doc: modified docstrings to comply with PEP257 standards

Author: ahsan-z-khan

.pydocstylerc

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

src/sagemaker/algorithm.py

@@ -25,8 +25,9 @@
 
 
 class AlgorithmEstimator(EstimatorBase):
-    """A generic Estimator to train using any algorithm object (with an
-    ``algorithm_arn``). The Algorithm can be your own, or any Algorithm from AWS
+    """A generic Estimator to train using any algorithm object (with an ``algorithm_arn``).
+
+    The Algorithm can be your own, or any Algorithm from AWS
     Marketplace that you have a valid subscription for. This class will perform
     client-side validation on all the inputs.
     """
@@ -212,10 +213,7 @@ def validate_train_spec(self):
             )
 
     def set_hyperparameters(self, **kwargs):
-        """
-        Args:
-            **kwargs:
-        """
+        """Placeholder docstring"""
         for k, v in kwargs.items():
             value = self._validate_and_cast_hyperparameter(k, v)
             self.hyperparam_dict[k] = value
@@ -330,9 +328,9 @@ def transformer(
         role=None,
         volume_kms_key=None,
     ):
-        """Return a ``Transformer`` that uses a SageMaker Model based on the
-        training job. It reuses the SageMaker Session and base job name used by
-        the Estimator.
+        """Return a ``Transformer`` that uses a SageMaker Model based on the  training job.
+
+        It reuses the SageMaker Session and base job name used by the Estimator.
 
         Args:
             instance_count (int): Number of EC2 instances to use.
@@ -413,32 +411,18 @@ def _prepare_for_training(self, job_name=None):
         # Validate hyperparameters
         # an explicit call to set_hyperparameters() will also validate the hyperparameters
         # but it is possible that the user never called it.
-        """
-        Args:
-            job_name:
-        """
         self._validate_and_set_default_hyperparameters()
 
         super(AlgorithmEstimator, self)._prepare_for_training(job_name)
 
     def fit(self, inputs=None, wait=True, logs=True, job_name=None):
-        """
-        Args:
-            inputs:
-            wait:
-            logs:
-            job_name:
-        """
+        """Placeholder docstring"""
         if inputs:
             self._validate_input_channels(inputs)
 
         super(AlgorithmEstimator, self).fit(inputs, wait, logs, job_name)
 
     def _validate_input_channels(self, channels):
-        """
-        Args:
-            channels:
-        """
         train_spec = self.algorithm_spec["TrainingSpecification"]
         algorithm_name = self.algorithm_spec["AlgorithmName"]
         training_channels = {c["Name"]: c for c in train_spec["TrainingChannels"]}
@@ -456,11 +440,6 @@ def _validate_input_channels(self, channels):
                 raise ValueError("Required input channel: %s Was not provided." % (name))
 
     def _validate_and_cast_hyperparameter(self, name, v):
-        """
-        Args:
-            name:
-            v:
-        """
         algorithm_name = self.algorithm_spec["AlgorithmName"]
 
         if name not in self.hyperparameter_definitions:
@@ -514,11 +493,6 @@ def _parse_hyperparameters(self):
         return definitions
 
     def _hyperparameter_range_and_class(self, parameter_type, hyperparameter):
-        """
-        Args:
-            parameter_type:
-            hyperparameter:
-        """
         if parameter_type in self._hyperpameters_with_range:
             range_name = parameter_type + "ParameterRangeSpecification"
 
@@ -559,10 +533,6 @@ def _hyperparameter_range_and_class(self, parameter_type, hyperparameter):
         return parameter_class, parameter_range
 
     def _algorithm_training_input_modes(self, training_channels):
-        """
-        Args:
-            training_channels:
-        """
         current_input_modes = {"File", "Pipe"}
         for channel in training_channels:
             supported_input_modes = set(channel["SupportedInputModes"])
@@ -572,8 +542,7 @@ def _algorithm_training_input_modes(self, training_channels):
 
     @classmethod
     def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
-        """Convert the job description to init params that can be handled by the
-        class constructor
+        """Convert the job description to init params that can be handled by the class constructor.
 
         Args:
             job_details (dict): the returned job details from a DescribeTrainingJob

src/sagemaker/amazon/common.py

@@ -92,12 +92,7 @@ def deserialize(self, data, content_type):
 
 
 def _write_feature_tensor(resolved_type, record, vector):
-    """
-    Args:
-        resolved_type:
-        record:
-        vector:
-    """
+    """Placeholder Docstring"""
     if resolved_type == "Int32":
         record.features["values"].int32_tensor.values.extend(vector)
     elif resolved_type == "Float64":
@@ -107,12 +102,7 @@ def _write_feature_tensor(resolved_type, record, vector):
 
 
 def _write_label_tensor(resolved_type, record, scalar):
-    """
-    Args:
-        resolved_type:
-        record:
-        scalar:
-    """
+    """Placeholder Docstring"""
     if resolved_type == "Int32":
         record.label["values"].int32_tensor.values.extend([scalar])
     elif resolved_type == "Float64":
@@ -122,12 +112,7 @@ def _write_label_tensor(resolved_type, record, scalar):
 
 
 def _write_keys_tensor(resolved_type, record, vector):
-    """
-    Args:
-        resolved_type:
-        record:
-        vector:
-    """
+    """Placeholder Docstring"""
     if resolved_type == "Int32":
         record.features["values"].int32_tensor.keys.extend(vector)
     elif resolved_type == "Float64":
@@ -137,12 +122,7 @@ def _write_keys_tensor(resolved_type, record, vector):
 
 
 def _write_shape(resolved_type, record, scalar):
-    """
-    Args:
-        resolved_type:
-        record:
-        scalar:
-    """
+    """Placeholder Docstring"""
     if resolved_type == "Int32":
         record.features["values"].int32_tensor.shape.extend([scalar])
     elif resolved_type == "Float64":
@@ -285,10 +265,7 @@ def _write_recordio(f, data):
 
