Access to network instance for neural net estimators

README.md

@@ -819,6 +819,7 @@ A type of classifier that uses the logistic (sigmoid) function to distinguish be
 | Method | Description |
 |--|--|
 | `progress() : array` | Returns an array with the training progress at each epoch of training. |
+| `network() : Network` | Returns the underlying neural network instance or *null* if not trained. |
 
 ##### Example:
 ```php
@@ -849,6 +850,7 @@ Multiclass [Neural Network](#neural-network) model that uses a series of user-de
 | Method | Description |
 |--|--|
 | `progress() : array` | Returns an array with the training progress at each epoch of training. |
+| `network() : Network` | Returns the underlying neural network instance or *null* if not trained. |
 
 ##### Example:
 ```php
@@ -930,6 +932,7 @@ A generalization of logistic regression for multiple class outcomes using a sing
 | Method | Description |
 |--|--|
 | `progress() : array` | Returns an array with the training progress at each epoch of training. |
+| `network() : Network` | Returns the underlying neural network instance or *null* if not trained. |
 
 ##### Example:
 ```php
@@ -1118,6 +1121,7 @@ A [Neural Network](#neural-network) with a continuous output layer suitable for
 | Method | Description |
 |--|--|
 | `progress() : array` | Returns an array with the validation score at each epoch of training. |
+| `network() : Network` | Returns the underlying neural network instance or *null* if not trained. |
 
 ##### Example:
 ```php

src/Classifiers/LogisticRegression.php

@@ -64,7 +64,7 @@ class LogisticRegression implements Binary, Online, Probabilistic, Persistable
     /**
      * The underlying computational graph.
      *
-     * @var \Rubix\ML\NeuralNet\Network
+     * @var \Rubix\ML\NeuralNet\Network|null
      */
     protected $network;
 
@@ -122,13 +122,25 @@ public function __construct(int $batchSize = 10, Optimizer $optimizer = null,
     }
 
     /**
+     * Return the training progress of the estimator.
+     *
      * @return array
      */
     public function progress() : array
     {
         return $this->progress;
     }
 
+    /**
+     * Return the underlying neural network instance or null if not trained.
+     *
+     * @return \Rubix\ML\NeuralNet\Network|null
+     */
+    public function network() : ?Network
+    {
+        return $this->network;
+    }
+
     /**
     * @param  \Rubix\ML\Datasets\Dataset  $dataset
     * @throws \InvalidArgumentException

src/Classifiers/MultiLayerPerceptron.php

@@ -100,7 +100,7 @@ class MultiLayerPerceptron implements Multiclass, Online, Probabilistic, Persist
     /**
      * The underlying computational graph.
      *
-     * @var \Rubix\ML\NeuralNet\Network
+     * @var \Rubix\ML\NeuralNet\Network|null
      */
     protected $network;
 
@@ -180,13 +180,25 @@ public function __construct(array $hidden = [], int $batchSize = 50, Optimizer $
     }
 
     /**
+     * Return the training progress of the estimator.
+     *
      * @return array
      */
     public function progress() : array
     {
         return $this->progress;
     }
 
+    /**
+     * Return the underlying neural network instance or null if not trained.
+     *
+     * @return \Rubix\ML\NeuralNet\Network|null
+     */
+    public function network() : ?Network
+    {
+        return $this->network;
+    }
+
     /**
     * @param  \Rubix\ML\Datasets\Dataset  $dataset
     * @throws \InvalidArgumentException

src/Classifiers/SoftmaxClassifier.php

@@ -64,7 +64,7 @@ class SoftmaxClassifier implements Multiclass, Online, Probabilistic, Persistabl
     /**
      * The underlying computational graph.
      *
-     * @var \Rubix\ML\NeuralNet\Network
+     * @var \Rubix\ML\NeuralNet\Network|null
      */
     protected $network;
 
@@ -122,13 +122,25 @@ public function __construct(int $batchSize = 10, Optimizer $optimizer = null,
     }
 
     /**
+     * Return the training progress of the estimator.
+     *
      * @return array
      */
     public function progress() : array
     {
         return $this->progress;
     }
 
+    /**
+     * Return the underlying neural network instance or null if not trained.
+     *
+     * @return \Rubix\ML\NeuralNet\Network|null
+     */
+    public function network() : ?Network
+    {
+        return $this->network;
+    }
+
     /**
      * @param  \Rubix\ML\Datasets\Dataset  $dataset
      * @throws \InvalidArgumentException

src/NeuralNet/Network.php

@@ -116,13 +116,13 @@ public function parametric() : array
     }
 
     /**
-     * The depth of the network. i.e. the number of hidden layers.
+     * The depth of the network. i.e. the number of parametric layers.
      *
      * @return int
      */
     public function depth() : int
     {
-        return count($this->hidden());
+        return count($this->parametric());
     }
 
