Machine Learning for the rescue

Gathering the data is not a problem today. The bigger challenge is to understand these informations and draw some conclusions. Fortunately, we can use some techniques like machine learning to „teach” computer how to learn from our data. Fast artificial neural networks, random forests, SVMs, classification, clustering - just to name a few concepts ready to use… We will apply all these solutions to PHP application to deliver automatic insights/predictions and create a real business value for a client. By the end of this session you will be familiar with Machine Learning ideas and prepared to solve unsolvable problems in PHP.

Transcript

1. RESCUE Machine Learning for a Mariusz Gil
4. CLIENT PROBLEM
5. 1M BACKLINKS CLASSIFY THEM
7. OK NOT OK I DON’T CARE
8. OK NOT OK I DON’T CARE
9. OK NOT OK I DON’T CARE
11. T(URL) → [1, 2, 3, …]
12. IF-OLOGY UGLY CODE FOR POC 1ST APPROACH
13. I DON’ KNOW
14. NAIVE MACHINE LEARNING 2ND APPROACH
15. NAIVE MACHINE LEARNING 2ND APPROACH
16. DATA ML TASK SEND TO RESULTS CALCULATE
18. RECIPE FOR A FAILURE DOING WITHOUT KNOWING
19. DATA ORIENTED MACHINE LEARNING WORKFLOW 3RD APPROACH, FINAL
20. A COMPUTER PROGRAM IS SAID TO LEARN FROM EXPERIENCE E WITH RESPECT TO SOME CLASS OF TASKS T AND PERFORMANCE MEASURE P IF ITS PERFORMANCE AT TASKS IN T, AS MEASURED BY P, IMPROVES WITH EXPERIENCE E
21. DATA ML TASK PREPARED, INPUT FOR RESULTS WITH PERFORMANCE EXPERIENCE FEEDBACK LOOP LEARNING, VALIDATING
22. ML TASK CLASSIFICATION REGRESSION CLUSTERING DIMENSIONALITY REDUCTION ASSOCIATION RULES
23. EXAMPLE TIME :)
24. FAST ARTIFICIAL NEURAL NETWORK CLASSIFICATION
26. 80 2 4 2 1 1 0 0 0 1 9 1 0 0 0 1 8 1 0 0 0 9 8 1 0 0 0 4 3 1 0 0 0 5 8 1 0 0 0 5 1 1 0 0 0 9 10 1 0 0 0 4 7 1 0 0 0 5 9 …
28. <?php $num_input = 2; $num_output = 4; $num_layers = 3; $num_neurons_hidden = 4; $desired_error = 0.001; $max_epochs = 500000; $epochs_between_reports = 1000; $ann = fann_create_standard($num_layers, $num_input, $num_neurons_hidden, $num_output); if ($ann) { fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC); $filename = dirname(__FILE__) . "/coordination_system.data"; if (fann_train_on_file( $ann, $filename, $max_epochs, $epochs_between_reports, $desired_error )) { fann_save($ann, dirname(__FILE__) . "/coordination_system.net"); } fann_destroy($ann); }
29. <?php $train_file = (dirname(__FILE__) . "/coordination_system.net"); $ann = fann_create_from_file($train_file); if ($ann) { $input = array($argv[1], $argv[2]); $calc_out = fann_run($ann, $input); fann_destroy($ann); }
30. SUPERVISED LEARNING
31. SUPPORT VECTOR MACHINES CLASSIFICATION
32. A SUPPORT VECTOR MACHINE PERFORMS CLASSIFICATION BY FINDING THE HYPERPLANE THAT MAXIMIZES THE MARGIN BETWEEN THE GIVEN CLASSES
33. SUPERVISED LEARNING
34. K-MEANS CLUSTERING
35. IRIS DATASET 1936, RONALD FISHER
