Machine Learning in PHP: Overview of Libraries and Tools

Machine Learning in PHP: Libraries and Tools

Traditionally, machine learning (ML) is associated with Python, R, or Julia. However, PHP, being one of the most popular languages for web development, also has an ecosystem for implementing ML algorithms. Although PHP does not have the same performance and abundance of low-level libraries (like NumPy or TensorFlow), for many practical tasks — classification, regression, clustering, working with neural networks — there are mature solutions. In this article, we will look at three main libraries for machine learning in PHP: PHP-ML, Rubix ML, and Keras-PHP.

1. PHP-ML (PHP Machine Learning)

PHP-ML is the most popular and stable library for machine learning in PHP. It requires no external dependencies, is written entirely in PHP, and provides a wide range of algorithms. The library is ideal for learning, prototyping, and integrating ML into web applications.

Key Features:

• Classification: Naive Bayes, SVM (based on LibSVM), k-Nearest Neighbors (k-NN), Decision Trees.
• Regression: Linear Regression, Support Vector Regression (SVR), k-NN Regression.
• Clustering: k-Means, DBSCAN, Hierarchical Clustering.
• Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix.
• Data Preprocessing: Normalization, Standardization, Encoding of categorical features.

Example: Training a classifier for text sentiment analysis

use Phpml\\Classification\\NaiveBayes;use Phpml\\FeatureExtraction\\TfIdfTransformer;use Phpml\\Tokenization\\WhitespaceTokenizer;

// Training data$samples = [    'great movie',    'terrible movie',    'really enjoyed it',    'boring and stupid'];$labels = ['positive', 'negative', 'positive', 'negative'];

// Converting text to numeric vectors$tokenizer = new WhitespaceTokenizer();$transformer = new TfIdfTransformer();

// Create and train the model$classifier = new NaiveBayes();$classifier->train($samples, $labels);

// Prediction$prediction = $classifier->predict(['interesting plot']);echo $prediction; // positive

2. Rubix ML

Rubix ML is a more modern and advanced library focused on high performance and flexibility. It supports working with large datasets through generators (lazy loading), has a built-in pipeline system, and integrates with TensorFlow via NDArray. Unlike PHP-ML, Rubix ML requires PHP 7.4+ and uses extensions like intl and mbstring.

Key Features:

• Neural Networks: Multilayer Perceptrons (MLP) with support for various activation functions (ReLU, Sigmoid, Softmax).
• Pipelines: Combining multiple steps (normalization, encoding, training) into a single pipeline.
• Sparse Data Handling: Efficient algorithms for large matrices.
• Visualization: Built-in tools for plotting graphs (via SVG).
• Model Saving and Loading: Serialization to JSON or PHP-serialized format.

Example: Regression for predicting housing prices

use Rubix\\ML\\Regressors\\Ridge;use Rubix\\ML\\Datasets\\Labeled;use Rubix\\ML\\Transformers\\NumericStringConverter;

// Load data (assuming CSV)$dataset = Labeled::fromIterator(new \\ArrayIterator([    ['area' => 120, 'rooms' => 3, 'price' => 150000],    ['area' => 80, 'rooms' => 2, 'price' => 90000],    ['area' => 200, 'rooms' => 4, 'price' =&g