     /**

src/Regressors/DummyRegressor.php

@@ -7,6 +7,7 @@
 use Rubix\ML\Datasets\Labeled;
 use Rubix\ML\Transformers\Strategies\Continuous;
 use Rubix\ML\Transformers\Strategies\BlurryMean;
+use InvalidArgumentException;
 
 class DummyRegressor implements Regressor, Persistable
 {
@@ -34,10 +35,16 @@ public function __construct(Continuous $strategy = null)
      * Fit the training set to the given guessing strategy.
      *
      * @param  \Rubix\ML\Datasets\Labeled  $dataset
+     * @throws \InvalidArgumentException
      * @return void
      */
     public function train(Dataset $dataset) : void
     {
+        if (!$dataset instanceof Labeled) {
+            throw new InvalidArgumentException('This Estimator requires a'
+                . ' Labeled training set.');
+        }
+
         $this->strategy->fit($dataset->labels());
     }
 

src/Regressors/MLPRegressor.php

@@ -88,7 +88,7 @@ class MLPRegressor implements Regressor, Online, Persistable
     /**
      * The underlying computational graph.
      *
-     * @var \Rubix\ML\NeuralNet\Network
+     * @var \Rubix\ML\NeuralNet\Network|null
      */
     protected $network;
 
@@ -168,13 +168,25 @@ public function __construct(array $hidden, int $batchSize = 50, Optimizer $optim
     }
 
     /**
+     * Return the training progress of the estimator.
+     *
      * @return array
      */
     public function progress() : array
     {
         return $this->progress;
     }
 
+    /**
+     * Return the underlying neural network instance or null if not trained.
+     *
+     * @return \Rubix\ML\NeuralNet\Network|null
+     */
+    public function network() : ?Network
+    {
+        return $this->network;
+    }
+
     /**
     * @param  \Rubix\ML\Datasets\Dataset  $dataset
     * @throws \InvalidArgumentException

tests/Classifiers/AdaBoostTest.php

@@ -5,11 +5,13 @@
 use Rubix\ML\Estimator;
 use Rubix\ML\Persistable;
 use Rubix\ML\Datasets\Labeled;
+use Rubix\ML\Datasets\Unlabeled;
 use Rubix\ML\Classifiers\Binary;
 use Rubix\ML\Classifiers\AdaBoost;
 use Rubix\ML\Classifiers\Classifier;
 use Rubix\ML\Classifiers\DecisionTree;
 use PHPUnit\Framework\TestCase;
+use InvalidArgumentException;
 
 class AdaBoostTest extends TestCase
 {
@@ -81,4 +83,13 @@ public function test_make_prediction()
         $this->assertEquals('male', $predictions[0]);
         $this->assertEquals('female', $predictions[1]);
     }
+
+    public function test_train_with_unlabeled()
+    {
+        $dataset = new Unlabeled([['bad']]);
+
+        $this->expectException(InvalidArgumentException::class);
+
+        $this->estimator->train($dataset);
+    }
 }

tests/Classifiers/CommitteeMachineTest.php

@@ -5,12 +5,14 @@
 use Rubix\ML\Estimator;
 use Rubix\ML\Probabilistic;
 use Rubix\ML\Datasets\Labeled;
+use Rubix\ML\Datasets\Unlabeled;
 use Rubix\ML\Classifiers\Multiclass;
 use Rubix\ML\Classifiers\Classifier;
 use Rubix\ML\Kernels\Distance\Euclidean;
 use Rubix\ML\Classifiers\CommitteeMachine;
 use Rubix\ML\Classifiers\KNearestNeighbors;
 use PHPUnit\Framework\TestCase;
+use InvalidArgumentException;
 
 class CommitteeMachineTest extends TestCase
 {
@@ -100,4 +102,13 @@ public function test_predict_proba()
         $this->assertLessThan(0.5, $probabilities[1]['male']);
         $this->assertGreaterThan(0.5, $probabilities[1]['female']);
     }
+
+    public function test_train_with_unlabeled()
+    {
+        $dataset = new Unlabeled([['bad']]);
+
+        $this->expectException(InvalidArgumentException::class);
+
+        $this->estimator->train($dataset);
+    }
 }

