Fix flake8 'too complex' error

Author: laurenyu

setup.py

@@ -49,7 +49,7 @@ def read(fname):
 
       extras_require={
           'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
-                   'mock', 'tensorflow>=1.3.0', 'contextlib2', 'awslogs']},
+                   'mock', 'tensorflow>=1.3.0', 'contextlib2', 'awslogs', 'pandas']},
 
       entry_points={
           'console_scripts': ['sagemaker=sagemaker.cli.main:main'],

src/sagemaker/estimator.py

@@ -44,8 +44,7 @@ class EstimatorBase(with_metaclass(ABCMeta, object)):
 
     def __init__(self, role, train_instance_count, train_instance_type,
                  train_volume_size=30, train_max_run=24 * 60 * 60, input_mode='File',
-                 output_path=None, output_kms_key=None, base_job_name=None, sagemaker_session=None,
-                 metric_definitions=None):
+                 output_path=None, output_kms_key=None, base_job_name=None, sagemaker_session=None):
         """Initialize an ``EstimatorBase`` instance.
 
         Args:
@@ -74,7 +73,6 @@ def __init__(self, role, train_instance_count, train_instance_type,
             sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
                 Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
-            metric_definitions (list[dict]): Metrics definition with 'name' and 'regex' keys.
         """
         self.role = role
         self.train_instance_count = train_instance_count
@@ -95,7 +93,6 @@ def __init__(self, role, train_instance_count, train_instance_type,
         self.output_path = output_path
         self.output_kms_key = output_kms_key
         self.latest_training_job = None
-        self.metric_definitions = metric_definitions
 
     @abstractmethod
     def train_image(self):

src/sagemaker/job.py

@@ -68,7 +68,7 @@ def _load_config(inputs, estimator):
 
     @staticmethod
     def _format_inputs_to_input_config(inputs):
-        # Circular dependency
+        # Deferred import due to circular dependency
         from sagemaker.amazon.amazon_estimator import RecordSet
         if isinstance(inputs, RecordSet):
             inputs = inputs.data_channel()
@@ -84,14 +84,7 @@ def _format_inputs_to_input_config(inputs):
             for k, v in inputs.items():
                 input_dict[k] = _Job._format_string_uri_input(v)
         elif isinstance(inputs, list):
-            for record in inputs:
-                if not isinstance(record, RecordSet):
-                    raise ValueError('List compatible only with RecordSets.')
-
-                if record.channel in input_dict:
-                    raise ValueError('Duplicate channels not allowed.')
-
-                input_dict[record.channel] = record.records_s3_input()
+            input_dict = _Job._format_record_set_list_input(inputs)
         else:
             raise ValueError(
                 'Cannot format input {}. Expecting one of str, dict or s3_input'.format(inputs))
@@ -123,6 +116,23 @@ def _format_string_uri_input(input):
                 'Cannot format input {}. Expecting one of str, s3_input, or file_input'.format(
                     input))
 
+    @staticmethod
+    def _format_record_set_list_input(inputs):
+        # Deferred import due to circular dependency
+        from sagemaker.amazon.amazon_estimator import RecordSet
+
+        input_dict = {}
+        for record in inputs:
+            if not isinstance(record, RecordSet):
+                raise ValueError('List compatible only with RecordSets.')
+
+            if record.channel in input_dict:
+                raise ValueError('Duplicate channels not allowed.')
+
+            input_dict[record.channel] = record.records_s3_input()
+
+        return input_dict
+
     @staticmethod
     def _prepare_output_config(s3_path, kms_key_id):
         config = {'S3OutputPath': s3_path}