_info;
$learn_options = 'saveall=' . md5($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 = $dbml->query("SELECT count(*) as num FROM aloja_ml.learners WHERE id_learner = '" . md5($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', '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($row), ',', '"');
}
// run the R processor
exec('cd ' . getcwd() . '/cache/query ; touch ' . getcwd() . '/cache/query/' . md5($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 = $max_y = 0;
if (isset($dump)) {
echo "1";
exit(0);
}
if (isset($pass)) {
return 1;
}
} else {
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM aloja_ml.learners WHERE id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
if (!$is_cached) {
// register model to DB
$query = "INSERT IGNORE INTO aloja_ml.learners (id_learner,instance,model,algorithm,dataslice)";
$query = $query . " VALUES ('" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "','" . $learn_param . "','" . $slice_info . "');";
if ($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;
$query = "INSERT IGNORE INTO aloja_ml.predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,datanodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,hadoop_version,pred_time,id_learner,instance,predict_code) VALUES ";
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$specific_instance = implode(",", array_slice($data, 2, 21));
$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 aloja_ml.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);
$query = "INSERT INTO aloja_ml.model_storage (id_hash,type,file) VALUES ('" . md5($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');
}
$must_wait = "NO";
$count = 0;
$max_x = $max_y = 0;
$error_stats = '';
$query = "SELECT exe_time, pred_time, instance FROM aloja_ml.predictions WHERE id_learner='" . md5($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++;
}
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE, predict_code FROM aloja_ml.predictions WHERE id_learner='" . md5($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: ' . $row['RAE'] . '<br/>';
}
if (isset($dump)) {
$data = json_encode($jsonExecs);
echo "Observed, Predicted, Execution\n";
echo str_replace(array('},{"y":', '"x":', '"mydata":', '[{"y":', '"}]'), array("\n", '', '', '', ''), $data);
exit(0);
}
if (isset($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;
}
$return_params = array('jsonExecs' => json_encode($jsonExecs), 'max_p' => min(array($max_x, $max_y)), 'unrestricted' => $unrestricted, 'learn' => $learn_param, 'must_wait' => $must_wait, 'instance' => $instance, 'model_info' => $model_info, 'slice_info' => $slice_info, 'id_learner' => md5($config), 'error_stats' => $error_stats);
return $this->render('mltemplate/mlprediction.html.twig', $return_params);
}
public function mlfindattributesAction()
{
$instance = $instances = $message = $tree_descriptor = $model_html = $config = '';
$possible_models = $possible_models_id = $other_models = array();
$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();
if (array_key_exists('dump', $_GET)) {
$dump = $_GET["dump"];
unset($_GET["dump"]);
}
if (array_key_exists('pass', $_GET)) {
$pass = $_GET["pass"];
unset($_GET["pass"]);
}
$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'), 'unseen' => array('type' => 'checkbox', 'default' => 1, 'label' => 'Predict with unseen atributes ⚠', 'parseFunction' => function () {
$choice = isset($_GET['unseen']) && !isset($_GET['unseen']) ? 0 : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, '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', 'unseen'))));
$where_configs = $this->filters->getWhereClause();
$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]);
}
}
$learnParams = $this->filters->getFiltersSelectedChoices(array('current_model', 'unseen'));
$param_current_model = $learnParams['current_model'];
$unseen = $learnParams['unseen'] ? true : false;
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
$jsonData = $jsonHeader = "[]";
$mae = $rae = 0;
// compose instance
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, $unseen);
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names, $params, $unseen);
$instances = MLUtils::generateInstances($this->filters, $param_names, $params, $unseen, $db);
// Model for filling
MLUtils::findMatchingModels($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[] = $row['id_learner'];
}
if (!empty($possible_models_id) || $current_model != "") {
$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 . '-' . $current_model . '-' . ($unseen ? 'U' : 'R');
$is_cached_mysql = $dbml->query("SELECT count(*) as total FROM aloja_ml.trees WHERE id_findattrs = '" . md5($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 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);
// 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;
$query = "INSERT IGNORE INTO aloja_ml.predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,datanodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,hadoop_version,pred_time,id_learner,instance,predict_code) VALUES ";
if (($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 aloja_ml.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 aloja_ml.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 aloja_ml.predictions (id_exec,exe_time,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,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);
$query = "INSERT INTO aloja_ml.trees (id_findattrs,id_learner,instance,model,tree_code) VALUES ('" . md5($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');
$is_cached = true;
}
fclose($handle);
}
if (!$is_cached) {
$jsonData = $jsonHeader = $jsonColumns = $jsonColor = '[]';
$must_wait = 'YES';
if (isset($dump)) {
$dbml = null;
echo "1";
exit(0);
}
if (isset($pass)) {
$dbml = null;
return "1";
}
} else {
if (isset($pass) && $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', 'Datanodes', 'VM.OS', 'VM.Cores', 'VM.RAM', 'Provider', 'VM.Size', 'Type', 'Bench.Type', 'Version', '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 aloja_ml.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
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE FROM aloja_ml.predictions WHERE id_learner='" . md5($config) . "' AND predict_code > 0";
$result = $dbml->query($query);
$row = $result->fetch();
$mae = $row['MAE'];
$rae = $row['RAE'];
// Dump case
if (isset($dump)) {
echo "ID" . str_replace(array("[", "]", "{title:\"", "\"}"), array('', '', ''), $jsonHeader) . "\n";
echo str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonData);
$dbml = null;
exit(0);
}
if (isset($pass) && $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
$query = "SELECT tree_code FROM aloja_ml.trees WHERE id_findattrs = '" . md5($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.";
$must_wait = 'NO';
if (isset($dump)) {
echo "-1";
exit(0);
}
if (isset($pass)) {
return "-1";
}
}
$dbml = null;
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonData = $jsonHeader = "[]";
$must_wait = 'NO';
$mae = $rae = 0;
$dbml = null;
if (isset($pass)) {
return "-2";
}
}
