change: formatting changes from updates to black (#1855)

Author: metrizable

src/sagemaker/amazon/amazon_estimator.py

@@ -94,7 +94,9 @@ def __init__(
     def training_image_uri(self):
         """Placeholder docstring"""
         return image_uris.retrieve(
-            self.repo_name, self.sagemaker_session.boto_region_name, version=self.repo_version,
+            self.repo_name,
+            self.sagemaker_session.boto_region_name,
+            version=self.repo_version,
         )
 
     def hyperparameters(self):

src/sagemaker/amazon/kmeans.py

@@ -29,7 +29,7 @@ class KMeans(AmazonAlgorithmEstimatorBase):
 
     As the result of KMeans, members of a group are as similar as possible to one another and as
     different as possible from members of other groups. You define the attributes that you want
-    the algorithm to use to determine similarity. """
+    the algorithm to use to determine similarity."""
 
     repo_name = "kmeans"
     repo_version = 1
@@ -257,7 +257,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         image_uri = image_uris.retrieve(
-            KMeans.repo_name, sagemaker_session.boto_region_name, version=KMeans.repo_version,
+            KMeans.repo_name,
+            sagemaker_session.boto_region_name,
+            version=KMeans.repo_version,
         )
         super(KMeansModel, self).__init__(
             image_uri,

src/sagemaker/amazon/knn.py

@@ -246,7 +246,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         image_uri = image_uris.retrieve(
-            KNN.repo_name, sagemaker_session.boto_region_name, version=KNN.repo_version,
+            KNN.repo_name,
+            sagemaker_session.boto_region_name,
+            version=KNN.repo_version,
         )
         super(KNNModel, self).__init__(
             image_uri,

src/sagemaker/amazon/lda.py

@@ -27,10 +27,10 @@
 class LDA(AmazonAlgorithmEstimatorBase):
     """An unsupervised learning algorithm attempting to describe data as distinct categories.
 
-     LDA is most commonly used to discover a
-     user-specified number of topics shared by documents within a text corpus. Here each
-     observation is a document, the features are the presence (or occurrence count) of each
-     word, and the categories are the topics."""
+    LDA is most commonly used to discover a
+    user-specified number of topics shared by documents within a text corpus. Here each
+    observation is a document, the features are the presence (or occurrence count) of each
+    word, and the categories are the topics."""
 
     repo_name = "lda"
     repo_version = 1
@@ -230,7 +230,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         image_uri = image_uris.retrieve(
-            LDA.repo_name, sagemaker_session.boto_region_name, version=LDA.repo_version,
+            LDA.repo_name,
+            sagemaker_session.boto_region_name,
+            version=LDA.repo_version,
         )
         super(LDAModel, self).__init__(
             image_uri,

src/sagemaker/amazon/ntm.py

@@ -259,7 +259,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         image_uri = image_uris.retrieve(
-            NTM.repo_name, sagemaker_session.boto_region_name, version=NTM.repo_version,
+            NTM.repo_name,
+            sagemaker_session.boto_region_name,
+            version=NTM.repo_version,
         )
         super(NTMModel, self).__init__(
             image_uri,

src/sagemaker/amazon/pca.py

@@ -240,7 +240,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         image_uri = image_uris.retrieve(
-            PCA.repo_name, sagemaker_session.boto_region_name, version=PCA.repo_version,
+            PCA.repo_name,
+            sagemaker_session.boto_region_name,
+            version=PCA.repo_version,
         )
         super(PCAModel, self).__init__(
             image_uri,

src/sagemaker/analytics.py

@@ -416,8 +416,7 @@ def _metric_names_for_training_job(self):
 
 
 class ExperimentAnalytics(AnalyticsMetricsBase):
-    """Fetch trial component data and make them accessible for analytics.
-    """
+    """Fetch trial component data and make them accessible for analytics."""
 
     MAX_TRIAL_COMPONENTS = 10000
 
@@ -477,16 +476,14 @@ def __init__(
 
     @property
     def name(self):
-        """Name of the Experiment being analyzed
-        """
+        """Name of the Experiment being analyzed"""
         return self._experiment_name
 
     def __repr__(self):
         return "<sagemaker.ExperimentAnalytics for %s>" % self.name
 
     def clear_cache(self):
-        """Clear the object of all local caches of API methods.
-        """
+        """Clear the object of all local caches of API methods."""
         super(ExperimentAnalytics, self).clear_cache()
         self._trial_components = None
 
@@ -570,13 +567,13 @@ def _reshape(self, trial_component):
 
     def _fetch_dataframe(self):
         """Return a pandas dataframe with all the trial_components,
-            along with their parameters and metrics.
+        along with their parameters and metrics.
         """
         df = pd.DataFrame([self._reshape(component) for component in self._get_trial_components()])
         return df
 
     def _get_trial_components(self, force_refresh=False):
-        """ Get all trial components matching the given search query expression.
+        """Get all trial components matching the given search query expression.
 
         Args:
             force_refresh (bool): Set to True to fetch the latest data from SageMaker API.

src/sagemaker/automl/automl.py

@@ -26,8 +26,7 @@
 
 
 class AutoML(object):
-    """A class for creating and interacting with SageMaker AutoML jobs
-    """
+    """A class for creating and interacting with SageMaker AutoML jobs"""
 
     def __init__(
         self,

src/sagemaker/cli/framework_upgrade.py

@@ -158,7 +158,13 @@ def add_region(existing_content, region, account):
 
 
 def add_version(
-    existing_content, short_version, full_version, scope, processors, py_versions, tag_prefix,
+    existing_content,
+    short_version,
+    full_version,
+    scope,
+    processors,
+    py_versions,
+    tag_prefix,
 ):
     """Read framework image uri information from json file to a dictionary, update it with new
     framework version information, then write the dictionary back to json file.
@@ -172,7 +178,7 @@ def add_version(
         processors (str): Supported processors (e.g. "cpu,gpu").
         py_versions (str): Supported Python versions (e.g. "py3,py37").
         tag_prefix (str): Algorithm image's tag prefix.
-     """
+    """
     if py_versions:
         py_versions = py_versions.split(",")
     processors = processors.split(",")

src/sagemaker/local/local_session.py

@@ -448,8 +448,7 @@ class file_input(object):
     """Amazon SageMaker channel configuration for FILE data sources, used in local mode."""
 
     def __init__(self, fileUri, content_type=None):
-        """Create a definition for input data used by an SageMaker training job in local mode.
-        """
+        """Create a definition for input data used by an SageMaker training job in local mode."""
         self.config = {
             "DataSource": {
                 "FileDataSource": {