change: fix attach for 1P algorithm estimators

src/sagemaker/tuner.py

@@ -19,7 +19,7 @@
 from enum import Enum
 
 import sagemaker
-from sagemaker.amazon.amazon_estimator import RecordSet
+from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.analytics import HyperparameterTuningJobAnalytics
 from sagemaker.estimator import Framework
@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
             estimator_cls, job_details["TrainingJobDefinition"]
         )
         estimator = cls._prepare_estimator_from_job_description(
-            estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
+            estimator_cls, job_details, sagemaker_session
         )
         init_params = cls._prepare_init_params_from_job_description(job_details)
 
@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
         )
 
     @classmethod
-    def _prepare_estimator_from_job_description(
-        cls, estimator_cls, training_details, sagemaker_session
-    ):
+    def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
+        training_details = job_details["TrainingJobDefinition"]
+
         # Swap name for static hyperparameters to what an estimator would expect
         training_details["HyperParameters"] = training_details["StaticHyperParameters"]
         del training_details["StaticHyperParameters"]
 
         # Remove hyperparameter reserved by SageMaker for tuning jobs
         del training_details["HyperParameters"]["_tuning_objective_metric"]
 
+        # Add missing hyperparameters defined in the hyperparameter ranges,
+        # as potentially required in the Amazon algorithm estimator's constructor
+        if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
+            parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
+            additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
+                parameter_ranges
+            )
+            training_details["HyperParameters"].update(additional_hyperparameters)
+
         # Add items expected by the estimator (but aren't needed otherwise)
         training_details["TrainingJobName"] = ""
         if "KmsKeyId" not in training_details["OutputDataConfig"]:
@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):
 
         return ranges
 
+    @classmethod
+    def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
+        hyperparameters = {}
+
+        for parameter in parameter_ranges["CategoricalParameterRanges"]:
+            hyperparameters[parameter["Name"]] = parameter["Values"][0]
+
+        for parameter in parameter_ranges["ContinuousParameterRanges"]:
+            hyperparameters[parameter["Name"]] = float(parameter["MinValue"])
+
+        for parameter in parameter_ranges["IntegerParameterRanges"]:
+            hyperparameters[parameter["Name"]] = int(parameter["MinValue"])
+
+        return hyperparameters
+
     def hyperparameter_ranges(self):
         """Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
         hyperparameter tuning job.

tests/integ/test_tuner.py

@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
         time.sleep(15)
         tuner.wait()
 
-    desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
-        HyperParameterTuningJobName=latest_tuning_job_name
+    attached_tuner = HyperparameterTuner.attach(
+        tuning_job_name, sagemaker_session=sagemaker_session
     )
-    assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
+    assert attached_tuner.early_stopping_type == "Auto"
+    assert attached_tuner.estimator.alpha0 == 1.0
+    assert attached_tuner.estimator.num_topics == 1
+
+    best_training_job = attached_tuner.best_training_job()
 
-    best_training_job = tuner.best_training_job()
     with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
         predictor = tuner.deploy(1, "ml.c4.xlarge")
         predict_input = np.random.rand(1, feature_num)

tests/unit/test_tuner.py

@@ -78,7 +78,7 @@
             "IntegerParameterRanges": [
                 {
                     "MaxValue": "100",
-                    "Name": "mini_batch_size",
+                    "Name": "num_components",
                     "MinValue": "10",
                     "ScalingType": "Auto",
                 }
@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
     assert tuner.estimator.output_kms_key == ""
 
     assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
-    assert tuner.estimator.hyperparameters()["num_components"] == "1"
+    assert tuner.estimator.hyperparameters()["num_components"] == "10"
 
 
 def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(