$return_params = array('instance' => $instance, '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_descriptor' => $tree_descriptor);
$this->filters->setCurrentChoices('current_model', array_merge($possible_models_id, array('---Other models---'), $other_models));
return $this->render('mltemplate/mlfindattributes.html.twig', $return_params);
}
public function mlparamEvaluationAction()
{
$rows = $categories = $series = $instance = $model_info = $config = $current_model = $slice_info = '';
$arrayBenchs_pred = $possible_models = $possible_models_id = $other_models = array();
$jsonData = $jsonHeader = "[]";
$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();
if (array_key_exists('parameval', $_GET)) {
$paramEval = isset($_GET['parameval']) && Utils::get_GET_string('parameval') != '' ? Utils::get_GET_string('parameval') : 'maps';
unset($_GET["parameval"]);
}
$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'), 'minExecs' => array('default' => 0, 'type' => 'inputNumber', 'label' => 'Minimum executions:', 'parseFunction' => function () {
return 0;
}, 'filterGroup' => 'basic'), 'minexetime' => array('default' => 0), 'valid' => array('default' => 0), 'filter' => array('default' => 0), 'prepares' => array('default' => 0)));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true, 'filters' => array('current_model'))));
$where_configs = $this->filters->getWhereClause();
$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', 'minExecs'));
$param_current_model = $param_variables['current_model'];
$minExecs = $param_variables['minExecs'];
$where_configs = str_replace("AND .", "AND ", $where_configs);
$where_configs = str_replace("id_cluster", "e.id_cluster", $where_configs);
$minExecsFilter = "";
if ($minExecs > 0) {
$minExecsFilter = "HAVING COUNT(*) > {$minExecs}";
}
$filter_execs = DBUtils::getFilterExecs();
$options = $this->filters->getFilterChoices();
$paramOptions = array();
foreach ($options[$paramEval] as $option) {
if ($paramEval == 'comp') {
$paramOptions[] = Utils::getCompressionName($option);
} else {
if ($paramEval == 'net') {
$paramOptions[] = Utils::getNetworkName($option);
} else {
if ($paramEval == 'disk') {
$paramOptions[] = Utils::getDisksName($option);
} else {
$paramOptions[] = $option;
}
}
}
}
$param_eval_query = $paramEval == 'id_cluster' ? 'e.id_cluster' : $paramEval;
$benchOptions = $db->get_rows("SELECT DISTINCT bench FROM aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs} GROUP BY {$param_eval_query}, bench order by {$param_eval_query}");
// get the result rows
$query = "SELECT count(*) as count, {$param_eval_query}, e.id_exec, exec as conf, bench, " . "exe_time, avg(exe_time) avg_exe_time, min(exe_time) min_exe_time " . "from aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs}" . "GROUP BY {$param_eval_query},bench {$minExecsFilter} order by bench,{$param_eval_query}";
$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 == '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
// ----------------------------------------------------
$param_variables = $this->filters->getFiltersSelectedChoices(array('current_model'));
$param_current_model = $param_variables['current_model'];
$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[] = $row['id_learner'];
}
if (!empty($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 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 . ' ' . $slice_info . "-parameval";
$query_cache = "SELECT count(*) as total FROM aloja_ml.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';
$query = "SELECT count(*) as count, {$param_eval_query}, bench, exe_time, avg(pred_time) avg_pred_time, min(pred_time) min_pred_time " . "FROM aloja_ml.predictions e WHERE e.id_learner = '" . $current_model . "' {$filter_execs} {$where_configs}" . "GROUP BY {$param_eval_query}, bench {$minExecsFilter} order by bench,{$param_eval_query}";
$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 = '[]';
$must_wait = 'NO';
}
$return_params = array('title' => 'Improvement of Hadoop Execution by SW and HW Configurations', 'categories' => $categories, 'series' => $series, 'paramEval' => $paramEval, 'instance' => $instance, 'models' => '<li>' . implode('</li><li>', $possible_models) . '</li>', 'models_id' => $possible_models_id, 'current_model' => $current_model, 'gammacolors' => $colors, 'model_info' => $model_info, 'slice_info' => $slice_info, 'must_wait' => $must_wait);
$this->filters->setCurrentChoices('current_model', array_merge($possible_models_id, array('---Other models---'), $other_models));
return $this->render('mltemplate/mlparameval.html.twig', $return_params);
}
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 mlprecisionAction()
{
$jsonDiversity = $jsonPrecisions = $jsonDiscvars = $jsonHeaderDiv = $jsonPrecisionHeader = '[]';
$instance = $error_stats = '';
$jsonPrecexps = $jsonPrecexpsHeader = '[]';
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;
$this->buildFilters();
if ($instructions) {
MLUtils::getIndexPrecExps($jsonPrecexps, $jsonPrecexpsHeader, $dbml);
return $this->render('mltemplate/mlprecision.html.twig', array('precexps' => $jsonPrecexps, 'header_precexps' => $jsonPrecexpsHeader, 'discvars' => '[]', 'diversity' => '[]', 'precisions' => '[]', 'diversityHeader' => '[]', 'precisionHeader' => '[]', 'instructions' => 'YES'));
}
$where_configs = $this->filters->getWhereClause();
$where_configs = str_replace("AND .", "AND ", $where_configs);
$param_names = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'bench_type', 'vm_size', 'vm_cores', 'vm_RAM', 'type', 'hadoop_version', 'provider', 'vm_OS');
// 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]);
}
}
$param_names_additional = array('datefrom', 'dateto', 'minexetime', 'maxexetime', 'valid', 'filter');
// Order is important
$params_additional = $this->filters->getFiltersSelectedChoices($param_names_additional);
// 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);
$config = $model_info . ' ' . $slice_info . "-precision";
$cache_ds = getcwd() . '/cache/query/' . md5($config) . '-cache.csv';
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM aloja_ml.precisions WHERE id_precision = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
$eval_names = array('Cl.Name', 'Datanodes', 'Headnodes', 'VM.OS', 'VM.Cores', 'VM.RAM', 'Provider', 'VM.Size', 'Type', 'Bench.Type', 'Hadoop.Version');
$in_process = file_exists(getcwd() . '/cache/query/' . md5($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('e.id_exec' => 'ID', 'e.bench' => 'Benchmark', 'e.exe_time' => 'Exe.Time', 'e.net' => 'Net', 'e.disk' => 'Disk', 'e.maps' => 'Maps', 'e.iosf' => 'IO.SFac', 'e.replication' => 'Rep', 'e.iofilebuf' => 'IO.FBuf', 'e.comp' => 'Comp', 'e.blk_size' => 'Blk.size', 'e.id_cluster' => 'Cluster', 'c.name' => 'Cl.Name', 'c.datanodes' => 'Datanodes', 'c.headnodes' => 'Headnodes', 'c.vm_OS' => 'VM.OS', 'c.vm_cores' => 'VM.Cores', 'c.vm_RAM' => 'VM.RAM', 'c.provider' => 'Provider', 'c.vm_size' => 'VM.Size', 'c.type' => 'Type', 'e.bench_type' => 'Bench.Type', 'e.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 LEFT JOIN aloja_ml.predictions p USING (id_exec) WHERE e.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($row), ',', '"');
