Machine learning in php php con poland
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The document discusses machine learning applications in PHP, specifically focusing on how to analyze and identify code within comments using a neural network approach. It highlights the training process, showcasing real-case examples and the performance of the model on numerous comments, indicating a high issue detection rate but also a significant number of false positives. Additionally, it offers insights on improving training data, network configurations, and algorithm adjustments for enhanced results.
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Machine learning in php php con poland
- 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
- 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
- 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
- 23. Training • 47 cases • 5 characteristics • 3 hidden neurons • + 5 input + 1 output • Duration : 5.711 s
- 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%)
- 31. TRUE POSITIVE FALSE POSITIVE TRUE NEGATIVE FALSE NEGATIVE FOUND BY FANN (MACHINE LEARNING) TARGET (EXPERT WORK)
- 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/ PHP 7.1 P REPARATION WORKSHOP Dzięki Czemu