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MDL-65769 lib: update PHP-ML to 0.8.0

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This commit is contained in:
Simey Lameze
2019-07-12 06:28:31 +08:00
parent f7e108438f
commit e6c25fb057
126 changed files with 3636 additions and 3750 deletions
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lib/mlbackend/php/phpml/src/Phpml/SupportVectorMachine/DataTransformer.php
+42 -31
View File
@@ -4,18 +4,15 @@ declare(strict_types=1);
namespace Phpml\SupportVectorMachine;
use Phpml\Exception\InvalidArgumentException;
class DataTransformer
{
/**
* @param array $samples
* @param array $labels
* @param bool $targets
*
* @return string
*/
public static function trainingSet(array $samples, array $labels, bool $targets = false): string
{
$set = '';
$numericLabels = [];
if (!$targets) {
$numericLabels = self::numericLabels($labels);
}
@@ -27,13 +24,12 @@ class DataTransformer
return $set;
}
/**
* @param array $samples
*
* @return string
*/
public static function testSet(array $samples): string
{
if (count($samples) === 0) {
throw new InvalidArgumentException('The array has zero elements');
}
if (!is_array($samples[0])) {
$samples = [$samples];
}
@@ -46,30 +42,50 @@ class DataTransformer
return $set;
}
/**
* @param string $rawPredictions
* @param array $labels
*
* @return array
*/
public static function predictions(string $rawPredictions, array $labels): array
{
$numericLabels = self::numericLabels($labels);
$results = [];
foreach (explode(PHP_EOL, $rawPredictions) as $result) {
if (strlen($result) > 0) {
$results[] = array_search($result, $numericLabels);
if (isset($result[0])) {
$results[] = array_search((int) $result, $numericLabels, true);
}
}
return $results;
}
/**
* @param array $labels
*
* @return array
*/
public static function probabilities(string $rawPredictions, array $labels): array
{
$numericLabels = self::numericLabels($labels);
$predictions = explode(PHP_EOL, trim($rawPredictions));
$header = array_shift($predictions);
$headerColumns = explode(' ', (string) $header);
array_shift($headerColumns);
$columnLabels = [];
foreach ($headerColumns as $numericLabel) {
$columnLabels[] = array_search((int) $numericLabel, $numericLabels, true);
}
$results = [];
foreach ($predictions as $rawResult) {
$probabilities = explode(' ', $rawResult);
array_shift($probabilities);
$result = [];
foreach ($probabilities as $i => $prob) {
$result[$columnLabels[$i]] = (float) $prob;
}
$results[] = $result;
}
return $results;
}
public static function numericLabels(array $labels): array
{
$numericLabels = [];
@@ -84,16 +100,11 @@ class DataTransformer
return $numericLabels;
}
/**
* @param array $sample
*
* @return string
*/
private static function sampleRow(array $sample): string
{
$row = [];
foreach ($sample as $index => $feature) {
$row[] = sprintf('%s:%s', $index + 1, $feature);
$row[] = sprintf('%s:%F', $index + 1, $feature);
}
return implode(' ', $row);
lib/mlbackend/php/phpml/src/Phpml/SupportVectorMachine/Kernel.php
+4 -4
View File
@@ -9,20 +9,20 @@ abstract class Kernel
/**
* u'*v.
*/
const LINEAR = 0;
public const LINEAR = 0;
/**
* (gamma*u'*v + coef0)^degree.
*/
const POLYNOMIAL = 1;
public const POLYNOMIAL = 1;
/**
* exp(-gamma*|u-v|^2).
*/
const RBF = 2;
public const RBF = 2;
/**
* tanh(gamma*u'*v + coef0).