}
// run the R processor
exec('cd ' . getcwd() . '/cache/query ; touch ' . getcwd() . '/cache/query/' . md5($config) . '.lock');
$count = 1;
foreach ($eval_names as $name) {
exec(getcwd() . '/resources/queue -d -c "cd ' . getcwd() . '/cache/query ; ../../resources/aloja_cli.r -d ' . md5($config) . '-cache.csv -m aloja_diversity -p vdisc="' . $name . '":noout=1:json=1 -v > ' . md5($config) . '-D-' . $name . '.tmp 2>/dev/null; touch ' . md5($config) . '-' . $count++ . '.lock" >/dev/null 2>&1 &');
exec(getcwd() . '/resources/queue -d -c "cd ' . getcwd() . '/cache/query ; ../../resources/aloja_cli.r -d ' . md5($config) . '-cache.csv -m aloja_precision_split -p vdisc="' . $name . '":noout=1:json=1 -v > ' . md5($config) . '-P-' . $name . '.tmp 2>/dev/null; touch ' . md5($config) . '-' . $count++ . '.lock" >/dev/null 2>&1 &');
}
}
$finished_process = (int) shell_exec('ls ' . getcwd() . '/cache/query/' . md5($config) . '-*.lock | wc -w ') == 2 * count($eval_names);
if ($finished_process && !$is_cached) {
$token = 0;
$token_i = 0;
$query = "INSERT IGNORE INTO aloja_ml.precisions (id_precision,model,instance,dataslice,diversity,precisions,discvar) VALUES ";
foreach ($eval_names as $name) {
$treated_line_d = "";
$treated_line_p = "";
if (($handle = fopen(getcwd() . '/cache/query/' . md5($config) . '-D-' . $name . '.tmp', "r")) !== FALSE) {
$line = fgets($handle, 1000000);
$treated_line_d = substr($line, 5);
$treated_line_d = substr($treated_line_d, 0, -2);
$treated_line_d = preg_replace('/,Cmp(\\d+),/', ',${1},', $treated_line_d);
$treated_line_d = preg_replace('/,Cl(\\d+),/', ',${1},', $treated_line_d);
$treated_line_d = str_replace("'", "\"", $treated_line_d);
}
fclose($handle);
if (($handle = fopen(getcwd() . '/cache/query/' . md5($config) . '-P-' . $name . '.tmp', "r")) !== FALSE) {
$line = fgets($handle, 1000000);
$treated_line_p = substr($line, 5);
$treated_line_p = substr($treated_line_p, 0, -2);
$treated_line_p = preg_replace('/,Cmp(\\d+),/', ',${1},', $treated_line_p);
$treated_line_p = preg_replace('/,Cl(\\d+),/', ',${1},', $treated_line_p);
$treated_line_p = str_replace("'", "\"", $treated_line_p);
}
fclose($handle);
if ($token > 0) {
$query = $query . ",";
}
$token = 1;
$query = $query . "('" . md5($config) . "','" . substr($model_info, 1) . "','" . $instance . "','" . $slice_info . "','" . $treated_line_d . "','" . $treated_line_p . "','" . $name . "') ";
$token_i = 1;
}
if ($token_i > 0) {
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving into DB');
}
}
// remove remaining locks
shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.lock');
// Remove temporal files
shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.tmp');
shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.csv');
$is_cached = true;
}
if (!$is_cached) {
$jsonDiversity = $jsonPrecisions = $jsonDiscvars = $jsonHeaderDiv = $jsonPrecisionHeader = '[]';
$must_wait = 'YES';
} else {
$must_wait = 'NO';
$discvars = array();
$diversity = array();
$precisions = array();
$query = "SELECT id_precision,model,instance,dataslice,diversity,precisions,discvar FROM aloja_ml.precisions WHERE id_precision = '" . md5($config) . "'";
$result = $dbml->query($query);
foreach ($result as $row) {
$discvars[] = $row['discvar'];
$diversity[] = $row['diversity'];
$precisions[] = $row['precisions'];
}
$jsonDiscvars = "['" . implode("','", $discvars) . "']";
$jsonDiversity = "[" . implode(",", $diversity) . "]";
$jsonPrecisions = "[" . implode(",", $precisions) . "]";
$header = array('Benchmark', 'Net', 'Disk', 'Maps', 'IO.SFS', 'Rep', 'IO.FBuf', 'Comp', 'Blk.Size', 'Target', 'Exe.Time', 'Support');
$jsonHeaderDiv = '[';
foreach ($header as $title) {
if ($jsonHeaderDiv != '[') {
$jsonHeaderDiv = $jsonHeaderDiv . ',';
}
$jsonHeaderDiv = $jsonHeaderDiv . '{"title":"' . $title . '"}';
}
$jsonHeaderDiv = $jsonHeaderDiv . ']';
$jsonPrecisionHeader = '[{"title":"Target"},{"title":"Diversity"},{"title":"# Executions"},{"title":"Deviation (UnPrecision)"},{"title":"Mean [Stats]"},{"title":"StDev [Stats]"},{"title":"Max [Stats]"},{"title":"Min [Stats]"}]';
}
$dbml = null;
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage());
$jsonDiversity = $jsonPrecisions = $jsonDiscvars = $jsonHeaderDiv = $jsonPrecisionHeader = '[]';
$must_wait = 'NO';
$dbml = null;
}
$return_params = array('discvars' => $jsonDiscvars, 'diversity' => $jsonDiversity, 'precisions' => $jsonPrecisions, 'diversityHeader' => $jsonHeaderDiv, 'precisionHeader' => $jsonPrecisionHeader, 'precexps' => $jsonPrecexps, 'header_precexps' => $jsonPrecexpsHeader, 'must_wait' => $must_wait, 'instance' => $instance, 'model_info' => $model_info, 'slice_info' => $slice_info, 'id_precision' => md5($config), 'error_stats' => $error_stats);
return $this->render('mltemplate/mlprecision.html.twig', $return_params);
}
public function mlobservedtreesAction()
{
$model_info = $instance = $slice_info = $message = $config = $tree_descriptor_ordered = $tree_descriptor_gini = '';
$jsonData = $jsonHeader = '[]';
$jsonObstrees = $jsonObstreesHeader = '[]';
$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;
$this->buildFilters(array('minexetime' => array('default' => 0), 'valid' => array('default' => 0), 'filter' => array('default' => 0), 'prepares' => array('default' => 1)));
if ($instructions) {
MLUtils::getIndexObsTrees($jsonObstrees, $jsonObstreesHeader, $dbml);
return $this->render('mltemplate/mlobstrees.html.twig', array('obstrees' => $jsonObstrees, 'header_obstrees' => $jsonObstreesHeader, 'jsonData' => '[]', 'jsonHeader' => '[]', 'instructions' => 'YES'));
}
$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', 'datasize', 'scale_factor');
// 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);
$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);
$config = $instance . '-' . $slice_info . '-obstree';
$is_cached_mysql = $dbml->query("SELECT count(*) as total FROM aloja_ml.observed_trees WHERE id_obstrees = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$in_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.fin');
$tmp_file = getcwd() . '/cache/ml/' . md5($config) . '.tmp';
// get headers for csv
$header_names = array('bench' => 'Benchmark', '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', 'IFNULL(datasize,0)' => 'Datasize', 'scale_factor' => 'Scale.Factor');
$special_header_names = array('id_exec' => 'ID', 'exe_time' => 'Exe.Time');
$headers = array_keys($header_names);
$special_headers = array_keys($special_header_names);
if (!$in_process && !$finished_process && !$is_cached) {
// Dump the DB slice to csv
$query = "SELECT " . implode(",", $headers) . ", " . implode(",", $special_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($rows)) {
throw new \Exception('No data matches with your critteria.');
}
if (($key = array_search('e.id_cluster', $headers)) !== false) {
$headers[$key] = 'id_cluster';
}
$fp = fopen($tmp_file, 'w');
foreach ($rows as $row) {
$row['id_cluster'] = "Cl" . $row['id_cluster'];
// Cluster is numerically codified...
