Added a Custom Exception and supporting tests if a model file is not found.

composer.json

@@ -11,8 +11,9 @@
     ],
     "require": {
         "php": ">=7.1",
-        "laravel/framework": "^5.1||^6.0||^7.0||^8.0",
-        "rubix/ml": "0.2.*"
+        "laravel/framework": "^5.1||^6.0||^7.0||^8.0||^9.0",
+        "rubix/ml": "0.2.*",
+        "facade/ignition-contracts": "^1.0"
     },
     "require-dev": {
         "phpunit/phpunit": "^7.0||^8.0",

src/Exceptions/ModelFileNotFound.php

@@ -0,0 +1,21 @@
+<?php
+
+namespace DivineOmega\EloquentAttributeValuePrediction\Exceptions;
+
+use Exception;
+use Facade\IgnitionContracts\Solution;
+use Facade\IgnitionContracts\BaseSolution;
+use Facade\IgnitionContracts\ProvidesSolution;
+
+class ModelFileNotFound extends Exception implements ProvidesSolution
+{
+    public function getSolution(): Solution
+    {
+
+        return BaseSolution::create('Data Model File for '.$this->getMessage().' Not Found')
+            ->setSolutionDescription('Generate your data model by running `php artisan eavp:train '.addslashes($this->getMessage()).'`')
+            ->setDocumentationLinks([
+                'Training Documentation' => 'https://github.com/DivineOmega/eloquent-attribute-value-prediction#training',
+            ]);
+    }
+}

src/Traits/PredictsAttributes.php

@@ -2,6 +2,7 @@
 
 namespace DivineOmega\EloquentAttributeValuePrediction\Traits;
 
+use DivineOmega\EloquentAttributeValuePrediction\Exceptions\ModelFileNotFound;
 use DivineOmega\EloquentAttributeValuePrediction\Helpers\DatasetHelper;
 use DivineOmega\EloquentAttributeValuePrediction\Helpers\PathHelper;
 use Exception;
@@ -28,15 +29,23 @@ public function getPredictions(string $attribute): array
     {
         if ($this->isAttributeContinuous($attribute)) {
             throw new InvalidArgumentException(
-                'You can not get multiple predictions for a continious (numeric) argument. Try using the `predict` method instead.'
+                'You can not get multiple predictions for a continuous (numeric) argument. Try using the `predict` method instead.'
             );
         }
 
         $dataset = DatasetHelper::buildUnlabeledDataset($this, $attribute);
 
+        //If Model File is missing, throw a custom exception with a solution in it.
         $modelPath = PathHelper::getModelPath(get_class($this), $attribute);
 
-        $estimator = PersistentModel::load(new Filesystem($modelPath));
+        if(!is_file($modelPath))
+        {
+            throw new ModelFileNotFound(get_class($this));
+        }
+
+        $modelFile = new Filesystem($modelPath);
+
+        $estimator = PersistentModel::load($modelFile);
 
         $predictions = $estimator->proba($dataset)[0];
 

tests/Unit/PredictionTest.php

@@ -2,9 +2,11 @@
 
 namespace DivineOmega\EloquentAttributeValuePrediction\Tests\Unit;
 
+use DivineOmega\EloquentAttributeValuePrediction\Exceptions\ModelFileNotFound;
 use DivineOmega\EloquentAttributeValuePrediction\Helpers\PathHelper;
 use DivineOmega\EloquentAttributeValuePrediction\ServiceProvider;
 use DivineOmega\EloquentAttributeValuePrediction\Tests\Unit\TestClasses\Eloquent\IrisFlower;
+use DivineOmega\EloquentAttributeValuePrediction\Tests\Unit\TestClasses\Eloquent\Untrained;
 use Illuminate\Database\Eloquent\Model;
 use Orchestra\Testbench\TestCase;
 
@@ -80,4 +82,12 @@ public function testPetalWidthGetPredictionsFails()
         IrisFlower::first()->getPredictions('petal_width');
     }
 
+    public function testModelFileNotFoundThrown()
+    {
+
+        $this->expectException(ModelFileNotFound::class);
+
+        Untrained::first()->getPredictions('abr');
+
+    }
 }

tests/Unit/TestClasses/Eloquent/Untrained.php

@@ -0,0 +1,39 @@
+<?php
+
+namespace DivineOmega\EloquentAttributeValuePrediction\Tests\Unit\TestClasses\Eloquent;
+
+use DivineOmega\EloquentAttributeValuePrediction\Interfaces\HasPredictableAttributes;
+use DivineOmega\EloquentAttributeValuePrediction\Traits\PredictsAttributes;
+use Illuminate\Database\Eloquent\Model;
+use Sushi\Sushi;
+
+class Untrained extends Model implements HasPredictableAttributes
+{
+    use PredictsAttributes;
+    use Sushi;
+
+    protected $casts = [
+        'abr' => 'string'
+    ];
+
+    protected $rows = [
+        [
+            'abbr' => 'NY',
+            'name' => 'New York',
+        ],
+        [
+            'abbr' => 'CA',
+            'name' => 'California',
+        ],
+    ];
+
+    public function registerPredictableAttributes(): array
+    {
+        return [
+            'abr' => [
+                'name'
+                ]
+            ];
+    }
+
+}