265 lines
8.9 KiB
PHP
265 lines
8.9 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\Classification;
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use Phpml\Classification\MLPClassifier;
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use Phpml\Exception\InvalidArgumentException;
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use Phpml\ModelManager;
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use Phpml\NeuralNetwork\ActivationFunction;
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use Phpml\NeuralNetwork\ActivationFunction\HyperbolicTangent;
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use Phpml\NeuralNetwork\ActivationFunction\PReLU;
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use Phpml\NeuralNetwork\ActivationFunction\Sigmoid;
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use Phpml\NeuralNetwork\ActivationFunction\ThresholdedReLU;
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use Phpml\NeuralNetwork\Node\Neuron;
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use PHPUnit\Framework\TestCase;
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class MLPClassifierTest extends TestCase
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{
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public function testMLPClassifierLayersInitialization(): void
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{
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$mlp = new MLPClassifier(2, [2], [0, 1]);
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self::assertCount(3, $mlp->getLayers());
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$layers = $mlp->getLayers();
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// input layer
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self::assertCount(3, $layers[0]->getNodes());
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self::assertNotContainsOnly(Neuron::class, $layers[0]->getNodes());
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// hidden layer
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self::assertCount(3, $layers[1]->getNodes());
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self::assertNotContainsOnly(Neuron::class, $layers[1]->getNodes());
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// output layer
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self::assertCount(2, $layers[2]->getNodes());
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self::assertContainsOnly(Neuron::class, $layers[2]->getNodes());
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}
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public function testSynapsesGeneration(): void
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{
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$mlp = new MLPClassifier(2, [2], [0, 1]);
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$layers = $mlp->getLayers();
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foreach ($layers[1]->getNodes() as $node) {
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if ($node instanceof Neuron) {
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$synapses = $node->getSynapses();
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self::assertCount(3, $synapses);
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$synapsesNodes = $this->getSynapsesNodes($synapses);
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foreach ($layers[0]->getNodes() as $prevNode) {
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self::assertContains($prevNode, $synapsesNodes);
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}
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}
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}
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}
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public function testBackpropagationLearning(): void
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{
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// Single layer 2 classes.
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$network = new MLPClassifier(2, [2], ['a', 'b'], 1000);
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$network->train(
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[[1, 0], [0, 1], [1, 1], [0, 0]],
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['a', 'b', 'a', 'b']
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);
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self::assertEquals('a', $network->predict([1, 0]));
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self::assertEquals('b', $network->predict([0, 1]));
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self::assertEquals('a', $network->predict([1, 1]));
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self::assertEquals('b', $network->predict([0, 0]));
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}
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public function testBackpropagationTrainingReset(): void
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{
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// Single layer 2 classes.
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$network = new MLPClassifier(2, [2], ['a', 'b'], 1000);
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$network->train(
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[[1, 0], [0, 1]],
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['a', 'b']
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);
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self::assertEquals('a', $network->predict([1, 0]));
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self::assertEquals('b', $network->predict([0, 1]));
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$network->train(
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[[1, 0], [0, 1]],
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['b', 'a']
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);
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self::assertEquals('b', $network->predict([1, 0]));
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self::assertEquals('a', $network->predict([0, 1]));
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}
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public function testBackpropagationPartialTraining(): void
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{
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// Single layer 2 classes.
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$network = new MLPClassifier(2, [2], ['a', 'b'], 1000);
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$network->partialTrain(
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[[1, 0], [0, 1]],
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['a', 'b']
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);
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self::assertEquals('a', $network->predict([1, 0]));
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self::assertEquals('b', $network->predict([0, 1]));
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$network->partialTrain(
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[[1, 1], [0, 0]],
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['a', 'b']
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);
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self::assertEquals('a', $network->predict([1, 0]));
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self::assertEquals('b', $network->predict([0, 1]));
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self::assertEquals('a', $network->predict([1, 1]));
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self::assertEquals('b', $network->predict([0, 0]));
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}
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public function testBackpropagationLearningMultilayer(): void
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{
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// Multi-layer 2 classes.
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$network = new MLPClassifier(5, [3, 2], ['a', 'b', 'c'], 2000);
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$network->train(
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[[1, 0, 0, 0, 0], [0, 1, 1, 0, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0]],
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['a', 'b', 'a', 'c']
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);
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self::assertEquals('a', $network->predict([1, 0, 0, 0, 0]));
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self::assertEquals('b', $network->predict([0, 1, 1, 0, 0]));
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self::assertEquals('a', $network->predict([1, 1, 1, 1, 1]));
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self::assertEquals('c', $network->predict([0, 0, 0, 0, 0]));
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}
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public function testBackpropagationLearningMulticlass(): void
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{
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// Multi-layer more than 2 classes.