$row['comp'] = "Cmp" . $row['comp'];
// Compression is numerically codified...
$line = '';
foreach ($headers as $hn) {
$line = $line . ($line != '' ? ',' : '') . $row[$hn];
}
$line = $row['id_exec'] . ' ' . $line . ' ' . $row['exe_time'] . "\n";
fputs($fp, $line);
}
fclose($fp);
if (($key = array_search('id_cluster', $headers)) !== false) {
$headers[$key] = 'e.id_cluster';
}
// Execute R Engine
$exe_query = 'cd ' . getcwd() . '/cache/ml;';
$exe_query = $exe_query . ' touch ' . md5($config) . '.lock;';
$exe_query = $exe_query . ' ../../resources/aloja_cli.r -m aloja_representative_tree -p method=ordered:dump_file=' . $tmp_file . ':output=nodejson -v >' . md5($config) . '-split.dat 2>/dev/null;';
$exe_query = $exe_query . ' ../../resources/aloja_cli.r -m aloja_representative_tree -p method=gini:dump_file=' . $tmp_file . ':output=nodejson -v >' . md5($config) . '-gini.dat 2>/dev/null;';
$exe_query = $exe_query . ' rm -f ' . md5($config) . '.lock; rm -f ' . $tmp_file . '; touch ' . md5($config) . '.fin';
exec(getcwd() . '/resources/queue -d -c "' . $exe_query . '" >/dev/null 2>&1 &');
}
if (!$is_cached) {
$finished_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.fin');
if ($finished_process) {
// Read results and dump to DB
$tree_descriptor_ordered = '';
try {
$file = fopen(getcwd() . '/cache/ml/' . md5($config) . '-split.dat', "r");
$tree_descriptor_ordered = fgets($file);
$tree_descriptor_ordered = substr($tree_descriptor_ordered, 5, -2);
$tree_descriptor_ordered = str_replace("\\\"", "\"", $tree_descriptor_ordered);
$tree_descriptor_ordered = str_replace("desc:\"\"", "desc:\"---\"", $tree_descriptor_ordered);
fclose($file);
} catch (\Exception $e) {
throw new \Exception("Error on retrieving result file. Check that R is working properly.");
}
$tree_descriptor_gini = '';
/* try
{
$file = fopen(getcwd().'/cache/ml/'.md5($config).'-gini.dat', "r");
$tree_descriptor_gini = fgets($file);
$tree_descriptor_gini = substr($tree_descriptor_gini, 5, -2);
$tree_descriptor_gini = str_replace("\\\"","\"",$tree_descriptor_gini);
fclose($file);
} catch (\Exception $e) { throw new \Exception ("Error on retrieving result file. Check that R is working properly."); }
*/
$query = "INSERT INTO aloja_ml.observed_trees (id_obstrees,instance,model,dataslice,tree_code_split,tree_code_gain) VALUES ('" . md5($config) . "','" . $instance . "','" . $model_info . "','" . $slice_info . "','" . $tree_descriptor_ordered . "','" . $tree_descriptor_gini . "')";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving tree into DB');
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '-*.dat');
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '.fin');
} else {
$must_wait = 'YES';
throw new \Exception('WAIT');
}
}
// Fetch results and compose JSON
$header = array('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', 'Datasize', 'Scale.Factor', 'Observed');
$jsonHeader = '[{title:""}';
foreach ($header as $title) {
$jsonHeader = $jsonHeader . ',{title:"' . $title . '"}';
}
$jsonHeader = $jsonHeader . ']';
// Fetch observed values
$query = "SELECT " . implode(",", $headers) . ", " . implode(",", $special_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($rows)) {
throw new \Exception('No data matches with your critteria.');
}
if (($key = array_search('e.id_cluster', $headers)) !== false) {
$headers[$key] = 'id_cluster';
}
$jsonData = '[';
foreach ($rows as $row) {
$row['id_cluster'] = "Cl" . $row['id_cluster'];
// Cluster is numerically codified...
$row['comp'] = "Cmp" . $row['comp'];
// Compression is numerically codified...
