PHP code example of rubix / sentiment

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

    

rubix / sentiment example snippets

$samples = $labels = [];

foreach (['positive', 'negative'] as $label) {
    foreach (glob("train/$label/*.txt") as $file) {
        $samples[] = [file_get_contents($file)];
        $labels[] = $label;
    }
}

use Rubix\ML\Datasets\Labeled;

$dataset = new Labeled($samples, $labels);

use Rubix\ML\PersistentModel;
use Rubix\ML\Pipeline;
use Rubix\ML\Transformers\TextNormalizer;
use Rubix\ML\Transformers\WordCountVectorizer;
use Rubix\ML\Transformers\TfIdfTransformer;
use Rubix\ML\Transformers\ZScaleStandardizer;
use Rubix\ML\Other\Tokenizers\NGram;
use Rubix\ML\Classifiers\MultilayerPerceptron;
use Rubix\ML\NeuralNet\Layers\Dense;
use Rubix\ML\NeuralNet\Layers\PReLU;
use Rubix\ML\NeuralNet\Layers\Activation;
use Rubix\ML\NeuralNet\Layers\BatchNorm;
use Rubix\ML\NeuralNet\ActivationFunctions\LeakyReLU;
use Rubix\ML\NeuralNet\Optimizers\AdaMax;
use Rubix\ML\Persisters\Filesystem;

$estimator = new PersistentModel(
    new Pipeline([
        new TextNormalizer(),
        new WordCountVectorizer(10000, 2, 0.4, new NGram(1, 2)),
        new TfIdfTransformer(),
        new ZScaleStandardizer(),
    ], new MultilayerPerceptron([
        new Dense(100),
        new Activation(new LeakyReLU()),
        new Dense(100),
        new Activation(new LeakyReLU()),
        new Dense(100, 0.0, false),
        new BatchNorm(),
        new Activation(new LeakyReLU()),
        new Dense(50),
        new PReLU(),
        new Dense(50),
        new PReLU(),
    ], 256, new AdaMax(0.0001))),
    new Filesystem('sentiment.rbx', true)
);

$estimator->train($dataset);

use Rubix\ML\Extractors\CSV;

$extractor = new CSV('progress.csv', true);

$extractor->export($estimator->steps());

$estimator->save();

$ php train.php

$samples = $labels = [];

foreach (['positive', 'negative'] as $label) {
    foreach (glob("test/$label/*.txt") as $file) {
        $samples[] = [file_get_contents($file)];
        $labels[] = $label;
    }
}

use Rubix\ML\Datasets\Labeled;

$dataset = Labeled::build($samples, $labels)->randomize()->take(10000);

use Rubix\ML\PersistentModel;
use Rubix\ML\Persisters\Filesystem;

$estimator = PersistentModel::load(new Filesystem('sentiment.rbx'));

$predictions = $estimator->predict($dataset);

use Rubix\ML\CrossValidation\Reports\AggregateReport;
use Rubix\ML\CrossValidation\Reports\ConfusionMatrix;
use Rubix\ML\CrossValidation\Reports\MulticlassBreakdown;

$report = new AggregateReport([
    new MulticlassBreakdown(),
    new ConfusionMatrix(),
]);

$results = $report->generate($predictions, $dataset->labels());

echo $results;

$results->toJSON()->saveTo(new Filesystem('report.json'));

use Rubix\ML\PersistentModel;
use Rubix\ML\Persisters\Filesystem;

$estimator = PersistentModel::load(new Filesystem('sentiment.rbx'));

while (empty($text)) $text = readline("Enter some text to analyze:\n");

$prediction = $estimator->predictSample([$text]);

echo "The sentiment is: $prediction" . PHP_EOL;

sh
$ php validate.php

sh
php predict.php