From af9ccfe722f26c48307776842003ec2b1659fca0 Mon Sep 17 00:00:00 2001
From: Yuji Uchiyama <y.uchiyama.1015@gmail.com>
Date: Sat, 3 Mar 2018 19:19:58 +0900
Subject: [PATCH] Add tests for LogisticRegression (#248)

---
 .../Linear/LogisticRegressionTest.php         | 110 ++++++++++++++++++
 1 file changed, 110 insertions(+)

diff --git a/tests/Classification/Linear/LogisticRegressionTest.php b/tests/Classification/Linear/LogisticRegressionTest.php
index f60d308..ed9b878 100644
--- a/tests/Classification/Linear/LogisticRegressionTest.php
+++ b/tests/Classification/Linear/LogisticRegressionTest.php
@@ -8,9 +8,49 @@ use Phpml\Classification\Linear\LogisticRegression;
 use PHPUnit\Framework\TestCase;
 use ReflectionMethod;
 use ReflectionProperty;
+use Throwable;
 
 class LogisticRegressionTest extends TestCase
 {
+    public function testConstructorThrowWhenInvalidTrainingType(): void
+    {
+        $this->expectException(Throwable::class);
+
+        $classifier = new LogisticRegression(
+            500,
+            true,
+            -1,
+            'log',
+            'L2'
+        );
+    }
+
+    public function testConstructorThrowWhenInvalidCost(): void
+    {
+        $this->expectException(Throwable::class);
+
+        $classifier = new LogisticRegression(
+            500,
+            true,
+            LogisticRegression::CONJUGATE_GRAD_TRAINING,
+            'invalid',
+            'L2'
+        );
+    }
+
+    public function testConstructorThrowWhenInvalidPenalty(): void
+    {
+        $this->expectException(Throwable::class);
+
+        $classifier = new LogisticRegression(
+            500,
+            true,
+            LogisticRegression::CONJUGATE_GRAD_TRAINING,
+            'log',
+            'invalid'
+        );
+    }
+
     public function testPredictSingleSample(): void
     {
         // AND problem
@@ -22,6 +62,76 @@ class LogisticRegressionTest extends TestCase
         $this->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);
+        $this->assertEquals(0, $classifier->predict([0.1, 0.1]));
+        $this->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);
+        $this->assertEquals(0, $classifier->predict([0.1, 0.1]));
+        $this->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);
+        $this->assertEquals(0, $classifier->predict([0.1, 0.1]));
+        $this->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);
+        $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