$line = '';
foreach ($headers as $hn) {
$line = $line . ($line != '' ? ',' : '') . $row[$hn];
}
$line = $row['id_exec'] . ',' . $line . ',' . $row['exe_time'];
if ($jsonData != '[') {
$jsonData = $jsonData . ',';
}
$jsonData = $jsonData . "['" . str_replace(",", "','", $line) . "']";
}
$jsonData = $jsonData . ']';
foreach (range(1, 32) as $value) {
$jsonData = str_replace('Cmp' . $value, Utils::getCompressionName($value), $jsonData);
}
if ($tree_descriptor_ordered == '') {
// Display Descriptive Tree, if not processed yet
$query = "SELECT tree_code_split, tree_code_gain FROM aloja_ml.observed_trees WHERE id_obstrees = '" . md5($config) . "'";
$result = $dbml->query($query);
$row = $result->fetch();
$tree_descriptor_ordered = $row['tree_code_split'];
$tree_descriptor_gini = $row['tree_code_gain'];
}
} catch (\Exception $e) {
if ($e->getMessage() != "WAIT") {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
$jsonData = $jsonHeader = '[]';
}
$dbml = null;
$return_params = array('jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'obstrees' => $jsonObstrees, 'header_obstrees' => $jsonObstreesHeader, 'message' => $message, 'must_wait' => $must_wait, 'instance' => $instance, 'model_info' => $model_info, 'slice_info' => $slice_info, 'id_obstrees' => md5($config), 'tree_descriptor_ordered' => $tree_descriptor_ordered, 'tree_descriptor_gini' => $tree_descriptor_gini);
return $this->render('mltemplate/mlobstrees.html.twig', $return_params);
}
public function mldatacollapseAction()
{
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();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists("current_model", $_GET)) {
$preset = Utils::initDefaultPreset($db, 'mldatacollapse');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types', 'hadoop_versions');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$params_additional = array();
$param_names_additional = array('datefrom', 'dateto', 'minexetime', 'maxexetime', 'valids', 'filters');
// Order is important
foreach ($param_names_additional as $p) {
$params_additional[$p] = Utils::read_params($p, $where_configs, FALSE);
}
$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,');
$dims1 = $dims1 . (empty($params['hadoop_versionss']) ? '' : 'Version,') . (empty($params['bench_types']) ? '' : 'Bench.Type');
if (substr($dims1, -1) == ',') {
$dims1 = substr($dims1, 0, -1);
}
$dims2 = "Benchmark";
// 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);
// 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'];
}
*/
$learning_model = '';
if ($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 . '-' . $slice_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', 'hadoop_version' => 'Hadoop.Version');
$headers = array_keys($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 aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE hadoop_version IS NOT NULL" . $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 aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE hadoop_version IS NOT NULL" . $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 = '[]';
}
$return_params = array('selected' => 'mldatacollapse', 'jsonEncoded' => $jsonData, 'jsonHeader' => $jsonHeader, 'jsonColumns' => $jsonColumns, 'jsonColor' => $jsonColor, 'instance' => $instance, 'instance' => $instance, 'model_info' => $model_info, 'slice_info' => $slice_info, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db));
foreach ($param_names as $p) {
$return_params[$p] = $params[$p];
}
foreach ($param_names_additional as $p) {
$return_params[$p] = $params_additional[$p];
}
echo $this->container->getTwig()->render('mltemplate/mldatacollapse.html.twig', $return_params);
}
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 mlcrossvar3dfaAction()
{
$jsonData = $possible_models = $possible_models_id = $other_models = array();
$message = $instance = $possible_models_id = '';
$categories1 = $categories2 = "''";
$maxx = $minx = $maxy = $miny = $maxz = $minz = 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();
$this->buildFilters(array('variable2' => array('type' => 'selectOne', 'default' => array('net'), 'table' => 'execs', 'label' => 'Variable 2: ', 'generateChoices' => function () {
return array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'bench_type', 'vm_size', 'vm_cores', 'vm_RAM', 'type', 'hadoop_version', 'provider', 'vm_OS', 'exe_time', 'pred_time', 'TOTAL_MAPS');
}, 'beautifier' => function ($value) {
$labels = array('bench' => 'Benchmark', 'net' => 'Network', 'disk' => 'Disk', 'maps' => 'Maps', 'iosf' => 'I/O Sort Factor', 'replication' => 'Replication', 'iofilebuf' => 'I/O File Buffer', 'comp' => 'Compression', 'blk_size' => 'Block size', 'id_cluster' => 'Cluster', 'datanodes' => 'Datanodes', 'bench_type' => 'Benchmark Suite', 'vm_size' => 'VM Size', 'vm_cores' => 'VM cores', 'vm_RAM' => 'VM RAM', 'type' => 'Cluster type', 'hadoop_version' => 'Hadoop Version', 'provider' => 'Provider', 'vm_OS' => 'VM OS', 'exe_time' => 'Exeuction time', 'pred_time' => 'Prediction time', 'TOTAL_MAPS' => 'Total execution maps');
return $labels[$value];
}, 'parseFunction' => function () {
$value = isset($_GET['variable2']) ? $_GET['variable2'] : array('net');
return array('currentChoice' => $value, 'whereClause' => "");
}), 'variable1' => array('type' => 'selectOne', 'default' => array('maps'), 'table' => 'execs', 'label' => 'Variable 1: ', 'generateChoices' => function () {
return array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'bench_type', 'vm_size', 'vm_cores', 'vm_RAM', 'type', 'hadoop_version', 'provider', 'vm_OS', 'exe_time', 'pred_time', 'TOTAL_MAPS');
}, 'beautifier' => function ($value) {
$labels = array('bench' => 'Benchmark', 'net' => 'Network', 'disk' => 'Disk', 'maps' => 'Maps', 'iosf' => 'I/O Sort Factor', 'replication' => 'Replication', 'iofilebuf' => 'I/O File Buffer', 'comp' => 'Compression', 'blk_size' => 'Block size', 'id_cluster' => 'Cluster', 'datanodes' => 'Datanodes', 'bench_type' => 'Benchmark Suite', 'vm_size' => 'VM Size', 'vm_cores' => 'VM cores', 'vm_RAM' => 'VM RAM', 'type' => 'Cluster type', 'hadoop_version' => 'Hadoop Version', 'provider' => 'Provider', 'vm_OS' => 'VM OS', 'exe_time' => 'Exeuction time', 'pred_time' => 'Prediction time', 'TOTAL_MAPS' => 'Total execution maps');
return $labels[$value];
}, 'parseFunction' => function () {
$value = isset($_GET['variable1']) ? $_GET['variable1'] : array('maps');
return array('currentChoice' => $value, 'whereClause' => "");
}), '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'), 'unseen' => array('type' => 'checkbox', 'default' => 1, 'label' => 'Predict with unseen atributes ⚠', 'parseFunction' => function () {
