Merge branch '2.5' into 3.0

.github/workflows/ci.yml

@@ -20,7 +20,7 @@ jobs:
         with:
           php-version: ${{ matrix.php-versions }}
           tools: composer, pecl
-          extensions: svm, mbstring, gd, fileinfo
+          extensions: svm, mbstring, gd, fileinfo, swoole
           ini-values: memory_limit=-1
 
       - name: Validate composer.json

.gitignore

@@ -8,6 +8,7 @@ Thumbs.db
 .DS_Store
 debug.log
 /test.png
+pyvenv.cfg
 .venv
 .idea
 .vscode

CHANGELOG.md

@@ -12,6 +12,9 @@
 - 2.5.0
     - Added Vantage Point Spatial tree
     - Blob Generator can now `simulate()` a Dataset object
+    - Added Wrapper interface
+    - Added Swoole Backend
+    - Plus Plus added check for min number of sample seeds
 
 - 2.4.1
     - Sentence Tokenizer fix Arabic and Farsi language support

benchmarks/Classifiers/OneVsRestBench.php

@@ -2,18 +2,22 @@
 
 namespace Rubix\ML\Benchmarks\Classifiers;
 
+use Rubix\ML\Backends\Backend;
 use Rubix\ML\Classifiers\OneVsRest;
 use Rubix\ML\Datasets\Generators\Blob;
 use Rubix\ML\Classifiers\LogisticRegression;
 use Rubix\ML\NeuralNet\Optimizers\Stochastic;
 use Rubix\ML\Datasets\Generators\Agglomerate;
+use Rubix\ML\Tests\DataProvider\BackendProviderTrait;
 
 /**
  * @Groups({"Classifiers"})
  * @BeforeMethods({"setUp"})
  */
 class OneVsRestBench
 {
+    use BackendProviderTrait;
+
     protected const TRAINING_SIZE = 10000;
 
     protected const TESTING_SIZE = 10000;
@@ -52,9 +56,13 @@ public function setUp() : void
      * @Subject
      * @Iterations(5)
      * @OutputTimeUnit("seconds", precision=3)
+     * @ParamProviders("provideBackends")
+     * @param array{ backend: Backend } $params
      */
-    public function trainPredict() : void
+    public function trainPredict(array $params) : void
     {
+        $this->estimator->setBackend($params['backend']);
+
         $this->estimator->train($this->training);
 
         $this->estimator->predict($this->testing);

benchmarks/Classifiers/RandomForestBench.php

@@ -2,17 +2,21 @@
 
 namespace Rubix\ML\Benchmarks\Classifiers;
 
+use Rubix\ML\Backends\Backend;
 use Rubix\ML\Classifiers\RandomForest;
 use Rubix\ML\Datasets\Generators\Blob;
 use Rubix\ML\Classifiers\ClassificationTree;
 use Rubix\ML\Datasets\Generators\Agglomerate;
+use Rubix\ML\Tests\DataProvider\BackendProviderTrait;
 use Rubix\ML\Transformers\IntervalDiscretizer;
 
 /**
  * @Groups({"Classifiers"})
  */
 class RandomForestBench
 {
+    use BackendProviderTrait;
+
     protected const TRAINING_SIZE = 10000;
 
     protected const TESTING_SIZE = 10000;
@@ -70,9 +74,13 @@ public function setUpCategorical() : void
      * @Iterations(5)
      * @BeforeMethods({"setUpContinuous"})
      * @OutputTimeUnit("seconds", precision=3)
+     * @ParamProviders("provideBackends")
+     * @param array{ backend: Backend } $params
      */
-    public function continuous() : void
+    public function continuous(array $params) : void
     {
+        $this->estimator->setBackend($params['backend']);
+
         $this->estimator->train($this->training);
 
         $this->estimator->predict($this->testing);
@@ -83,9 +91,13 @@ public function continuous() : void
      * @Iterations(5)
      * @BeforeMethods({"setUpCategorical"})
      * @OutputTimeUnit("seconds", precision=3)
+     * @ParamProviders("provideBackends")
+     * @param array{ backend: Backend } $params
      */
-    public function categorical() : void
+    public function categorical(array $params) : void
     {
+        $this->estimator->setBackend($params['backend']);
+
         $this->estimator->train($this->training);
 
