change: [pr-827][followups]Improve documentation of some functions

Also some unit test fixes. See comments from mario in
aws#827

Author: Naresh Kumar Kolloju

src/sagemaker/session.py

@@ -514,9 +514,9 @@ def transform(self, job_name, model_name, strategy, max_concurrent_transforms, m
             input_config (dict): A dictionary describing the input data (and its location) for the job.
             output_config (dict): A dictionary describing the output location for the job.
             resource_config (dict): A dictionary describing the resources to complete the job.
-            tags (list[dict]): List of tags for labeling a transform job.
+            tags (list[dict]): List of tags for labeling a transform job. For more information,
+                see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
             data_processing(dict): A dictionary describing config for combining the input data and transformed data.
-                                   For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
         """
         transform_request = {
             'TransformJobName': job_name,

src/sagemaker/transformer.py

@@ -99,8 +99,10 @@ def transform(self, data, data_type='S3Prefix', content_type=None, compression_t
             job_name (str): job name (default: None). If not specified, one will be generated.
             input_filter (str): A JSONPath to select a portion of the input to pass to the algorithm container for
                 inference. If you omit the field, it gets the value '$', representing the entire input.
+                For more information, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTransformJob.html
                 Some examples: "$[1:]", "$.features"(default: None).
             output_filter (str): A JSONPath to select a portion of the joined/original output to return as the output.
+                For more information, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTransformJob.html
                 Some examples: "$[1:]", "$.prediction" (default: None).
             join_source (str): The source of data to be joined to the transform output. It can be set to 'Input'
                 meaning the entire input record will be joined to the inference result.

tests/integ/test_transformer.py

@@ -55,9 +55,10 @@ def test_transform_mxnet(sagemaker_session, mxnet_full_version):
 
     kms_key_arn = get_or_create_kms_key(sagemaker_session)
     output_filter = "$"
+    input_filter = "$"
 
     transformer = _create_transformer_and_transform_job(mx, transform_input, kms_key_arn,
-                                                        input_filter=None, output_filter=output_filter,
+                                                        input_filter=input_filter, output_filter=output_filter,
                                                         join_source=None)
     with timeout_and_delete_model_with_transformer(transformer, sagemaker_session,
                                                    minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
@@ -67,6 +68,7 @@ def test_transform_mxnet(sagemaker_session, mxnet_full_version):
         TransformJobName=transformer.latest_transform_job.name)
     assert kms_key_arn == job_desc['TransformResources']['VolumeKmsKeyId']
     assert output_filter == job_desc['DataProcessing']['OutputFilter']
+    assert input_filter == job_desc['DataProcessing']['InputFilter']
 
 
 @pytest.mark.canary_quick

tests/unit/test_session.py

@@ -578,17 +578,24 @@ def test_transform_pack_to_request(sagemaker_session):
         'InstanceType': INSTANCE_TYPE,
     }
 
+    data_processing = {
+        'OutputFilter': '$',
+        'InputFilter': '$',
+        'JoinSource': 'Input'
+    }
+
     expected_args = {
         'TransformJobName': JOB_NAME,
         'ModelName': model_name,
         'TransformInput': in_config,
         'TransformOutput': out_config,
         'TransformResources': resource_config,
+        'DataProcessing': data_processing,
     }
 
     sagemaker_session.transform(job_name=JOB_NAME, model_name=model_name, strategy=None, max_concurrent_transforms=None,
                                 max_payload=None, env=None, input_config=in_config, output_config=out_config,
-                                resource_config=resource_config, tags=None, data_processing=None)
+                                resource_config=resource_config, tags=None, data_processing=data_processing)
 
     _, _, actual_args = sagemaker_session.sagemaker_client.method_calls[0]
     assert actual_args == expected_args