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0.4.0 (#163)
* RBX file format (#158) * Initial commit * Fix coding style * Shorten revision number and header checksum * Filesystem test uses RBX format * Recurse into object tree * Check for header/body class match * Fix coding style * Variable compression levels * Remove length header * Outline of specification * Convert checksums to HMACs * Spiff up a little bit * Tidy up * Use Native base serializer under the hood * Better error messages * Base serializer now injectable * Introduce encrypted variant * Appease Stan * Add beef to RBXP payload hash * Rename RBX portable to RBX standard * Use password digests by default * Appease Stan * More appeasement * Unrestricted digest length * Benchmark serializers * Change default base serializers * Switch payload HMAC to sha256 * Fix hmac * Fix mkdocs nav * RBX use checksums instead of HMACs * No default password * Tidy up * Remove PHP 8.0 from CI due to 3rd party incompatibility * Move RBXE to Extras package * Appease Stan * Dynamic column width in console output (#149) * Added dynamic column size in console Refactoring Console.php * Cast columnSize to int * Replace array_reduce with foreach * Mark tests as skipped * GitHub CLA * GitHub CLA check * GitHub CLA check with other username * Embed library version in RBX format * Appease Stan * Added custom class revision mismatch exception * Add RBX stuff to the user guide * Deprecate Igbinary serializer * New Transformer: Boolean Converter (#159) * add a boolean converter which converts true to 1 and false to 0. * updating the BooleanConverter too accept a customizable true/false value. Also updated docs to include the BooleanConverter * fix up the PHPdoc. Failed static analysis. * working through static analysis failures. * using single quotes * improvements per Andrew's comments on the PR * Add windows latest to CI build environments * Add fileinfo to required CI extensions * add a boolean converter which converts true to 1 and false to 0. * updating the BooleanConverter too accept a customizable true/false value. Also updated docs to include the BooleanConverter * fix up the PHPdoc. Failed static analysis. * working through static analysis failures. * using single quotes * improvements per Andrew's comments on the PR Co-authored-by: Andrew DalPino <[email protected]> * Appease Stan * Remove debug.log that should have been ignored by Git * Appease Stan * Update changelog * Tighten up RBX format * Initial commit (#162) * Deprecate explainedVar() and noiseVar() methods on PCA and LDA * Add missing extension specification and exception * Rename Autotrack Revisions * No need to sort singular values * Implement transformer conduits * Add return transformers method to Conduit * Clean up * Revert conduits * Single-threaded by default * Bump Tensor version requirement * Polish up for release Co-authored-by: Vladimir Stepanov <[email protected]> Co-authored-by: Zachary Vander Velden <[email protected]>
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‎.php_cs.dist‎

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@@ -122,4 +122,4 @@ return Config::create()->setRules([
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'trim_array_spaces' => true,
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'unary_operator_spaces' => true,
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'whitespace_after_comma_in_array' => true,
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])->setFinder($finder);
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])->setFinder($finder);

‎CHANGELOG.md‎

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- 0.4.0
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- Added Truncated SVD transformer
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- Added Rubix Object File (RBX) format serializer
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- Added class revision() method to the Persistable interface
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- Added custom class revision mismatch exception
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- Add Boolean Converter transformer
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- Deprecated Igbinary serializer and move to Extras package
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- Deprecate explainedVar() and noiseVar() methods on PCA and LDA
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- Added missing extension specification and exception
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- 0.3.2
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- Fix t-SNE momentum gain bus error when using Tensor extension
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- Optimize t-SNE matrix instantiation

‎README.md‎

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#### Optional
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- [Extras Package](https://github.com/RubixML/Extras) for experimental features
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- [SVM extension](https://php.net/manual/en/book.svm.php) for Support Vector Machine engine (libsvm)
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- [Mbstring extension](https://www.php.net/manual/en/book.mbstring.php) for fast multibyte string manipulation
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- [GD extension](https://php.net/manual/en/book.image.php) for image manipulation
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- [Mbstring extension](https://www.php.net/manual/en/book.mbstring.php) for fast multibyte string manipulation
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- [SVM extension](https://php.net/manual/en/book.svm.php) for Support Vector Machine engine (libsvm)
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- [Redis extension](https://github.com/phpredis/phpredis) for persisting to a Redis DB
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- [Igbinary extension](https://github.com/igbinary/igbinary) for binary serialization of persistables
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## Documentation
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Read the latest docs [here](https://docs.rubixml.com).