39. <?php require 'vendor/autoload.php'; use Phpml\Dataset\Demo\Iris; use Phpml\Clustering\KMeans; $dataset = new Iris(); $kmeans = new KMeans(3); echo 'Dataset size: ' . count($dataset->getSamples()) . PHP_EOL; $clusters = $kmeans->cluster($dataset->getSamples()); foreach ($clusters as $i => $cluster) { echo 'Cluster #' . $i . ' :' . count(($cluster)) . PHP_EOL; }
40. $ php -f ./iris-clustering.php Dataset size: 150 Cluster #0 :39 Cluster #1 :50 Cluster #2 :61 $ php -f ./iris-clustering.php Dataset size: 150 Cluster #0 :38 Cluster #1 :50 Cluster #2 :62 $ php -f ./iris-clustering.php Dataset size: 150 Cluster #0 :39 Cluster #1 :50 Cluster #2 :61 $ php -f ./iris-clustering.php Dataset size: 150 Cluster #0 :96 Cluster #1 :24 Cluster #2 :30
41. RESULTS STABILITY
42. UNSUPERVISED LEARNING
43. RECIPE FOR A FAILURE DON’T YOU KNOW YOUR DATA?
44. PREDICTING VALUES REGRESSION
45. HOW MANY BRITISH POUNDS… EURO I SHOULD EARN AS DEVELOPER ACCORDING TO MY SKILLSET?
46. | age | linkedin_php | salary | |-----|--------------|--------| | 20 | 0 | 2000 | | 26 | 8 | 3975 | | 30 | 10 | 4000 |
47. YEARS → LINKEDIN PHP →
48. <?php require 'vendor/autoload.php'; use Phpml\Dataset\ArrayDataset; use Phpml\Regression\LeastSquares; $dataset = new ArrayDataset( [ [20, 0], [26, 8], [30, 10], ], [ 2000, 3975, 4000, ] ); $regression = new LeastSquares(); $regression->train($dataset->getSamples(), $dataset->getTargets()); echo $regression->predict(array_slice($argv, 1)) . PHP_EOL;
49. | age | city_size | linkedin_php | salary | |-----|-----------|--------------|--------| | 20 | 900000 | 0 | 2000 | | 20 | 400000 | 0 | 1800 | | 25 | 450000 | 8 | 3700 | | 26 | 900000 | 8 | 3975 | | 30 | 100000 | 10 | 4000 | | 30 | 500000 | 10 | 3500 |
50. SUPERVISED LEARNING
51. TECHNOLOGY
57. …JVM, PYTHON
59. ML IS NOT A SINGLE RUN OF ALGORITHM
60. IT’S A PROCESS
61. ML PROCESS DEFINE A PROBLEM ANALYZE YOUR DATA UNDERSTAND YOUR DATA PREPARE DATA FOR ML SELECT & RUN ALGO(S) TUNE ALGO(S) PARAMETERS SELECT FINAL MODEL VALIDATE FINAL MODEL
62. ML PROCESS DEFINE A PROBLEM ANALYZE YOUR DATA UNDERSTAND YOUR DATA PREPARE DATA FOR ML SELECT & RUN ALGO(S) TUNE ALGO(S) PARAMETERS SELECT FINAL MODEL VALIDATE FINAL MODEL
63. | age | city_size | linkedin_php | salary | |-----|-----------|--------------|--------| | 20 | 900000 | 0 | 2000 | | 20 | 400000 | 0 | 1800 | | 25 | 450000 | 8 | 3700 | | 26 | 900000 | 8 | 3975 | | 30 | 100000 | 10 | 4000 | | 30 | 500000 | 10 | 3500 |
64. | age | city_size | linkedin_php | salary | currency | |-----|-----------|--------------|--------|----------| | 20 | 900000 | 0 | 2000 | EUR | | 20 | 400000 | 0 | 1800 | USD | | 25 | 450000 | 8 | 3700 | USD | | 26 | 900000 | 8 | 3975 | USD | | 30 | 100000 | 10 | 4000 | USD | | 30 | 500000 | 10 | 3500 | USD |
65. ONE MORE THING…
66. PHPCON CFP WILL BE CLOSED TOMORROW! http://phpcon.pl/2016/en/cfp
67. THANKS mariuszgil HAPPY LEARNING YOUR MACHINES!