107 lines
4.2 KiB
PHP
107 lines
4.2 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\Classification\Linear;
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use Phpml\Classification\Linear\LogisticRegression;
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use PHPUnit\Framework\TestCase;
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use ReflectionMethod;
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use ReflectionProperty;
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class LogisticRegressionTest extends TestCase
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{
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public function testPredictSingleSample(): void
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{
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// AND problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
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$targets = [0, 0, 0, 1, 0, 1];
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$classifier = new LogisticRegression();
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$classifier->train($samples, $targets);
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$this->assertEquals(0, $classifier->predict([0.1, 0.1]));
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$this->assertEquals(1, $classifier->predict([0.9, 0.9]));
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}
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public function testPredictMultiClassSample(): void
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{
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// By use of One-v-Rest, Perceptron can perform multi-class classification
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// The samples should be separable by lines perpendicular to the dimensions
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$samples = [
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[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
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[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
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[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
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];
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$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
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$classifier = new LogisticRegression();
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$classifier->train($samples, $targets);
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$this->assertEquals(0, $classifier->predict([0.5, 0.5]));
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$this->assertEquals(1, $classifier->predict([6.0, 5.0]));
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$this->assertEquals(2, $classifier->predict([3.0, 9.5]));
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}
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public function testPredictProbabilitySingleSample(): void
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{
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
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$targets = [0, 0, 0, 1, 0, 1];
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$classifier = new LogisticRegression();
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$classifier->train($samples, $targets);
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$property = new ReflectionProperty($classifier, 'classifiers');
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$property->setAccessible(true);
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$predictor = $property->getValue($classifier)[0];
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$method = new ReflectionMethod($predictor, 'predictProbability');
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$method->setAccessible(true);
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$zero = $method->invoke($predictor, [0.1, 0.1], 0);
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$one = $method->invoke($predictor, [0.1, 0.1], 1);
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$this->assertEquals(1, $zero + $one, '', 1e-6);
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$this->assertTrue($zero > $one);
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$zero = $method->invoke($predictor, [0.9, 0.9], 0);
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$one = $method->invoke($predictor, [0.9, 0.9], 1);
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$this->assertEquals(1, $zero + $one, '', 1e-6);
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$this->assertTrue($zero < $one);
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}
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public function testPredictProbabilityMultiClassSample(): void
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{
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$samples = [
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[0, 0], [0, 1], [1, 0], [1, 1],
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[5, 5], [6, 5], [5, 6], [6, 6],
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[3, 10], [3, 10], [3, 8], [3, 9],
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];
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$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
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$classifier = new LogisticRegression();
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$classifier->train($samples, $targets);
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$property = new ReflectionProperty($classifier, 'classifiers');
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$property->setAccessible(true);
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$predictor = $property->getValue($classifier)[0];
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$method = new ReflectionMethod($predictor, 'predictProbability');
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$method->setAccessible(true);
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$zero = $method->invoke($predictor, [3.0, 9.5], 0);
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$not_zero = $method->invoke($predictor, [3.0, 9.5], 'not_0');
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$predictor = $property->getValue($classifier)[1];
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$method = new ReflectionMethod($predictor, 'predictProbability');
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$method->setAccessible(true);
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$one = $method->invoke($predictor, [3.0, 9.5], 1);
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$not_one = $method->invoke($predictor, [3.0, 9.5], 'not_1');
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$predictor = $property->getValue($classifier)[2];
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$method = new ReflectionMethod($predictor, 'predictProbability');
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$method->setAccessible(true);
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$two = $method->invoke($predictor, [3.0, 9.5], 2);
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$not_two = $method->invoke($predictor, [3.0, 9.5], 'not_2');
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$this->assertEquals(1, $zero + $not_zero, '', 1e-6);
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$this->assertEquals(1, $one + $not_one, '', 1e-6);
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$this->assertEquals(1, $two + $not_two, '', 1e-6);
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$this->assertTrue($zero < $two);
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$this->assertTrue($one < $two);
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}
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}
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