*/
const SIGMOID = 3;
public const SIGMOID = 3;
}
lib/mlbackend/php/phpml/src/Phpml/SupportVectorMachine/SupportVectorMachine.php
+146 -75
View File
@@ -4,6 +4,9 @@ declare(strict_types=1);
namespace Phpml\SupportVectorMachine;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Exception\InvalidOperationException;
use Phpml\Exception\LibsvmCommandException;
use Phpml\Helper\Trainable;
class SupportVectorMachine
@@ -36,7 +39,7 @@ class SupportVectorMachine
private $degree;
/**
* @var float
* @var float|null
*/
private $gamma;
@@ -90,24 +93,19 @@ class SupportVectorMachine
*/
private $targets = [];
/**
* @param int $type
* @param int $kernel
* @param float $cost
* @param float $nu
* @param int $degree
* @param float|null $gamma
* @param float $coef0
* @param float $epsilon
* @param float $tolerance
* @param int $cacheSize
* @param bool $shrinking
* @param bool $probabilityEstimates
*/
public function __construct(
int $type, int $kernel, float $cost = 1.0, float $nu = 0.5, int $degree = 3,
float $gamma = null, float $coef0 = 0.0, float $epsilon = 0.1, float $tolerance = 0.001,
int $cacheSize = 100, bool $shrinking = true, bool $probabilityEstimates = false
int $type,
int $kernel,
float $cost = 1.0,
float $nu = 0.5,
int $degree = 3,
?float $gamma = null,
float $coef0 = 0.0,
float $epsilon = 0.1,
float $tolerance = 0.001,
int $cacheSize = 100,
bool $shrinking = true,
bool $probabilityEstimates = false
) {
$this->type = $type;
$this->kernel = $kernel;
@@ -122,90 +120,71 @@ class SupportVectorMachine
$this->shrinking = $shrinking;
$this->probabilityEstimates = $probabilityEstimates;
$rootPath = realpath(implode(DIRECTORY_SEPARATOR, [__DIR__, '..', '..', '..'])).DIRECTORY_SEPARATOR;
$rootPath = realpath(implode(DIRECTORY_SEPARATOR, [__DIR__, '..', '..'])).DIRECTORY_SEPARATOR;
$this->binPath = $rootPath.'bin'.DIRECTORY_SEPARATOR.'libsvm'.DIRECTORY_SEPARATOR;
$this->varPath = $rootPath.'var'.DIRECTORY_SEPARATOR;
}
/**
* @param string $binPath
*
* @return $this
*/
public function setBinPath(string $binPath)
public function setBinPath(string $binPath): void
{
$this->ensureDirectorySeparator($binPath);
$this->verifyBinPath($binPath);
$this->binPath = $binPath;
return $this;
}
/**
* @param string $varPath
*
* @return $this
*/
public function setVarPath(string $varPath)
public function setVarPath(string $varPath): void
{
$this->varPath = $varPath;
if (!is_writable($varPath)) {
throw new InvalidArgumentException(sprintf('The specified path "%s" is not writable', $varPath));
}
return $this;
$this->ensureDirectorySeparator($varPath);
$this->varPath = $varPath;
}
/**
* @param array $samples
* @param array $targets
*/
public function train(array $samples, array $targets)
public function train(array $samples, array $targets): void
{
$this->samples = array_merge($this->samples, $samples);
$this->targets = array_merge($this->targets, $targets);
$trainingSet = DataTransformer::trainingSet($this->samples, $this->targets, in_array($this->type, [Type::EPSILON_SVR, Type::NU_SVR]));
$trainingSet = DataTransformer::trainingSet($this->samples, $this->targets, in_array($this->type, [Type::EPSILON_SVR, Type::NU_SVR], true));
file_put_contents($trainingSetFileName = $this->varPath.uniqid('phpml', true), $trainingSet);
$modelFileName = $trainingSetFileName.'-model';
$command = $this->buildTrainCommand($trainingSetFileName, $modelFileName);
$output = '';
exec(escapeshellcmd($command), $output);
$this->model = file_get_contents($modelFileName);
$output = [];
exec(escapeshellcmd($command).' 2>&1', $output, $return);
unlink($trainingSetFileName);
if ($return !== 0) {
throw new LibsvmCommandException(
sprintf('Failed running libsvm command: "%s" with reason: "%s"', $command, array_pop($output))
);
}
$this->model = (string) file_get_contents($modelFileName);
unlink($modelFileName);
}
/**
* @return string
*/
public function getModel()
public function getModel(): string
{
return $this->model;
}
/**
* @param array $samples
* @return array|string
*
* @return array
* @throws LibsvmCommandException
*/
public function predict(array $samples)
{
$testSet = DataTransformer::testSet($samples);
file_put_contents($testSetFileName = $this->varPath.uniqid('phpml', true), $testSet);
file_put_contents($modelFileName = $testSetFileName.'-model', $this->model);
$outputFileName = $testSetFileName.'-output';
$predictions = $this->runSvmPredict($samples, false);
