php-ml/tests/Phpml/Classification/MLPClassifierTest.php
Marcin Michalski 3ac658c397 php-cs-fixer - more rules (#118)
* Add new cs-fixer rules and run them

* Do not align double arrows/equals
2017-08-17 08:50:37 +02:00

207 lines
6.4 KiB
PHP

<?php
declare(strict_types=1);
namespace tests\Phpml\Classification;
use Phpml\Classification\MLPClassifier;
use Phpml\NeuralNetwork\Node\Neuron;
use Phpml\ModelManager;
use PHPUnit\Framework\TestCase;
class MLPClassifierTest extends TestCase
{
public function testMLPClassifierLayersInitialization()
{
$mlp = new MLPClassifier(2, [2], [0, 1]);
$this->assertCount(3, $mlp->getLayers());
$layers = $mlp->getLayers();
// input layer
$this->assertCount(3, $layers[0]->getNodes());
$this->assertNotContainsOnly(Neuron::class, $layers[0]->getNodes());
// hidden layer
$this->assertCount(3, $layers[1]->getNodes());
$this->assertNotContainsOnly(Neuron::class, $layers[1]->getNodes());
// output layer
$this->assertCount(2, $layers[2]->getNodes());
$this->assertContainsOnly(Neuron::class, $layers[2]->getNodes());
}
public function testSynapsesGeneration()
{
$mlp = new MLPClassifier(2, [2], [0, 1]);
$layers = $mlp->getLayers();
foreach ($layers[1]->getNodes() as $node) {
if ($node instanceof Neuron) {
$synapses = $node->getSynapses();
$this->assertCount(3, $synapses);
$synapsesNodes = $this->getSynapsesNodes($synapses);
foreach ($layers[0]->getNodes() as $prevNode) {
$this->assertContains($prevNode, $synapsesNodes);
}
}
}
}
public function testBackpropagationLearning()
{
// Single layer 2 classes.
$network = new MLPClassifier(2, [2], ['a', 'b']);
$network->train(
[[1, 0], [0, 1], [1, 1], [0, 0]],
['a', 'b', 'a', 'b']
);
$this->assertEquals('a', $network->predict([1, 0]));
$this->assertEquals('b', $network->predict([0, 1]));
$this->assertEquals('a', $network->predict([1, 1]));
$this->assertEquals('b', $network->predict([0, 0]));
}
public function testBackpropagationTrainingReset()
{
// Single layer 2 classes.
$network = new MLPClassifier(2, [2], ['a', 'b'], 1000);
$network->train(
[[1, 0], [0, 1]],
['a', 'b']
);
$this->assertEquals('a', $network->predict([1, 0]));
$this->assertEquals('b', $network->predict([0, 1]));
$network->train(
[[1, 0], [0, 1]],
['b', 'a']
);
$this->assertEquals('b', $network->predict([1, 0]));
$this->assertEquals('a', $network->predict([0, 1]));
}
public function testBackpropagationPartialTraining()
{
// Single layer 2 classes.
$network = new MLPClassifier(2, [2], ['a', 'b'], 1000);
$network->partialTrain(
[[1, 0], [0, 1]],
['a', 'b']
);
$this->assertEquals('a', $network->predict([1, 0]));
$this->assertEquals('b', $network->predict([0, 1]));
$network->partialTrain(
[[1, 1], [0, 0]],
['a', 'b']
);
$this->assertEquals('a', $network->predict([1, 0]));
$this->assertEquals('b', $network->predict([0, 1]));
$this->assertEquals('a', $network->predict([1, 1]));
$this->assertEquals('b', $network->predict([0, 0]));
}
public function testBackpropagationLearningMultilayer()
{
// Multi-layer 2 classes.
$network = new MLPClassifier(5, [3, 2], ['a', 'b']);
$network->train(
[[1, 0, 0, 0, 0], [0, 1, 1, 0, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0]],
['a', 'b', 'a', 'b']
);
$this->assertEquals('a', $network->predict([1, 0, 0, 0, 0]));
$this->assertEquals('b', $network->predict([0, 1, 1, 0, 0]));
$this->assertEquals('a', $network->predict([1, 1, 1, 1, 1]));
$this->assertEquals('b', $network->predict([0, 0, 0, 0, 0]));
}
public function testBackpropagationLearningMulticlass()
{
// Multi-layer more than 2 classes.
$network = new MLPClassifier(5, [3, 2], ['a', 'b', 4]);
$network->train(
[[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]],
['a', 'b', 'a', 'a', 4]
);
$this->assertEquals('a', $network->predict([1, 0, 0, 0, 0]));
$this->assertEquals('b', $network->predict([0, 1, 0, 0, 0]));
$this->assertEquals('a', $network->predict([0, 0, 1, 1, 0]));
$this->assertEquals('a', $network->predict([1, 1, 1, 1, 1]));
$this->assertEquals(4, $network->predict([0, 0, 0, 0, 0]));
}
public function testSaveAndRestore()
{
// Instantinate new Percetron trained for OR problem
$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
$targets = [0, 1, 1, 1];
$classifier = new MLPClassifier(2, [2], [0, 1]);
$classifier->train($samples, $targets);
$testSamples = [[0, 0], [1, 0], [0, 1], [1, 1]];
$predicted = $classifier->predict($testSamples);
$filename = 'perceptron-test-'.rand(100, 999).'-'.uniqid();
$filepath = tempnam(sys_get_temp_dir(), $filename);
$modelManager = new ModelManager();
$modelManager->saveToFile($classifier, $filepath);
$restoredClassifier = $modelManager->restoreFromFile($filepath);
$this->assertEquals($classifier, $restoredClassifier);
$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
}
/**
* @expectedException \Phpml\Exception\InvalidArgumentException
*/
public function testThrowExceptionOnInvalidLayersNumber()
{
new MLPClassifier(2, [], [0, 1]);
}
/**
* @expectedException \Phpml\Exception\InvalidArgumentException
*/
public function testThrowExceptionOnInvalidPartialTrainingClasses()
{
$classifier = new MLPClassifier(2, [2], [0, 1]);
$classifier->partialTrain(
[[0, 1], [1, 0]],
[0, 2],
[0, 1, 2]
);
}
/**
* @expectedException \Phpml\Exception\InvalidArgumentException
*/
public function testThrowExceptionOnInvalidClassesNumber()
{
new MLPClassifier(2, [2], [0]);
}
/**
* @param array $synapses
*
* @return array
*/
private function getSynapsesNodes(array $synapses): array
{
$nodes = [];
foreach ($synapses as $synapse) {
$nodes[] = $synapse->getNode();
}
return $nodes;
}
}