157 lines
4 KiB
PHP
157 lines
4 KiB
PHP
<?php
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namespace Rubix\ML\Tests;
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use Rubix\ML\Learner;
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use Rubix\ML\Verbose;
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use Rubix\ML\Wrapper;
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use Rubix\ML\DataType;
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use Rubix\ML\Estimator;
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use Rubix\ML\GridSearch;
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use Rubix\ML\Persistable;
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use Rubix\ML\EstimatorType;
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use Rubix\ML\Backends\Serial;
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use Rubix\ML\Other\Loggers\BlackHole;
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use Rubix\ML\CrossValidation\HoldOut;
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use Rubix\ML\Kernels\Distance\Euclidean;
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use Rubix\ML\Kernels\Distance\Manhattan;
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use Rubix\ML\Datasets\Generators\Circle;
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use Rubix\ML\Classifiers\KNearestNeighbors;
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use Rubix\ML\CrossValidation\Metrics\FBeta;
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use Rubix\ML\Datasets\Generators\Agglomerate;
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use Rubix\ML\CrossValidation\Metrics\Accuracy;
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use PHPUnit\Framework\TestCase;
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/**
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* @group MetaEstimators
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* @covers \Rubix\ML\GridSearch
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*/
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class GridSearchTest extends TestCase
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{
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protected const TRAIN_SIZE = 300;
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protected const TEST_SIZE = 10;
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protected const MIN_SCORE = 0.9;
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protected const RANDOM_SEED = 0;
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/**
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* @var \Rubix\ML\Datasets\Generators\Agglomerate
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*/
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protected $generator;
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/**
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* @var \Rubix\ML\GridSearch
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*/
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protected $estimator;
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/**
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* @var \Rubix\ML\CrossValidation\Metrics\Accuracy
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*/
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protected $metric;
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/**
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* @before
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*/
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protected function setUp() : void
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{
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$this->generator = new Agglomerate([
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'inner' => new Circle(0.0, 0.0, 1.0, 0.05),
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'middle' => new Circle(0.0, 0.0, 5.0, 0.10),
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'outer' => new Circle(0.0, 0.0, 10.0, 0.15),
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]);
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$this->estimator = new GridSearch(KNearestNeighbors::class, [
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[1, 3, 5], [true], [new Euclidean(), new Manhattan()],
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], new FBeta(), new HoldOut(0.2));
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$this->metric = new Accuracy();
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srand(self::RANDOM_SEED);
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}
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/**
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* @test
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*/
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public function build() : void
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{
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$this->assertInstanceOf(GridSearch::class, $this->estimator);
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$this->assertInstanceOf(Learner::class, $this->estimator);
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$this->assertInstanceOf(Verbose::class, $this->estimator);
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$this->assertInstanceOf(Wrapper::class, $this->estimator);
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$this->assertInstanceOf(Persistable::class, $this->estimator);
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$this->assertInstanceOf(Estimator::class, $this->estimator);
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}
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/**
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* @test
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*/
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public function type() : void
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{
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$this->assertEquals(EstimatorType::classifier(), $this->estimator->type());
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}
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/**
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* @test
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*/
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public function compatibility() : void
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{
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$this->assertEquals(DataType::all(), $this->estimator->compatibility());
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}
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/**
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* @test
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*/
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public function params() : void
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{
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$expected = [
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'base' => KNearestNeighbors::class,
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'params' => [
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[1, 3, 5], [true], [new Euclidean(), new Manhattan()],
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],
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'metric' => new FBeta(),
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'validator' => new HoldOut(0.2),
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];
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$this->assertEquals($expected, $this->estimator->params());
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}
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/**
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* @test
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*/
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public function trainPredictBest() : void
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{
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$this->estimator->setLogger(new BlackHole());
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$this->estimator->setBackend(new Serial());
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$training = $this->generator->generate(self::TRAIN_SIZE);
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$testing = $this->generator->generate(self::TEST_SIZE);
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$this->estimator->train($training);
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$this->assertTrue($this->estimator->trained());
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$results = $this->estimator->results();
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$this->assertIsArray($results);
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$this->assertCount(6, $results);
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$best = $this->estimator->best();
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$this->assertIsArray($best);
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$this->assertCount(3, $best);
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$this->assertEquals($results[0][1], $best);
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$predictions = $this->estimator->predict($testing);
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$score = $this->metric->score($predictions, $testing->labels());
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$this->assertGreaterThanOrEqual(self::MIN_SCORE, $score);
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}
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protected function assertPreConditions() : void
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{
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$this->assertFalse($this->estimator->trained());
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}
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}
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