Machine learning is teaching the computer how to learn by itself. It is far easier to be done, especially when you have small data set and a good level of expertise in your field. Classifying objects, predicting who will buy, spotting comments in code is achieved with grassy algorithms like neural networks, genetic algorithms or ant herding. PHP is in good position to make use of such teachings, and take advantages of related technologies like fann. By the end of the session, you'll know where you want to try it.

1. MACHINE LEARNING IN PHP
The roots of education are bitter, but the fruit is sweet
PHP conf asia, Singapore, 2016

2. Agenda
• How to teach tricks to your PHP
• Application : searching for code in comments
• Complex learning

3. Speaker
• Damien Seguy
• Exakat CTO
• Static analysis of PHP code

4. Machine Learning
• Teaching the machine
• Supervised learning : learning then applying
• Application build its own model : training phase
• It applies its model to real cases : applying phase

5. Applications
• Play go, chess, tic-tac-toe and beat everyone else
• Fraud detection and risk analysis
• Automated translation or automated transcription
• OCR and face recognition
• Medical diagnostics
• Walk, welcome guest at hotels, play football
• Finding good PHP code

6. PHP Applications
• Recommendations systems
• Predicting user behavior
• SPAM
• conversion user to customer
• ETA
• Detect code in comments

7. Real use case
• Identify code in comments
• Classic problem
• Good problem for machine learning
• Complex, no simple solution
• A lot of data and expertise are available

8. Supervised Training
History
data
Training
ModelReal data Results

9. The Fann Extension
• ext/fann (https://pecl.php.net/package/fann)
• Fast Artificial Neural Network
• http://leenissen.dk/fann/wp/
• Neural networks in PHP
• Works on PHP 7, thanks to the hard work of Jakub
Zelenka
• https://github.com/bukka/php-fann

10. NEURAL NETWORKS
• Imitation of nature
• Input layer
• Output layer
• Intermediate layers

11. Neural network
• Imitation of nature
• Input layer
• Output layer
• Intermediate layers

12. Initialisation
<?php
$num_layers = 1;
$num_input = 5;
$num_neurons_hidden = 3;
$num_output = 1;
$ann = fann_create_standard($num_layers, $num_input,
$num_neurons_hidden, $num_output);
// Activation function
fann_set_activation_function_hidden($ann,
FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output($ann,
FANN_SIGMOID_SYMMETRIC);

13. Preparing data
Raw data Extract Filter Human review Fann ready

14. Expert at work
// Test if the if is in a compressed format
// none need yet
// There is a parser specified in `Parser::$KEYWORD_PARSERS`
// $result should exist, regardless of $_message
// $a && $b and multidimensional
// numGlyphs + 1
// TODO : fix this; var_dump($var);
// if(ob_get_clean()){
//$annots .= ' /StructParent ';
// $cfg['Servers'][$i]['controlpass'] = 'pmapass';

15. Input vector
• 'length' : size of the comment
• 'countDollar' : number of $
• 'countEqual' : number of =
• 'countObjectOperator' number of -> operator ($o->p)
• 'countSemicolon' : number of semi-colon ;

16. Input data
46 5 1
825 0 0 0 1
0
37 2 0 0 0
0
55 2 2 0 1
1
61 2 1 3 1
1
...
* This file is part of Exakat.
*
* Exakat is free software: you can redist
* it under the terms of the GNU Affero Ge
* the Free Software Foundation, either ve
* (at your option) any later version.
*
* Exakat is distributed in the hope that
* but WITHOUT ANY WARRANTY; without even
* MERCHANTABILITY or FITNESS FOR A PARTIC
* GNU Affero General Public License for m
*
* You should have received a copy of the
* along with Exakat. If not, see <http:/
*
* The latest code can be found at <http:/
*
*/
// $x[3] or $x[] and multidimensional
//if ($round == 3) { die('Round '.$round);
//$this->errors[] = $this->language->get('
Number of input
Number of incoming data
Number of outgoing data

17. 1 5 1
37 2 0 0 0
0
// $x[3] or $x[] and multidimensional
ext/Fann
It's a comment

18. Training
$max_epochs = 500000;
$desired_error = 0.001;
// the actual training
if (fann_train_on_file($ann,
'incoming.data',
$max_epochs,
$epochs_between_reports,
$desired_error)) {
fann_save($ann, 'model.out');
}
fann_destroy($ann);
?>