‎benchmarks/Persisters/Serializers/GzipBench.php‎

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<?php
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namespace Rubix\ML\Benchmarks\Persisters\Serializers;
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use Rubix\ML\Datasets\Generators\Blob;
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use Rubix\ML\Classifiers\KNearestNeighbors;
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use Rubix\ML\Datasets\Generators\Agglomerate;
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use Rubix\ML\Persisters\Serializers\Gzip;
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/**
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* @Groups({"Serializers"})
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* @BeforeMethods({"setUp"})
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*/
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class GzipBench
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{
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protected const TRAINING_SIZE = 2500;
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/**
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* @var \Rubix\ML\Persisters\Serializers\Gzip
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*/
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protected $serializer;
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/**
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* @var \Rubix\ML\Persistable
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*/
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protected $persistable;
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public function setUp() : void
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{
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$generator = new Agglomerate([
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'Iris-setosa' => new Blob([5.0, 3.42, 1.46, 0.24], [0.35, 0.38, 0.17, 0.1]),
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'Iris-versicolor' => new Blob([5.94, 2.77, 4.26, 1.33], [0.51, 0.31, 0.47, 0.2]),
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'Iris-virginica' => new Blob([6.59, 2.97, 5.55, 2.03], [0.63, 0.32, 0.55, 0.27]),
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]);
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$training = $generator->generate(self::TRAINING_SIZE);
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$estimator = new KNearestNeighbors(5, true);
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$estimator->train($training);
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$this->persistable = $estimator;
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$this->serializer = new Gzip();
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}
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/**
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* @Subject
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* @revs(10)
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* @Iterations(5)
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* @OutputTimeUnit("milliseconds", precision=3)
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*/
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public function serializeUnserialize() : void
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{
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$encoding = $this->serializer->serialize($this->persistable);
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$persistable = $this->serializer->unserialize($encoding);
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}
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}

‎benchmarks/Persisters/Serializers/NativeBench.php‎

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<?php
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namespace Rubix\ML\Benchmarks\Persisters\Serializers;
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use Rubix\ML\Datasets\Generators\Blob;
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use Rubix\ML\Classifiers\KNearestNeighbors;
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use Rubix\ML\Datasets\Generators\Agglomerate;
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use Rubix\ML\Persisters\Serializers\Native;
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/**
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* @Groups({"Serializers"})
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* @BeforeMethods({"setUp"})
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*/
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class NativeBench
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{
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protected const TRAINING_SIZE = 2500;
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/**
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* @var \Rubix\ML\Persisters\Serializers\Native
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*/
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protected $serializer;
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/**
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* @var \Rubix\ML\Persistable
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*/
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protected $persistable;
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public function setUp() : void
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{
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$generator = new Agglomerate([
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'Iris-setosa' => new Blob([5.0, 3.42, 1.46, 0.24], [0.35, 0.38, 0.17, 0.1]),
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'Iris-versicolor' => new Blob([5.94, 2.77, 4.26, 1.33], [0.51, 0.31, 0.47, 0.2]),
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'Iris-virginica' => new Blob([6.59, 2.97, 5.55, 2.03], [0.63, 0.32, 0.55, 0.27]),
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]);
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$training = $generator->generate(self::TRAINING_SIZE);
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$estimator = new KNearestNeighbors(5, true);
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$estimator->train($training);
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$this->persistable = $estimator;
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$this->serializer = new Native();
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}
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/**
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* @Subject
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* @revs(10)
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* @Iterations(5)
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* @OutputTimeUnit("milliseconds", precision=3)
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*/
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public function serializeUnserialize() : void
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{
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$encoding = $this->serializer->serialize($this->persistable);
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$persistable = $this->serializer->unserialize($encoding);
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}
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}