$command = sprintf('%ssvm-predict%s %s %s %s', $this->binPath, $this->getOSExtension(), $testSetFileName, $modelFileName, $outputFileName);
$output = '';
exec(escapeshellcmd($command), $output);
$predictions = file_get_contents($outputFileName);
unlink($testSetFileName);
unlink($modelFileName);
unlink($outputFileName);
if (in_array($this->type, [Type::C_SVC, Type::NU_SVC])) {
if (in_array($this->type, [Type::C_SVC, Type::NU_SVC], true)) {
$predictions = DataTransformer::predictions($predictions, $this->targets);
} else {
$predictions = explode(PHP_EOL, trim($predictions));
@@ -219,9 +198,63 @@ class SupportVectorMachine
}
/**
* @return string
* @return array|string
*
* @throws LibsvmCommandException
*/
private function getOSExtension()
public function predictProbability(array $samples)
{
if (!$this->probabilityEstimates) {
throw new InvalidOperationException('Model does not support probabiliy estimates');
}
$predictions = $this->runSvmPredict($samples, true);
if (in_array($this->type, [Type::C_SVC, Type::NU_SVC], true)) {
$predictions = DataTransformer::probabilities($predictions, $this->targets);
} else {
$predictions = explode(PHP_EOL, trim($predictions));
}
if (!is_array($samples[0])) {
return $predictions[0];
}
return $predictions;
}
private function runSvmPredict(array $samples, bool $probabilityEstimates): string
{
$testSet = DataTransformer::testSet($samples);
file_put_contents($testSetFileName = $this->varPath.uniqid('phpml', true), $testSet);
file_put_contents($modelFileName = $testSetFileName.'-model', $this->model);
$outputFileName = $testSetFileName.'-output';
$command = $this->buildPredictCommand(
$testSetFileName,
$modelFileName,
$outputFileName,
$probabilityEstimates
);
$output = [];
exec(escapeshellcmd($command).' 2>&1', $output, $return);
unlink($testSetFileName);
unlink($modelFileName);
$predictions = (string) file_get_contents($outputFileName);
unlink($outputFileName);
if ($return !== 0) {
throw new LibsvmCommandException(
sprintf('Failed running libsvm command: "%s" with reason: "%s"', $command, array_pop($output))
);
}
return $predictions;
}
private function getOSExtension(): string
{
$os = strtoupper(substr(PHP_OS, 0, 3));
if ($os === 'WIN') {
@@ -233,15 +266,10 @@ class SupportVectorMachine
return '';
}
/**
* @param string $trainingSetFileName
* @param string $modelFileName
*
* @return string
*/
private function buildTrainCommand(string $trainingSetFileName, string $modelFileName): string
{
return sprintf('%ssvm-train%s -s %s -t %s -c %s -n %s -d %s%s -r %s -p %s -m %s -e %s -h %d -b %d %s %s',
return sprintf(
'%ssvm-train%s -s %s -t %s -c %s -n %F -d %s%s -r %s -p %F -m %F -e %F -h %d -b %d %s %s',
$this->binPath,
$this->getOSExtension(),
$this->type,
@@ -260,4 +288,47 @@ class SupportVectorMachine
escapeshellarg($modelFileName)
);
}
private function buildPredictCommand(
string $testSetFileName,
string $modelFileName,
string $outputFileName,
bool $probabilityEstimates
): string {
return sprintf(
'%ssvm-predict%s -b %d %s %s %s',
$this->binPath,
$this->getOSExtension(),
$probabilityEstimates ? 1 : 0,
escapeshellarg($testSetFileName),
escapeshellarg($modelFileName),
escapeshellarg($outputFileName)
);
}
private function ensureDirectorySeparator(string &$path): void
{
if (substr($path, -1) !== DIRECTORY_SEPARATOR) {
$path .= DIRECTORY_SEPARATOR;
}
}
private function verifyBinPath(string $path): void
{
if (!is_dir($path)) {
throw new InvalidArgumentException(sprintf('The specified path "%s" does not exist', $path));
}
$osExtension = $this->getOSExtension();
foreach (['svm-predict', 'svm-scale', 'svm-train'] as $filename) {
$filePath = $path.$filename.$osExtension;
if (!file_exists($filePath)) {
throw new InvalidArgumentException(sprintf('File "%s" not found', $filePath));
}
if (!is_executable($filePath)) {
throw new InvalidArgumentException(sprintf('File "%s" is not executable', $filePath));
}
}
}
}
lib/mlbackend/php/phpml/src/Phpml/SupportVectorMachine/Type.php
+5 -5
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@@ -9,25 +9,25 @@ abstract class Type
/**
* classification.
*/
const C_SVC = 0;
public const C_SVC = 0;
/**
* classification.
*/
const NU_SVC = 1;
public const NU_SVC = 1;
/**
* distribution estimation.
*/
const ONE_CLASS_SVM = 2;
public const ONE_CLASS_SVM = 2;
/**
* regression.
*/
const EPSILON_SVR = 3;
public const EPSILON_SVR = 3;
/**
* regression.
*/
const NU_SVR = 4;
public const NU_SVR = 4;
}