Machine learning in php Using PHP-ML

Machine Learning in Php
Classifying email attachments as logos or photos using Php-ml
By Agbagbara Omokhoa

Content
 Introduction
 Our Use Case
 Naïve Solution
 Feature Extraction
 Visualize Solution
 Training a Model

Introduction
 Presentation is about Supervised Machine Learning Only.
 Hopefully by now you would have read a lot about machine learning,
there has been a lot written about it.
 Two types of learning
 Supervised machine learning – Learning then applying
 Unsupervised machine learning – Learning without labels
 Most examples for supervised machine learning are about sentiment
analysis of reviews, either from twitter or for restaurant reviews.
 Most online courses or Youtube also follow the same pattern.
 Most examples use python or R.
 Is it possible to do machine learning in php?

Introduction : Supervised Learning ,
Examples
 Sentiment Analysis
 Given a review, is the sentiment positive or negative , for example
 The rice was awful = -ve
 The drinks were reasonably priced = +ve
 Claim Classification of Loss Category
 Given a description, an insurance claim should be classified under which loss
category
 Roos Have Hit Vehicle Damaging Drivers Side = Impact
 I was driving and suddenly a hail storm hit and hit the bonnet of my car. Roof doesn't
look bad but bonnet has damage = Hail Damage
 INS has had some dirty go through their vehicle, unsure where the fuel was put into the
car, have just noticed smoke coming from the vehicle = Mechanical Damage

Our Use Case
 We normally request that claimants and body smash repairers provide
images of damages to support their claims.
 Images are sent to an email address, where a php cron script, parses the
emails and attach images or documents to the claim in an application.
 The script sometimes attaches company logos to the claims as well.
 Given images which has been parsed from an email, can these images be
classified as either logos or claims photos.
Logo Image

Naïve Solution
 Get all the images which have been identified as logos.
 Use php hash_file(md5, filename) to create a array, with the hash values as
the keys.
 For each new file that is received, check if the hash_file value exists in the
array.
 Works quite well, for all known logo files, but not for new logo files.

Feature Extraction
 Machine learning requires that input be supplied to a model, to provide an
output. These input must be numeric representation of the problem.
 What features can be extracted from image files
 File size
 Bag of colours
 There are other ways to represent the features of image [ search image
descriptors]

Feature Extraction : Image Description
 Bag of colours ie, count the number of occurrences of a colour in a image.
 Resize and convert image to a grayscale first, since the colour has no real
effect on determining the class of image.
Logo Image

 Create an array of 256 possible gray values
 Foreach pixel position, add the gray value to the bin
 Results in a 256 dimension array, for best result reduce the number of
dimensions to 32

 Reduce the 256 features array to a 32 features array

 Scale all feature values to between 0 and 1
 Produces smoother histograms.
 32 features values are then summed to create an extra feature “pixel sum”

Feature Extraction : Output
 Machine learning requires processing a lot input data need to structure input
data,
 Create a csv file for each image with heading “Grey”,”Hit”, this can be used to
visualise the individual results (Create a histogram)
 Create a csv file for all input files which belong to the same class.
 filename,pixel_0,pixel_1,pixel_2,pixel_3,pixel_4,pixel_5,pixel_6,pixel_7,pixel_8,pixel_9,pixel_10,pixel_1
1,pixel_12,pixel_13,pixel_14,pixel_15,pixel_16,pixel_17,pixel_18,pixel_19,pixel_20,pixel_21,pixel_22,pix
el_23,pixel_24,pixel_25,pixel_26,pixel_27,pixel_28,pixel_29,pixel_30,pixel_31,pixel_sum,feature_class
 Example of command line
 echo "Create Features for Logos"
 php image.creation.php training.logos 0 > "featureslogo.csv"
 echo "Create Features for Photos"
 php image.creation.php training.photos 1 > "featuresphotos.csv"
 Possible to create a scatter plot of the csv feature file.

Feature Extraction : Visualize Results
 Using a graphing class to produce simple histograms
Logos Photos

Feature Extraction : Visualize Results
 Visualize pixel sum feature in excel using scatter plots
0
5
10
15
20
25
0 10 20 30 40 50 60 70 80
Logos
Claim Photos

Conclusion from Scatter Plot.
 By looking at the scatter plot we can assume that
 If pixel sum > 5 then photo
 Else if pixel sum < 5 then logo.

Training A Model (1)
 Use Php-ML
 Requires Php >= 7.0
 https://php-ml.readthedocs.io/en/latest/
 Load the different feature csv files
 Extract the samples and the labels

 Merge the dataset for the different classes and create cross validation
dataset

 Instantiate a classifier using one of the implementations from Php-ml
 Need to tweak a few of the parameters to get the best results
 Calculate the accuracy of the prediction
 Save the trained model to a file.
 For best result you can run the training process multiple times and save the
different models

Test Models / Validation
 Load features csv file which contains known classifications
 Load a trained model
 For each input feature, predict the class
 Compare the result to the actual result

Finally / What’s Next
 Source Code can be found at https://github.com/deltastateonline/ml-php
 Can we classify images as damage photos or images of quotes so that if a
quote is received, the assessor gets an alert requesting immediate
attention.
Damage Photos Quote Images

Machine learning in php Using PHP-ML

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