$choice = isset($_GET['unseen']) && !isset($_GET['unseen']) ? 0 : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, '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', 'unseen'))));
$where_configs = $this->filters->getWhereClause();
$model_html = '';
$model_info = $db->get_rows("SELECT id_learner, model, algorithm, dataslice FROM aloja_ml.learners");
foreach ($model_info as $row) {
$model_html = $model_html . "<li><b>" . $row['id_learner'] . "</b> => " . $row['algorithm'] . " : " . $row['model'] . " : " . $row['dataslice'] . "</li>";
}
$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);
$variables = $this->filters->getFiltersSelectedChoices(array('variable1', 'variable2', 'current_model', 'unseen'));
$cross_var1 = $variables['variable1'];
$cross_var2 = $variables['variable2'];
$param_current_model = $variables['current_model'];
$unseen = $variables['unseen'] ? true : false;
$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
$instance = MLUtils::generateSimpleInstance($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);
$current_model = in_array($param_current_model, $possible_models_id) ? $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[] = $row['id_learner'];
}
// Call to MLPrediction, to create a model
if (empty($possible_models_id)) {
$_GET['pass'] = 1;
$mltc1 = new MLPredictionController();
// FIXME - Choose the default modeling algorithm
$mltc1->container = $this->container;
$ret_learn = $mltc1->mlpredictionAction();
$rows = null;
if ($ret_data == 1) {
$must_wait = "YES";
throw new \Exception("WAIT");
} else {
if ($ret_data == -1) {
throw new \Exception("There was an error when creating a model for [" . $instance . "]");
}
}
}
// Call to MLFindAttributes, to generate data
if ($current_model != '') {
$_GET['pass'] = 2;
$mlfa1 = new MLFindAttributesController();
$mlfa1->container = $this->container;
$ret_data = $mlfa1->mlfindattributesAction();
$rows = null;
if ($ret_data == 1) {
$must_wait = "YES";
throw new \Exception("WAIT");
} else {
if ($ret_data == -1) {
throw new \Exception("There was an error when creating predictions for [" . $instance . "]");
}
}
}
// Get stuff from the DB
$query = "SELECT " . $cross_var1 . " AS V1, " . $cross_var2 . " AS V2, AVG(e.pred_time) as V3, e.instance\n\t\t\t\tFROM aloja_ml.predictions as e\n\t\t\t\tWHERE e.id_learner " . ($current_model != '' ? "='" . $current_model . "'" : "IN (SELECT id_learner FROM aloja_ml.trees WHERE model='" . $model_info . "')") . $where_configs . "\n\t\t\t\tGROUP BY e.instance\n\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 = $db->get_rows($query);
if (empty($rows)) {
if ($current_model == '') {
throw new \Exception('No data matches with your critteria. Try to select a specific model to generate data.');
} else {
throw new \Exception('No data matches with your critteria.');
}
}
// Show the results
$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'] = (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 = "''";
}
} catch (\Exception $e) {
if ($e->getMessage() != "WAIT") {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
$jsonData = '[]';
}
$dbml = null;
$return_params = array('jsonData' => $jsonData, 'variable1' => str_replace("e.id_cluster", "id_cluster", $cross_var1), 'variable2' => str_replace("e.id_cluster", "id_cluster", $cross_var2), 'categories1' => $categories1, 'categories2' => $categories2, 'maxx' => $maxx, 'minx' => $minx, 'maxy' => $maxy, 'miny' => $miny, 'maxz' => $maxz, 'minz' => $minz, 'instance' => $instance, 'model_info' => $model_info, 'slice_info' => $slice_info, 'models' => '<li>' . implode('</li><li>', $possible_models) . '</li>', 'must_wait' => $must_wait, 'models' => $model_html, 'current_model' => $current_model);
$this->filters->setCurrentChoices('current_model', array_merge(array("Aggregation of Models"), $possible_models_id, !empty($other_models) ? array('---Other models---') : array(), $other_models));
return $this->render('mltemplate/mlcrossvar3dfa.html.twig', $return_params);
}
public function mlsummariesAction()
{
$displaydata = $separate_feat = $instance = $model_info = $slice_info = '';
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();
$this->filters->removeFilters(array('upred', 'uobsr'));
$this->filters->removeFIltersFromGroup("MLearning", array('upred', 'uobsr'));
$this->buildFilters(array('feature' => array('type' => 'selectOne', 'default' => array('joined'), 'label' => 'Separate by: ', 'generateChoices' => function () {
return array('joined', 'Benchmark', 'Net', 'Disk', 'Maps', 'IO.SFac', 'Rep', 'IO.FBuf', 'Comp', 'Blk.size', 'Cluster');
}, 'beautifier' => function ($value) {
$labels = array('joined' => 'None', 'Benchmark' => 'Benchmarks', 'Net' => 'Networks', 'Disk' => 'Disks', 'Maps' => 'Maps', 'IO.SFac' => 'IO Sort Factor', 'Rep' => 'Replication', 'Rep' => 'Replication', 'IO.FBuf' => 'IO File Buffer', 'Comp' => 'Compressions', 'Blk.size' => 'Block sizes', 'Cluster' => 'Clusters');
return $labels[$value];
}, 'parseFunction' => function () {
$choice = isset($_GET['feature']) ? $_GET['feature'] : array('joined');
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning')));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true)));
$where_configs = $this->filters->getWhereClause();
$param_names = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'bench_type', 'vm_size', 'vm_cores', 'vm_RAM', 'type', 'hadoop_version', 'provider', 'vm_OS');
// 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]);
}
}
$param_names_additional = array('datefrom', 'dateto', 'minexetime', 'maxexetime', 'valid', 'filter');
// Order is important
$params_additional = $this->filters->getFiltersSelectedChoices($param_names_additional);
$feature = $this->filters->getFiltersSelectedChoices(array('feature'));
$separate_feat = $feature['feature'];
// 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);
$config = $model_info . ' ' . $separate_feat . ' ' . $slice_info . ' SUMMARY';
$cache_ds = getcwd() . '/cache/query/' . md5($config) . '-cache.csv';
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM aloja_ml.summaries WHERE id_summaries = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
if (!$is_cached) {
// get headers for csv
$header_names = array('e.id_exec' => 'ID', 'e.bench' => 'Benchmark', 'e.exe_time' => 'Exe.Time', 'e.net' => 'Net', 'e.disk' => 'Disk', 'e.maps' => 'Maps', 'e.iosf' => 'IO.SFac', 'e.replication' => 'Rep', 'e.iofilebuf' => 'IO.FBuf', 'e.comp' => 'Comp', 'e.blk_size' => 'Blk.size', 'e.id_cluster' => 'Cluster', 'c.name' => 'Cl.Name', 'c.datanodes' => 'Datanodes', 'c.headnodes' => 'Headnodes', 'c.vm_OS' => 'VM.OS', 'c.vm_cores' => 'VM.Cores', 'c.vm_RAM' => 'VM.RAM', 'c.provider' => 'Provider', 'c.vm_size' => 'VM.Size', 'c.type' => 'Type', 'e.bench_type' => 'Bench.Type', 'e.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 LEFT JOIN aloja_ml.predictions p USING (id_exec) WHERE e.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), ',', '"');
