Refactor recommender service (#4)

* Refactor recommender service

* Change register recommender function

* Change the tests

* Add cron job to install package

Author: mhdcodes

.github/workflows/cron.yml

@@ -0,0 +1,30 @@
+name: Check Package
+
+on:
+  schedule:
+    - cron: '0 0 * * *'  # every day at midnight
+
+jobs:
+  install:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: true
+      matrix:
+        os: [ ubuntu-latest, windows-latest ]
+        php: [ 8.1, 8.0, 7.4 ]
+        stability: [ prefer-stable ]
+
+    name: PHP${{ matrix.php }} - ${{ matrix.stability }} - ${{ matrix.os }}
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Setup PHP
+        uses: shivammathur/setup-php@v2
+        with:
+          php-version: ${{ matrix.php }}
+          extensions: dom, curl, libxml, mbstring, zip, pcntl, pdo, sqlite, pdo_sqlite, bcmath, soap
+
+      - name: Install package
+        run: composer require phpjuice/opencf

README.md

@@ -23,10 +23,8 @@ composer require phpjuice/opencf
 
 OpenCF Package is designed to be very simple and straightforward to use. All you have to do is:
 
-1. Load training set
-2. Select recommendation engine
-3. Build a model
-4. Predict future ratings based on the training set provided
+1. Load a training set (dataset)
+3. Predict future ratings using a recommender. (Weighted Slopeone,Cosine, Weighted Cosine)
 
 ### Create Recommender Service
 
@@ -39,34 +37,6 @@ use OpenCF\RecommenderService;
 $recommenderService = new RecommenderService($dataset);
 ```
 
-### Registering a recommendation engine
-
-The recommender service support's 3 recommendation engines (Weighted Slopeone,Cosine, Weighted Cosine).
-
-#### Cosine
-
-```php
-// Register a `Cosine` recommendation engine
-$recommenderService->registerRecommender('Cosine');
-$recommenderService->getRecommender('Cosine'); // OpenCF\Algorithms\Similarity\Cosine::class
-```
-
-#### Weighted Cosine
-
-```php
-// Register a `Weighted Cosine` recommendation engine
-$recommenderService->registerRecommender('WeightedCosine');
-$recommenderService->getRecommender('WeightedCosine'); // OpenCF\Algorithms\Similarity\WeightedCosine::class
-```
-
-#### Weighted Slopeone
-
-```php
-// Register a `Weighted Slopeone` recommendation engine
-$recommenderService->registerRecommender('WeightedSlopeone');
-$recommenderService->getRecommender('WeightedSlopeone'); // OpenCF\Algorithms\Slopeone\WeightedSlopeone::class
-```
-
 ### Adding dataset
 
 Adding a dataset to the recommender can be done using the constructor or can be easily done by providing an array of
@@ -103,29 +73,30 @@ $recommenderService->setDataset($dataset);
 
 ### Getting Predictions
 
-All you have to do to predict ratings for a new user is to retrieve an engine from the recommender service and
-, build the model & run the `predict()` method.
+All you have to do to predict ratings for a new user is to retrieve an engine from the recommender service and & run
+the `predict()` method.
 
 ```php
-// Get engine
-$weightedSlopeone = $recommenderService->getRecommender('WeightedSlopeone');
-
-// Build model
-$weightedSlopeone->buildModel();
-
-// Get predictions
-$results = $weightedSlopeone->predict([
+// Get a recommender
+$recommender = $recommenderService->cosine(); // Cosine recommender
+// OR
+$recommender = $recommenderService->weightedCosine(); // WeightedCosine recommender
+// OR
+$recommender = $recommenderService->weightedSlopeone(); // WeightedSlopeone recommender
+
+// Predict future ratings
+$results = $recommender->predict([
     "squid" => 0.4
 ]);
 ```
 
-This should produce the following results
+This should produce the following results when using `WeightedSlopeone` recommender
 
