Fix hyperparameter strategy docs (aws#5045)

Author: sage-maker

src/sagemaker/tuner.py

@@ -18,21 +18,20 @@
 import inspect
 import json
 import logging
-
 from enum import Enum
-from typing import Union, Dict, Optional, List, Set
+from typing import Dict, List, Optional, Set, Union
 
 import sagemaker
 from sagemaker.amazon.amazon_estimator import (
-    RecordSet,
     AmazonAlgorithmEstimatorBase,
     FileSystemRecordSet,
+    RecordSet,
 )
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.analytics import HyperparameterTuningJobAnalytics
 from sagemaker.deprecations import removed_function
-from sagemaker.estimator import Framework, EstimatorBase
-from sagemaker.inputs import TrainingInput, FileSystemInput
+from sagemaker.estimator import EstimatorBase, Framework
+from sagemaker.inputs import FileSystemInput, TrainingInput
 from sagemaker.job import _Job
 from sagemaker.jumpstart.utils import (
     add_jumpstart_uri_tags,
@@ -44,18 +43,17 @@
     IntegerParameter,
     ParameterRange,
 )
-from sagemaker.workflow.entities import PipelineVariable
-from sagemaker.workflow.pipeline_context import runnable_by_pipeline
-
 from sagemaker.session import Session
 from sagemaker.utils import (
+    Tags,
     base_from_name,
     base_name_from_image,
+    format_tags,
     name_from_base,
     to_string,
-    format_tags,
-    Tags,
 )
+from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.pipeline_context import runnable_by_pipeline
 
 AMAZON_ESTIMATOR_MODULE = "sagemaker"
 AMAZON_ESTIMATOR_CLS_NAMES = {
@@ -133,15 +131,12 @@ def __init__(
 
         if warm_start_type not in list(WarmStartTypes):
             raise ValueError(
-                "Invalid type: {}, valid warm start types are: {}".format(
-                    warm_start_type, list(WarmStartTypes)
-                )
+                f"Invalid type: {warm_start_type}, "
+                f"valid warm start types are: {list(WarmStartTypes)}"
             )
 
         if not parents:
-            raise ValueError(
-                "Invalid parents: {}, parents should not be None/empty".format(parents)
-            )
+            raise ValueError(f"Invalid parents: {parents}, parents should not be None/empty")
 
         self.type = warm_start_type
         self.parents = set(parents)
@@ -1455,9 +1450,7 @@ def _get_best_training_job(self):
             return tuning_job_describe_result["BestTrainingJob"]
         except KeyError:
             raise Exception(
-                "Best training job not available for tuning job: {}".format(
-                    self.latest_tuning_job.name
-                )
+                f"Best training job not available for tuning job: {self.latest_tuning_job.name}"
             )
 
     def _ensure_last_tuning_job(self):
@@ -1920,8 +1913,11 @@ def create(
                 :meth:`~sagemaker.tuner.HyperparameterTuner.fit` method launches.
                 If not specified, a default job name is generated,
                 based on the training image name and current timestamp.
-            strategy (str): Strategy to be used for hyperparameter estimations
-                (default: 'Bayesian').
+            strategy (str or PipelineVariable): Strategy to be used for hyperparameter estimations.
+                More information about different strategies:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html.
+                Available options are: 'Bayesian', 'Random', 'Hyperband',
+                'Grid' (default: 'Bayesian')
             strategy_config (dict): The configuration for a training job launched by a
                 hyperparameter tuning job.
             completion_criteria_config (dict): The configuration for tuning job completion criteria.
@@ -2080,21 +2076,19 @@ def _validate_dict_argument(cls, name, value, allowed_keys, require_same_keys=Fa
             return
 
         if not isinstance(value, dict):
-            raise ValueError(
-                "Argument '{}' must be a dictionary using {} as keys".format(name, allowed_keys)
-            )
+            raise ValueError(f"Argument '{name}' must be a dictionary using {allowed_keys} as keys")
 
         value_keys = sorted(value.keys())
 
         if require_same_keys:
             if value_keys != allowed_keys:
                 raise ValueError(
-                    "The keys of argument '{}' must be the same as {}".format(name, allowed_keys)
+                    f"The keys of argument '{name}' must be the same as {allowed_keys}"
                 )
         else:
             if not set(value_keys).issubset(set(allowed_keys)):
                 raise ValueError(
-                    "The keys of argument '{}' must be a subset of {}".format(name, allowed_keys)
+                    f"The keys of argument '{name}' must be a subset of {allowed_keys}"
                 )
 
     def _add_estimator(