         $this->estimator->predict($this->testing);

composer.json

@@ -49,7 +49,8 @@
         "phpstan/extension-installer": "^1.0",
         "phpstan/phpstan": "^1.0",
         "phpstan/phpstan-phpunit": "^1.0",
-        "phpunit/phpunit": "^9.0"
+        "phpunit/phpunit": "^9.0",
+        "swoole/ide-helper": "^5.1"
     },
     "suggest": {
         "ext-tensor": "For fast Matrix/Vector computing",

docs/cross-validation/metrics/rand-index.md

@@ -7,7 +7,7 @@ $$
 {\displaystyle ARI = {\frac {\left.\sum _{ij}{\binom {n_{ij}}{2}}-\left[\sum _{i}{\binom {a_{i}}{2}}\sum _{j}{\binom {b_{j}}{2}}\right]\right/{\binom {n}{2}}}{\left.{\frac {1}{2}}\left[\sum _{i}{\binom {a_{i}}{2}}+\sum _{j}{\binom {b_{j}}{2}}\right]-\left[\sum _{i}{\binom {a_{i}}{2}}\sum _{j}{\binom {b_{j}}{2}}\right]\right/{\binom {n}{2}}}}}
 $$
 
-**Estimator Compatibility:** Regressor
+**Estimator Compatibility:** Clusterer
 
 **Score Range:** -1 to 1
 

phpstan.neon

@@ -6,3 +6,4 @@ parameters:
         - 'benchmarks'
     excludePaths:
         - src/Backends/Amp.php
+        - src/Backends/Swoole.php

phpunit.xml

@@ -1,5 +1,18 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<phpunit xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" backupGlobals="false" backupStaticAttributes="false" bootstrap="vendor/autoload.php" colors="true" convertErrorsToExceptions="true" convertNoticesToExceptions="true" convertWarningsToExceptions="true" forceCoversAnnotation="true" processIsolation="false" stopOnFailure="false" xsi:noNamespaceSchemaLocation="https://schema.phpunit.de/9.3/phpunit.xsd">
+<phpunit
+  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+  backupGlobals="false"
+  backupStaticAttributes="false"
+  bootstrap="vendor/autoload.php"
+  colors="true"
+  convertErrorsToExceptions="true"
+  convertNoticesToExceptions="true"
+  convertWarningsToExceptions="true"
+  forceCoversAnnotation="true"
+  processIsolation="true"
+  stopOnFailure="false"
+  xsi:noNamespaceSchemaLocation="https://schema.phpunit.de/9.3/phpunit.xsd"
+>
   <coverage processUncoveredFiles="true">
     <include>
       <directory suffix=".php">src</directory>

src/AnomalyDetectors/LocalOutlierFactor.php

@@ -67,7 +67,7 @@ class LocalOutlierFactor implements Estimator, Learner, Scoring, Persistable
      *
      * @var Spatial
      */
-    protected \Rubix\ML\Graph\Trees\Spatial $tree;
+    protected Spatial $tree;
 
     /**
      * The precomputed k distances between each training sample and its k'th nearest neighbor.

src/AnomalyDetectors/Loda.php

@@ -100,7 +100,7 @@ class Loda implements Estimator, Learner, Online, Scoring, Persistable
      *
      * @var \Tensor\Matrix|null
      */
-    protected ?\Tensor\Matrix $r = null;
+    protected ?Matrix $r = null;
 
     /**
      * The edges and bin counts of each histogram.

src/AnomalyDetectors/OneClassSVM.php

@@ -44,7 +44,7 @@ class OneClassSVM implements Estimator, Learner
      *
      * @var svm
      */
-    protected \svm $svm;
+    protected svm $svm;
 
     /**
      * The hyper-parameters of the model.
@@ -58,7 +58,7 @@ class OneClassSVM implements Estimator, Learner
      *
      * @var \svmmodel|null
      */
-    protected ?\svmmodel $model = null;
+    protected ?svmmodel $model = null;
 