}
// 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 aloja_ml.summaries (id_summaries,instance,model,dataslice,summary)";
$query = $query . " VALUES ('" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "','" . $slice_info . "','" . $displaydata . "');";
if ($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 = $dbml->query("SELECT summary FROM aloja_ml.summaries WHERE id_summaries = '" . md5($config) . "' LIMIT 1");
$tmp_result = $is_cached_mysql->fetch();
$displaydata = $tmp_result['summary'];
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
$return_params = array('displaydata' => $displaydata, 'feature' => $separate_feat, 'instance' => $instance, 'model_info' => $model_info, 'slice_info' => $slice_info);
foreach ($param_names as $p) {
$return_params[$p] = $params[$p];
}
foreach ($param_names_additional as $p) {
$return_params[$p] = $params_additional[$p];
}
return $this->render('mltemplate/mlsummaries.html.twig', $return_params);
}
public function mlminconfigsAction()
{
$jsonData = $jsonHeader = $configs = $jsonMinconfs = $jsonMinconfsHeader = '[]';
$model_info = $slice_info = $message = $instance = $config = '';
$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;
$this->buildFilters(array('learn' => array('type' => 'selectOne', 'default' => array('regtree'), 'label' => 'Learning method: ', 'generateChoices' => function () {
return array('regtree', 'nneighbours', 'nnet', 'polyreg');
}, 'beautifier' => function ($value) {
$labels = array('regtree' => 'Regression Tree', 'nneighbours' => 'k-NN', 'nnet' => 'NNets', 'polyreg' => 'PolyReg-3');
return $labels[$value];
}, 'parseFunction' => function () {
$choice = isset($_GET['learn']) ? $_GET['learn'] : array('regtree');
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'umodel' => array('type' => 'checkbox', 'default' => 1, 'label' => 'Unrestricted to new values', 'parseFunction' => function () {
$choice = isset($_GET['submit']) && !isset($_GET['umodel']) ? 0 : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning')));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true, 'filters' => array('learn', 'umodel'))));
if ($instructions) {
MLUtils::getIndexMinconfs($jsonMinconfs, $jsonMinconfsHeader, $dbml);
return $this->render('mltemplate/mlminconfigs.html.twig', array('jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'configs' => $configs, 'minconfs' => $jsonMinconfs, 'header_minconfs' => $jsonMinconfsHeader, 'instructions' => 'YES'));
}
$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', 'datasize', 'scale_factor');
// 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);
$learnParams = $this->filters->getFiltersSelectedChoices(array('learn', 'umodel'));
$learn_param = $learnParams['learn'];
$unrestricted = $learnParams['umodel'] ? true : false;
$where_configs = $this->filters->getWhereClause();
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names, $params, $unrestricted, true);
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, $unrestricted, true);
$slice_info = MLUtils::generateDatasliceInfo($this->filters, $param_names_additional, $params_additional);
$config = $model_info . ' ' . $learn_param . ' ' . ($unrestricted ? 'U' : 'R') . ' ' . $slice_info . ' 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';
}
}
}
}
$cache_ds = getcwd() . '/cache/ml/' . md5($config) . '-cache.csv';
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM aloja_ml.learners WHERE id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['num'] > 0;
$is_cached_mysql = $dbml->query("SELECT count(*) as num FROM aloja_ml.minconfigs WHERE id_minconfigs = '" . md5($config . 'R') . "' AND id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $is_cached && $tmp_result['num'] > 0;
$in_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/ml/' . md5($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', 'e.net' => 'Net', 'e.disk' => 'Disk', 'maps' => 'Maps', 'iosf' => 'IO.SFac', 'replication' => 'Rep', 'iofilebuf' => 'IO.FBuf', 'comp' => 'Comp', 'blk_size' => 'Blk.size', 'e.id_cluster' => 'Cluster', 'datanodes' => 'Datanodes', 'c.vm_OS' => 'VM.OS', 'c.vm_cores' => 'VM.Cores', 'c.vm_RAM' => 'VM.RAM', 'c.provider' => 'Provider', 'c.vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type', 'hadoop_version' => 'Hadoop.Version', 'IFNULL(datasize,0)' => 'Datasize', 'scale_factor' => 'Scale.Factor');
$added_names = array('maxtxkbs' => 'Net.maxtxKB.s', 'maxrxkbs' => 'Net.maxrxKB.s', 'maxtxpcks' => 'Net.maxtxPck.s', 'maxrxpcks' => 'Net.maxrxPck.s', 'maxtxcmps' => 'Net.maxtxCmp.s', 'maxrxcmps' => 'Net.maxrxCmp.s', 'maxrxmscts' => 'Net.maxrxmsct.s', 'maxtps' => 'Disk.maxtps', 'maxsvctm' => 'Disk.maxsvctm', 'maxrds' => 'Disk.maxrd.s', 'maxwrs' => 'Disk.maxwr.s', 'maxrqsz' => 'Disk.maxrqsz', 'maxqusz' => 'Disk.maxqusz', 'maxawait' => 'Disk.maxawait', 'maxutil' => 'Disk.maxutil');
// dump the result to csv
$query = "SELECT " . implode(",", array_keys($header_names)) . ",\n\t\t\t\t\tn.maxtxkbs, n.maxrxkbs, n.maxtxpcks, n.maxrxpcks, n.maxtxcmps, n.maxrxcmps, n.maxrxmscts,\n\t\t\t\t\td.maxtps, d.maxsvctm, d.maxrds, d.maxwrs, d.maxrqsz, d.maxqusz, d.maxawait, d.maxutil\n\t\t\t\t\tFROM aloja2.execs AS e LEFT JOIN aloja2.clusters AS c ON e.id_cluster = c.id_cluster,\n\t\t\t\t\t(\n\t\t\t\t\t SELECT MAX(n1.`maxtxkB/s`) AS maxtxkbs, MAX(n1.`maxrxkB/s`) AS maxrxkbs,\n\t\t\t\t\t MAX(n1.`maxtxpck/s`) AS maxtxpcks, MAX(n1.`maxrxpck/s`) AS maxrxpcks,\n\t\t\t\t\t MAX(n1.`maxtxcmp/s`) AS maxtxcmps, MAX(n1.`maxrxcmp/s`) AS maxrxcmps,\n\t\t\t\t\t MAX(n1.`maxrxmcst/s`) AS maxrxmscts,\n\t\t\t\t\t e1.net AS net, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t FROM aloja2.precal_network_metrics AS n1,\n\t\t\t\t\t aloja2.execs AS e1 LEFT JOIN aloja2.clusters AS c1 ON e1.id_cluster = c1.id_cluster\n\t\t\t\t\t WHERE e1.id_exec = n1.id_exec\n\t\t\t\t\t GROUP BY e1.net, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t) AS n,\n\t\t\t\t\t(\n\t\t\t\t\t SELECT MAX(d1.maxtps) AS maxtps, MAX(d1.maxsvctm) as maxsvctm,\n\t\t\t\t\t MAX(d1.`maxrd_sec/s`) as maxrds, MAX(d1.`maxwr_sec/s`) as maxwrs,\n\t\t\t\t\t MAX(d1.maxrq_sz) as maxrqsz, MAX(d1.maxqu_sz) as maxqusz,\n\t\t\t\t\t MAX(d1.maxawait) as maxawait, MAX(d1.`max%util`) as maxutil,\n\t\t\t\t\t e2.disk AS disk, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t FROM aloja2.precal_disk_metrics AS d1,\n\t\t\t\t\t aloja2.execs AS e2 LEFT JOIN aloja2.clusters AS c1 ON e2.id_cluster = c1.id_cluster\n\t\t\t\t\t WHERE e2.id_exec = d1.id_exec\n\t\t\t\t\t GROUP BY e2.disk, c1.vm_cores, c1.vm_RAM, c1.vm_size, c1.vm_OS, c1.provider\n\t\t\t\t\t) AS d\n\t\t\t\t\tWHERE e.net = n.net AND c.vm_cores = n.vm_cores AND c.vm_RAM = n.vm_RAM AND c.vm_size = n.vm_size\n\t\t\t\t\tAND c.vm_OS = n.vm_OS AND c.provider = n.provider AND e.disk = d.disk AND c.vm_cores = d.vm_cores\n\t\t\t\t\tAND c.vm_RAM = d.vm_RAM AND c.vm_size = d.vm_size AND c.vm_OS = d.vm_OS AND c.provider = d.provider\n\t\t\t\t\tAND 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($fp, array_values(array_merge($header_names, $added_names)), ',', '"');
