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Fix string representation of integer labels issue in NaiveBayes (#206)
* Update NaiveBayes.php This fixes an issue using string labels that are string representations of integers, e.g. "1998" getting cast to (int)1998. * Update NaiveBayes.php fixes superfluous whitespace error * added tests for naive bayes with numeric labels * added array_unique * nested array_flips for speed * nested the array flips inside the array map * to appear style CI test
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@ -66,8 +66,7 @@ class NaiveBayes implements Classifier
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$this->sampleCount = count($this->samples);
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$this->sampleCount = count($this->samples);
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$this->featureCount = count($this->samples[0]);
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$this->featureCount = count($this->samples[0]);
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$labelCounts = array_count_values($this->targets);
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$this->labels = array_map('strval', array_flip(array_flip($this->targets)));
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$this->labels = array_keys($labelCounts);
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foreach ($this->labels as $label) {
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foreach ($this->labels as $label) {
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$samples = $this->getSamplesByLabel($label);
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$samples = $this->getSamplesByLabel($label);
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$this->p[$label] = count($samples) / $this->sampleCount;
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$this->p[$label] = count($samples) / $this->sampleCount;
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@ -68,4 +68,63 @@ class NaiveBayesTest extends TestCase
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$this->assertEquals($classifier, $restoredClassifier);
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$this->assertEquals($classifier, $restoredClassifier);
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$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
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$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
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}
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}
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public function testPredictSimpleNumericLabels(): void
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{
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$samples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
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$labels = ['1996', '1997', '1998'];
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$classifier = new NaiveBayes();
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$classifier->train($samples, $labels);
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$this->assertEquals('1996', $classifier->predict([3, 1, 1]));
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$this->assertEquals('1997', $classifier->predict([1, 4, 1]));
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$this->assertEquals('1998', $classifier->predict([1, 1, 6]));
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}
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public function testPredictArrayOfSamplesNumericalLabels(): void
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{
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$trainSamples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
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$trainLabels = ['1996', '1997', '1998'];
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$testSamples = [[3, 1, 1], [5, 1, 1], [4, 3, 8], [1, 1, 2], [2, 3, 2], [1, 2, 1], [9, 5, 1], [3, 1, 2]];
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$testLabels = ['1996', '1996', '1998', '1998', '1997', '1997', '1996', '1996'];
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$classifier = new NaiveBayes();
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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$this->assertEquals($testLabels, $predicted);
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// Feed an extra set of training data.
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$samples = [[1, 1, 6]];
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$labels = ['1999'];
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$classifier->train($samples, $labels);
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$testSamples = [[1, 1, 6], [5, 1, 1]];
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$testLabels = ['1999', '1996'];
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$this->assertEquals($testLabels, $classifier->predict($testSamples));
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}
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public function testSaveAndRestoreNumericLabels(): void
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{
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$trainSamples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
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$trainLabels = ['1996', '1997', '1998'];
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$testSamples = [[3, 1, 1], [5, 1, 1], [4, 3, 8]];
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$testLabels = ['1996', '1996', '1998'];
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$classifier = new NaiveBayes();
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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$filename = 'naive-bayes-test-'.random_int(100, 999).'-'.uniqid();
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$filepath = tempnam(sys_get_temp_dir(), $filename);
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$modelManager = new ModelManager();
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$modelManager->saveToFile($classifier, $filepath);
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$restoredClassifier = $modelManager->restoreFromFile($filepath);
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$this->assertEquals($classifier, $restoredClassifier);
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$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
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}
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}
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}
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