 ```php
 [
-  "cuttlefish"=>0.25,
-  "octopus"=>0.23333333333333,
-  "nautilus"=>0.1
+  "cuttlefish" => 0.25,
+  "octopus" => 0.23333333333333,
+  "nautilus" => 0.1
 ];
 ```
 

composer.json

@@ -1,6 +1,6 @@
 {
     "name": "phpjuice/opencf",
-    "description": "OpenCF - an Item-based Collaborative Filtering Engine.",
+    "description": "PHP implementation of the (Weighted Slopeone,Cosine, Weighted Cosine) rating-based collaborative filtering schemes.",
     "keywords": [
         "recommendation",
         "recommender",
@@ -40,7 +40,7 @@
     },
     "scripts": {
         "test": "vendor/bin/pest --colors=always",
-        "analyse": "phpstan analyse --ansi --debug",
+        "analyse": "vendor/bin/phpstan analyse --ansi --debug",
         "php-cs-fixer": [
             "php-cs-fixer fix src --rules=@PSR2",
             "php-cs-fixer fix tests --rules=@PSR2"

phpstan.neon

@@ -1,14 +1,32 @@
-includes:
-    # -
-
 parameters:
+
     level: max
+
     paths:
         - src
         - tests
 
+    scanFiles:
+
+        # Pest handles loading custom helpers only when running tests
+        # @see https://pestphp.com/docs/helpers#usage
+        - tests/Pest.php
+
     checkMissingIterableValueType: false
-    checkGenericClassInNonGenericObjectType: false
     reportUnmatchedIgnoredErrors: true
 
     ignoreErrors:
+
+        # Pest implicitly binds $this to the current test case
+        # @see https://pestphp.com/docs/underlying-test-case
+        -
+            message: '#^Undefined variable: \$this$#'
+            paths:
+                - tests/*
+
+        # Pest custom expectations are dynamic and not conducive static analysis
+        # @see https://pestphp.com/docs/expectations#custom-expectations
+        -
+            message: '#Call to an undefined method Pest\\Expectation|Pest\\Support\\Extendable::#'
+            paths:
+                - tests/*

src/Algorithms/Slopeone/WeightedSlopeone.php

@@ -14,9 +14,7 @@ public function name(): string
         return 'WeightedSlopeone';
     }
 
-    /**
-     * @inheritdoc
-     */
+    /** @inheritdoc */
     public function buildModel(): self
     {
         $this->similarityFunction = new Similarity($this->vector);

src/Contracts/IRecommenderService.php

@@ -9,19 +9,36 @@ interface IRecommenderService
     /**
      * Returns a registered recommender instance.
      *
-     * @param  string  $name
-     *
+     * @param  string  $recommender
      * @return IRecommender
      * @throws NotRegisteredRecommenderException
      */
-    public function getRecommender(string $name): IRecommender;
+    public function getRecommender(string $recommender): IRecommender;
 
     /**
      * Registers a Recommender instance for later use.
      *
-     * @param  string  $name  recommender used to build the model
+     * @param  string  $recommender  recommender used to build the model
      *
      * @return $this
      */
-    public function registerRecommender(string $name): self;
+    public function registerRecommender(string $recommender): self;
+
+    /**
+     * Return's a weighted slopeone recommender.
+     * @return IRecommender
+     */
+    public function weightedSlopeone(): IRecommender;
+
+    /**
+     * Return's a weighted cosine recommender.
+     * @return IRecommender
+     */
+    public function weightedCosine(): IRecommender;
+
+    /**
+     * Return's a cosine recommender.
+     * @return IRecommender
+     */
+    public function cosine(): IRecommender;
 }

src/RecommenderService.php

@@ -10,23 +10,27 @@
 use OpenCF\Exceptions\EmptyDatasetException;
 use OpenCF\Exceptions\NotRegisteredRecommenderException;
 use OpenCF\Exceptions\NotSupportedSchemeException;
+use ReflectionClass;
+use ReflectionException;
 
 class RecommenderService implements IRecommenderService
 {
     /**
-     * @var IRecommender[]
+     * list of registered engines.
+     *
+     * @var array<IRecommender>
      */
-    private array $engines = [];
+    private array $recommenders = [];
 
     /**
      * list of the supported Engines.
      *
-     * @var array
+     * @var array<string>
      */
-    private array $supportedEngines = [
-        'Cosine',
-        'WeightedCosine',
-        'WeightedSlopeone',
+    private array $defaultRecommenders = [
+        Cosine::class,
+        WeightedCosine::class,
+        WeightedSlopeone::class,
     ];
 
     /**
@@ -42,6 +46,11 @@ class RecommenderService implements IRecommenderService
     public function __construct(array $dataset)