tests/Classifiers/DecisionTreeTest.php

@@ -4,13 +4,14 @@
 
 use Rubix\ML\Estimator;
 use Rubix\ML\Persistable;
+use Rubix\ML\Probabilistic;
 use Rubix\ML\Datasets\Labeled;
+use Rubix\ML\Datasets\Unlabeled;
 use Rubix\ML\Classifiers\Multiclass;
 use Rubix\ML\Classifiers\Classifier;
 use Rubix\ML\Classifiers\DecisionTree;
-use Rubix\ML\Probabilistic;
-
 use PHPUnit\Framework\TestCase;
+use InvalidArgumentException;
 
 class DecisionTreeTest extends TestCase
 {
@@ -97,4 +98,13 @@ public function test_predict_proba()
         $this->assertLessThan(0.5, $probabilities[1]['male']);
         $this->assertGreaterThan(0.5, $probabilities[1]['female']);
     }
+
+    public function test_train_with_unlabeled()
+    {
+        $dataset = new Unlabeled([['bad']]);
+
+        $this->expectException(InvalidArgumentException::class);
+
+        $this->estimator->train($dataset);
+    }
 }

tests/Classifiers/DummyClassifierTest.php

@@ -5,10 +5,12 @@
 use Rubix\ML\Estimator;
 use Rubix\ML\Persistable;
 use Rubix\ML\Datasets\Labeled;
+use Rubix\ML\Datasets\Unlabeled;
 use Rubix\ML\Classifiers\Classifier;
 use Rubix\ML\Classifiers\DummyClassifier;
 use Rubix\ML\Transformers\Strategies\PopularityContest;
 use PHPUnit\Framework\TestCase;
+use InvalidArgumentException;
 
 class DummyClassifierTest extends TestCase
 {
@@ -77,4 +79,13 @@ public function test_make_prediction()
         $this->assertContains($predictions[0], ['male', 'female']);
         $this->assertContains($predictions[1], ['male', 'female']);
     }
+
+    public function test_train_with_unlabeled()
+    {
+        $dataset = new Unlabeled([['bad']]);
+
+        $this->expectException(InvalidArgumentException::class);
+
+        $this->estimator->train($dataset);
+    }
 }

tests/Classifiers/KNearestNeighborsTest.php

@@ -7,11 +7,13 @@
 use Rubix\ML\Persistable;
 use Rubix\ML\Probabilistic;
 use Rubix\ML\Datasets\Labeled;
+use Rubix\ML\Datasets\Unlabeled;
 use Rubix\ML\Classifiers\Multiclass;
 use Rubix\ML\Classifiers\Classifier;
 use Rubix\ML\Kernels\Distance\Euclidean;
 use Rubix\ML\Classifiers\KNearestNeighbors;
 use PHPUnit\Framework\TestCase;
+use InvalidArgumentException;
 
 class KNearestNeighborsTest extends TestCase
 {
@@ -99,4 +101,13 @@ public function test_predict_proba()
         $this->assertLessThan(0.5, $probabilities[1]['male']);
         $this->assertGreaterThan(0.5, $probabilities[1]['female']);
     }
+
+    public function test_train_with_unlabeled()
+    {
+        $dataset = new Unlabeled([['bad']]);
+
+        $this->expectException(InvalidArgumentException::class);
+
+        $this->estimator->train($dataset);
+    }
 }

tests/Classifiers/LogisticRegressionTest.php

@@ -7,11 +7,13 @@
 use Rubix\ML\Persistable;
 use Rubix\ML\Probabilistic;
 use Rubix\ML\Datasets\Labeled;
+use Rubix\ML\Datasets\Unlabeled;
 use Rubix\ML\Classifiers\Binary;
 use Rubix\ML\Classifiers\Classifier;
 use Rubix\ML\NeuralNet\Optimizers\Adam;
 use Rubix\ML\Classifiers\LogisticRegression;
 use PHPUnit\Framework\TestCase;
+use InvalidArgumentException;
 
 class LogisticRegressionTest extends TestCase
 {
@@ -99,4 +101,13 @@ public function test_predict_proba()
         $this->assertLessThan(0.5, $probabilities[1]['male']);
         $this->assertGreaterThan(0.5, $probabilities[1]['female']);
     }
+
+    public function test_train_with_unlabeled()
+    {
+        $dataset = new Unlabeled([['bad']]);
+
+        $this->expectException(InvalidArgumentException::class);
+
+        $this->estimator->train($dataset);
+    }
 }