Update NaiveBayes.php (#30)
* Update NaiveBayes.php * Update NaiveBayes.php * Update NaiveBayes.php Update to fix "predictSample" function to enable it handle samples given as multi-dimensional arrays. * Update NaiveBayes.php * Update NaiveBayes.php
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Mustafa Karabulut
Arkadiusz Kondas
parent
cb5a9956c6
commit
e603d60841
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@ -6,31 +6,152 @@ namespace Phpml\Classification;
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use Phpml\Helper\Predictable;
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use Phpml\Helper\Trainable;
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use Phpml\Math\Statistic\Mean;
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use Phpml\Math\Statistic\StandardDeviation;
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class NaiveBayes implements Classifier
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{
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use Trainable, Predictable;
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const CONTINUOS = 1;
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const NOMINAL = 2;
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const EPSILON = 1e-10;
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private $std = array();
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private $mean= array();
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private $discreteProb = array();
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private $dataType = array();
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private $p = array();
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private $sampleCount = 0;
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private $featureCount = 0;
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private $labels = array();
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public function train(array $samples, array $targets)
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{
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$this->samples = $samples;
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$this->targets = $targets;
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$this->sampleCount = count($samples);
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$this->featureCount = count($samples[0]);
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// Get distinct targets
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$this->labels = $targets;
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array_unique($this->labels);
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foreach ($this->labels as $label) {
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$samples = $this->getSamplesByLabel($label);
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$this->p[$label] = count($samples) / $this->sampleCount;
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$this->calculateStatistics($label, $samples);
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}
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}
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/**
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* Calculates vital statistics for each label & feature. Stores these
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* values in private array in order to avoid repeated calculation
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* @param string $label
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* @param array $samples
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*/
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private function calculateStatistics($label, $samples)
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{
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$this->std[$label] = array_fill(0, $this->featureCount, 0);
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$this->mean[$label]= array_fill(0, $this->featureCount, 0);
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$this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
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$this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
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for ($i=0; $i<$this->featureCount; $i++) {
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// Get the values of nth column in the samples array
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// Mean::arithmetic is called twice, can be optimized
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$values = array_column($samples, $i);
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$numValues = count($values);
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// if the values contain non-numeric data,
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// then it should be treated as nominal/categorical/discrete column
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if ($values !== array_filter($values, 'is_numeric')) {
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$this->dataType[$label][$i] = self::NOMINAL;
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$this->discreteProb[$label][$i] = array_count_values($values);
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$db = &$this->discreteProb[$label][$i];
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$db = array_map(function ($el) use ($numValues) {
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return $el / $numValues;
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}, $db);
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} else {
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$this->mean[$label][$i] = Mean::arithmetic($values);
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// Add epsilon in order to avoid zero stdev
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$this->std[$label][$i] = 1e-10 + StandardDeviation::population($values, false);
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}
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}
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}
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/**
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* Calculates the probability P(label|sample_n)
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*
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* @param array $sample
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* @param int $feature
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* @param string $label
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*/
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private function sampleProbability($sample, $feature, $label)
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{
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$value = $sample[$feature];
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if ($this->dataType[$label][$feature] == self::NOMINAL) {
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if (! isset($this->discreteProb[$label][$feature][$value]) ||
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$this->discreteProb[$label][$feature][$value] == 0) {
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return self::EPSILON;
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}
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return $this->discreteProb[$label][$feature][$value];
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}
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$std = $this->std[$label][$feature] ;
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$mean= $this->mean[$label][$feature];
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// Calculate the probability density by use of normal/Gaussian distribution
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// Ref: https://en.wikipedia.org/wiki/Normal_distribution
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//
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// In order to avoid numerical errors because of small or zero values,
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// some libraries adopt taking log of calculations such as
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// scikit-learn did.
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// (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
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$pdf = -0.5 * log(2.0 * pi() * $std * $std);
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$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
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return $pdf;
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}
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/**
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* Return samples belonging to specific label
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* @param string $label
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* @return array
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*/
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private function getSamplesByLabel($label)
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{
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$samples = array();
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for ($i=0; $i<$this->sampleCount; $i++) {
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if ($this->targets[$i] == $label) {
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$samples[] = $this->samples[$i];
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}
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}
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return $samples;
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}
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/**
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* @param array $sample
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*
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* @return mixed
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*/
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protected function predictSample(array $sample)
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{
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$predictions = [];
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foreach ($this->targets as $index => $label) {
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$predictions[$label] = 0;
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foreach ($sample as $token => $count) {
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if (array_key_exists($token, $this->samples[$index])) {
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$predictions[$label] += $count * $this->samples[$index][$token];
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}
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}
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$isArray = is_array($sample[0]);
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$samples = $sample;
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if (!$isArray) {
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$samples = array($sample);
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}
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arsort($predictions, SORT_NUMERIC);
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reset($predictions);
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return key($predictions);
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$samplePredictions = array();
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foreach ($samples as $sample) {
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// Use NaiveBayes assumption for each label using:
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// P(label|features) = P(label) * P(feature0|label) * P(feature1|label) .... P(featureN|label)
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// Then compare probability for each class to determine which label is most likely
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$predictions = array();
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foreach ($this->labels as $label) {
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$p = $this->p[$label];
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for ($i=0; $i<$this->featureCount; $i++) {
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$Plf = $this->sampleProbability($sample, $i, $label);
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$p += $Plf;
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}
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$predictions[$label] = $p;
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}
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arsort($predictions, SORT_NUMERIC);
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reset($predictions);
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$samplePredictions[] = key($predictions);
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}
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if (! $isArray) {
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return $samplePredictions[0];
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}
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return $samplePredictions;
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}
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}
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