php-ml/tests/SupportVectorMachine/SupportVectorMachineTest.php
Marcin Michalski db82afa263 Update to phpunit 8 and bump min php to 7.2 (#367)
* Update to phpunit 8

* Require at least PHP 7.2
2019-04-10 20:42:59 +02:00

207 lines
5.9 KiB
PHP

<?php
declare(strict_types=1);
namespace Phpml\Tests\SupportVectorMachine;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Exception\InvalidOperationException;
use Phpml\Exception\LibsvmCommandException;
use Phpml\SupportVectorMachine\Kernel;
use Phpml\SupportVectorMachine\SupportVectorMachine;
use Phpml\SupportVectorMachine\Type;
use PHPUnit\Framework\TestCase;
class SupportVectorMachineTest extends TestCase
{
public function testTrainCSVCModelWithLinearKernel(): void
{
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
$model =
'svm_type c_svc
kernel_type linear
nr_class 2
total_sv 2
rho 0
label 0 1
nr_sv 1 1
SV
0.25 1:2 2:4
-0.25 1:4 2:2
';
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::LINEAR, 100.0);
$svm->train($samples, $labels);
self::assertEquals($model, $svm->getModel());
}
public function testTrainCSVCModelWithProbabilityEstimate(): void
{
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
$svm = new SupportVectorMachine(
Type::C_SVC,
Kernel::LINEAR,
100.0,
0.5,
3,
null,
0.0,
0.1,
0.01,
100,
true,
true
);
$svm->train($samples, $labels);
self::assertStringContainsString(PHP_EOL.'probA ', $svm->getModel());
self::assertStringContainsString(PHP_EOL.'probB ', $svm->getModel());
}
public function testPredictSampleWithLinearKernel(): void
{
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::LINEAR, 100.0);
$svm->train($samples, $labels);
$predictions = $svm->predict([
[3, 2],
[2, 3],
[4, -5],
]);
self::assertEquals('b', $predictions[0]);
self::assertEquals('a', $predictions[1]);
self::assertEquals('b', $predictions[2]);
}
public function testPredictSampleFromMultipleClassWithRbfKernel(): void
{
$samples = [
[1, 3], [1, 4], [1, 4],
[3, 1], [4, 1], [4, 2],
[-3, -1], [-4, -1], [-4, -2],
];
$labels = [
'a', 'a', 'a',
'b', 'b', 'b',
'c', 'c', 'c',
];
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::RBF, 100.0);
$svm->train($samples, $labels);
$predictions = $svm->predict([
[1, 5],
[4, 3],
[-4, -3],
]);
self::assertEquals('a', $predictions[0]);
self::assertEquals('b', $predictions[1]);
self::assertEquals('c', $predictions[2]);
}
public function testPredictProbability(): void
{
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
$svm = new SupportVectorMachine(
Type::C_SVC,
Kernel::LINEAR,
100.0,
0.5,
3,
null,
0.0,
0.1,
0.01,
100,
true,
true
);
$svm->train($samples, $labels);
$predictions = $svm->predictProbability([
[3, 2],
[2, 3],
[4, -5],
]);
self::assertTrue($predictions[0]['a'] < $predictions[0]['b']);
self::assertTrue($predictions[1]['a'] > $predictions[1]['b']);
self::assertTrue($predictions[2]['a'] < $predictions[2]['b']);
// Should be true because the latter is farther from the decision boundary
self::assertTrue($predictions[0]['b'] < $predictions[2]['b']);
}
public function testThrowExceptionWhenVarPathIsNotWritable(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('is not writable');
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::RBF);
$svm->setVarPath('var-path');
}
public function testThrowExceptionWhenBinPathDoesNotExist(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('does not exist');
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::RBF);
$svm->setBinPath('bin-path');
}
public function testThrowExceptionWhenFileIsNotFoundInBinPath(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('not found');
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::RBF);
$svm->setBinPath('var');
}
public function testThrowExceptionWhenLibsvmFailsDuringTrain(): void
{
$this->expectException(LibsvmCommandException::class);
$this->expectExceptionMessage('ERROR: unknown svm type');
$svm = new SupportVectorMachine(99, Kernel::RBF);
$svm->train([], []);
}
public function testThrowExceptionWhenLibsvmFailsDuringPredict(): void
{
$this->expectException(LibsvmCommandException::class);
$this->expectExceptionMessage('can\'t open model file');
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::RBF);
$svm->predict([1]);
}
public function testThrowExceptionWhenPredictProbabilityCalledWithoutProperModel(): void
{
$this->expectException(InvalidOperationException::class);
$this->expectExceptionMessage('Model does not support probabiliy estimates');
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
$svm = new SupportVectorMachine(Type::C_SVC, Kernel::LINEAR, 100.0);
$svm->train($samples, $labels);
$svm->predictProbability([
[3, 2],
[2, 3],
[4, -5],
]);
}
}