endroid / prediction-io
Endroid PredictionIo Client
Requires
- php: >=7.1
- guzzlehttp/guzzle: >=6.3.0
- predictionio/predictionio: ^0.9
Requires (Dev)
- phpunit/phpunit: ^5.7|^6.0
README
By endroid
The Prediction IO library provides a client which offers easy access to a PredictionIo recommendation engine. PredictionIo is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.
Through a small set of simple calls, all server functionality is exposed to your application. You can add users and items,
register actions between these users and items and retrieve recommendations deduced from this information by any
PredictionIo
recommendation engine. Applications range from showing recommended products in a
web shop to discovering relevant experts in a social collaboration network.
Requirements
- Symfony
- Dependencies:
PredictionIo-PHP-SDK
Installation
Use Composer to install the library.
$ composer require endroid/prediction-io
Usage
use Endroid\PredictionIo\EventClient; use Endroid\PredictionIo\EngineClient; $apiKey = '...'; $eventClient = new EventClient($apiKey); $recommendationEngineClient = new EngineClient('http://localhost:8000'); $similarProductEngineClient = new EngineClient('http://localhost:8001'); // Populate with users and items $userProperties = ['address' => '1234 Street, San Francisco, CA 94107', 'birthday' => '22-04-1991']; $eventClient->createUser('user_1', $userProperties); $itemProperties = ['categories' => [123, 1234, 12345]]; $eventClient->createItem('product_1', $itemProperties); // Record actions $actionProperties = ['firstView' => true]; $eventClient->recordUserActionOnItem('view', 'user_1', 'product_1', $actionProperties); // Return recommendations $itemCount = 20; $recommendedProducts = $recommendationEngineClient->getRecommendedItems('user_1', $itemCount); $similarProducts = $similarProductEngineClient->getSimilarItems('product_1', $itemCount);
Symfony integration
Register the Symfony bundle in the kernel.
// app/AppKernel.php public function registerBundles() { $bundles = [ // ... new Endroid\PredictionIo\Bundle\PredictionIoBundle\EndroidPredictionIoBundle(), ]; }
The default parameters can be overridden via the configuration.
endroid_prediction_io: event_server: url: http://localhost:7070 apps: app_one: key: '...' engines: recommendation: url: http://localhost:8000 similarproduct: url: http://localhost:8001 viewedthenbought: url: http://localhost:8002 complementarypurchase: url: http://localhost:8003 productranking: url: http://localhost:8004 leadscoring: url: http://localhost:8005 app_two: key: '...' engines: complementarypurchase: url: http://localhost:8006 leadscoring: url: http://localhost:8007
Now you can retrieve the event and engine clients as follows.
/** @var EventClient $eventClient */ $eventClient = $this->get('endroid.prediction_io.app_one.event_client'); /** @var EngineClient $recommendationEngineClient */ $recommendationEngineClient = $this->get('endroid.prediction_io.app_one.recommendation.engine_client'); /** @var EngineClient $similarProductEngineClient */ $similarProductEngineCl![Recommendations](https://raw.githubusercontent.com/endroid/PredictionIo/master/assets/recommendations.png)ient = $this->get('endroid.prediction_io.app_one.similarproduct.engine_client');
Docker
Many Docker images exist for running a PredictionIo server. Personally I used the
spereio
image to create an image
that creates, trains and deploys a recommendation engine and starts the PIO server. It
starts a cron that trains the model every 5 minutes. You can find that image
here
.
Vagrant box
PredictionIo provides a Vagrant box
containing an out-of-the-box PredictionIo server.
Versioning
Version numbers follow the MAJOR.MINOR.PATCH scheme. Backwards compatible changes will be kept to a minimum but be aware that these can occur. Lock your dependencies for production and test your code when upgrading.
License
This bundle is under the MIT license. For the full copyright and license information please view the LICENSE file that was distributed with this source code.