Revert "Add WrapperAware trait"

This reverts commit 241abc4.

src/GridSearch.php

@@ -23,7 +23,6 @@
 use Rubix\ML\Specifications\EstimatorIsCompatibleWithMetric;
 use Rubix\ML\Specifications\SamplesAreCompatibleWithEstimator;
 use Rubix\ML\Exceptions\InvalidArgumentException;
-use Rubix\ML\Traits\WrapperAware;
 
 /**
  * Grid Search
@@ -42,7 +41,7 @@
  */
 class GridSearch implements Wrapper, Learner, Parallel, Verbose, Persistable
 {
-    use AutotrackRevisions, Multiprocessing, LoggerAware, WrapperAware;
+    use AutotrackRevisions, Multiprocessing, LoggerAware;
 
     /**
      * The class name of the base estimator.
@@ -72,6 +71,13 @@ class GridSearch implements Wrapper, Learner, Parallel, Verbose, Persistable
      */
     protected \Rubix\ML\CrossValidation\Validator $validator;
 
+    /**
+     * The base estimator instance.
+     *
+     * @var Learner
+     */
+    protected \Rubix\ML\Learner $base;
+
     /**
      * The validation scores obtained from the last search.
      *
@@ -173,6 +179,18 @@ public function __construct(
         $this->backend = new Serial();
     }
 
+    /**
+     * Return the estimator type.
+     *
+     * @internal
+     *
+     * @return EstimatorType
+     */
+    public function type() : EstimatorType
+    {
+        return $this->base->type();
+    }
+
     /**
      * Return the data types that the estimator is compatible with.
      *
@@ -214,6 +232,16 @@ public function trained() : bool
         return $this->base->trained();
     }
 
+    /**
+     * Return the base learner instance.
+     *
+     * @return Estimator
+     */
+    public function base() : Estimator
+    {
+        return $this->base;
+    }
+
     /**
      * Train one estimator per combination of parameters given by the grid and
      * assign the best one as the base estimator of this instance.
@@ -276,6 +304,18 @@ public function train(Dataset $dataset) : void
         }
     }
 
+    /**
+     * Make a prediction on a given sample dataset.
+     *
+     * @param Dataset $dataset
+     * @throws Exceptions\RuntimeException
+     * @return mixed[]
+     */
+    public function predict(Dataset $dataset) : array
+    {
+        return $this->base->predict($dataset);
+    }
+
     /**
      * The callback that executes after the cross validation task.
      *

src/PersistentModel.php

@@ -10,7 +10,6 @@
 use Rubix\ML\AnomalyDetectors\Scoring;
 use Rubix\ML\Exceptions\InvalidArgumentException;
 use Rubix\ML\Exceptions\RuntimeException;
-use Rubix\ML\Traits\WrapperAware;
 
 /**
  * Persistent Model
@@ -24,7 +23,12 @@
  */
 class PersistentModel implements Wrapper, Learner, Probabilistic, Scoring
 {
-    use WrapperAware;
+    /**
+     * The persistable base learner.
+     *
+     * @var Learner
+     */
+    protected \Rubix\ML\Learner $base;
 
     /**
      * The persister used to interface with the storage layer.
@@ -80,6 +84,30 @@ public function __construct(Learner $base, Persister $persister, ?Serializer $se
         $this->serializer = $serializer ?? new RBX();
     }
 
+    /**
+     * Return the estimator type.
+     *
+     * @internal
+     *
+     * @return EstimatorType
+     */
+    public function type() : EstimatorType
+    {
+        return $this->base->type();
+    }
+
+    /**
+     * Return the data types that the estimator is compatible with.
+     *
+     * @internal
+     *
+     * @return list<\Rubix\ML\DataType>
+     */
+    public function compatibility() : array
+    {
+        return $this->base->compatibility();
+    }
+
     /**
      * Return the settings of the hyper-parameters in an associative array.
      *
@@ -106,6 +134,16 @@ public function trained() : bool
         return $this->base->trained();
     }
 
+    /**
+     * Return the base estimator instance.
+     *
+     * @return Estimator
+     */
+    public function base() : Estimator
+    {
+        return $this->base;
+    }
+
     /**
      * Save the model to storage.
      */
@@ -130,6 +168,17 @@ public function train(Dataset $dataset) : void
         $this->base->train($dataset);
     }
 
+    /**
+     * Make a prediction on a given sample dataset.
+     *
+     * @param Dataset $dataset
+     * @return mixed[]
+     */
+    public function predict(Dataset $dataset) : array
+    {
+        return $this->base->predict($dataset);
+    }
+
     /**
      * Estimate the joint probabilities for each possible outcome.
      *

src/Pipeline.php

@@ -4,7 +4,6 @@
 
 use Rubix\ML\Helpers\Params;
 use Rubix\ML\Datasets\Dataset;
-use Rubix\ML\Traits\WrapperAware;
 use Rubix\ML\Transformers\Elastic;
 use Rubix\ML\Transformers\Stateful;
 use Rubix\ML\Transformers\Transformer;
@@ -28,7 +27,7 @@
  */
 class Pipeline implements Online, Probabilistic, Scoring, Persistable, Wrapper
 {
-    use AutotrackRevisions, WrapperAware;
+    use AutotrackRevisions;
 
     /**
      * A list of transformers to be applied in series.
@@ -39,6 +38,13 @@ class Pipeline implements Online, Probabilistic, Scoring, Persistable, Wrapper
         //
     ];
 
+    /**
+     * An instance of a base estimator to receive the transformed data.
+     *
+     * @var Estimator
+     */
+    protected \Rubix\ML\Estimator $base;
+
     /**
      * Should we update the elastic transformers during partial train?
      *
@@ -66,6 +72,30 @@ public function __construct(array $transformers, Estimator $base, bool $elastic
         $this->elastic = $elastic;
     }
 
+    /**
+     * Return the estimator type.
+     *
+     * @internal
+     *
+     * @return EstimatorType
+     */
+    public function type() : EstimatorType
+    {
+        return $this->base->type();
+    }
+
+    /**
+     * Return the data types that the estimator is compatible with.
+     *
+     * @internal
+     *
+     * @return list<\Rubix\ML\DataType>
+     */
+    public function compatibility() : array
+    {
+        return $this->base->compatibility();
+    }
+
     /**
      * Return the settings of the hyper-parameters in an associative array.
      *
@@ -94,6 +124,16 @@ public function trained() : bool
             : true;
     }
 
+    /**
+     * Return the base estimator instance.
+     *
+     * @return Estimator
+     */
+    public function base() : Estimator
+    {
+        return $this->base;
+    }
+
     /**
      * Run the training dataset through all transformers in order and use the
      * transformed dataset to train the estimator.