How to implement machine learning in PHP?

As the applications of machine learning gradually increase in the future, developers' demand for machine learning will also gradually increase. Because PHP is a widely used programming language, many developers wonder how to do machine learning in PHP. This article will cover the basics and guidelines for implementing machine learning in PHP.

First, let’s look at the basics of machine learning. Machine learning refers to the use of algorithms and data to enable computers to simulate human learning abilities. Common machine learning tasks include classification, clustering, and regression. Classification is the task of classifying objects into different categories. Clustering is the process of discovering similar objects. Regression is predicting the value of a variable.

Machine learning requires many skills and background knowledge, so this article assumes that you already have some basic knowledge of machine learning. Now, we will get into machine learning in PHP.

PHP does not have a native machine learning library, but you can still use some open source machine learning libraries, such as Weka, TensorFlow and Scikit-learn. Here, we’ll cover ways to do machine learning using the PHP-ML library.

PHP-ML is an open source PHP machine learning library with many machine learning algorithms and data preprocessing functions. To use PHP-ML for machine learning, you need to install it with Composer. Please run the following command to install:

composer require php-ai/php-ml

After installation, you can reference PHP-ML in your PHP code:

require_once __DIR__ . '/vendor/autoload.php';

Next, let’s see how to use PHP-ML for classification tasks . Let's assume we have a CSV file that contains some variables and category labels. We want to use a classification algorithm to predict the class label of a given variable. First, we need to load the data from the CSV file:

use PhpmlDatasetCsvDataset;

$dataset = new CsvDataset('path/to/dataset.csv', $header = true);

We set the $dataset variable to a new instance of CsvDataset and pass the path to the CSV file as a parameter. Setting $header to true specifies that the first line is a header file. You can view the loaded data using the following code:

print_r($dataset->getSamples());
print_r($dataset->getTargets());

Next, we will use the KNN algorithm to train the model and classify new data. In PHP-ML, you can use the Estimator interface to access many machine learning algorithms. Please note that the Estimator interface only provides learning functionality. To make predictions on test data, you need to use the makePrediction method in Predictor.

use PhpmlClassificationKNearestNeighbors;

$classifier = new KNearestNeighbors($k = 3);
$classifier->train($dataset->getSamples(), $dataset->getTargets());

$newSample = [5.7, 2.9, 4.2, 1.3];
echo $classifier->predict($newSample);

Here, the $classifier variable is set to an instance of KNN. After training the model, we will use the predict method to classify new samples. The output should be the predicted class of the sample.

You can use many other algorithms in Scikit-learn for classification tasks. In PHP-ML, there are many other data preprocessing functions, such as data normalization and feature extraction.

Unlike classification tasks, regression tasks involve predicting the value of a variable. In PHP-ML, you can use many regression algorithms such as linear regression, KNN regression, and SVM regression.

Here, we will introduce using linear regression to predict the value of a continuous variable. We will use the Boston Home Prices dataset, which contains many variables and one continuous variable.

use PhpmlDatasetCsvDataset;
use PhpmlRegressionLeastSquares;

$dataset = new CsvDataset(__DIR__.'/../examples/datasets/boston.csv', 14, true);
$regression = new LeastSquares();
$regression->train($dataset->getSamples(), $dataset->getTargets());

$newSample = [0.02731,0.0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14];
echo $regression->predict($newSample);

Here we set the $dataset variable to a new instance of CsvDataset and the number of columns in the CSV file (14) as the second parameter. Setting $header to true allows us to identify columns as integers instead of strings. Set $regression to a new instance of LeastSquares, train the model and predict new labels using the predict method.

In short, PHP-ML is a powerful PHP machine learning library that can help you implement many machine learning tasks in PHP. Although PHP is not the best choice for machine learning, in more complex web applications, PHP may be necessary. Hopefully this article helped you understand how to use machine learning and the PHP-ML library in PHP.

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