     /**
      * @param float $nu

src/Backends/Swoole.php

@@ -0,0 +1,173 @@
+<?php
+
+namespace Rubix\ML\Backends;
+
+use Rubix\ML\Backends\Tasks\Task;
+use Rubix\ML\Specifications\ExtensionIsLoaded;
+use Rubix\ML\Specifications\SwooleExtensionIsLoaded;
+use RuntimeException;
+use Swoole\Atomic;
+use Swoole\Process;
+
+use function Swoole\Coroutine\run;
+
+/**
+ * Swoole
+ *
+ * Works both with Swoole and OpenSwoole.
+ *
+ * @category    Machine Learning
+ * @package     Rubix/ML
+ */
+class Swoole implements Backend
+{
+    /**
+     * The queue of tasks to be processed in parallel.
+     */
+    protected array $queue = [];
+
+    private int $cpus;
+
+    private int $hasIgbinary;
+
+    public function __construct()
+    {
+        SwooleExtensionIsLoaded::create()->check();
+
+        $this->cpus = swoole_cpu_num();
+        $this->hasIgbinary = ExtensionIsLoaded::with('igbinary')->passes();
+    }
+
+    /**
+     * Queue up a deferred task for backend processing.
+     *
+     * @internal
+     *
+     * @param Task $task
+     * @param callable(mixed,mixed):void $after
+     * @param mixed $context
+     */
+    public function enqueue(Task $task, ?callable $after = null, $context = null) : void
+    {
+        $this->queue[] = function () use ($task, $after, $context) {
+            $result = $task();
+
+            if ($after) {
+                $after($result, $context);
+            }
+
+            return $result;
+        };
+    }
+
+    /**
+     * Process the queue and return the results.
+     *
+     * @internal
+     *
+     * @return mixed[]
+     */
+    public function process() : array
+    {
+        $results = [];
+
+        $maxMessageLength = new Atomic(0);
+        $workerProcesses = [];
+
+        $currentCpu = 0;
+
+        foreach ($this->queue as $index => $queueItem) {
+            $workerProcess = new Process(
+                function (Process $worker) use ($maxMessageLength, $queueItem) {
+                    $serialized = $this->serialize($queueItem());
+
+                    $serializedLength = strlen($serialized);
+                    $currentMaxSerializedLength = $maxMessageLength->get();
+
+                    if ($serializedLength > $currentMaxSerializedLength) {
+                        $maxMessageLength->set($serializedLength);
+                    }
+
+                    $worker->exportSocket()->send($serialized);
+                },
+                // redirect_stdin_and_stdout
+                false,
+                // pipe_type
+                SOCK_DGRAM,
+                // enable_coroutine
+                true,
+            );
+
+            $workerProcess->setAffinity([$currentCpu]);
+            $workerProcess->setBlocking(false);
+            $workerProcess->start();
+
+            $workerProcesses[$index] = $workerProcess;
+
+            $currentCpu = ($currentCpu + 1) % $this->cpus;
+        }
+
+        run(function () use ($maxMessageLength, &$results, $workerProcesses) {
+            foreach ($workerProcesses as $index => $workerProcess) {
+                $status = $workerProcess->wait();
+
+                if (0 !== $status['code']) {
+                    throw new RuntimeException('Worker process exited with an error');
+                }
+
+                $socket = $workerProcess->exportSocket();
+
+                if ($socket->isClosed()) {
+                    throw new RuntimeException('Coroutine socket is closed');
+                }
+
+                $maxMessageLengthValue = $maxMessageLength->get();
+
+                $receivedData = $socket->recv($maxMessageLengthValue);
+                $unserialized = $this->unserialize($receivedData);
+
+                $results[] = $unserialized;
+            }
+        });
+
+        return $results;
+    }
+
+    /**
+     * Flush the queue
+     */
+    public function flush() : void
+    {
+        $this->queue = [];
+    }
+
+    private function serialize(mixed $data) : string
+    {
+        if ($this->hasIgbinary) {
+            return igbinary_serialize($data);
+        }
+
+        return serialize($data);
+    }
+
+    private function unserialize(string $serialized) : mixed
+    {
+        if ($this->hasIgbinary) {
+            return igbinary_unserialize($serialized);
+        }
+
+        return unserialize($serialized);
+    }
+
+    /**
+     * Return the string representation of the object.
+     *
+     * @internal
+     *
+     * @return string
+     */
+    public function __toString() : string
+    {
+        return 'Swoole';
+    }
+}

src/BootstrapAggregator.php

@@ -64,7 +64,7 @@ class BootstrapAggregator implements Estimator, Learner, Parallel, Persistable
      *
      * @var Learner
      */
-    protected \Rubix\ML\Learner $base;
+    protected Learner $base;
 
     /**
      * The number of base learners to train in the ensemble.

src/Classifiers/AdaBoost.php

@@ -72,7 +72,7 @@ class AdaBoost implements Estimator, Learner, Probabilistic, Verbose, Persistabl
      *
      * @var Learner
      */
-    protected \Rubix\ML\Learner $base;
+    protected Learner $base;
 
     /**
      * The learning rate of the ensemble i.e. the *shrinkage* applied to each step.

src/Classifiers/KDNeighbors.php

@@ -60,7 +60,7 @@ class KDNeighbors implements Estimator, Learner, Probabilistic, Persistable
      *
      * @var Spatial
      */
-    protected \Rubix\ML\Graph\Trees\Spatial $tree;
+    protected Spatial $tree;
 
     /**
      * The zero vector for the possible class outcomes.