158 lines
4.2 KiB
PHP
158 lines
4.2 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests;
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use Phpml\Classification\SVC;
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use Phpml\FeatureExtraction\TfIdfTransformer;
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use Phpml\FeatureExtraction\TokenCountVectorizer;
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\ModelManager;
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use Phpml\Pipeline;
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use Phpml\Preprocessing\Imputer;
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use Phpml\Preprocessing\Imputer\Strategy\MostFrequentStrategy;
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use Phpml\Preprocessing\Normalizer;
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use Phpml\Regression\SVR;
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use Phpml\Tokenization\WordTokenizer;
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use PHPUnit\Framework\TestCase;
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class PipelineTest extends TestCase
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{
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public function testPipelineConstruction(): void
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{
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$transformers = [
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new TfIdfTransformer(),
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];
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$estimator = new SVC();
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$pipeline = new Pipeline($transformers, $estimator);
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self::assertEquals($transformers, $pipeline->getTransformers());
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self::assertEquals($estimator, $pipeline->getEstimator());
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}
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public function testPipelineEstimatorSetter(): void
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{
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$pipeline = new Pipeline([new TfIdfTransformer()], new SVC());
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$estimator = new SVR();
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$pipeline->setEstimator($estimator);
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self::assertEquals($estimator, $pipeline->getEstimator());
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}
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public function testPipelineWorkflow(): void
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{
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$transformers = [
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new Imputer(null, new MostFrequentStrategy()),
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new Normalizer(),
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];
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$estimator = new SVC();
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$samples = [
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[1, -1, 2],
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[2, 0, null],
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[null, 1, -1],
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];
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$targets = [
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4,
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1,
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4,
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];
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$pipeline = new Pipeline($transformers, $estimator);
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$pipeline->train($samples, $targets);
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$predicted = $pipeline->predict([[0, 0, 0]]);
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self::assertEquals(4, $predicted[0]);
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}
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public function testPipelineTransformers(): void
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{
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$transformers = [
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new TokenCountVectorizer(new WordTokenizer()),
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new TfIdfTransformer(),
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];
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$estimator = new SVC();
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$samples = [
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'Hello Paul',
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'Hello Martin',
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'Goodbye Tom',
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'Hello John',
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'Goodbye Alex',
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'Bye Tony',
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];
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$targets = [
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'greetings',
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'greetings',
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'farewell',
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'greetings',
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'farewell',
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'farewell',
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];
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$pipeline = new Pipeline($transformers, $estimator);
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$pipeline->train($samples, $targets);
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$expected = ['greetings', 'farewell'];
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$predicted = $pipeline->predict(['Hello Max', 'Goodbye Mark']);
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self::assertEquals($expected, $predicted);
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}
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public function testPipelineTransformersWithTargets(): void
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{
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$samples = [[1, 2, 1], [1, 3, 4], [5, 2, 1], [1, 3, 3], [1, 3, 4], [0, 3, 5]];
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$targets = ['a', 'a', 'a', 'b', 'b', 'b'];
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$pipeline = new Pipeline([$selector = new SelectKBest(2)], new SVC());
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$pipeline->train($samples, $targets);
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self::assertEqualsWithDelta([1.47058823, 4.0, 3.0], $selector->scores(), 0.00000001);
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self::assertEquals(['b'], $pipeline->predict([[1, 3, 5]]));
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}
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public function testSaveAndRestore(): void
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{
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$pipeline = new Pipeline([
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new TokenCountVectorizer(new WordTokenizer()),
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new TfIdfTransformer(),
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], new SVC());
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$pipeline->train([
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'Hello Paul',
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'Hello Martin',
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'Goodbye Tom',
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'Hello John',
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'Goodbye Alex',
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'Bye Tony',
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], [
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'greetings',
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'greetings',
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'farewell',
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'greetings',
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'farewell',
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'farewell',
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]);
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$testSamples = ['Hello Max', 'Goodbye Mark'];
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$predicted = $pipeline->predict($testSamples);
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$filepath = (string) tempnam(sys_get_temp_dir(), uniqid('pipeline-test', true));
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$modelManager = new ModelManager();
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$modelManager->saveToFile($pipeline, $filepath);
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$restoredClassifier = $modelManager->restoreFromFile($filepath);
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self::assertEquals($pipeline, $restoredClassifier);
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self::assertEquals($predicted, $restoredClassifier->predict($testSamples));
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unlink($filepath);
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}
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}
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