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$network = new MLPClassifier(5, [3, 2], ['a', 'b', 4], 1000);
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$network->train(
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[[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 1, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0]],
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['a', 'b', 'a', 'a', 4]
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);
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self::assertEquals('a', $network->predict([1, 0, 0, 0, 0]));
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self::assertEquals('b', $network->predict([0, 1, 0, 0, 0]));
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self::assertEquals('a', $network->predict([0, 0, 1, 1, 0]));
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self::assertEquals('a', $network->predict([1, 1, 1, 1, 1]));
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self::assertEquals(4, $network->predict([0, 0, 0, 0, 0]));
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}
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/**
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* @dataProvider activationFunctionsProvider
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*/
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public function testBackpropagationActivationFunctions(ActivationFunction $activationFunction): void
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{
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$network = new MLPClassifier(5, [3], ['a', 'b'], 1000, $activationFunction);
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$network->train(
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[[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 1, 0], [1, 1, 1, 1, 1]],
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['a', 'b', 'a', 'a']
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);
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self::assertEquals('a', $network->predict([1, 0, 0, 0, 0]));
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self::assertEquals('b', $network->predict([0, 1, 0, 0, 0]));
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self::assertEquals('a', $network->predict([0, 0, 1, 1, 0]));
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self::assertEquals('a', $network->predict([1, 1, 1, 1, 1]));
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}
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public function activationFunctionsProvider(): array
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{
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return [
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[new Sigmoid()],
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[new HyperbolicTangent()],
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[new PReLU()],
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[new ThresholdedReLU()],
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];
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}
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public function testSaveAndRestore(): void
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{
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// Instantinate new Percetron trained for OR problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 1, 1, 1];
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$classifier = new MLPClassifier(2, [2], [0, 1], 1000);
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$classifier->train($samples, $targets);
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$testSamples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$predicted = $classifier->predict($testSamples);
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$filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid('', false);
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$filepath = (string) tempnam(sys_get_temp_dir(), $filename);
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$modelManager = new ModelManager();
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$modelManager->saveToFile($classifier, $filepath);
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$restoredClassifier = $modelManager->restoreFromFile($filepath);
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self::assertEquals($classifier, $restoredClassifier);
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self::assertEquals($predicted, $restoredClassifier->predict($testSamples));
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}
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public function testSaveAndRestoreWithPartialTraining(): void
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{
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$network = new MLPClassifier(2, [2], ['a', 'b'], 1000);
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$network->partialTrain(
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[[1, 0], [0, 1]],
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['a', 'b']
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);
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self::assertEquals('a', $network->predict([1, 0]));
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self::assertEquals('b', $network->predict([0, 1]));
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$filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid('', false);
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$filepath = (string) tempnam(sys_get_temp_dir(), $filename);
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$modelManager = new ModelManager();
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$modelManager->saveToFile($network, $filepath);
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/** @var MLPClassifier $restoredNetwork */
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$restoredNetwork = $modelManager->restoreFromFile($filepath);
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$restoredNetwork->partialTrain(
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[[1, 1], [0, 0]],
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['a', 'b']
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);
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self::assertEquals('a', $restoredNetwork->predict([1, 0]));
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self::assertEquals('b', $restoredNetwork->predict([0, 1]));
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self::assertEquals('a', $restoredNetwork->predict([1, 1]));
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self::assertEquals('b', $restoredNetwork->predict([0, 0]));
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}
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public function testThrowExceptionOnInvalidLayersNumber(): void
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{
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$this->expectException(InvalidArgumentException::class);
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new MLPClassifier(2, [], [0, 1]);
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}
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public function testThrowExceptionOnInvalidPartialTrainingClasses(): void
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{
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$this->expectException(InvalidArgumentException::class);
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$classifier = new MLPClassifier(2, [2], [0, 1]);
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$classifier->partialTrain(
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[[0, 1], [1, 0]],
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[0, 2],
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[0, 1, 2]
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);
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}
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public function testThrowExceptionOnInvalidClassesNumber(): void
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{
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$this->expectException(InvalidArgumentException::class);
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new MLPClassifier(2, [2], [0]);
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}
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public function testOutputWithLabels(): void
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{
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$output = (new MLPClassifier(2, [2, 2], ['T', 'F']))->getOutput();
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self::assertEquals(['T', 'F'], array_keys($output));
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}
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private function getSynapsesNodes(array $synapses): array
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{
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$nodes = [];
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foreach ($synapses as $synapse) {
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$nodes[] = $synapse->getNode();
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}
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return $nodes;
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}
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}
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