22. TRAINING
• 47 cases
• 5 characteristics
• 3 hidden neurons
• + 5 input + 1 output
• Duration : 5.711 s

23. Application
History
data
Training
ModelReal data Results

24. Application
<?php
$ann = fann_create_from_file('model.out');
$comment = '//$gvars = $this->getGraphicVars();';
$input = makeVector($comment);
$results = fann_run($ann, $input);
if ($results[0] > 0.8) {
print ""$comment" -> $results[0] n";
}
?>

25. Results > 0.8
• Answer between 0 and 1
• Values ranges from -14 to 0,999
• The closer to 1, the safer. The closer to 0, the safer.
• Is this a percentage? Is this a carrots count ?
• It's a mix of counts…

26. -16
-12
-8
-4
0
60.000000
70.000000
80.000000
90.000000
100.000000

27. REAL CASES
• Tested on 14093 comments
• Duration 68.01ms
• Found 1960 issues (14%)

28. 0.99999893
// $cfg['Servers'][$i]['controlhost'] = '';
0.99999928
//$_SESSION['Import_message'] = $message->getDisplay();
/* 0.99999928
if (defined('SESSIONUPLOAD')) {
// write sessionupload back into the loaded PMA session
$sessionupload = unserialize(SESSIONUPLOAD);
foreach ($sessionupload as $key => $value) {
$_SESSION[$key] = $value;
}
// remove session upload data that are not set anymore
foreach ($_SESSION as $key => $value) {
if (mb_substr($key, 0, mb_strlen(UPLOAD_PREFIX))
== UPLOAD_PREFIX
&& ! isset($sessionupload[$key])
) {
unset($_SESSION[$key]);
}
}

29. 0.98780382
//LEAD_OFFSET = (0xD800 - (0x10000 >> 10)) = 55232
0.99361396
// We have server(s) => apply default configuration
0.98383027
// Duration = as configured
0.99999928
// original -> translation mapping
0.97590065
// = ( 59 x 84 ) mm = ( 2.32 x 3.31 ) in

30. True positive False positive
True negative False negative
Found by
FANN
Target

31. True
positive
False
positive
True
negative
False
negative
Found by
FANN
Target
// $cfg['Servers'][$i]['table_coords'] = 'pma__tabl
//(isset($attribs['height'])?$attribs['height']: 1)
// if ($key != null) did not work for index "0"
// the PASSWORD() function
0.99999923
0.73295981
0.99999851
0.2104115

32. RESULTS
• 1960 issues
• 50+% of false positive
• With an easy clean, 822 issues reported
• 14k comments, analyzed in 68 ms (367ms in PHP5)
• Total time of coding : 27 mins.
// = ( 59 x 84 ) mm = ( 2.32 x 3.31 ) in
/* vim: set expandtab sw=4 ts=4 sts=4: */

33. Learn better, not harder
• Better training data
• Improve characteristics
• Configure the neural network
• Change algorithm
• Automate learning
• Update constantly
Real data
History
data
Training
Model Results
Retroaction

34. Better training data
• More data, more data, more data
• Varied situations, real case situations
• Include specific cases
• Experience is capital
• https://homes.cs.washington.edu/~pedrod/
papers/cacm12.pdf

35. Improve characteristics
• Add new characteristics
• Remove the one that are less interesting
• Find the right set of characteristics

36. Network Configuration
• Input vector
• Intermediate neurons
• Activation function
• Output vector
0
5000
10000
15000
20000
1 2 3 4 5 6 7 8 9 10
1 layer 2 layers 3 layers 4 layers
Time of training (ms)

37. Change algorithm
• First add more data before changing algorithm
• Try cascade2 algorithm from FANN
• 0.6 => 0 found
• 0.5 => 2 found
• Not found by the first algorithm

38. Finding the BEST
• Test with 2-4
layers
10 neurons
• Measure
results
0
2250
4500
6750
9000
1 2 3 4 5 6 7 8 9 10 11 12 13
1 layer 2 layers 3 layers 4 layers

39. DEEP LEARNING
• Chaining the neural networks
• Auto-encoders
• Unsupervised Learning
• Genetic algorithm, ant, random forest, naive Bayes

40. Other tools
• PHP ext/fann
• Langage R
• https://github.com/kachkaev/php-r
• Scikit-learn
• https://github.com/scikit-learn/scikit-learn
• Mahout
• https://mahout.apache.org/

41. Conclusion
• Machine learning is about data, not code
• There are tools to use it with PHP
• Fast to try, easy results or fast fail
• Use it for complex problems, that accepts error

42. THANKYOU!
@exakat