Fix logistic regression implementation (#169)

* Fix target value of LogisticRegression

* Fix probability calculation in LogisticRegression

* Change the default cost function to log-likelihood

* Remove redundant round function

* Fix for coding standard

src/Phpml/Classification/Linear/LogisticRegression.php

@@ -32,7 +32,7 @@ class LogisticRegression extends Adaline
      *
      * @var string
      */
-    protected $costFunction = 'sse';
+    protected $costFunction = 'log';
 
     /**
      * Regularization term: only 'L2' is supported
@@ -67,7 +67,7 @@ class LogisticRegression extends Adaline
         int $maxIterations = 500,
         bool $normalizeInputs = true,
         int $trainingType = self::CONJUGATE_GRAD_TRAINING,
-        string $cost = 'sse',
+        string $cost = 'log',
         string $penalty = 'L2'
     ) {
         $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
@@ -190,6 +190,8 @@ class LogisticRegression extends Adaline
                         $hX = 1e-10;
                     }
 
+                    $y = $y < 0 ? 0 : 1;
+
                     $error = -$y * log($hX) - (1 - $y) * log(1 - $hX);
                     $gradient = $hX - $y;
 
@@ -213,6 +215,8 @@ class LogisticRegression extends Adaline
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 
+                    $y = $y < 0 ? 0 : 1;
+
                     $error = ($y - $hX) ** 2;
                     $gradient = -($y - $hX) * $hX * (1 - $hX);
 
@@ -243,7 +247,7 @@ class LogisticRegression extends Adaline
     {
         $output = $this->output($sample);
 
-        if (round($output) > 0.5) {
+        if ($output > 0.5) {
             return 1;
         }
 
@@ -260,14 +264,13 @@ class LogisticRegression extends Adaline
      */
     protected function predictProbability(array $sample, $label): float
     {
-        $predicted = $this->predictSampleBinary($sample);
-
-        if ((string) $predicted == (string) $label) {
         $sample = $this->checkNormalizedSample($sample);
+        $probability = $this->output($sample);
 
-            return (float) abs($this->output($sample) - 0.5);
+        if (array_search($label, $this->labels, true) > 0) {
+            return $probability;
         }
 
-        return 0.0;
+        return 1 - $probability;
     }
 }

tests/Phpml/Classification/Linear/LogisticRegressionTest.php

@@ -0,0 +1,106 @@
+<?php
+
+declare(strict_types=1);
+
+namespace tests\Phpml\Classification\Linear;
+
+use Phpml\Classification\Linear\LogisticRegression;
+use PHPUnit\Framework\TestCase;
+use ReflectionMethod;
+use ReflectionProperty;
+
+class LogisticRegressionTest extends TestCase
+{
+    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);
+        $this->assertEquals(0, $classifier->predict([0.1, 0.1]));
+        $this->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);
+        $this->assertEquals(0, $classifier->predict([0.5, 0.5]));
+        $this->assertEquals(1, $classifier->predict([6.0, 5.0]));
+        $this->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);
+        $this->assertEquals(1, $zero + $one, null, 1e-6);
+        $this->assertTrue($zero > $one);
+
+        $zero = $method->invoke($predictor, [0.9, 0.9], 0);
+        $one = $method->invoke($predictor, [0.9, 0.9], 1);
+        $this->assertEquals(1, $zero + $one, null, 1e-6);
+        $this->assertTrue($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');
+
+        $this->assertEquals(1, $zero + $not_zero, null, 1e-6);
+        $this->assertEquals(1, $one + $not_one, null, 1e-6);
+        $this->assertEquals(1, $two + $not_two, null, 1e-6);
+        $this->assertTrue($zero < $two);
+        $this->assertTrue($one < $two);
+    }
+}