change: include additional docstyle improvements (#1889)

Co-authored-by: Ajay Karpur <[email protected]>

Author: metrizable

.pydocstylerc

@@ -1,4 +1,4 @@
 [pydocstyle]
 inherit = false
-ignore = D104,D107,D202,D203,D205,D209,D212,D213,D214,D400,D401,D404,D406,D407,D411,D413,D414,D415,D417
+ignore = D104,D107,D202,D203,D205,D212,D213,D214,D400,D401,D404,D406,D407,D411,D413,D414,D415,D417
 match = (?!record_pb2).*\.py

src/sagemaker/amazon/factorization_machines.py

@@ -30,7 +30,8 @@ class FactorizationMachines(AmazonAlgorithmEstimatorBase):
     Factorization Machines combine the advantages of Support Vector Machines
     with factorization models. It is an extension of a linear model that is
     designed to capture interactions between features within high dimensional
-    sparse datasets economically."""
+    sparse datasets economically.
+    """
 
     repo_name = "factorization-machines"
     repo_version = 1

src/sagemaker/amazon/ipinsights.py

@@ -29,7 +29,8 @@ class IPInsights(AmazonAlgorithmEstimatorBase):
     """An unsupervised learning algorithm that learns the usage patterns for IPv4 addresses.
 
     It is designed to capture associations between IPv4 addresses and various entities, such
-    as user IDs or account numbers."""
+    as user IDs or account numbers.
+    """
 
     repo_name = "ipinsights"
     repo_version = 1

src/sagemaker/amazon/kmeans.py

@@ -29,7 +29,8 @@ 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

src/sagemaker/amazon/knn.py

@@ -30,7 +30,8 @@ class KNN(AmazonAlgorithmEstimatorBase):
     For classification problems, the algorithm queries the k points that are closest to the sample
     point and returns the most frequently used label of their class as the predicted label. For
     regression problems, the algorithm queries the k closest points to the sample point and returns
-    the average of their feature values as the predicted value."""
+    the average of their feature values as the predicted value.
+    """
 
     repo_name = "knn"
     repo_version = 1

src/sagemaker/amazon/lda.py

@@ -30,7 +30,8 @@ class LDA(AmazonAlgorithmEstimatorBase):
     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."""
+    word, and the categories are the topics.
+    """
 
     repo_name = "lda"
     repo_version = 1

src/sagemaker/amazon/linear_learner.py

@@ -32,7 +32,8 @@ class LinearLearner(AmazonAlgorithmEstimatorBase):
     For multiclass classification problems, the labels must be from 0 to num_classes - 1. For
     regression problems, y is a real number. The algorithm learns a linear function, or, for
     classification problems, a linear threshold function, and maps a vector x to an approximation
-    of the label y."""
+    of the label y.
+    """
 
     repo_name = "linear-learner"
     repo_version = 1

src/sagemaker/amazon/ntm.py

@@ -25,11 +25,12 @@
 
 
 class NTM(AmazonAlgorithmEstimatorBase):
-    """An unsupervised learning algorithm used to organize a corpus of documents into topics
+    """An unsupervised learning algorithm used to organize a corpus of documents into topics.
 
     The resulting topics contain word groupings based on their statistical distribution.
     Documents that contain frequent occurrences of words such as "bike", "car", "train",
-    "mileage", and "speed" are likely to share a topic on "transportation" for example."""
+    "mileage", and "speed" are likely to share a topic on "transportation" for example.
+    """
 
     repo_name = "ntm"
     repo_version = 1

src/sagemaker/amazon/object2vec.py

@@ -24,7 +24,8 @@
 
 
 def _list_check_subset(valid_super_list):
-    """
+    """Provides a function to check validity of list subset.
+
     Args:
         valid_super_list:
     """
@@ -45,7 +46,8 @@ class Object2Vec(AmazonAlgorithmEstimatorBase):
 
     It can learn low-dimensional dense embeddings of high-dimensional objects. The embeddings
     are learned in a way that preserves the semantics of the relationship between pairs of
-    objects in the original space in the embedding space."""
+    objects in the original space in the embedding space.
+    """
 
     repo_name = "object2vec"
     repo_version = 1

src/sagemaker/amazon/pca.py

@@ -28,7 +28,8 @@ class PCA(AmazonAlgorithmEstimatorBase):
     """An unsupervised machine learning algorithm to reduce feature dimensionality.
 
     As a result, number of features within a dataset is reduced but the dataset still
-    retain as much information as possible."""
+    retain as much information as possible.
+    """
 
     repo_name = "pca"
     repo_version = 1