PHP code example of rubix / churn

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

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

    

rubix / churn example snippets

use Rubix\ML\Extractors\CSV;
use Rubix\ML\Extractors\ColumnPicker;

$extractor = new ColumnPicker(new CSV('dataset.csv', true), [
    'Gender', 'SeniorCitizen', 'Partner', 'Dependents', 'MonthsInService', 'Phone',
    'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
    'TechSupport', 'TV', 'Movies', 'Contract', 'PaperlessBilling', 'PaymentMethod',
    'MonthlyCharges', 'TotalCharges', 'Region', 'Churn',
]);

use Rubix\ML\Datasets\Labeled;

$dataset = Labeled::fromIterator($extractor);

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

use Rubix\ML\Classifiers\NaiveBayes;

$estimator = new NaiveBayes([
    "Yes" => 0.05,
    "No" => 0.95,
]);

use Rubix\ML\Pipeline;
use Rubix\ML\Transformers\NumericStringConverter;
use Rubix\ML\Transformers\IntervalDiscretizer;

$estimator = new Pipeline([
    new NumericStringConverter(),
    new IntervalDiscretizer(3, true),
], $estimator);

$estimator->train($training);

var_dump($estimator->trained());

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

print_r($predictions);

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

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

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

echo $report

use Rubix\ML\Persisters\Filesystem;

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

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

$estimator = new PersistentModel($estimator, new Filesystem('model.rbx'));

$estimator->save();

use Rubix\ML\Extractors\SqlTable;
use PDO;

$connection = new PDO('sqlite:database.sqlite');

$extractor = new SqlTable($connection, 'customers');

$extractor = new LimitIterator($extractor->getIterator(), 0, 100);

$extractor = new ColumnPicker($extractor, [
    'Id', 'Gender', 'SeniorCitizen', 'Partner', 'Dependents', 'MonthsInService', 'Phone',
    'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
    'TechSupport', 'TV', 'Movies', 'Contract', 'PaperlessBilling', 'PaymentMethod',
    'MonthlyCharges', 'TotalCharges', 'Region',
]);

$dataset = Unlabeled::fromIterator($extractor);

$ids = $dataset->feature(0);

$dataset->dropFeature(0);

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

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

$statement = $connection->prepare("UPDATE customers SET churn=? WHERE id=?");

foreach ($predictions as $i => $prediction) {
    $statement->execute([$prediction, $ids[$i]]);
}