foreach ($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($row), ',', '"');
}
// run the R processor
exec('cd ' . getcwd() . '/cache/ml; touch ' . md5($config) . '.lock');
$command = getcwd() . '/resources/queue -c "cd ' . getcwd() . '/cache/ml; ../../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);
}
$in_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.lock');
if ($in_process) {
$must_wait = "YES";
throw new \Exception('WAIT');
}
// Save learning model to DB, with predictions
$is_cached_mysql = $dbml->query("SELECT id_learner FROM aloja_ml.learners WHERE id_learner = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
if ($tmp_result['id_learner'] != md5($config)) {
// register model to DB
$query = "INSERT INTO aloja_ml.learners (id_learner,instance,model,algorithm,dataslice)";
$query = $query . " VALUES ('" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "','" . $learn_param . "','" . $slice_info . "');";
if ($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/ml/' . md5($config) . '-' . $value . '.csv', 'r')) !== FALSE) {
$header = fgetcsv($handle, 1000, ",");
$token = 0;
$query = "INSERT IGNORE INTO aloja_ml.predictions (\n\t\t\t\t\t\t\tid_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,\n\t\t\t\t\t\t\tid_cluster,datanodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,hadoop_version,\n\t\t\t\t\t\t\tdatasize,scale_factor,\n\t\t\t\t\t\t\tnet_maxtxkbs,net_maxrxkbs,net_maxtxpcks,net_maxrxpcks,net_maxtxcmps,net_maxrxcmps,net_maxrxmscts,\n\t\t\t\t\t\t\tdisk_maxtps,disk_maxsvctm,disk_maxrds,disk_maxwrs,disk_maxrqsz,disk_maxqusz,disk_maxawait, disk_maxutil,\n\t\t\t\t\t\t\tpred_time,id_learner,instance,predict_code) VALUES ";
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$specific_instance = implode(",", array_slice($data, 2, 36));
$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 aloja_ml.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);
} else {
throw new \Exception('Error on R processing. Result file ' . md5($config) . '-' . $value . '.csv not present');
}
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '*.csv');
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '*.dat');
}
// Save minconfigs to DB, with props and centers
$is_cached_mysql = $dbml->query("SELECT id_minconfigs FROM aloja_ml.minconfigs WHERE id_minconfigs = '" . md5($config . 'R') . "'");
$tmp_result = $is_cached_mysql->fetch();
if ($tmp_result['id_minconfigs'] != md5($config . 'R')) {
// register minconfigs to DB
$query = "INSERT INTO aloja_ml.minconfigs (id_minconfigs,id_learner,instance,model,dataslice)";
$query = $query . " VALUES ('" . md5($config . 'R') . "','" . md5($config) . "','" . $instance . "','" . substr($model_info, 1) . "','" . $slice_info . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving minconfis into DB');
}
$clusters = array();
// Save results of the CSV - MAE or RAE
if (file_exists(getcwd() . '/cache/ml/' . md5($config . 'R') . '-raes.csv')) {
$error_file = 'raes.csv';
} else {
$error_file = 'maes.csv';
}
$handle = fopen(getcwd() . '/cache/ml/' . md5($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 aloja_ml.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
$handle_sizes = fopen(getcwd() . '/cache/ml/' . md5($config . 'R') . '-sizes.csv', 'r');
foreach ($clusters as $cluster) {
// Get supports from sizes
$sizes = fgetcsv($handle_sizes, 1000, ",");
// Get clusters
$handle = fopen(getcwd() . '/cache/ml/' . md5($config . 'R') . '-dsk' . $cluster . '.csv', 'r');
$header = fgetcsv($handle, 1000, ",");
$i = 0;
while (($data = fgetcsv($handle, 1000, ",")) !== FALSE) {
$subdata1 = array_slice($data, 0, 12);
$subdata2 = array_slice($data, 19, 4);
$specific_data = implode(',', array_merge($subdata1, $subdata2));
$specific_data = preg_replace('/,Cmp(\\d+),/', ',${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 aloja_ml.minconfigs_centers (id_minconfigs,cluster,id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,bench_type,hadoop_version,datasize,scale_factor,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
$filemodel = getcwd() . '/cache/ml/' . md5($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) . "','learner','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file model into DB');
}
$filemodel = getcwd() . '/cache/ml/' . md5($config . 'R') . '-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 . 'R') . "','minconf','" . $content . "');";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving file minconf into DB');
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'R') . '*.csv');
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config . 'R') . '*.rds');
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '*.rds');
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '.fin');
}
// Retrieve minconfig progression results from DB
$header = "id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,bench_type,hadoop_version,datasize,scale_factor,id_cluster,support";
$header_array = explode(",", $header);
$last_y = 9000000000000000.0;
$configs = '[';
$jsonData = array();
$query = "SELECT cluster, MAE, RAE FROM aloja_ml.minconfigs_props WHERE id_minconfigs='" . md5($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 " . $header . " FROM aloja_ml.minconfigs_centers WHERE id_minconfigs='" . md5($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:"Bench.Type"},{title:"Hadoop.Ver"},{title:"Data.Size"},{title:"Scale.Factor"},{title:"Main Ref. Cluster"},{title:"Support"}]';
$is_cached_mysql = $dbml->query("SELECT MAX(cluster) as mcluster, MAX(MAE) as mmae, MAX(RAE) as mrae FROM aloja_ml.minconfigs_props WHERE id_minconfigs='" . md5($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 $e) {
if ($e->getMessage() != "WAIT") {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
$jsonData = $jsonHeader = $configs = '[]';
}
$dbml = null;
$return_params = array('jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'minconfs' => $jsonMinconfs, 'header_minconfs' => $jsonMinconfsHeader, 'configs' => $configs, 'max_p' => min(array($max_x, $max_y)), 'instance' => $instance, 'id_learner' => md5($config), 'id_minconf' => md5($config . 'R'), 'model_info' => $model_info, 'slice_info' => $slice_info, 'must_wait' => $must_wait);
return $this->render('mltemplate/mlminconfigs.html.twig', $return_params);
}
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)));
}