     {
         $this->setDataset($dataset);
+
+        // register default recommenders
+        foreach ($this->defaultRecommenders as $recommender) {
+            $this->recommenders[$recommender] = new $recommender($this->dataset);
+        }
     }
 
     /**
@@ -62,35 +71,61 @@ public function setDataset(array $dataset = []): self
         return $this;
     }
 
-    public function getRecommender(string $name): IRecommender
+    public function weightedSlopeone(): IRecommender
     {
-        if (! array_key_exists($name, $this->engines)) {
-            throw new NotRegisteredRecommenderException(sprintf('The Recommendation engine "%s" is not registered in the Recommender Service',
-                $name));
+        if (!in_array(WeightedSlopeone::class, $this->recommenders)) {
+            $this->registerRecommender(WeightedSlopeone::class);
         }
 
-        return $this->engines[$name];
+        return $this->getRecommender(WeightedSlopeone::class);
     }
 
-    public function registerRecommender(string $name): self
+    public function registerRecommender(string $recommender): self
     {
-        if (! in_array($name, $this->supportedEngines)) {
-            throw new NotSupportedSchemeException(sprintf('The Recommendation engine "%s" is not supported yet',
-                $name));
+        try {
+            $rf = new ReflectionClass($recommender);
+        } catch (ReflectionException $e) {
+            throw new NotSupportedSchemeException(sprintf('Recommendation engine "%s" must implement "%s" interface',
+                $recommender, IRecommender::class));
+        }
+
+        if (!$rf->implementsInterface(IRecommender::class)) {
+            throw new NotSupportedSchemeException(sprintf('Recommendation engine "%s" must implement "%s" interface',
+                $recommender, IRecommender::class));
         }
-        switch ($name) {
-            case 'WeightedCosine':
-                $recommendationEngine = new WeightedCosine($this->dataset);
-                break;
-            case 'WeightedSlopeone':
-                $recommendationEngine = new WeightedSlopeone($this->dataset);
-                break;
-            default:
-                $recommendationEngine = new Cosine($this->dataset);
-                break;
+
+        if (!in_array($recommender, $this->recommenders)) {
+            $this->recommenders[$recommender] = new $recommender($this->dataset);
         }
-        $this->engines[$recommendationEngine->name()] = $recommendationEngine;
 
         return $this;
     }
+
+    public function getRecommender(string $recommender): IRecommender
+    {
+        if (!array_key_exists($recommender, $this->recommenders)) {
+            throw new NotRegisteredRecommenderException(sprintf('The Recommendation engine "%s" is not registered in the Recommender Service',
+                $recommender));
+        }
+
+        return $this->recommenders[$recommender]->buildModel();
+    }
+
+    public function weightedCosine(): IRecommender
+    {
+        if (!in_array(WeightedCosine::class, $this->recommenders)) {
+            $this->registerRecommender(WeightedCosine::class);
+        }
+
+        return $this->getRecommender(WeightedCosine::class);
+    }
+
+    public function cosine(): IRecommender
+    {
+        if (!in_array(Cosine::class, $this->recommenders)) {
+            $this->registerRecommender(Cosine::class);
+        }
+
+        return $this->getRecommender(Cosine::class);
+    }
 }

tests/Algorithms/Similarity/CosineTest.php

@@ -1,6 +1,7 @@
 <?php
 
 use OpenCF\Algorithms\Similarity\Cosine;
+
 beforeEach(function () {
     $this->dataset = [
         'Batman V Superman' => [
@@ -67,7 +68,7 @@
     // $this->dataset
 });
 
-test('slopeone scheme predict method', function () {
+it('returns correct predictions', function () {
     $scheme = new Cosine($this->dataset);
     $scheme->buildModel();
 

tests/Algorithms/Similarity/PredictorTest.php

@@ -3,6 +3,7 @@
 use OpenCF\Algorithms\Similarity\Cosine;
 use OpenCF\Algorithms\Similarity\Predictor;
 use OpenCF\Support\Vector;
+
 beforeEach(function () {
     $this->dataset = [
         'Item1' => [
@@ -29,7 +30,7 @@
     ];
 });
 
-test('get prediction with cosine', function () {
+it('returns correct prediction for a single item', function () {
     $cosine = new Cosine($this->dataset);
     $cosine->buildModel();
     $pred = new Predictor($this->dataset, $cosine->getModel(), new Vector());