php-ml/tests/Classification/Linear/LogisticRegressionTest.php

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<?php
declare(strict_types=1);
namespace Phpml\Tests\Classification\Linear;
use Phpml\Classification\Linear\LogisticRegression;
use Phpml\Exception\InvalidArgumentException;
use PHPUnit\Framework\TestCase;
use ReflectionMethod;
use ReflectionProperty;
class LogisticRegressionTest extends TestCase
{
public function testConstructorThrowWhenInvalidTrainingType(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Logistic regression can only be trained with '.
'batch (gradient descent), online (stochastic gradient descent) '.
'or conjugate batch (conjugate gradients) algorithms');
new LogisticRegression(
500,
true,
-1,
'log',
'L2'
);
}
public function testConstructorThrowWhenInvalidCost(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage("Logistic regression cost function can be one of the following: \n".
"'log' for log-likelihood and 'sse' for sum of squared errors");
new LogisticRegression(
500,
true,
LogisticRegression::CONJUGATE_GRAD_TRAINING,
'invalid',
'L2'
);
}
public function testConstructorThrowWhenInvalidPenalty(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Logistic regression supports only \'L2\' regularization');
new LogisticRegression(
500,
true,
LogisticRegression::CONJUGATE_GRAD_TRAINING,
'log',
'invalid'
);
}
public function testPredictSingleSample(): void
{
// AND problem
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
$targets = [0, 0, 0, 1, 0, 1];
$classifier = new LogisticRegression();
$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.1, 0.1]));
self::assertEquals(1, $classifier->predict([0.9, 0.9]));
}
public function testPredictSingleSampleWithBatchTraining(): void
{
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
$targets = [0, 0, 0, 1, 0, 1];
// $maxIterations is set to 10000 as batch training needs more
// iteration to converge than CG method in general.
$classifier = new LogisticRegression(
10000,
true,
LogisticRegression::BATCH_TRAINING,
'log',
'L2'
);
$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.1, 0.1]));
self::assertEquals(1, $classifier->predict([0.9, 0.9]));
}
public function testPredictSingleSampleWithOnlineTraining(): void
{
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
$targets = [0, 0, 0, 1, 0, 1];
// $penalty is set to empty (no penalty) because L2 penalty seems to
// prevent convergence in online training for this dataset.
$classifier = new LogisticRegression(
10000,
true,
LogisticRegression::ONLINE_TRAINING,
'log',
''
);
$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.1, 0.1]));
self::assertEquals(1, $classifier->predict([0.9, 0.9]));
}
public function testPredictSingleSampleWithSSECost(): void
{
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
$targets = [0, 0, 0, 1, 0, 1];
$classifier = new LogisticRegression(
500,
true,
LogisticRegression::CONJUGATE_GRAD_TRAINING,
'sse',
'L2'
);
$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.1, 0.1]));
self::assertEquals(1, $classifier->predict([0.9, 0.9]));
}
public function testPredictSingleSampleWithoutPenalty(): void
{
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
$targets = [0, 0, 0, 1, 0, 1];
$classifier = new LogisticRegression(
500,
true,
LogisticRegression::CONJUGATE_GRAD_TRAINING,
'log',
''
);
$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.1, 0.1]));
self::assertEquals(1, $classifier->predict([0.9, 0.9]));
}
public function testPredictMultiClassSample(): void
{
// By use of One-v-Rest, Perceptron can perform multi-class classification
// The samples should be separable by lines perpendicular to the dimensions
$samples = [
[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
];
$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
$classifier = new LogisticRegression();
$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.5, 0.5]));
self::assertEquals(1, $classifier->predict([6.0, 5.0]));
self::assertEquals(2, $classifier->predict([3.0, 9.5]));
}
public function testPredictProbabilitySingleSample(): void
{
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.4, 0.4], [0.6, 0.6]];
$targets = [0, 0, 0, 1, 0, 1];
$classifier = new LogisticRegression();
$classifier->train($samples, $targets);
$property = new ReflectionProperty($classifier, 'classifiers');
$property->setAccessible(true);
$predictor = $property->getValue($classifier)[0];
$method = new ReflectionMethod($predictor, 'predictProbability');
$method->setAccessible(true);
$zero = $method->invoke($predictor, [0.1, 0.1], 0);
$one = $method->invoke($predictor, [0.1, 0.1], 1);
self::assertEqualsWithDelta(1, $zero + $one, 1e-6);
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self::assertTrue($zero > $one);
$zero = $method->invoke($predictor, [0.9, 0.9], 0);
$one = $method->invoke($predictor, [0.9, 0.9], 1);
self::assertEqualsWithDelta(1, $zero + $one, 1e-6);
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self::assertTrue($zero < $one);
}
public function testPredictProbabilityMultiClassSample(): void
{
$samples = [
[0, 0], [0, 1], [1, 0], [1, 1],
[5, 5], [6, 5], [5, 6], [6, 6],
[3, 10], [3, 10], [3, 8], [3, 9],
];
$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
$classifier = new LogisticRegression();
$classifier->train($samples, $targets);
$property = new ReflectionProperty($classifier, 'classifiers');
$property->setAccessible(true);
$predictor = $property->getValue($classifier)[0];
$method = new ReflectionMethod($predictor, 'predictProbability');
$method->setAccessible(true);
$zero = $method->invoke($predictor, [3.0, 9.5], 0);
$not_zero = $method->invoke($predictor, [3.0, 9.5], 'not_0');
$predictor = $property->getValue($classifier)[1];
$method = new ReflectionMethod($predictor, 'predictProbability');
$method->setAccessible(true);
$one = $method->invoke($predictor, [3.0, 9.5], 1);
$not_one = $method->invoke($predictor, [3.0, 9.5], 'not_1');
$predictor = $property->getValue($classifier)[2];
$method = new ReflectionMethod($predictor, 'predictProbability');
$method->setAccessible(true);
$two = $method->invoke($predictor, [3.0, 9.5], 2);
$not_two = $method->invoke($predictor, [3.0, 9.5], 'not_2');
self::assertEqualsWithDelta(1, $zero + $not_zero, 1e-6);
self::assertEqualsWithDelta(1, $one + $not_one, 1e-6);
self::assertEqualsWithDelta(1, $two + $not_two, 1e-6);
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self::assertTrue($zero < $two);
self::assertTrue($one < $two);
}
}