wine class dataset

data/wine.csv

@@ -0,0 +1,179 @@
+alcohol,malic acid,ash,alcalinity of ash,magnesium,total phenols,flavanoids,nonflavanoid phenols,proanthocyanins,color intensity,hue,OD280/OD315 of diluted wines,proline,class
+14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065,1
+13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050,1
+13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185,1
+14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480,1
+13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735,1
+14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450,1
+14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290,1
+14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295,1
+14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045,1
+13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045,1
+14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510,1
+14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280,1
+13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320,1
+14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150,1
+14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547,1
+13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310,1
+14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280,1
+13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130,1
+14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680,1
+13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845,1
+14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780,1
+12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770,1
+13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035,1
+12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015,1
+13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845,1
+13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830,1
+13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195,1
+13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285,1
+13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915,1
+14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035,1
+13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285,1
+13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515,1
+13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990,1
+13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235,1
+13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095,1
+13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920,1
+13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880,1
+13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105,1
+13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020,1
+14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760,1
+13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795,1
+13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035,1
+13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095,1
+13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680,1
+13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885,1
+14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080,1
+14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065,1
+13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985,1
+14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060,1
+13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260,1
+13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150,1
+13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265,1
+13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190,1
+13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375,1
+13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060,1
+13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120,1
+14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970,1
+13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270,1
+13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285,1
+12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520,2
+12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680,2
+12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450,2
+13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630,2
+12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420,2
+12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355,2
+12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678,2
+13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502,2
+12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510,2
+13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750,2
+12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718,2
+12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870,2
+13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410,2
+13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472,2
+12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985,2
+11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886,2
+11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428,2
+13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392,2
+11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500,2
+12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750,2
+12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463,2
+12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278,2
+12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714,2
+12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630,2
+13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515,2
+11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520,2
+12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450,2
+12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495,2
+11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562,2
+11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680,2
+12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625,2
+12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480,2
+12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450,2
+12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495,2
+12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290,2
+11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345,2
+12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937,2
+11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625,2
+12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428,2
+12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660,2
+12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406,2
+12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710,2
+12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562,2
+12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438,2
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+12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312,2
+11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680,2
+11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562,2
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+12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510,3
+12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470,3
+14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660,3
+13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740,3
+13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750,3
+13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835,3
+13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840,3
+14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560,3

src/Phpml/Dataset/Demo/Wine.php

@@ -0,0 +1,22 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Dataset\Demo;
+
+use Phpml\Dataset\CsvDataset;
+
+/**
+ * Classes: 3
+ * Samples per class: class 1 59; class 2 71; class 3 48
+ * Samples total: 178
+ * Features per sample: 13
+ */
+class Wine extends CsvDataset
+{
+    public function __construct()
+    {
+        $filepath = dirname(__FILE__).'/../../../../data/wine.csv';
+        parent::__construct($filepath, 13, true);
+    }
+}

src/Phpml/Metric/Accuracy.php

@@ -25,7 +25,7 @@ public static function score(array $actualLabels, array $predictedLabels, bool $
 
         $score = 0;
         foreach ($actualLabels as $index => $label) {
-            if ($label === $predictedLabels[$index]) {
+            if ($label == $predictedLabels[$index]) {
                 ++$score;
             }
         }

tests/Phpml/Classifier/KNearestNeighborsTest.php

@@ -7,6 +7,7 @@
 use Phpml\Classifier\KNearestNeighbors;
 use Phpml\CrossValidation\RandomSplit;
 use Phpml\Dataset\Demo\Iris;
+use Phpml\Dataset\Demo\Wine;
 use Phpml\Metric\Accuracy;
 
 class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
@@ -55,4 +56,16 @@ public function testAccuracyOnIrisDataset()
 
         $this->assertEquals(0.96, $score);
     }
+
+    public function testAccuracyOnWineDataset()
+    {
+        $dataset = new RandomSplit(new Wine(), $testSize = 0.3, $seed = 321);
+        $classifier = new KNearestNeighbors(1);
+        $classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
+        $predicted = $classifier->predict($dataset->getTestSamples());
+        $score = Accuracy::score($dataset->getTestLabels(), $predicted);
+
+        $this->assertEquals(0.85185185185185186, $score);
+    }
+
 }

tests/Phpml/Dataset/Demo/WineTest.php

@@ -0,0 +1,22 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\Dataset\Demo;
+
+use Phpml\Dataset\Demo\Wine;
+
+class WineTest extends \PHPUnit_Framework_TestCase
+{
+    public function testLoadingWineDataset()
+    {
+        $iris = new Wine();
+
+        // whole dataset
+        $this->assertEquals(178, count($iris->getSamples()));
+        $this->assertEquals(178, count($iris->getLabels()));
+
+        // one sample features count
+        $this->assertEquals(13, count($iris->getSamples()[0]));
+    }
+}