PHP code example of rubix / credit

1. Go to this page and download the library: Download rubix/credit library. Choose the download type require.

2. Extract the ZIP file and open the index.php.

3. Add this code to the index.php.

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

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

rubix / credit example snippets


use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Extractors\CSV;

$dataset = Labeled::fromIterator(new CSV('dataset.csv', true));


use Rubix\ML\Transformers\NumericStringConverter;
use Rubix\ML\Transformers\OneHotEncoder;
use Rubix\ML\Transformers\ZScaleStandardizer;

$dataset->apply(new NumericStringConverter())
    ->apply(new OneHotEncoder())
    ->apply(new ZScaleStandardizer());


[$training, $testing] = $dataset->stratifiedSplit(0.8);


use Rubix\ML\Classifiers\LogisticRegression;
use Rubix\ML\NeuralNet\Optimizers\StepDecay;

$estimator = new LogisticRegression(256, new StepDecay(0.01, 100));


use Rubix\ML\Other\Loggers\Screen;

$estimator->setLogger(new Screen());


$estimator->train($dataset);


use Rubix\ML\Extractors\CSV;

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

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


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

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


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


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


use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Extractors\CSV;
use Rubix\ML\Transformers\NumericStringConverter;

$dataset = Labeled::fromIterator(new CSV('dataset.csv', true))
    ->apply(new NumericStringConverter());


$stats = $dataset->describe();

echo $stats;


use Rubix\ML\Persisters\Filesystem;

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


use Rubix\ML\Datasets\Labeled;

$dataset = $dataset->randomize()->head(2500);


use Rubix\ML\Embedders\TSNE;

$embedder = new TSNE(2, 20.0, 20);


use Rubix\ML\Transformers\OneHotEncoder;
use Rubix\ML\Transformers\ZScaleStandardizer;

$dataset->apply(new OneHotEncoder())
    ->apply(new ZScaleStandardizer());


$dataset->apply($embedder);


use Rubix\ML\Extractors\CSV;

$dataset->exportTo(new CSV('embedding.csv'));

sh
$ php train.php

sh
$ php explore.php