‎benchmarks/Persisters/Serializers/RBXBench.php‎

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<?php
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namespace Rubix\ML\Benchmarks\Persisters\Serializers;
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use Rubix\ML\Datasets\Generators\Blob;
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use Rubix\ML\Classifiers\KNearestNeighbors;
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use Rubix\ML\Datasets\Generators\Agglomerate;
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use Rubix\ML\Persisters\Serializers\RBX;
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/**
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* @Groups({"Serializers"})
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* @BeforeMethods({"setUp"})
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*/
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class RBXBench
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{
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protected const TRAINING_SIZE = 2500;
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/**
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* @var \Rubix\ML\Persisters\Serializers\RBX
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*/
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protected $serializer;
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/**
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* @var \Rubix\ML\Persistable
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*/
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protected $persistable;
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public function setUp() : void
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{
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$generator = new Agglomerate([
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'Iris-setosa' => new Blob([5.0, 3.42, 1.46, 0.24], [0.35, 0.38, 0.17, 0.1]),
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'Iris-versicolor' => new Blob([5.94, 2.77, 4.26, 1.33], [0.51, 0.31, 0.47, 0.2]),
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'Iris-virginica' => new Blob([6.59, 2.97, 5.55, 2.03], [0.63, 0.32, 0.55, 0.27]),
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]);
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$training = $generator->generate(self::TRAINING_SIZE);
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$estimator = new KNearestNeighbors(5, true);
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$estimator->train($training);
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$this->persistable = $estimator;
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$this->serializer = new RBX();
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}
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/**
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* @Subject
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* @revs(10)
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* @Iterations(5)
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* @OutputTimeUnit("milliseconds", precision=3)
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*/
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public function serializeUnserialize() : void
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{
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$encoding = $this->serializer->serialize($this->persistable);
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$persistable = $this->serializer->unserialize($encoding);
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}
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}

‎benchmarks/Transformers/NumericStringConverterBench.php‎

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<?php
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namespace Rubix\ML\Benchmarks\Transformers;
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use Rubix\ML\Datasets\Generators\Blob;
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use Rubix\ML\Transformers\NumericStringConverter;
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/**
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* @Groups({"Transformers"})
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* @BeforeMethods({"setUp"})
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*/
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class NumericStringConverterBench
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{
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protected const DATASET_SIZE = 100000;
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/**
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* @var \Rubix\ML\Datasets\Dataset
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*/
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public $dataset;
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/**
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* @var \Rubix\ML\Transformers\NumericStringConverter
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*/
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protected $transformer;
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public function setUp() : void
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{
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$generator = new Blob([0.0, 0.0, 0.0, 0.0]);
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$this->dataset = $generator->generate(self::DATASET_SIZE)
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->transformColumn(1, 'strval')
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->transformColumn(3, 'strval');
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$this->transformer = new NumericStringConverter();
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}
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/**
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* @Subject
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* @Iterations(3)
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* @OutputTimeUnit("milliseconds", precision=3)
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*/
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public function apply() : void
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{
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$this->dataset->apply($this->transformer);
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}
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}

‎benchmarks/Transformers/TruncatedSVDBench.php‎

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<?php
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namespace Rubix\ML\Benchmarks\Transformers;
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use Rubix\ML\Datasets\Generators\Blob;
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use Rubix\ML\Datasets\Generators\Agglomerate;
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use Rubix\ML\Transformers\TruncatedSVD;
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/**
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* @Groups({"Transformers"})
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* @BeforeMethods({"setUp"})
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*/
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class TruncatedSVDBench
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{
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protected const DATASET_SIZE = 10000;
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/**
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* @var \Rubix\ML\Datasets\Labeled
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*/
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public $dataset;
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/**
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* @var \Rubix\ML\Transformers\TruncatedSVD
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*/
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protected $transformer;
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public function setUp() : void
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{
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$generator = new Agglomerate([
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'Iris-setosa' => new Blob([5.0, 3.42, 1.46, 0.24], [0.35, 0.38, 0.17, 0.1]),
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'Iris-versicolor' => new Blob([5.94, 2.77, 4.26, 1.33], [0.51, 0.31, 0.47, 0.2]),
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'Iris-virginica' => new Blob([6.59, 2.97, 5.55, 2.03], [0.63, 0.32, 0.55, 0.27]),
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]);
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$this->dataset = $generator->generate(self::DATASET_SIZE);
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$this->transformer = new TruncatedSVD(1);
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}
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/**
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* @Subject
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* @Iterations(3)
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* @OutputTimeUnit("milliseconds", precision=3)
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*/
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public function apply() : void
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{
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$this->dataset->apply($this->transformer);
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}
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}

