Remove extra hyperparameters for 1P (aws#38)

Author: laurenyu

src/sagemaker/tuner.py

@@ -94,9 +94,13 @@ def prepare_for_training(self):
         for hyperparameter_name in self._hyperparameter_ranges.keys():
             self.static_hyperparameters.pop(hyperparameter_name, None)
 
-        # For attach() to know what estimator to use
-        self.static_hyperparameters[self.SAGEMAKER_ESTIMATOR_CLASS_NAME] = self.estimator.__class__.__name__
-        self.static_hyperparameters[self.SAGEMAKER_ESTIMATOR_MODULE] = self.estimator.__module__
+        # For attach() to know what estimator to use for non-1P algorithms
+        # (1P algorithms don't accept extra hyperparameters)
+        from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
+
+        if not isinstance(self.estimator, AmazonAlgorithmEstimatorBase):
+            self.static_hyperparameters[self.SAGEMAKER_ESTIMATOR_CLASS_NAME] = self.estimator.__class__.__name__
+            self.static_hyperparameters[self.SAGEMAKER_ESTIMATOR_MODULE] = self.estimator.__module__
 
     def fit(self, inputs, job_name=None, **kwargs):
         """Start a hyperparameter tuning job.

tests/integ/test_tuner.py

@@ -52,7 +52,7 @@ def test_fit_1p(sagemaker_session):
     # specify which hp you want to optimize over
     hyperparameter_ranges = {'extra_center_factor': IntegerParameter(1, 10),
                              'mini_batch_size': IntegerParameter(10, 100),
-                             'local_lloyd_tol': ContinuousParameter(1.0, 2.0),
+                             'local_lloyd_tol': ContinuousParameter(0.5, 0.75),
                              'local_lloyd_init_method': CategoricalParameter(['kmeans++', 'random'])}
     objective_metric_name = 'test:msd'
 

tests/unit/test_tuner.py

@@ -121,8 +121,7 @@ def test_fit_1p(sagemaker_session, tuner):
 
     _, _, tune_kwargs = sagemaker_session.tune.mock_calls[0]
 
-    assert len(tune_kwargs['static_hyperparameters']) == 6
-    assert tune_kwargs['static_hyperparameters']['sagemaker_estimator_module'] == pca.__module__
+    assert len(tune_kwargs['static_hyperparameters']) == 4
     assert tune_kwargs['static_hyperparameters']['extra_components'] == '5'
     assert len(tune_kwargs['parameter_ranges']['IntegerParameterRanges']) == 1
     assert tune_kwargs['job_name'].startswith('pca')