 
 def read_recordio(f):
-    """
-    Args:
-        f:
-    """
+    """Placeholder Docstring"""
     while True:
         try:
             (read_kmagic,) = struct.unpack("I", f.read(4))
@@ -303,10 +280,7 @@ def read_recordio(f):
 
 
 def _resolve_type(dtype):
-    """
-    Args:
-        dtype:
-    """
+    """Placeholder Docstring"""
     if dtype == np.dtype(int):
         return "Int32"
     if dtype == np.dtype(float):

src/sagemaker/amazon/factorization_machines.py

@@ -112,8 +112,7 @@ def __init__(
         factors_init_value=None,
         **kwargs
     ):
-        """Factorization Machines is :class:`Estimator` for general-purpose
-        supervised learning.
+        """Factorization Machines is :class:`Estimator` for general-purpose supervised learning.
 
         Amazon SageMaker Factorization Machines is a general-purpose
         supervised learning algorithm that you can use for both classification
@@ -247,8 +246,9 @@ def __init__(
         self.factors_init_value = factors_init_value
 
     def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
-        """Return a :class:`~sagemaker.amazon.FactorizationMachinesModel`
-        referencing the latest s3 model data produced by this Estimator.
+        """Return a :class:`~sagemaker.amazon.FactorizationMachinesModel`.
+
+        It references the latest s3 model data produced by this Estimator.
 
         Args:
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
@@ -267,8 +267,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
 
 
 class FactorizationMachinesPredictor(Predictor):
-    """Performs binary-classification or regression prediction from input
-    vectors.
+    """Performs binary-classification or regression prediction from input vectors.
 
     The implementation of
     :meth:`~sagemaker.predictor.Predictor.predict` in this
@@ -292,6 +291,8 @@ def __init__(
         deserializer=RecordDeserializer(),
     ):
         """
+        Initialization for FactorizationMachinesPredictor class.
+
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
                 requests are sent.
@@ -314,12 +315,15 @@ def __init__(
 
 class FactorizationMachinesModel(Model):
     """Reference S3 model data created by FactorizationMachines estimator.
+
     Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and
     returns :class:`FactorizationMachinesPredictor`.
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
+        Initialization for FactorizationMachinesModel class.
+
         Args:
             model_data (str): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.

src/sagemaker/amazon/hyperparameter.py

@@ -23,7 +23,9 @@ class Hyperparameter(object):
     """
 
     def __init__(self, name, validate=lambda _: True, validation_message="", data_type=str):
-        """Args: name (str): The name of this hyperparameter validate
+        """Args:
+
+        name (str): The name of this hyperparameter validate
         (callable[object]->[bool]): A validation function or list of validation
         functions.
 
@@ -49,10 +51,7 @@ def __init__(self, name, validate=lambda _: True, validation_message="", data_ty
             self.validation = [self.validation]
 
     def validate(self, value):
-        """
-        Args:
-            value:
-        """
+        """Placeholder docstring"""
         if value is None:  # We allow assignment from None, but Nones are not sent to training.
             return
 
@@ -64,11 +63,7 @@ def validate(self, value):
                 raise ValueError(error_message)
 
     def __get__(self, obj, objtype):
-        """
-        Args:
-            obj:
-            objtype:
-        """
+        """Placeholder docstring"""
         if "_hyperparameters" not in dir(obj) or self.name not in obj._hyperparameters:
             raise AttributeError()
         return obj._hyperparameters[self.name]
@@ -96,8 +91,7 @@ def __delete__(self, obj):
 
     @staticmethod
     def serialize_all(obj):
-        """Return all non-None ``hyperparameter`` values on ``obj`` as a
-        ``dict[str,str].``
+        """Return all non-None ``hyperparameter`` values on ``obj`` as a ``dict[str,str].``
 
         Args:
             obj:

src/sagemaker/amazon/ipinsights.py

@@ -73,8 +73,9 @@ def __init__(
         weight_decay=None,
         **kwargs
     ):
-        """This estimator is for IP Insights, an unsupervised algorithm that
-        learns usage patterns of IP addresses.
+        """This estimator is for IP Insights.
+
+        An unsupervised algorithm that learns usage patterns of IP addresses.
 
         This Estimator may be fit via calls to
         :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`.
@@ -167,12 +168,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
-        """
-        Args:
-            records:
-            mini_batch_size:
-            job_name:
-        """
+        """Placeholder docstring"""
         if mini_batch_size is not None and (mini_batch_size < 1 or mini_batch_size > 500000):
             raise ValueError("mini_batch_size must be in [1, 500000]")
         super(IPInsights, self)._prepare_for_training(
@@ -181,8 +177,7 @@ def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
 
 
 class IPInsightsPredictor(Predictor):
-    """Returns dot product of entity and IP address embeddings as a score for
-    compatibility.
+    """Returns dot product of entity and IP address embeddings as a score for compatibility.
 
     The implementation of
     :meth:`~sagemaker.predictor.Predictor.predict` in this
@@ -200,6 +195,7 @@ def __init__(
     ):
         """
         Args:
+
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
                 requests are sent.
             sagemaker_session (sagemaker.session.Session): A SageMaker Session
@@ -220,14 +216,16 @@ def __init__(
 
 
 class IPInsightsModel(Model):
-    """Reference IPInsights s3 model data. Calling
-    :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns a
+    """Reference IPInsights s3 model data.
+
+    Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns a
     Predictor that calculates anomaly scores for data points.
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         Args:
+
             model_data (str): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.
             role (str): An AWS IAM role (either name or full ARN). The Amazon