‎composer.json‎

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"ext-json": "*",
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"amphp/parallel": "^1.3",
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"psr/log": "^1.1",
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"rubix/tensor": "^2.0",
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"rubix/tensor": "^2.2",
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"symfony/polyfill-mbstring": "^1.0",
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"symfony/polyfill-php73": "^1.20",
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"symfony/polyfill-php80": "^1.17",
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"require-dev": {
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"friendsofphp/php-cs-fixer": "2.18.*",
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"league/flysystem-memory": "^2.0",
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"phpbench/phpbench": "1.0.0-alpha4",
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"phpbench/phpbench": "1.0.0-alpha6",
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"phpstan/extension-installer": "^1.0",
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"phpstan/phpstan": "0.12.*",
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"phpstan/phpstan-phpunit": "0.12.*",
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"sort-packages": true,
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"process-timeout": 3000
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},
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"minimum-stability": "dev",
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"prefer-stable": true,
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"funding": [
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{
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"type": "github",

‎docs/hyper-parameter-tuning.md‎

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Hyper-parameter tuning is an experimental process that incorporates [cross-validation](cross-validation.md) to guide hyper-parameter selection. When choosing an estimator for your project it often helps to fine-tune its hyper-parameters in order to get the best accuracy and performance from the model.
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## Manual Tuning
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In a manual scenario, a user will train an estimator with one set of hyper-parameters, obtain a validation score, and then use that as a baseline to make future adjustments. The goal at each iteration is to determine whether the adjustments improve accuracy or cause it to decrease. We can consider a model to be *fully* tuned when adjustments to the hyper-parameters can no longer make improvements to the validation score. In the example below, we'll tune the *radius* parameter of [Radius Neighbors Regressor](regressors/radius-neighbors-regressor.md) by iterating over the following block of code with a different setting each time. At first, we can start by choosing radius from a set of values and then honing in on the best value once we have obtained the settings with the highest [SMAPE](cross-validation/metrics/smape.md) score.
5+
When actively tuning a model, we will train an estimator with one set of hyper-parameters, obtain a validation score, and then use that as a baseline to make future adjustments. The goal at each iteration is to determine whether the adjustments improve accuracy or cause it to decrease. We can consider a model to be *fully* tuned when adjustments to the hyper-parameters can no longer make improvements to the validation score. With practice, we'll develop an intuition for which parameters need adjusting. Refer to the API documentation for each learner for a description of each hyper-parameter. In the example below, we'll tune the *radius* parameter of [Radius Neighbors Regressor](regressors/radius-neighbors-regressor.md) by iterating over the following block of code with a different setting each time. At first, we can start by choosing radius from a set of values and then honing in on the best value once we have obtained the settings with the highest [SMAPE](cross-validation/metrics/smape.md) score.
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```php
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use Rubix\ML\Regressors\RadiusNeighborsRegressor;
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use Rubix\ML\CrossValidation\Metrics\SMAPE;
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[$training, $testing] = $dataset->randomize()->split(0.8);
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$metric = new SMAPE();
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$estimator = new RadiusNeighborsRegressor(0.5); // 0.1, 0.5, 1.0, 2.0, 5.0
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$estimator->train($training);
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$predictions = $estimator->predict($testing);
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$metric = new SMAPE();
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$score = $metric->score($predictions, $testing->labels());
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echo $score;

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