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. MachineLearning
inPHP
Poland,Warsaw,October2016
"Learn, someday this pain will be useful to you"

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. MachineLearning
• 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. PhpApplications
• Recommendations systems
• Predicting user behavior
• SPAM
• conversion user to customer
• ETA
• Detect code in comments

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

8. SupervisedTraining
History
data
Training
ModelReal data Results

9. SupervisedTraining
History
data
Training
ModelReal data Results

10. TheFannExtension
• 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

11. NeuralNetworks
• Imitation of nature
• Input layer
• Output layer
• Intermediate layers

12. NeuralNetworks
• Imitation of nature
• Input layer
• Output layer
• Intermediate layers

13. <?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);
Initialisation

14. PreparingData
Raw data Extract Filter Human review Fann ready
• Extract data from raw source
• Remove any useless data from extract
• Apply some human review to filtered data
• Format data for FANN

15. ExpertAtWork
// Test if the if is in a compressed format
// nie mowie po polsku
// There is a parser specified in `Parser::$KEYWORD_PARSERS`
// $result should exist, regardless of $_message
// TODO : fix this; var_dump($var);
// $a && $b and multidimensional
// numGlyphs + 1
//$annots .= ' /StructParent ';
// $cfg['Servers'][$i]['controlpass'] = 'pmapass';
// if(ob_get_clean()){

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

17. InputData
47 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
...
NumberOfInput
NumberOfIncomingData
NumberOfOutgoingData
* (at your option) any later v
*
* Exakat is distributed in the
* but WITHOUT ANY WARRANTY; wi
* MERCHANTABILITY or FITNESS F
* GNU Affero General Public Li
*
* You should have received a c
* along with Exakat. If not,
*
* The latest code can be found
*
*/
// $x[3] or $x[] and multidimen
//if ($round == 3) { die('Round
//$this->errors[] = $this->lang

18. BlackMagic
151
372000
0
// $X[3] Or $X[] And Multidimensional
EXT/FANN
It'sAComment

19. Training
<?php
$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);
?>

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

24. Application
History
data
Training
ModelReal data Results

25. 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";
}
?>

26. 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…

27. ScoresDistribution
-16
-12
-8
-4
0
6 0 . 0 0 0 0 0 0
7 0 . 0 0 0 0 0 0
8 0 . 0 0 0 0 0 0
9 0 . 0 0 0 0 0 0
1 0 0 . 0 0 0 0 0 0

28. RealCases
• Tested on 14093 comments
• Duration 68.01ms
• Found 1960 issues (14%)

29. 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])

30. 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

31. TRUE POSITIVE FALSE POSITIVE
TRUE NEGATIVE FALSE NEGATIVE
FOUND BY
FANN
(MACHINE
LEARNING)
TARGET (EXPERT WORK)

32. TRUE
POSITIVE
FALSE
POSITIVE
TRUE
NEGATIVE
FALSE
NEGATIVE
FOUND BY
FANN
TARGET
0.99999923
0.73295981
0.99999851
0.2104115
// $cfg['Servers'][$i]['table_coords'] = 'pma__
//(isset($attribs['height'])?$attribs['height']
// if ($key != null) did not work for index "0"
// the PASSWORD() function

33. 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: */

34. Learn Better,NotHarder
• Better training data
• Improve characteristics
• Configure the neural network
• Change algorithm
• Automate learning
• Update constantly
Real data
History
data
Training
Model Results
Retroaction

35. BetterTrainingData
• 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

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

37. NetworkConfiguration
• Input vector
• Intermediate neurons
• Activation function
• Output vector
0
5 0 0 0
1 0 0 0 0
1 5 0 0 0
2 0 0 0 0
1 2 3 4 5 6 7 8 9 1 0
1 layer 2 layers 3 layers 4 layers
TimeOfTraining(Ms)

38. ChangeAlgorithm
• 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
• Ant colony, genetics algorithm, gravitational search,
artificial immune, nie mowie po polsku, annealing,
harmony search, interior point search, taboo search

39. FindingTheBest
• Test with 2-4 layers
10 neurons
• Measure results
0
2 2 5 0
4 5 0 0
6 7 5 0
9 0 0 0
1 2 3 4 5 6 7 8 9 1 0 11 1 2 1 3
1 layer 2 layers 3 layers 4 layers

40. DeepLearning
• Chaining the neural networks
• Translators, scorers, auto-encoders
• Unsupervised Learning

41. OtherTools
• 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/

42. 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

43. HTTP ://WWW.EXAKAT.IO
@EXAKAT
HTTP ://WWW.SLIDESH ARE.NET /DSEG UY/
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